diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000000000000000000000000000000000000..7915b34603a4e187a3537d00aec765916de193bb --- /dev/null +++ b/.dockerignore @@ -0,0 +1,94 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +env/ +venv/ +ENV/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# Environment +.env +.env.local +.env.*.local + +# IDE +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# Logs +*.log +logs/ +*.log.* + +# Testing +.pytest_cache/ +.coverage +htmlcov/ +.tox/ + +# OS +.DS_Store +Thumbs.db + +# Git +.git/ +.gitignore +.gitattributes + +# Documentation +README.md +*.md + +# Test files +test_*.py +*_test.py +tests/ +test_urls.txt + +# Agent files (large binary) +agent + +# Specific log files +agent_direct_run.log +agent_test.log +agent_uvicorn_new.log +agent_uvicorn.log +ingestion.log +local_test.log +retrieve.log +test_run.log +test_simple.log +validate_rag.log + +# Scripts not needed in production +check_collection.py +check_detailed.py +extract_sitemap.py +test_local.py +validate_rag.py +validate.py +retrieve.py +main.py + +# Data files +sitemap_urls.txt diff --git a/.env b/.env new file mode 100644 index 0000000000000000000000000000000000000000..0ffb709e394d2be32465b59c7cba2755d30fc72b --- /dev/null +++ b/.env @@ -0,0 +1,19 @@ +# Cohere API key for embedding generation +COHERE_API_KEY="fYDA4euHeuxeMn2FmfxGqLqWyG8PN16BjLT0N4O9" + +# Qdrant Cloud connection details +QDRANT_URL="https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333" +QDRANT_API_KEY="eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhY2Nlc3MiOiJtIn0.xIHDfcROMhK4TPiIr4Q-P5yKHaofGf41M0FmQbxvJdE" +QDRANT_COLLECTION=book_embeddings + +# Deployment vercel +DEPLOY_VERCEL_URL="https://humanoid-ai-robotics-book-1.vercel.app" # /sitemap.xml + + +# ⚠️ IMPORTANT: Replace with your own OpenAI API key with available quota +# Current key has insufficient quota - get a new key from: https://platform.openai.com/api-keys +# OPENAI_API_KEY="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" +# AGENT_MODEL="gpt-4o-mini" + + +# OPENROUTER_API_KEY="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" \ No newline at end of file diff --git a/.env.example b/.env.example new file mode 100644 index 0000000000000000000000000000000000000000..90dce1d156bd13b73e67fde387cfaf82f65fc6cf --- /dev/null +++ b/.env.example @@ -0,0 +1,22 @@ +# Cohere API key for embedding generation +# Get your key from: https://dashboard.cohere.com/api-keys +COHERE_API_KEY="fYDA4euHeuxeMn2FmfxGqLqWyG8PN16BjLT0N4O9" + +# Qdrant Cloud connection details +# Get these from your Qdrant Cloud dashboard +QDRANT_URL="https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333" +QDRANT_API_KEY="eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhY2Nlc3MiOiJtIn0.xIHDfcROMhK4TPiIr4Q-P5yKHaofGf41M0FmQbxvJdE" +QDRANT_COLLECTION="book_embeddings" + +# Deployment URL (optional - used for documentation references) +DEPLOY_VERCEL_URL="https://humanoid-ai-robotics-book-1.vercel.app" # /sitemap.xml + + +# OpenAI API key for the agent +# Get your key from: https://platform.openai.com/api-keys +# ⚠️ Ensure your key has available quota (not expired or exhausted) +# OPENAI_API_KEY="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" +# AGENT_MODEL="gpt-4o-mini" + + +# OPENROUTER_API_KEY="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" \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..24c977380d4e06eb41e9abc4366008f261e37339 --- /dev/null +++ b/Dockerfile @@ -0,0 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Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/agent.py", "thread": 133059106440896, "threadName": "ThreadPoolExecutor-4_0", "processName": "MainProcess", "process": 10618, "taskName": null} +{"timestamp": "2026-02-16T23:10:21.247103Z", "level": "ERROR", "name": "root", "message": "[ab5d56e5] Agent failed after 0.00s: attempted relative import with no known parent package", "module": "agent", "lineno": 269, "exc_info": "Traceback (most recent call last):\n File \"/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/agent.py\", line 208, in run\n chunks = self.retrieve_chunks(question, top_k=5)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/agent.py\", line 144, in retrieve_chunks\n from .config import get_config\nImportError: attempted relative import with no known parent package", "pathname": "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/agent.py", "thread": 133059106440896, "threadName": "ThreadPoolExecutor-4_0", "processName": "MainProcess", "process": 10618, "taskName": null} +{"timestamp": "2026-02-16T23:10:21.249964Z", "level": "ERROR", "name": "root", "message": "[ab5d56e5] Agent error: attempted relative import with no known parent package", "module": "agent", "lineno": 359, "exc_info": "Traceback (most recent call last):\n File \"/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/agent.py\", line 336, in chat_endpoint\n result = await asyncio.wait_for(\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/lib/python3.12/asyncio/tasks.py\", line 520, in wait_for\n return await fut\n ^^^^^^^^^\n File \"/usr/lib/python3.12/concurrent/futures/thread.py\", line 58, in run\n result = self.fn(*self.args, **self.kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/agent.py\", line 208, in run\n chunks = self.retrieve_chunks(question, top_k=5)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/agent.py\", line 144, in retrieve_chunks\n from .config import get_config\nImportError: attempted relative import with no known parent package", "pathname": "/mnt/e/7. 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\\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2\\', \\'chunk_index\\': 0, \\'text\\': \\'Introduction to ROS 2 for Physical AI | Humanoid Robotics with ROS 2\\\\nSkip to main content\\\\nOn this page\\\\nWhat is ROS 2?\\\\n\\\\u200b\\\\nROS 2 (Robot Operating System 2) is not an operating system, but rather a flexible framework for writing robot software. It is a collection of tools, libraries, and conventions that aim to simplify the task of creating complex and robust robot behavior across a wide variety of robot platforms, applications, and use cases.\\\\nROS 2 is the second generation of the Robot Operating System, designed to address the limitations of the original ROS and to provide a more robust, scalable, and production-ready framework for robotics development.\\\\nWhy Middleware is Essential for Humanoid Robots\\\\n\\\\u200b\\\\nHumanoid robots are complex systems that require coordination between multiple subsystems including perception, planning, control, and actuation. Middleware like ROS 2 provides the essential communication infrastructure that allows these different subsystems to work together seamlessly.\\\\nFor\\', \\'score\\': 0.6773571, \\'source_number\\': 1}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2\\', \\'chunk_index\\': 4, \\'text\\': \"bots to execute actions through standardized interfaces to actuators and control systems, allowing them to interact with the environment.\\\\n3. Perception-Action Loops\\\\n\\\\u200b\\\\nROS 2\\'s communication infrastructure supports the implementation of perception-action loops that are fundamental to embodied intelligence.\\\\n4. Learning from Interaction\\\\n\\\\u200b\\\\nROS 2\\'s data logging and replay capabilities enable robots to learn from their interactions with the environment, a key aspect of embodied intelligence.\\\\n5. Distributed Intelligence\\\\n\\\\u200b\\\\nROS 2 allows intelligence to be distributed across different nodes, enabling more sophisticated behaviors as the robot interacts with its environment.\\\\nSummary\\\\n\\\\u200b\\\\nROS 2 represents a significant advancement in robotics middleware, specifically designed to address the challenges of modern robotics applications including humanoid robots. Its foundation on DDS provides robust, scalable, and configurable communication that is essential for complex robotic systems. As we continue to explore the potential of embodied intelligence, ROS 2 provides the necessary infrastructure to creat\", \\'score\\': 0.63640505, \\'source_number\\': 2}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2\\', \\'chunk_index\\': 2, \\'text\\': \\'-robot systems, making it ideal for scenarios where multiple humanoid robots need to coordinate.\\\\n3. Security\\\\n\\\\u200b\\\\nROS 2 includes built-in security features including authentication, authorization, and encryption, which are critical for deploying robots in real-world environments.\\\\n4. Deterministic Behavior\\\\n\\\\u200b\\\\nROS 2 provides more deterministic behavior compared to ROS 1, which is important for predictable robot performance.\\\\n5. Professional Use\\\\n\\\\u200b\\\\nROS 2 is designed to support professional and commercial applications, with better support for deployment, maintenance, and lifecycle management.\\\\nDDS Concepts\\\\n\\\\u200b\\\\nROS 2 uses DDS (Data Distribution Service) as its underlying communication middleware. DDS is a specification that provides a standardized API for machine-to-machine communication.\\\\nKey DDS Concepts:\\\\n\\\\u200b\\\\n1. Data-Centric Architecture\\\\n\\\\u200b\\\\nUnlike traditional request-reply patterns, DDS uses a data-centric approach where data producers and consumers are decoupled in time, space, and synchronization.\\\\n2. Quality of Service (QoS) Policies\\\\n\\\\u200b\\\\nDDS provides QoS policies that allow fine-tuning of communicat\\', \\'score\\': 0.60405266, \\'source_number\\': 3}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2\\', \\'chunk_index\\': 5, \\'text\\': \"explore the potential of embodied intelligence, ROS 2 provides the necessary infrastructure to create robots that can intelligently interact with their environment.\\\\nWhat is ROS 2?\\\\nWhy Middleware is Essential for Humanoid Robots\\\\nROS 2 Design Goals\\\\n1. Real-time Support\\\\n2. Multi-Robot Support\\\\n3. Security\\\\n4. Deterministic Behavior\\\\n5. Professional Use\\\\nDDS Concepts\\\\nKey DDS Concepts:\\\\nROS 2\\'s Role in Embodied Intelligence\\\\n1. Sensor Integration\\\\n2. Action Execution\\\\n3. Perception-Action Loops\\\\n4. Learning from Interaction\\\\n5. Distributed Intelligence\\\\nSummary\", \\'score\\': 0.5426538, \\'source_number\\': 4}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2\\', \\'chunk_index\\': 3, \\'text\\': \". Quality of Service (QoS) Policies\\\\n\\\\u200b\\\\nDDS provides QoS policies that allow fine-tuning of communication behavior:\\\\nReliability\\\\n: Best effort or reliable delivery\\\\nDurability\\\\n: Volatile or transient data\\\\nHistory\\\\n: Keep all samples or only the most recent\\\\nDeadline\\\\n: Maximum time between sample updates\\\\nLiveliness\\\\n: How to determine if a participant is alive\\\\n3. 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It is a collection of tools, libraries, and conventions that aim to simplify the task of creating complex and robust robot behavior across a wide variety of robot platforms, applications, and use cases.\\\\nROS 2 is the second generation of the Robot Operating System, designed to address the limitations of the original ROS and to provide a more robust, scalable, and production-ready framework for robotics development.\\\\nWhy Middleware is Essential for Humanoid Robots\\\\n\\\\u200b\\\\nHumanoid robots are complex systems that require coordination between multiple subsystems including perception, planning, control, and actuation. Middleware like ROS 2 provides the essential communication infrastructure that allows these different subsystems to work together seamlessly.\\\\nFor\\', \\'score\\': 0.6773571, \\'source_number\\': 1}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2\\', \\'chunk_index\\': 4, \\'text\\': \"bots to execute actions through standardized interfaces to actuators and control systems, allowing them to interact with the environment.\\\\n3. Perception-Action Loops\\\\n\\\\u200b\\\\nROS 2\\'s communication infrastructure supports the implementation of perception-action loops that are fundamental to embodied intelligence.\\\\n4. Learning from Interaction\\\\n\\\\u200b\\\\nROS 2\\'s data logging and replay capabilities enable robots to learn from their interactions with the environment, a key aspect of embodied intelligence.\\\\n5. Distributed Intelligence\\\\n\\\\u200b\\\\nROS 2 allows intelligence to be distributed across different nodes, enabling more sophisticated behaviors as the robot interacts with its environment.\\\\nSummary\\\\n\\\\u200b\\\\nROS 2 represents a significant advancement in robotics middleware, specifically designed to address the challenges of modern robotics applications including humanoid robots. Its foundation on DDS provides robust, scalable, and configurable communication that is essential for complex robotic systems. As we continue to explore the potential of embodied intelligence, ROS 2 provides the necessary infrastructure to creat\", \\'score\\': 0.63640505, \\'source_number\\': 2}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2\\', \\'chunk_index\\': 2, \\'text\\': \\'-robot systems, making it ideal for scenarios where multiple humanoid robots need to coordinate.\\\\n3. Security\\\\n\\\\u200b\\\\nROS 2 includes built-in security features including authentication, authorization, and encryption, which are critical for deploying robots in real-world environments.\\\\n4. Deterministic Behavior\\\\n\\\\u200b\\\\nROS 2 provides more deterministic behavior compared to ROS 1, which is important for predictable robot performance.\\\\n5. Professional Use\\\\n\\\\u200b\\\\nROS 2 is designed to support professional and commercial applications, with better support for deployment, maintenance, and lifecycle management.\\\\nDDS Concepts\\\\n\\\\u200b\\\\nROS 2 uses DDS (Data Distribution Service) as its underlying communication middleware. DDS is a specification that provides a standardized API for machine-to-machine communication.\\\\nKey DDS Concepts:\\\\n\\\\u200b\\\\n1. Data-Centric Architecture\\\\n\\\\u200b\\\\nUnlike traditional request-reply patterns, DDS uses a data-centric approach where data producers and consumers are decoupled in time, space, and synchronization.\\\\n2. Quality of Service (QoS) Policies\\\\n\\\\u200b\\\\nDDS provides QoS policies that allow fine-tuning of communicat\\', \\'score\\': 0.60405266, \\'source_number\\': 3}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2\\', \\'chunk_index\\': 5, \\'text\\': \"explore the potential of embodied intelligence, ROS 2 provides the necessary infrastructure to create robots that can intelligently interact with their environment.\\\\nWhat is ROS 2?\\\\nWhy Middleware is Essential for Humanoid Robots\\\\nROS 2 Design Goals\\\\n1. Real-time Support\\\\n2. Multi-Robot Support\\\\n3. Security\\\\n4. Deterministic Behavior\\\\n5. Professional Use\\\\nDDS Concepts\\\\nKey DDS Concepts:\\\\nROS 2\\'s Role in Embodied Intelligence\\\\n1. Sensor Integration\\\\n2. Action Execution\\\\n3. Perception-Action Loops\\\\n4. Learning from Interaction\\\\n5. Distributed Intelligence\\\\nSummary\", \\'score\\': 0.5426538, \\'source_number\\': 4}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2\\', \\'chunk_index\\': 3, \\'text\\': \". Quality of Service (QoS) Policies\\\\n\\\\u200b\\\\nDDS provides QoS policies that allow fine-tuning of communication behavior:\\\\nReliability\\\\n: Best effort or reliable delivery\\\\nDurability\\\\n: Volatile or transient data\\\\nHistory\\\\n: Keep all samples or only the most recent\\\\nDeadline\\\\n: Maximum time between sample updates\\\\nLiveliness\\\\n: How to determine if a participant is alive\\\\n3. 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Answer ONLY using the retrieved book content provided by the retrieve_chunks tool.\\n2. Do NOT use external knowledge or make up information.\\n3. If the retrieved content does not contain relevant information, say \"I couldn\\'t find relevant information in the book.\"\\n4. Always cite your sources using the format [Source 1], [Source 2], etc. Each source number corresponds to the chunk number from the tool.\\n5. 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It is a collection of tools, libraries, and conventions that aim to simplify the task of creating complex and robust robot behavior across a wide variety of robot platforms, applications, and use cases.\\\\nROS 2 is the second generation of the Robot Operating System, designed to address the limitations of the original ROS and to provide a more robust, scalable, and production-ready framework for robotics development.\\\\nWhy Middleware is Essential for Humanoid Robots\\\\n\\\\u200b\\\\nHumanoid robots are complex systems that require coordination between multiple subsystems including perception, planning, control, and actuation. Middleware like ROS 2 provides the essential communication infrastructure that allows these different subsystems to work together seamlessly.\\\\nFor\\', \\'score\\': 0.6184138, \\'source_number\\': 1}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2\\', \\'chunk_index\\': 4, \\'text\\': \"bots to execute actions through standardized interfaces to actuators and control systems, allowing them to interact with the environment.\\\\n3. Perception-Action Loops\\\\n\\\\u200b\\\\nROS 2\\'s communication infrastructure supports the implementation of perception-action loops that are fundamental to embodied intelligence.\\\\n4. Learning from Interaction\\\\n\\\\u200b\\\\nROS 2\\'s data logging and replay capabilities enable robots to learn from their interactions with the environment, a key aspect of embodied intelligence.\\\\n5. Distributed Intelligence\\\\n\\\\u200b\\\\nROS 2 allows intelligence to be distributed across different nodes, enabling more sophisticated behaviors as the robot interacts with its environment.\\\\nSummary\\\\n\\\\u200b\\\\nROS 2 represents a significant advancement in robotics middleware, specifically designed to address the challenges of modern robotics applications including humanoid robots. Its foundation on DDS provides robust, scalable, and configurable communication that is essential for complex robotic systems. As we continue to explore the potential of embodied intelligence, ROS 2 provides the necessary infrastructure to creat\", \\'score\\': 0.59927875, \\'source_number\\': 2}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2\\', \\'chunk_index\\': 2, \\'text\\': \\'-robot systems, making it ideal for scenarios where multiple humanoid robots need to coordinate.\\\\n3. Security\\\\n\\\\u200b\\\\nROS 2 includes built-in security features including authentication, authorization, and encryption, which are critical for deploying robots in real-world environments.\\\\n4. Deterministic Behavior\\\\n\\\\u200b\\\\nROS 2 provides more deterministic behavior compared to ROS 1, which is important for predictable robot performance.\\\\n5. Professional Use\\\\n\\\\u200b\\\\nROS 2 is designed to support professional and commercial applications, with better support for deployment, maintenance, and lifecycle management.\\\\nDDS Concepts\\\\n\\\\u200b\\\\nROS 2 uses DDS (Data Distribution Service) as its underlying communication middleware. DDS is a specification that provides a standardized API for machine-to-machine communication.\\\\nKey DDS Concepts:\\\\n\\\\u200b\\\\n1. Data-Centric Architecture\\\\n\\\\u200b\\\\nUnlike traditional request-reply patterns, DDS uses a data-centric approach where data producers and consumers are decoupled in time, space, and synchronization.\\\\n2. Quality of Service (QoS) Policies\\\\n\\\\u200b\\\\nDDS provides QoS policies that allow fine-tuning of communicat\\', \\'score\\': 0.55866444, \\'source_number\\': 3}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2\\', \\'chunk_index\\': 5, \\'text\\': \"explore the potential of embodied intelligence, ROS 2 provides the necessary infrastructure to create robots that can intelligently interact with their environment.\\\\nWhat is ROS 2?\\\\nWhy Middleware is Essential for Humanoid Robots\\\\nROS 2 Design Goals\\\\n1. Real-time Support\\\\n2. Multi-Robot Support\\\\n3. Security\\\\n4. Deterministic Behavior\\\\n5. Professional Use\\\\nDDS Concepts\\\\nKey DDS Concepts:\\\\nROS 2\\'s Role in Embodied Intelligence\\\\n1. Sensor Integration\\\\n2. Action Execution\\\\n3. Perception-Action Loops\\\\n4. Learning from Interaction\\\\n5. Distributed Intelligence\\\\nSummary\", \\'score\\': 0.503533, \\'source_number\\': 4}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/ros2-communication-model\\', \\'chunk_index\\': 0, \\'text\\': \"ROS 2 Communication Model | Humanoid Robotics with ROS 2\\\\nSkip to main content\\\\nOn this page\\\\nNodes\\\\n\\\\u200b\\\\nIn ROS 2, a\\\\nNode\\\\nis the fundamental unit of execution. Nodes are processes that perform computation and communicate with other nodes through messages. Each node typically performs a specific task or function within the larger robotic system.\\\\nNode Characteristics\\\\n\\\\u200b\\\\nProcess-based\\\\n: Each node runs as a separate process\\\\nSingle-threaded execution\\\\n: By default, nodes execute callbacks in a single thread\\\\nNamespaced\\\\n: Nodes can have namespaces for organization\\\\nComposable\\\\n: Multiple nodes can be combined into a single process for efficiency\\\\nCreating Nodes\\\\n\\\\u200b\\\\nNodes are created by inheriting from the\\\\nNode\\\\nclass in your chosen client library (rclpy for Python, rclcpp for C++):\\\\nimport\\\\nrclpy\\\\nfrom\\\\nrclpy\\\\n.\\\\nnode\\\\nimport\\\\nNode\\\\nclass\\\\nMyNode\\\\n(\\\\nNode\\\\n)\\\\n:\\\\ndef\\\\n__init__\\\\n(\\\\nself\\\\n)\\\\n:\\\\nsuper\\\\n(\\\\n)\\\\n.\\\\n__init__\\\\n(\\\\n\\'my_node_name\\'\\\\n)\\\\n# Initialize node-specific components 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Answer ONLY using the retrieved book content provided by the retrieve_chunks tool.\\n2. Do NOT use external knowledge or make up information.\\n3. If the retrieved content does not contain relevant information, say \"I couldn\\'t find relevant information in the book.\"\\n4. Always cite your sources using the format [Source 1], [Source 2], etc. Each source number corresponds to the chunk number from the tool.\\n5. Be concise and accurate.\\n\\nYour responses should be helpful, clear, and grounded exclusively in the provided context.', 'role': 'system'}, {'role': 'user', 'content': 'vla sytems'}, {'role': 'assistant', 'content': None, 'tool_calls': [{'id': 'call_38f000a59a5749209232a7ac', 'type': 'function', 'function': {'name': 'retrieve_chunks', 'arguments': '{\"query\": \"VLA systems\", \"top_k\": 5}'}}]}, {'role': 'tool', 'tool_call_id': 'call_38f000a59a5749209232a7ac', 'content': '[{\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 7, \\'text\\': \\'collaborative tasks\\\\nChallenges in Embodied Intelligence\\\\n\\\\u200b\\\\nReal-World Complexity\\\\n\\\\u200b\\\\nDealing with uncertainty and noise in sensory inputs\\\\nHandling dynamic and unpredictable environments\\\\nManaging the complexity of real-world physics\\\\nLearning Efficiency\\\\n\\\\u200b\\\\nBalancing exploration with exploitation\\\\nTransferring learning across different contexts\\\\nScaling learning to complex real-world tasks\\\\nSafety and Reliability\\\\n\\\\u200b\\\\nEnsuring safe behavior in human environments\\\\nHandling failures gracefully\\\\nMaintaining reliable operation over extended periods\\\\nTechnical Architecture\\\\n\\\\u200b\\\\nA typical VLA system architecture includes:\\\\n[Human Language Input] \u2192 [Language Encoder] \u2192 [Fusion Module] \u2192 [Action Planner]\\\\n\u2191 \u2193 \u2193\\\\n[Visual Input] \u2192 [Vision Encoder] \u2192 [Memory] \u2192 [World Model] \u2192 [Action Executor]\\\\nVisual Architecture Diagram\\\\n\\\\u200b\\\\ngraph TB\\\\nsubgraph \"Human Input\"\\\\nA[Human Language] --> D[Language Encoder]\\\\nB[Visual Scene] --> E[Vision Encoder]\\\\nend\\\\nsubgraph \"VLA Processing\"\\\\nD --> F[Fusion Module]\\\\nE --> F\\\\nF --> G[Memory System]\\\\nF --> H[World Model]\\\\nG --> I[Action Planner]\\\\nH --> I\\\\nI --> J[Action\\', \\'score\\': 0.5488081, \\'source_number\\': 1}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 20, \\'text\\': \\'l perception, language understanding, and action execution, these systems can perform complex tasks that require understanding both linguistic commands and visual contexts. In humanoid robotics, VLA systems enable more intuitive interaction and more capable task execution, making robots more useful and accessible to human users.\\\\nThe complete VLA system combines the foundational concepts from this chapter with the voice-to-action capabilities from Chapter 2 and the cognitive planning from Chapter 3, creating a comprehensive framework for human-robot interaction that can handle complex, real-world tasks.\\\\nIntroduction\\\\nThe Three Modalities of VLA Systems\\\\nVision Processing\\\\nLanguage Understanding\\\\nAction Execution\\\\nConvergence in Embodied Intelligence\\\\nMulti-Modal Integration\\\\nClosed-Loop Interaction\\\\nLearning from Interaction\\\\nApplications in Humanoid Robotics\\\\nHuman-Robot Interaction\\\\nComplex Task Execution\\\\nSocial Navigation\\\\nEmbodied Intelligence Systems\\\\nKey Principles of Embodied Intelligence\\\\nEmbodied Intelligence in VLA Systems\\\\nBenefits of Embodied Intelligence\\\\nChallenges in Embodied Intellige\\', \\'score\\': 0.5286542, \\'source_number\\': 2}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 0, \\'text\\': \\'Vision-Language-Action (VLA) Overview | Humanoid Robotics with ROS 2\\\\nSkip to main content\\\\nOn this page\\\\nIntroduction\\\\n\\\\u200b\\\\nVision-Language-Action (VLA) systems represent a paradigm shift in robotics, where visual perception, natural language understanding, and robotic action execution are tightly integrated to enable more intuitive and capable robotic systems. This integration allows robots to understand complex human instructions, perceive their environment, and execute sophisticated tasks in a coordinated manner.\\\\nFor humanoid robotics, VLA systems are particularly important as they enable robots to interact naturally with humans and their environments using multiple modalities simultaneously. This chapter explores the convergence of these three critical components and how they work together to create embodied intelligence.\\\\nThe Three Modalities of VLA Systems\\\\n\\\\u200b\\\\nVision Processing\\\\n\\\\u200b\\\\nVision processing in VLA systems goes beyond simple object detection to include:\\\\nScene Understanding\\\\n: Compreh\\', \\'score\\': 0.5286358, \\'source_number\\': 3}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 1, \\'text\\': \\'cessing in VLA systems goes beyond simple object detection to include:\\\\nScene Understanding\\\\n: Comprehending the spatial relationships between objects and understanding the context of the environment\\\\nVisual Question Answering\\\\n: Answering questions about the visual scene that require both perception and reasoning\\\\nVisual Grounding\\\\n: Connecting visual elements with language concepts, allowing robots to understand references like \"the red cup on the table\"\\\\nKey components of vision processing include:\\\\nObject detection and recognition\\\\nDepth estimation and 3D scene reconstruction\\\\nSemantic segmentation\\\\nVisual tracking and motion analysis\\\\nLanguage Understanding\\\\n\\\\u200b\\\\nLanguage understanding in VLA systems encompasses:\\\\nNatural Language Processing\\\\n: Converting human language into structured representations that robots can process\\\\nIntent Extraction\\\\n: Identifying the underlying goals and intentions behind human commands\\\\nContextual Reasoning\\\\n: Understanding language in the context of the visual scene and robot capabilities\\\\nThe language component must handle various forms of human communication:\\\\nDirect co\\', \\'score\\': 0.49321204, \\'source_number\\': 4}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 3, \\'text\\': \\'es high-level goals and context for visual processing\\\\nAction execution is guided by both visual perception and language commands\\\\nClosed-Loop Interaction\\\\n\\\\u200b\\\\nVLA systems operate in a closed-loop manner:\\\\nPerception\\\\n: The robot observes its environment\\\\nUnderstanding\\\\n: Visual and linguistic information is processed\\\\nPlanning\\\\n: Actions are planned based on goals and current state\\\\nExecution\\\\n: Actions are performed in the environment\\\\nFeedback\\\\n: New observations inform the next cycle\\\\nLearning from Interaction\\\\n\\\\u200b\\\\nVLA systems can learn from their interactions:\\\\nReinforcement Learning\\\\n: Learning which action sequences lead to successful outcomes\\\\nImitation Learning\\\\n: Learning from human demonstrations that combine visual, linguistic, and action components\\\\nLanguage-Guided Learning\\\\n: Using language to specify what to learn and how to evaluate success\\\\nApplications in Humanoid Robotics\\\\n\\\\u200b\\\\nVLA systems enable humanoid robots to perform complex tasks that require 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Answer ONLY using the retrieved book content provided by the retrieve_chunks tool.\\n2. Do NOT use external knowledge or make up information.\\n3. If the retrieved content does not contain relevant information, say \"I couldn\\'t find relevant information in the book.\"\\n4. Always cite your sources using the format [Source 1], [Source 2], etc. Each source number corresponds to the chunk number from the tool.\\n5. 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It is a collection of tools, libraries, and conventions that aim to simplify the task of creating complex and robust robot behavior across a wide variety of robot platforms, applications, and use cases.\\\\nROS 2 is the second generation of the Robot Operating System, designed to address the limitations of the original ROS and to provide a more robust, scalable, and production-ready framework for robotics development.\\\\nWhy Middleware is Essential for Humanoid Robots\\\\n\\\\u200b\\\\nHumanoid robots are complex systems that require coordination between multiple subsystems including perception, planning, control, and actuation. 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Distributed Intelligence\\\\n\\\\u200b\\\\nROS 2 allows intelligence to be distributed across different nodes, enabling more sophisticated behaviors as the robot interacts with its environment.\\\\nSummary\\\\n\\\\u200b\\\\nROS 2 represents a significant advancement in robotics middleware, specifically designed to address the challenges of modern robotics applications including humanoid robots. Its foundation on DDS provides robust, scalable, and configurable communication that is essential for complex robotic systems. As we continue to explore the potential of embodied intelligence, ROS 2 provides the necessary infrastructure to creat\", \\'score\\': 0.59883505, \\'source_number\\': 2}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2\\', \\'chunk_index\\': 2, \\'text\\': \\'-robot systems, making it ideal for scenarios where multiple humanoid robots need to coordinate.\\\\n3. Security\\\\n\\\\u200b\\\\nROS 2 includes built-in security features including authentication, authorization, and encryption, which are critical for deploying robots in real-world environments.\\\\n4. Deterministic Behavior\\\\n\\\\u200b\\\\nROS 2 provides more deterministic behavior compared to ROS 1, which is important for predictable robot performance.\\\\n5. Professional Use\\\\n\\\\u200b\\\\nROS 2 is designed to support professional and commercial applications, with better support for deployment, maintenance, and lifecycle management.\\\\nDDS Concepts\\\\n\\\\u200b\\\\nROS 2 uses DDS (Data Distribution Service) as its underlying communication middleware. DDS is a specification that provides a standardized API for machine-to-machine communication.\\\\nKey DDS Concepts:\\\\n\\\\u200b\\\\n1. 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Each node typically performs a specific task or function within the larger robotic system.\\\\nNode Characteristics\\\\n\\\\u200b\\\\nProcess-based\\\\n: Each node runs as a separate process\\\\nSingle-threaded execution\\\\n: By default, nodes execute callbacks in a single thread\\\\nNamespaced\\\\n: Nodes can have namespaces for organization\\\\nComposable\\\\n: Multiple nodes can be combined into a single process for efficiency\\\\nCreating Nodes\\\\n\\\\u200b\\\\nNodes are created by inheriting from the\\\\nNode\\\\nclass in your chosen client library (rclpy for Python, rclcpp for C++):\\\\nimport\\\\nrclpy\\\\nfrom\\\\nrclpy\\\\n.\\\\nnode\\\\nimport\\\\nNode\\\\nclass\\\\nMyNode\\\\n(\\\\nNode\\\\n)\\\\n:\\\\ndef\\\\n__init__\\\\n(\\\\nself\\\\n)\\\\n:\\\\nsuper\\\\n(\\\\n)\\\\n.\\\\n__init__\\\\n(\\\\n\\'my_node_name\\'\\\\n)\\\\n# Initialize node-specific components 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Security\\\\n\\\\u200b\\\\nROS 2 includes built-in security features including authentication, authorization, and encryption, which are critical for deploying robots in real-world environments.\\\\n4. Deterministic Behavior\\\\n\\\\u200b\\\\nROS 2 provides more deterministic behavior compared to ROS 1, which is important for predictable robot performance.\\\\n5. Professional Use\\\\n\\\\u200b\\\\nROS 2 is designed to support professional and commercial applications, with better support for deployment, maintenance, and lifecycle management.\\\\nDDS Concepts\\\\n\\\\u200b\\\\nROS 2 uses DDS (Data Distribution Service) as its underlying communication middleware. DDS is a specification that provides a standardized API for machine-to-machine communication.\\\\nKey DDS Concepts:\\\\n\\\\u200b\\\\n1. 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Answer ONLY using the retrieved book content provided by the retrieve_chunks tool.\\n2. Do NOT use external knowledge or make up information.\\n3. If the retrieved content does not contain relevant information, say \"I couldn\\'t find relevant information in the book.\"\\n4. Always cite your sources using the format [Source 1], [Source 2], etc. Each source number corresponds to the chunk number from the tool.\\n5. 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Perception-Action Loops\\\\n\\\\u200b\\\\nROS 2\\'s communication infrastructure supports the implementation of perception-action loops that are fundamental to embodied intelligence.\\\\n4. Learning from Interaction\\\\n\\\\u200b\\\\nROS 2\\'s data logging and replay capabilities enable robots to learn from their interactions with the environment, a key aspect of embodied intelligence.\\\\n5. Distributed Intelligence\\\\n\\\\u200b\\\\nROS 2 allows intelligence to be distributed across different nodes, enabling more sophisticated behaviors as the robot interacts with its environment.\\\\nSummary\\\\n\\\\u200b\\\\nROS 2 represents a significant advancement in robotics middleware, specifically designed to address the challenges of modern robotics applications including humanoid robots. Its foundation on DDS provides robust, scalable, and configurable communication that is essential for complex robotic systems. As we continue to explore the potential of embodied intelligence, ROS 2 provides the necessary infrastructure to creat\", \\'score\\': 0.6439354, \\'source_number\\': 2}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/ros2-communication-model\\', \\'chunk_index\\': 10, \\'text\\': \"te command\\\\nself\\\\n.\\\\ncmd_pub\\\\n.\\\\npublish\\\\n(\\\\ncmd_vel\\\\n)\\\\ndef\\\\nmain\\\\n(\\\\nargs\\\\n=\\\\nNone\\\\n)\\\\n:\\\\nrclpy\\\\n.\\\\ninit\\\\n(\\\\nargs\\\\n=\\\\nargs\\\\n)\\\\nloop_node\\\\n=\\\\nPerceptionDecisionActionLoop\\\\n(\\\\n)\\\\nrclpy\\\\n.\\\\nspin\\\\n(\\\\nloop_node\\\\n)\\\\nloop_node\\\\n.\\\\ndestroy_node\\\\n(\\\\n)\\\\nrclpy\\\\n.\\\\nshutdown\\\\n(\\\\n)\\\\nif\\\\n__name__\\\\n==\\\\n\\'__main__\\'\\\\n:\\\\nmain\\\\n(\\\\n)\\\\nSummary\\\\n\\\\u200b\\\\nThe ROS 2 communication model provides a flexible and robust framework for building complex robotic systems. The combination of nodes, topics, and services enables the creation of distributed systems where AI agents can seamlessly interface with robot controllers. The perception-decision-action loop provides a fundamental pattern for implementing intelligent behavior in robotic systems.\\\\nNodes\\\\nNode Characteristics\\\\nCreating Nodes\\\\nTopics and Publish/Subscribe Data Flow\\\\nPublishers and Subscribers\\\\nExample: Publisher\\\\nExample: Subscriber\\\\nServices\\\\nService Characteristics\\\\nExample: Service Server\\\\nExample: Service Client\\\\nBridging Python AI Agents to Robot Controllers using rclpy\\\\nrclpy Overview\\\\nIntegration Pattern\\\\nExample: AI Agent Bridge\\\\nThe Perception \u2192 Decision \u2192 Action Loop\\\\nLoop Components\\\\nROS 2 Implementation\\\\nExample Compl\", \\'score\\': 0.6318886, \\'source_number\\': 3}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/navigation-intelligence\\', \\'chunk_index\\': 10, \\'text\\': \"eleration\\\\nPerception Outputs\\\\n: Object detection, localization, mapping\\\\nData Fusion\\\\n: Combines multiple sensor inputs\\\\nWorld Model\\\\n: Maintains current understanding of environment\\\\nNavigation Planning\\\\n\\\\u200b\\\\nGoal Setting\\\\n: High-level navigation goals\\\\nPath Planning\\\\n: Global and local path computation\\\\nObstacle Avoidance\\\\n: Real-time obstacle detection and avoidance\\\\nExecution\\\\n: Sending commands to robot controllers\\\\nExample Integration Pipeline\\\\n\\\\u200b\\\\nimport\\\\nrclpy\\\\nfrom\\\\nrclpy\\\\n.\\\\nnode\\\\nimport\\\\nNode\\\\nfrom\\\\ngeometry_msgs\\\\n.\\\\nmsg\\\\nimport\\\\nPoseStamped\\\\nfrom\\\\nnav2_msgs\\\\n.\\\\naction\\\\nimport\\\\nNavigateToPose\\\\nfrom\\\\nsensor_msgs\\\\n.\\\\nmsg\\\\nimport\\\\nImage\\\\n,\\\\nPointCloud2\\\\nfrom\\\\nisaac_ros_detectnet_interfaces\\\\n.\\\\nmsg\\\\nimport\\\\nDetection2DArray\\\\nfrom\\\\nnav2_simple_commander\\\\n.\\\\nrobot_navigator\\\\nimport\\\\nBasicNavigator\\\\nclass\\\\nHumanoidNavigationNode\\\\n(\\\\nNode\\\\n)\\\\n:\\\\ndef\\\\n__init__\\\\n(\\\\nself\\\\n)\\\\n:\\\\nsuper\\\\n(\\\\n)\\\\n.\\\\n__init__\\\\n(\\\\n\\'humanoid_navigation_node\\'\\\\n)\\\\n# Initialize navigation system\\\\nself\\\\n.\\\\nnavigator\\\\n=\\\\nBasicNavigator\\\\n(\\\\n)\\\\n# Subscribe to Isaac ROS perception outputs\\\\nself\\\\n.\\\\ndetection_sub\\\\n=\\\\nself\\\\n.\\\\ncreate_subscription\\\\n(\\\\nDetection2DArray\\\\n,\\\\n\\'/detectnet/detections\\'\\\\n,\\\\nself\\\\n.\\\\ndetection_cal\", \\'score\\': 0.60370934, \\'source_number\\': 4}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/navigation-intelligence\\', \\'chunk_index\\': 0, \\'text\\': \"Navigation & Intelligence | Humanoid Robotics with ROS 2\\\\nSkip to main content\\\\nOn this page\\\\nIsaac ROS for Accelerated Perception and VSLAM\\\\n\\\\u200b\\\\nIntroduction to Isaac ROS\\\\n\\\\u200b\\\\nIsaac ROS is a collection of hardware-accelerated perception and autonomy packages that seamlessly integrate with the ROS 2 ecosystem. 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Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/agent.py", "thread": 131317680305856, "threadName": "ThreadPoolExecutor-4_0", "processName": "MainProcess", "process": 3886, "taskName": null} +{"timestamp": "2026-02-17T22:01:43.156803Z", "level": "DEBUG", "name": "httpcore.connection", "message": "close.started", "module": "_trace", "lineno": 47, "pathname": "/home/m-ahmad-official/venv/lib/python3.12/site-packages/httpcore/_trace.py", "thread": 131317680305856, "threadName": "ThreadPoolExecutor-4_0", "processName": "MainProcess", "process": 3886, "taskName": null} +{"timestamp": "2026-02-17T22:01:43.157704Z", "level": "DEBUG", "name": "httpcore.connection", "message": "close.complete", "module": "_trace", "lineno": 47, "pathname": "/home/m-ahmad-official/venv/lib/python3.12/site-packages/httpcore/_trace.py", "thread": 131317680305856, "threadName": "ThreadPoolExecutor-4_0", "processName": "MainProcess", "process": 3886, "taskName": null} +{"timestamp": "2026-02-17T22:01:43.160445Z", "level": "DEBUG", "name": "openai.agents", "message": "Tool retrieve_chunks completed.", "module": "tool", "lineno": 916, "pathname": "/home/m-ahmad-official/venv/lib/python3.12/site-packages/agents/tool.py", "thread": 131318150766720, "threadName": "MainThread", "processName": "MainProcess", "process": 3886, "taskName": "Task-398"} +{"timestamp": "2026-02-17T22:01:43.162709Z", "level": "DEBUG", "name": "openai.agents", "message": "Running agent RAG Book Assistant (turn 2)", "module": "run", "lineno": 930, "pathname": "/home/m-ahmad-official/venv/lib/python3.12/site-packages/agents/run.py", "thread": 131318150766720, "threadName": "MainThread", "processName": "MainProcess", "process": 3886, "taskName": "Task-380"} +{"timestamp": "2026-02-17T22:01:43.164323Z", "level": "DEBUG", "name": "openai.agents", "message": "No conversation_id available for request", "module": "run_loop", "lineno": 1462, "pathname": "/home/m-ahmad-official/venv/lib/python3.12/site-packages/agents/run_internal/run_loop.py", "thread": 131318150766720, "threadName": "MainThread", "processName": "MainProcess", "process": 3886, "taskName": "Task-380"} +{"timestamp": "2026-02-17T22:01:43.165269Z", "level": "DEBUG", "name": "openai.agents", "message": "Creating span with id None", "module": "provider", "lineno": 350, "pathname": "/home/m-ahmad-official/venv/lib/python3.12/site-packages/agents/tracing/provider.py", "thread": 131318150766720, "threadName": "MainThread", "processName": "MainProcess", "process": 3886, "taskName": "Task-380"} +{"timestamp": "2026-02-17T22:01:43.166451Z", "level": "DEBUG", "name": "openai.agents", "message": "Calling LLM", "module": "openai_chatcompletions", "lineno": 308, "pathname": "/home/m-ahmad-official/venv/lib/python3.12/site-packages/agents/models/openai_chatcompletions.py", "thread": 131318150766720, "threadName": "MainThread", "processName": "MainProcess", "process": 3886, "taskName": "Task-380"} +{"timestamp": "2026-02-17T22:01:43.171097Z", "level": "DEBUG", "name": "openai._base_client", "message": "Request options: {'method': 'post', 'url': '/chat/completions', 'headers': {'User-Agent': 'Agents/Python 0.8.1'}, 'files': None, 'idempotency_key': 'stainless-python-retry-6ae80771-68a8-41ea-9105-102ff4e2f1aa', 'content': None, 'json_data': {'messages': [{'content': 'You are a helpful assistant answering questions about a humanoid robotics book.\\n\\nIMPORTANT GROUNDING RULES:\\n1. Answer ONLY using the retrieved book content provided by the retrieve_chunks tool.\\n2. Do NOT use external knowledge or make up information.\\n3. If the retrieved content does not contain relevant information, say \"I couldn\\'t find relevant information in the book.\"\\n4. Always cite your sources using the format [Source 1], [Source 2], etc. Each source number corresponds to the chunk number from the tool.\\n5. Be concise and accurate.\\n\\nYour responses should be helpful, clear, and grounded exclusively in the provided context.', 'role': 'system'}, {'role': 'user', 'content': 'show me code ROS 2 Integration'}, {'role': 'assistant', 'content': None, 'tool_calls': [{'id': 'call_9cbe9e3e1e22400a94ae59de', 'type': 'function', 'function': {'name': 'retrieve_chunks', 'arguments': '{\"query\": \"ROS 2 Integration\", \"top_k\": 10}'}}]}, {'role': 'tool', 'tool_call_id': 'call_9cbe9e3e1e22400a94ae59de', 'content': '[{\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2\\', \\'chunk_index\\': 4, \\'text\\': \"bots to execute actions through standardized interfaces to actuators and control systems, allowing them to interact with the environment.\\\\n3. Perception-Action Loops\\\\n\\\\u200b\\\\nROS 2\\'s communication infrastructure supports the implementation of perception-action loops that are fundamental to embodied intelligence.\\\\n4. Learning from Interaction\\\\n\\\\u200b\\\\nROS 2\\'s data logging and replay capabilities enable robots to learn from their interactions with the environment, a key aspect of embodied intelligence.\\\\n5. Distributed Intelligence\\\\n\\\\u200b\\\\nROS 2 allows intelligence to be distributed across different nodes, enabling more sophisticated behaviors as the robot interacts with its environment.\\\\nSummary\\\\n\\\\u200b\\\\nROS 2 represents a significant advancement in robotics middleware, specifically designed to address the challenges of modern robotics applications including humanoid robots. Its foundation on DDS provides robust, scalable, and configurable communication that is essential for complex robotic systems. As we continue to explore the potential of embodied intelligence, ROS 2 provides the necessary infrastructure to creat\", \\'score\\': 0.60485435, \\'source_number\\': 1}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2\\', \\'chunk_index\\': 0, \\'text\\': \\'Introduction to ROS 2 for Physical AI | Humanoid Robotics with ROS 2\\\\nSkip to main content\\\\nOn this page\\\\nWhat is ROS 2?\\\\n\\\\u200b\\\\nROS 2 (Robot Operating System 2) is not an operating system, but rather a flexible framework for writing robot software. It is a collection of tools, libraries, and conventions that aim to simplify the task of creating complex and robust robot behavior across a wide variety of robot platforms, applications, and use cases.\\\\nROS 2 is the second generation of the Robot Operating System, designed to address the limitations of the original ROS and to provide a more robust, scalable, and production-ready framework for robotics development.\\\\nWhy Middleware is Essential for Humanoid Robots\\\\n\\\\u200b\\\\nHumanoid robots are complex systems that require coordination between multiple subsystems including perception, planning, control, and actuation. Middleware like ROS 2 provides the essential communication infrastructure that allows these different subsystems to work together seamlessly.\\\\nFor\\', \\'score\\': 0.5935259, \\'source_number\\': 2}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2\\', \\'chunk_index\\': 2, \\'text\\': \\'-robot systems, making it ideal for scenarios where multiple humanoid robots need to coordinate.\\\\n3. Security\\\\n\\\\u200b\\\\nROS 2 includes built-in security features including authentication, authorization, and encryption, which are critical for deploying robots in real-world environments.\\\\n4. Deterministic Behavior\\\\n\\\\u200b\\\\nROS 2 provides more deterministic behavior compared to ROS 1, which is important for predictable robot performance.\\\\n5. Professional Use\\\\n\\\\u200b\\\\nROS 2 is designed to support professional and commercial applications, with better support for deployment, maintenance, and lifecycle management.\\\\nDDS Concepts\\\\n\\\\u200b\\\\nROS 2 uses DDS (Data Distribution Service) as its underlying communication middleware. DDS is a specification that provides a standardized API for machine-to-machine communication.\\\\nKey DDS Concepts:\\\\n\\\\u200b\\\\n1. Data-Centric Architecture\\\\n\\\\u200b\\\\nUnlike traditional request-reply patterns, DDS uses a data-centric approach where data producers and consumers are decoupled in time, space, and synchronization.\\\\n2. Quality of Service (QoS) Policies\\\\n\\\\u200b\\\\nDDS provides QoS policies that allow fine-tuning of communicat\\', \\'score\\': 0.56961155, \\'source_number\\': 3}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/cognitive-planning-llms\\', \\'chunk_index\\': 21, \\'text\\': \\'\\\\nplan_from_goal\\\\n(\\\\ngoal\\\\n=\\\\ngoal\\\\n,\\\\nrobot_capabilities\\\\n=\\\\nrobot_capabilities\\\\n,\\\\nenvironment_context\\\\n=\\\\nenvironment_context\\\\n)\\\\n# Execute the action sequence in ROS 2\\\\nexecution_result\\\\n=\\\\nawait\\\\nself\\\\n.\\\\naction_executor\\\\n.\\\\nexecute_action_sequence\\\\n(\\\\nplan\\\\n[\\\\n\"action_sequence\"\\\\n]\\\\n)\\\\nreturn\\\\n{\\\\n\"plan\"\\\\n:\\\\nplan\\\\n,\\\\n\"execution_result\"\\\\n:\\\\nexecution_result\\\\n,\\\\n\"success\"\\\\n:\\\\nexecution_result\\\\n[\\\\n\"success\"\\\\n]\\\\n}\\\\n# Example usage in a ROS 2 node\\\\nasync\\\\ndef\\\\nmain\\\\n(\\\\n)\\\\n:\\\\nrclpy\\\\n.\\\\ninit\\\\n(\\\\n)\\\\nnode\\\\n=\\\\nrclpy\\\\n.\\\\ncreate_node\\\\n(\\\\n\\\\\\'llm_planner_node\\\\\\'\\\\n)\\\\n# Initialize the LLM planner (with your API key)\\\\nllm_planner\\\\n=\\\\nLLMBasedPlanner\\\\n(\\\\napi_key\\\\n=\\\\n\"your-api-key\"\\\\n)\\\\n# Create the integration\\\\nintegration\\\\n=\\\\nROS2LLMPlannerIntegration\\\\n(\\\\nnode\\\\n,\\\\nllm_planner\\\\n)\\\\n# Execute a complex goal\\\\nresult\\\\n=\\\\nawait\\\\nintegration\\\\n.\\\\nexecute_llm_plan\\\\n(\\\\ngoal\\\\n=\\\\n\"Go to the kitchen, find a red cup, pick it up, and bring it to me\"\\\\n,\\\\nrobot_capabilities\\\\n=\\\\n[\\\\n\"navigate\"\\\\n,\\\\n\"grasp\"\\\\n,\\\\n\"speak\"\\\\n]\\\\n,\\\\nenvironment_context\\\\n=\\\\nget_current_environment_context\\\\n(\\\\n)\\\\n)\\\\nprint\\\\n(\\\\nf\"Plan execution result:\\\\n{\\\\nresult\\\\n[\\\\n\\\\\\'success\\\\\\'\\\\n]\\\\n}\\\\n\"\\\\n)\\\\nrclpy\\\\n.\\\\nshutdown\\\\n(\\\\n)\\\\nService Integration\\\\n\\\\u200b\\\\nfrom\\\\nrclpy\\\\n.\\\\nservice\\\\nimport\\\\nService\\\\nfro\\', \\'score\\': 0.5666875, \\'source_number\\': 4}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/intro-to-ai-robot-brain\\', \\'chunk_index\\': 4, \\'text\\': \"saac ROS nodes publish and subscribe to standard ROS 2 message types\\\\nSame Tools\\\\n: Continue using familiar ROS 2 tools like rviz, rqt, and ros2 CLI\\\\nExisting Packages\\\\n: Isaac components can work alongside existing ROS 2 packages\\\\nLaunch System\\\\n: Compatible with ROS 2 launch files and lifecycle management\\\\nEnhanced Capabilities\\\\n\\\\u200b\\\\nPerformance\\\\n: GPU acceleration dramatically speeds up compute-intensive tasks\\\\nAI Integration\\\\n: Direct access to NVIDIA\\'s AI frameworks (TensorRT, cuDNN)\\\\nSimulation Quality\\\\n: High-fidelity simulation for more effective training\\\\nDevelopment Speed\\\\n: Pre-built components accelerate development cycles\\\\nBenefits of Isaac in the ROS 2 Ecosystem\\\\n\\\\u200b\\\\nThe combination of Isaac and ROS 2 provides unique advantages for robotics development:\\\\nAccelerated Development\\\\n\\\\u200b\\\\nRapid Prototyping\\\\n: Pre-built components and reference applications\\\\nSimulation-Reality Transfer\\\\n: Better generalization from simulation to real robots\\\\nAI-First Approach\\\\n: Native integration of artificial intelligence capabilities\\\\nImproved Performance\\\\n\\\\u200b\\\\nHardware Utilization\\\\n: Optimized for NVIDIA GPUs and Jetson platf\", \\'score\\': 0.5646477, \\'source_number\\': 5}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/urdf-humanoids\\', \\'chunk_index\\': 6, \\'text\\': \"\\\\n<\\\\nmechanicalReduction\\\\n>\\\\n1\\\\n\\\\n\\\\n\\\\nURDF\\'s Role in ROS 2 and Simulators\\\\n\\\\u200b\\\\nIntegration with ROS 2\\\\n\\\\u200b\\\\nURDF integrates with ROS 2 through several key components:\\\\nRobot State Publisher\\\\n: Publishes transforms based on joint states\\\\nTF2\\\\n: Provides coordinate transformations between links\\\\nRViz\\\\n: Visualizes the robot model\\\\nMoveIt\\\\n: Uses URDF for motion planning\\\\nROS 2 Launch Files\\\\n\\\\u200b\\\\nURDF files are typically loaded in ROS 2 launch files:\\\\nfrom\\\\nlaunch\\\\nimport\\\\nLaunchDescription\\\\nfrom\\\\nlaunch\\\\n.\\\\nsubstitutions\\\\nimport\\\\nCommand\\\\n,\\\\nPathJoinSubstitution\\\\nfrom\\\\nlaunch_ros\\\\n.\\\\nactions\\\\nimport\\\\nNode\\\\nfrom\\\\nament_index_python\\\\n.\\\\npackages\\\\nimport\\\\nget_package_share_directory\\\\ndef\\\\ngenerate_launch_description\\\\n(\\\\n)\\\\n:\\\\nurdf_path\\\\n=\\\\nPathJoinSubstitution\\\\n(\\\\n[\\\\nget_package_share_directory\\\\n(\\\\n\\'my_robot_description\\'\\\\n)\\\\n,\\\\n\\'urdf\\'\\\\n,\\\\n\\'robot.urdf\\'\\\\n]\\\\n)\\\\nrobot_state_publisher\\\\n=\\\\nNode\\\\n(\\\\npackage\\\\n=\\\\n\\'robot_state_publisher\\'\\\\n,\\\\nexecutable\\\\n=\\\\n\\'robot_state_publisher\\'\\\\n,\\\\nparameters\\\\n=\\\\n[\\\\n{\\\\n\\'robot_description\\'\\\\n:\\\\nCommand\\\\n(\\\\n[\\\\n\\'xacro \\'\\\\n,\\\\nurdf_path\\\\n]\\\\n)\\\\n}\\\\n]\\\\n)\\\\nreturn\\\\nLaunchDescription\\\\n(\\\\n[\\\\nrobot_state_publisher\\\\n]\\\\n)\\\\nSimulation\", \\'score\\': 0.5434972, \\'source_number\\': 6}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2\\', \\'chunk_index\\': 3, \\'text\\': \". Quality of Service (QoS) Policies\\\\n\\\\u200b\\\\nDDS provides QoS policies that allow fine-tuning of communication behavior:\\\\nReliability\\\\n: Best effort or reliable delivery\\\\nDurability\\\\n: Volatile or transient data\\\\nHistory\\\\n: Keep all samples or only the most recent\\\\nDeadline\\\\n: Maximum time between sample updates\\\\nLiveliness\\\\n: How to determine if a participant is alive\\\\n3. Topics, Publishers, and Subscribers\\\\n\\\\u200b\\\\nTopics\\\\n: Named data channels for communication\\\\nPublishers\\\\n: Entities that send data to topics\\\\nSubscribers\\\\n: Entities that receive data from topics\\\\nROS 2\\'s Role in Embodied Intelligence\\\\n\\\\u200b\\\\nEmbodied intelligence refers to the idea that intelligence emerges from the interaction between an agent and its environment. ROS 2 plays a crucial role in enabling embodied intelligence by:\\\\n1. Sensor Integration\\\\n\\\\u200b\\\\nROS 2 provides standardized interfaces for integrating various sensors (cameras, LIDAR, IMU, force/torque sensors) that allow robots to perceive their environment.\\\\n2. Action Execution\\\\n\\\\u200b\\\\nROS 2 enables robots to execute actions through standardized interfaces to actuators and control systems, allowing t\", \\'score\\': 0.5431277, \\'source_number\\': 7}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper\\', \\'chunk_index\\': 36, \\'text\\': \\'der data privacy when using cloud-based ASR\\\\nImplement local processing where sensitive information is involved\\\\nFollow appropriate data handling practices\\\\nIntegration with ROS 2\\\\n\\\\u200b\\\\nVoice-to-action systems can be integrated with ROS 2 using action servers and clients:\\\\nimport\\\\nrclpy\\\\nfrom\\\\nrclpy\\\\n.\\\\naction\\\\nimport\\\\nActionClient\\\\nfrom\\\\nrclpy\\\\n.\\\\nnode\\\\nimport\\\\nNode\\\\nclass\\\\nVoiceCommandActionClient\\\\n(\\\\nNode\\\\n)\\\\n:\\\\ndef\\\\n__init__\\\\n(\\\\nself\\\\n)\\\\n:\\\\nsuper\\\\n(\\\\n)\\\\n.\\\\n__init__\\\\n(\\\\n\\\\\\'voice_command_client\\\\\\'\\\\n)\\\\nself\\\\n.\\\\n_action_client\\\\n=\\\\nActionClient\\\\n(\\\\nself\\\\n,\\\\nVoiceCommand\\\\n,\\\\n# Custom action message\\\\n\\\\\\'voice_command\\\\\\'\\\\n)\\\\ndef\\\\nsend_voice_command\\\\n(\\\\nself\\\\n,\\\\ntext\\\\n,\\\\nintent\\\\n)\\\\n:\\\\ngoal_msg\\\\n=\\\\nVoiceCommand\\\\n.\\\\nGoal\\\\n(\\\\n)\\\\ngoal_msg\\\\n.\\\\ncommand_text\\\\n=\\\\ntext\\\\ngoal_msg\\\\n.\\\\nintent\\\\n=\\\\nintent\\\\nself\\\\n.\\\\n_action_client\\\\n.\\\\nwait_for_server\\\\n(\\\\n)\\\\nreturn\\\\nself\\\\n.\\\\n_action_client\\\\n.\\\\nsend_goal_async\\\\n(\\\\ngoal_msg\\\\n)\\\\nExercises for Students\\\\n\\\\u200b\\\\nBasic Implementation\\\\n: Implement a simple voice-to-action system that can recognize and execute the commands \"move forward\", \"turn left\", and \"stop\". Test the system with different audio inputs and evaluate its accuracy.\\\\nIntent Extraction Challenge\\\\n: Modify the \\', \\'score\\': 0.5340003, \\'source_number\\': 8}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/navigation-intelligence\\', \\'chunk_index\\': 0, \\'text\\': \"Navigation & Intelligence | Humanoid Robotics with ROS 2\\\\nSkip to main content\\\\nOn this page\\\\nIsaac ROS for Accelerated Perception and VSLAM\\\\n\\\\u200b\\\\nIntroduction to Isaac ROS\\\\n\\\\u200b\\\\nIsaac ROS is a collection of hardware-accelerated perception and autonomy packages that seamlessly integrate with the ROS 2 ecosystem. These packages leverage NVIDIA\\'s GPU computing capabilities to deliver significant performance improvements for computationally intensive robotics tasks, particularly in perception and simultaneous localization and mapping (SLAM).\\\\nIsaac ROS Architecture\\\\n\\\\u200b\\\\nThe Isaac ROS framework is designed with several key principles:\\\\nHardware Acceleration\\\\n\\\\u200b\\\\nGPU Computing\\\\n: Leverage CUDA cores for parallel processing\\\\nTensor Cores\\\\n: Utilize specialized AI acceleration hardware\\\\nHardware Interfaces\\\\n: Direct integration with NVIDIA hardware platforms\\\\nMemory Management\\\\n: Optimized data transfers between CPU and GPU\\\\nROS 2 Compatibility\\\\n\\\\u200b\\\\nStandard Interfaces\\\\n: Full compatibility with ROS 2 message types\\\\nNode In\", \\'score\\': 0.5330334, \\'source_number\\': 9}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/unity-interaction-sensors\\', \\'chunk_index\\': 9, \\'text\\': \\'tion.x * deltaRotation.w, 1 - 2 * deltaRotation.x * deltaRotation.x),\\\\nMathf.Atan2(2 * deltaRotation.y * deltaRotation.w, 1 - 2 * deltaRotation.y * deltaRotation.y),\\\\nMathf.Atan2(2 * deltaRotation.z * deltaRotation.w, 1 - 2 * deltaRotation.z * deltaRotation.z)\\\\n) / deltaTime;\\\\n// Add noise to simulate real sensor characteristics\\\\nlinearAcceleration += GenerateNoise(accelerometerNoise);\\\\nangularVelocity += GenerateNoise(gyroscopeNoise);\\\\n// Publish IMU data\\\\nPublishIMUData(linearAcceleration, angularVelocity);\\\\n}\\\\nlastPosition = transform.position;\\\\nlastRotation = transform.rotation;\\\\nlastTime = Time.time;\\\\n}\\\\nVector3 GenerateNoise(float magnitude)\\\\n{\\\\nreturn new Vector3(\\\\nRandom.Range(-magnitude, magnitude),\\\\nRandom.Range(-magnitude, magnitude),\\\\nRandom.Range(-magnitude, magnitude)\\\\n);\\\\n}\\\\nvoid PublishIMUData(Vector3 linearAcceleration, Vector3 angularVelocity)\\\\n{\\\\n// Implementation for publishing IMU data\\\\n}\\\\n}\\\\nUnity-ROS Integration\\\\n\\\\u200b\\\\nROS-TCP-Connector\\\\n\\\\u200b\\\\nThe ROS-TCP-Connector package enables communication between Unity and ROS 2:\\\\nusing UnityEngine;\\\\nusing Unity.Robotics.ROSTCPConnector;\\\\nusing Unity.Robotics.R\\', \\'score\\': 0.53202516, \\'source_number\\': 10}]'}], 'model': 'stepfun/step-3.5-flash:free', 'max_tokens': 500, 'temperature': 0.7, 'tools': [{'type': 'function', 'function': {'name': 'retrieve_chunks', 'description': 'Retrieve relevant book chunks from Qdrant.', 'parameters': {'properties': {'query': {'description': \"User's question\", 'title': 'Query', 'type': 'string'}, 'top_k': {'default': 5, 'description': 'Number of chunks to retrieve (default: 5, max: 10)', 'title': 'Top K', 'type': 'integer'}}, 'required': ['query', 'top_k'], 'title': 'retrieve_chunks_args', 'type': 'object', 'additionalProperties': False}, 'strict': True}}]}}", "module": "_base_client", "lineno": 486, "pathname": 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Be concise and accurate.\\n\\nYour responses should be helpful, clear, and grounded exclusively in the provided context.', 'role': 'system'}, {'role': 'user', 'content': 'what is vla systems?'}, {'role': 'assistant', 'content': None, 'tool_calls': [{'id': 'call_95ce32a63d3f459f8be0c55e', 'type': 'function', 'function': {'name': 'retrieve_chunks', 'arguments': '{\"query\": \"VLA systems vision-language-action\", \"top_k\": 10}'}}]}, {'role': 'tool', 'tool_call_id': 'call_95ce32a63d3f459f8be0c55e', 'content': '[{\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 0, \\'text\\': \\'Vision-Language-Action (VLA) Overview | Humanoid Robotics with ROS 2\\\\nSkip to main content\\\\nOn this page\\\\nIntroduction\\\\n\\\\u200b\\\\nVision-Language-Action (VLA) systems represent a paradigm shift in robotics, where visual perception, natural language understanding, and robotic action execution are tightly integrated to enable more intuitive and capable robotic systems. This integration allows robots to understand complex human instructions, perceive their environment, and execute sophisticated tasks in a coordinated manner.\\\\nFor humanoid robotics, VLA systems are particularly important as they enable robots to interact naturally with humans and their environments using multiple modalities simultaneously. This chapter explores the convergence of these three critical components and how they work together to create embodied intelligence.\\\\nThe Three Modalities of VLA Systems\\\\n\\\\u200b\\\\nVision Processing\\\\n\\\\u200b\\\\nVision processing in VLA systems goes beyond simple object detection to include:\\\\nScene Understanding\\\\n: Compreh\\', \\'score\\': 0.76044405, \\'source_number\\': 1}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 20, \\'text\\': \\'l perception, language understanding, and action execution, these systems can perform complex tasks that require understanding both linguistic commands and visual contexts. In humanoid robotics, VLA systems enable more intuitive interaction and more capable task execution, making robots more useful and accessible to human users.\\\\nThe complete VLA system combines the foundational concepts from this chapter with the voice-to-action capabilities from Chapter 2 and the cognitive planning from Chapter 3, creating a comprehensive framework for human-robot interaction that can handle complex, real-world tasks.\\\\nIntroduction\\\\nThe Three Modalities of VLA Systems\\\\nVision Processing\\\\nLanguage Understanding\\\\nAction Execution\\\\nConvergence in Embodied Intelligence\\\\nMulti-Modal Integration\\\\nClosed-Loop Interaction\\\\nLearning from Interaction\\\\nApplications in Humanoid Robotics\\\\nHuman-Robot Interaction\\\\nComplex Task Execution\\\\nSocial Navigation\\\\nEmbodied Intelligence Systems\\\\nKey Principles of Embodied Intelligence\\\\nEmbodied Intelligence in VLA Systems\\\\nBenefits of Embodied Intelligence\\\\nChallenges in Embodied Intellige\\', \\'score\\': 0.6882441, \\'source_number\\': 2}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 19, \\'text\\': \\' Common Issues\\\\n\\\\u200b\\\\nPerformance Issues\\\\n\\\\u200b\\\\nSlow Response\\\\n: Check API call optimization and consider caching common responses\\\\nHigh Latency\\\\n: Optimize network calls and consider local processing for time-sensitive tasks\\\\nResource Consumption\\\\n: Monitor computational requirements and optimize accordingly\\\\nAccuracy Issues\\\\n\\\\u200b\\\\nMisunderstood Commands\\\\n: Improve prompt engineering and add context to LLM calls\\\\nFailed Object Recognition\\\\n: Verify vision system calibration and lighting conditions\\\\nAction Failures\\\\n: Implement robust error handling and recovery strategies\\\\nIntegration Issues\\\\n\\\\u200b\\\\nComponent Miscommunication\\\\n: Ensure consistent data formats between components\\\\nTiming Problems\\\\n: Implement proper synchronization between vision, language, and action systems\\\\nContext Loss\\\\n: Maintain state information across the entire VLA pipeline\\\\nSummary\\\\n\\\\u200b\\\\nVision-Language-Action systems represent a significant advancement in robotics, enabling more natural and capable human-robot interaction. By tightly integrating visual perception, language understanding, and action execution, these systems can perform complex tasks \\', \\'score\\': 0.68204457, \\'source_number\\': 3}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 9, \\'text\\': \\'o safety protocols and fallback mechanisms.\\\\nExamples of Vision-Language-Action Integration\\\\n\\\\u200b\\\\nTo illustrate how VLA systems work together, let\\\\\\'s examine some concrete examples:\\\\nExample 1: Object Retrieval Task\\\\n\\\\u200b\\\\n# Example of VLA integration for retrieving an object\\\\ndef\\\\nretrieve_object_task\\\\n(\\\\nrobot\\\\n,\\\\ncommand\\\\n)\\\\n:\\\\n\"\"\"\\\\nExample: \"Please bring me the red cup from the kitchen\"\\\\n\"\"\"\\\\n# Vision component: Detect objects in the environment\\\\nvision_data\\\\n=\\\\nrobot\\\\n.\\\\nvision_system\\\\n.\\\\ncapture_scene\\\\n(\\\\n)\\\\nobjects\\\\n=\\\\nrobot\\\\n.\\\\nvision_system\\\\n.\\\\ndetect_objects\\\\n(\\\\nvision_data\\\\n)\\\\n# Language component: Parse the command to extract intent\\\\nintent\\\\n=\\\\nrobot\\\\n.\\\\nlanguage_system\\\\n.\\\\nparse_command\\\\n(\\\\ncommand\\\\n)\\\\n# Result: {\"action\": \"retrieve\", \"object\": \"red cup\", \"location\": \"kitchen\"}\\\\n# Action component: Plan and execute the retrieval\\\\nif\\\\nintent\\\\n[\\\\n\"action\"\\\\n]\\\\n==\\\\n\"retrieve\"\\\\n:\\\\n# Find the red cup in the kitchen\\\\ntarget_object\\\\n=\\\\nfind_object_by_attributes\\\\n(\\\\nobjects\\\\n,\\\\ncolor\\\\n=\\\\n\"red\"\\\\n,\\\\ntype\\\\n=\\\\n\"cup\"\\\\n,\\\\nlocation\\\\n=\\\\nintent\\\\n[\\\\n\"location\"\\\\n]\\\\n)\\\\nif\\\\ntarget_object\\\\n:\\\\n# Plan navigation to kitchen\\\\nnavigation_action\\\\n=\\\\nrobot\\\\n.\\\\naction_planner\\\\n.\\\\nplan_navigation\\\\n\\', \\'score\\': 0.6687312, \\'source_number\\': 4}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 3, \\'text\\': \\'es high-level goals and context for visual processing\\\\nAction execution is guided by both visual perception and language commands\\\\nClosed-Loop Interaction\\\\n\\\\u200b\\\\nVLA systems operate in a closed-loop manner:\\\\nPerception\\\\n: The robot observes its environment\\\\nUnderstanding\\\\n: Visual and linguistic information is processed\\\\nPlanning\\\\n: Actions are planned based on goals and current state\\\\nExecution\\\\n: Actions are performed in the environment\\\\nFeedback\\\\n: New observations inform the next cycle\\\\nLearning from Interaction\\\\n\\\\u200b\\\\nVLA systems can learn from their interactions:\\\\nReinforcement Learning\\\\n: Learning which action sequences lead to successful outcomes\\\\nImitation Learning\\\\n: Learning from human demonstrations that combine visual, linguistic, and action components\\\\nLanguage-Guided Learning\\\\n: Using language to specify what to learn and how to evaluate success\\\\nApplications in Humanoid Robotics\\\\n\\\\u200b\\\\nVLA systems enable humanoid robots to perform complex tasks that require understanding both language commands and visual scenes:\\\\nHuman-Robot Interaction\\\\n\\\\u200b\\\\nUnderstanding natural language commands in the context of the cur\\', \\'score\\': 0.6677358, \\'source_number\\': 5}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 2, \\'text\\': \\'obot capabilities\\\\nThe language component must handle various forms of human communication:\\\\nDirect commands (\"Pick up the red cup\")\\\\nDescriptive requests (\"Bring me something to drink\")\\\\nComplex multi-step instructions\\\\nConversational interactions\\\\nAction Execution\\\\n\\\\u200b\\\\nAction execution involves:\\\\nTask Planning\\\\n: Breaking down high-level goals into sequences of executable actions\\\\nMotion Planning\\\\n: Determining how to physically execute actions while avoiding obstacles\\\\nControl Execution\\\\n: Sending commands to robot actuators to perform physical movements\\\\nFeedback Integration\\\\n: Using sensory information to adjust actions in real-time\\\\nConvergence in Embodied Intelligence\\\\n\\\\u200b\\\\nThe true power of VLA systems emerges when these three modalities converge to create embodied intelligence:\\\\nMulti-Modal Integration\\\\n\\\\u200b\\\\nVLA systems integrate information from all three modalities simultaneously:\\\\nVisual information informs language understanding (e.g., disambiguating \"the cup\" based on what\\\\\\'s visible)\\\\nLanguage provides high-level goals and context for visual processing\\\\nAction execution is guided by both visual perc\\', \\'score\\': 0.6665145, \\'source_number\\': 6}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 7, \\'text\\': \\'collaborative tasks\\\\nChallenges in Embodied Intelligence\\\\n\\\\u200b\\\\nReal-World Complexity\\\\n\\\\u200b\\\\nDealing with uncertainty and noise in sensory inputs\\\\nHandling dynamic and unpredictable environments\\\\nManaging the complexity of real-world physics\\\\nLearning Efficiency\\\\n\\\\u200b\\\\nBalancing exploration with exploitation\\\\nTransferring learning across different contexts\\\\nScaling learning to complex real-world tasks\\\\nSafety and Reliability\\\\n\\\\u200b\\\\nEnsuring safe behavior in human environments\\\\nHandling failures gracefully\\\\nMaintaining reliable operation over extended periods\\\\nTechnical Architecture\\\\n\\\\u200b\\\\nA typical VLA system architecture includes:\\\\n[Human Language Input] \u2192 [Language Encoder] \u2192 [Fusion Module] \u2192 [Action Planner]\\\\n\u2191 \u2193 \u2193\\\\n[Visual Input] \u2192 [Vision Encoder] \u2192 [Memory] \u2192 [World Model] \u2192 [Action Executor]\\\\nVisual Architecture Diagram\\\\n\\\\u200b\\\\ngraph TB\\\\nsubgraph \"Human Input\"\\\\nA[Human Language] --> D[Language Encoder]\\\\nB[Visual Scene] --> E[Vision Encoder]\\\\nend\\\\nsubgraph \"VLA Processing\"\\\\nD --> F[Fusion Module]\\\\nE --> F\\\\nF --> G[Memory System]\\\\nF --> H[World Model]\\\\nG --> I[Action Planner]\\\\nH --> I\\\\nI --> J[Action\\', \\'score\\': 0.66535616, \\'source_number\\': 7}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 8, \\'text\\': \\']\\\\nE --> F\\\\nF --> G[Memory System]\\\\nF --> H[World Model]\\\\nG --> I[Action Planner]\\\\nH --> I\\\\nI --> J[Action Executor]\\\\nend\\\\nsubgraph \"Robot Output\"\\\\nJ --> K[Robot Actions]\\\\nend\\\\nstyle A fill:#e1f5fe\\\\nstyle B fill:#e1f5fe\\\\nstyle K fill:#f3e5f5\\\\nstyle F fill:#fff3e0\\\\nstyle I fill:#e8f5e8\\\\nKey Components\\\\n\\\\u200b\\\\nModality Encoders\\\\n: Convert raw inputs (images, text) into feature representations\\\\nFusion Mechanisms\\\\n: Combine information from different modalities\\\\nMemory Systems\\\\n: Store and retrieve relevant information for decision making\\\\nAction Planners\\\\n: Generate sequences of actions to achieve goals\\\\nWorld Models\\\\n: Maintain understanding of the current state and predict outcomes\\\\nChallenges and Considerations\\\\n\\\\u200b\\\\nReal-Time Processing\\\\n\\\\u200b\\\\nVLA systems must process multiple modalities in real-time while maintaining responsive interaction with humans.\\\\nRobustness\\\\n\\\\u200b\\\\nSystems must handle variations in lighting, language, and environmental conditions.\\\\nSafety\\\\n\\\\u200b\\\\nIntegration of multiple complex systems requires careful attention to safety protocols and fallback mechanisms.\\\\nExamples of Vision-Language-Action Integration\\\\n\\\\u200b\\\\nTo illu\\', \\'score\\': 0.6650098, \\'source_number\\': 8}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 1, \\'text\\': \\'cessing in VLA systems goes beyond simple object detection to include:\\\\nScene Understanding\\\\n: Comprehending the spatial relationships between objects and understanding the context of the environment\\\\nVisual Question Answering\\\\n: Answering questions about the visual scene that require both perception and reasoning\\\\nVisual Grounding\\\\n: Connecting visual elements with language concepts, allowing robots to understand references like \"the red cup on the table\"\\\\nKey components of vision processing include:\\\\nObject detection and recognition\\\\nDepth estimation and 3D scene reconstruction\\\\nSemantic segmentation\\\\nVisual tracking and motion analysis\\\\nLanguage Understanding\\\\n\\\\u200b\\\\nLanguage understanding in VLA systems encompasses:\\\\nNatural Language Processing\\\\n: Converting human language into structured representations that robots can process\\\\nIntent Extraction\\\\n: Identifying the underlying goals and intentions behind human commands\\\\nContextual Reasoning\\\\n: Understanding language in the context of the visual scene and robot capabilities\\\\nThe language component must handle various forms of human communication:\\\\nDirect co\\', \\'score\\': 0.6527283, \\'source_number\\': 9}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 17, \\'text\\': \\'haviors based on the integrated understanding\\\\nExercises for Students\\\\n\\\\u200b\\\\nConceptual Understanding\\\\n: Explain how the three components of VLA (Vision, Language, Action) work together in the object retrieval example above.\\\\nImplementation Challenge\\\\n: Modify the object retrieval example to handle cases where the requested object is not visible. What would the robot do?\\\\nDesign Thinking\\\\n: In the closed-loop interaction example, what safety measures would you add to ensure the robot behaves appropriately?\\\\nReal-World Application\\\\n: Think of a task in your daily life that would benefit from VLA integration. Describe how vision, language, and action would work together to accomplish it.\\\\nIntegration of VLA Components\\\\n\\\\u200b\\\\nThe true power of Vision-Language-Action systems emerges when all three components work together seamlessly. Let\\\\\\'s examine how the concepts from all three chapters integrate:\\\\nComplete VLA Pipeline Example\\\\n\\\\u200b\\\\nHere\\\\\\'s how a complete VLA system would process a complex command like \"Please go to the kitchen, find a clean glass, fill it with water, and bring it to me\":\\\\nVision Component\\\\n:\\\\nPe\\', \\'score\\': 0.63759947, \\'source_number\\': 10}]'}], 'model': 'stepfun/step-3.5-flash:free', 'max_tokens': 500, 'temperature': 0.7, 'tools': [{'type': 'function', 'function': {'name': 'retrieve_chunks', 'description': 'Retrieve relevant book chunks from Qdrant.', 'parameters': {'properties': {'query': {'description': \"User's question\", 'title': 'Query', 'type': 'string'}, 'top_k': {'default': 5, 'description': 'Number of chunks to retrieve (default: 5, max: 10)', 'title': 'Top K', 'type': 'integer'}}, 'required': ['query', 'top_k'], 'title': 'retrieve_chunks_args', 'type': 'object', 'additionalProperties': False}, 'strict': True}}]}}", "module": 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Be concise and accurate.\\n\\nYour responses should be helpful, clear, and grounded exclusively in the provided context.', 'role': 'system'}, {'role': 'user', 'content': 'what is ASR systems?'}, {'role': 'assistant', 'content': None, 'tool_calls': [{'id': 'call_1c4ca417e7384d51b6d469a4', 'type': 'function', 'function': {'name': 'retrieve_chunks', 'arguments': '{\"query\": \"ASR systems\", \"top_k\": 5}'}}]}, {'role': 'tool', 'tool_call_id': 'call_1c4ca417e7384d51b6d469a4', 'content': '[{\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper\\', \\'chunk_index\\': 1, \\'text\\': \\'tween human speech and machine-understandable commands.\\\\nCore Components of ASR Systems\\\\n\\\\u200b\\\\n1. Audio Preprocessing\\\\n\\\\u200b\\\\nNoise Reduction\\\\n: Filtering out background noise to improve recognition accuracy\\\\nNormalization\\\\n: Adjusting audio levels and format for consistent processing\\\\nFeature Extraction\\\\n: Converting audio signals into features suitable for neural networks\\\\n2. Acoustic Models\\\\n\\\\u200b\\\\nSignal Processing\\\\n: Converting audio waveforms into spectrograms or other feature representations\\\\nPhoneme Recognition\\\\n: Identifying basic speech sounds that make up words\\\\nLanguage Modeling\\\\n: Using context to improve recognition accuracy\\\\n3. Language Models\\\\n\\\\u200b\\\\nGrammar Integration\\\\n: Understanding syntactic structures to improve recognition\\\\nContext Awareness\\\\n: Using surrounding words to disambiguate similar-sounding words\\\\nDomain Adaptation\\\\n: Optimizing for specific vocabularies or speaking styles\\\\nTypes of ASR Systems\\\\n\\\\u200b\\\\nCloud-Based ASR\\\\n\\\\u200b\\\\nAdvantages\\\\n: High accuracy, multilingual support, continuous updates\\\\nDisadvantages\\\\n: Requires internet connection, potential privacy concerns, API costs\\\\nExamples\\\\n: OpenAI Whisper AP\\', \\'score\\': 0.53937304, \\'source_number\\': 1}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper\\', \\'chunk_index\\': 0, \\'text\\': \\'Voice-to-Action Systems with OpenAI Whisper | Humanoid Robotics with ROS 2\\\\nSkip to main content\\\\nOn this page\\\\nIntroduction\\\\n\\\\u200b\\\\nVoice-to-action systems enable natural human-robot interaction by converting spoken language into executable robot commands. These systems are crucial for humanoid robotics, as they allow humans to communicate with robots using their natural language. OpenAI Whisper provides a powerful automatic speech recognition (ASR) capability that can be integrated into voice-to-action pipelines.\\\\nThis chapter explores how to implement voice-to-action systems using OpenAI Whisper, covering the complete pipeline from audio capture to robot command execution.\\\\nUnderstanding Automatic Speech Recognition (ASR) Systems\\\\n\\\\u200b\\\\nAutomatic Speech Recognition (ASR) systems convert spoken language into text. These systems are fundamental to voice-to-action applications, as they bridge the gap between human speech and machine-understandable commands.\\\\nCore Components of ASR Systems\\\\n\\\\u200b\\\\n1. Audio Pr\\', \\'score\\': 0.45678365, \\'source_number\\': 2}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper\\', \\'chunk_index\\': 42, \\'text\\': \\'=\\\\nVoiceToActionService\\\\n(\\\\nasr_url\\\\n=\\\\n\"http://asr-service:8000\"\\\\n,\\\\nnlp_url\\\\n=\\\\n\"http://nlp-service:8000\"\\\\n,\\\\nrobot_control_url\\\\n=\\\\n\"http://robot-control:8000\"\\\\n)\\\\nresult\\\\n=\\\\nvta_service\\\\n.\\\\nprocess_voice_command\\\\n(\\\\naudio_data\\\\n,\\\\ncontext\\\\n)\\\\nSummary\\\\n\\\\u200b\\\\nVoice-to-action systems using OpenAI Whisper provide a natural and intuitive way for humans to interact with humanoid robots. The complete pipeline includes audio capture, speech recognition, intent extraction, and command mapping to robot actions.\\\\nKey considerations for implementation include:\\\\nHandling ambiguity and error conditions gracefully\\\\nOptimizing for latency and accuracy\\\\nEnsuring privacy and security\\\\nIntegrating effectively with robot control systems\\\\nThe next chapter will explore cognitive planning with large language models, which builds on these voice-to-action concepts to enable more sophisticated task planning and execution.\\\\nIntroduction\\\\nUnderstanding Automatic Speech Recognition (ASR) Systems\\\\nCore Components of ASR Systems\\\\nTypes of ASR Systems\\\\nUnderstanding OpenAI Whisper\\\\nWhisper Architecture\\\\nWhisper Model Variants\\\\nWhisper in Voice-to-Action C\\', \\'score\\': 0.41504884, \\'source_number\\': 3}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper\\', \\'chunk_index\\': 36, \\'text\\': \\'der data privacy when using cloud-based ASR\\\\nImplement local processing where sensitive information is involved\\\\nFollow appropriate data handling practices\\\\nIntegration with ROS 2\\\\n\\\\u200b\\\\nVoice-to-action systems can be integrated with ROS 2 using action servers and clients:\\\\nimport\\\\nrclpy\\\\nfrom\\\\nrclpy\\\\n.\\\\naction\\\\nimport\\\\nActionClient\\\\nfrom\\\\nrclpy\\\\n.\\\\nnode\\\\nimport\\\\nNode\\\\nclass\\\\nVoiceCommandActionClient\\\\n(\\\\nNode\\\\n)\\\\n:\\\\ndef\\\\n__init__\\\\n(\\\\nself\\\\n)\\\\n:\\\\nsuper\\\\n(\\\\n)\\\\n.\\\\n__init__\\\\n(\\\\n\\\\\\'voice_command_client\\\\\\'\\\\n)\\\\nself\\\\n.\\\\n_action_client\\\\n=\\\\nActionClient\\\\n(\\\\nself\\\\n,\\\\nVoiceCommand\\\\n,\\\\n# Custom action message\\\\n\\\\\\'voice_command\\\\\\'\\\\n)\\\\ndef\\\\nsend_voice_command\\\\n(\\\\nself\\\\n,\\\\ntext\\\\n,\\\\nintent\\\\n)\\\\n:\\\\ngoal_msg\\\\n=\\\\nVoiceCommand\\\\n.\\\\nGoal\\\\n(\\\\n)\\\\ngoal_msg\\\\n.\\\\ncommand_text\\\\n=\\\\ntext\\\\ngoal_msg\\\\n.\\\\nintent\\\\n=\\\\nintent\\\\nself\\\\n.\\\\n_action_client\\\\n.\\\\nwait_for_server\\\\n(\\\\n)\\\\nreturn\\\\nself\\\\n.\\\\n_action_client\\\\n.\\\\nsend_goal_async\\\\n(\\\\ngoal_msg\\\\n)\\\\nExercises for Students\\\\n\\\\u200b\\\\nBasic Implementation\\\\n: Implement a simple voice-to-action system that can recognize and execute the commands \"move forward\", \"turn left\", and \"stop\". Test the system with different audio inputs and evaluate its accuracy.\\\\nIntent Extraction Challenge\\\\n: Modify the \\', \\'score\\': 0.38896814, \\'source_number\\': 4}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper\\', \\'chunk_index\\': 35, \\'text\\': \\')\",\\\\n\"grasp_object(\\\\\\'glass_001\\\\\\')\",\\\\n\"fill_container(\\\\\\'glass_001\\\\\\', \\\\\\'water\\\\\\')\",\\\\n\"move_to_location(\\\\\\'user_position\\\\\\')\",\\\\n\"offer_object(\\\\\\'glass_001\\\\\\')\"\\\\n]\\\\nError Handling and Robustness\\\\n\\\\u200b\\\\nVoice-to-action systems must handle various error conditions:\\\\nAudio Quality Issues\\\\n\\\\u200b\\\\nLow volume or noisy environments\\\\nAudio clipping or distortion\\\\nBackground noise interference\\\\nRecognition Errors\\\\n\\\\u200b\\\\nMisrecognized words or phrases\\\\nAccents or speech patterns not well-handled by ASR\\\\nTechnical jargon or domain-specific terminology\\\\nIntent Ambiguity\\\\n\\\\u200b\\\\nCommands that could have multiple interpretations\\\\nCommands that conflict with robot capabilities\\\\nCommands that are unsafe or infeasible\\\\nPerformance Considerations\\\\n\\\\u200b\\\\nLatency\\\\n\\\\u200b\\\\nMinimize the time between speech and action execution\\\\nOptimize network calls to Whisper API\\\\nConsider local Whisper models for reduced latency\\\\nAccuracy\\\\n\\\\u200b\\\\nBalance between false positives and false negatives\\\\nImplement confidence thresholds for command execution\\\\nUse context to improve accuracy\\\\nPrivacy\\\\n\\\\u200b\\\\nConsider data privacy when using cloud-based ASR\\\\nImplement local processing where sensitive information i\\', \\'score\\': 0.3864075, \\'source_number\\': 5}]'}], 'model': 'stepfun/step-3.5-flash:free', 'max_tokens': 500, 'temperature': 0.7, 'tools': [{'type': 'function', 'function': {'name': 'retrieve_chunks', 'description': 'Retrieve relevant book chunks from Qdrant.', 'parameters': 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Answer ONLY using the retrieved book content provided by the retrieve_chunks tool.\\n2. Do NOT use external knowledge or make up information.\\n3. If the retrieved content does not contain relevant information, say \"I couldn\\'t find relevant information in the book.\"\\n4. Always cite your sources using the format [Source 1], [Source 2], etc. Each source number corresponds to the chunk number from the tool.\\n5. 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Audio Preprocessing\\\\n\\\\u200b\\\\nNoise Reduction\\\\n: Filtering out background noise to improve recognition accuracy\\\\nNormalization\\\\n: Adjusting audio levels and format for consistent processing\\\\nFeature Extraction\\\\n: Converting audio signals into features suitable for neural networks\\\\n2. Acoustic Models\\\\n\\\\u200b\\\\nSignal Processing\\\\n: Converting audio waveforms into spectrograms or other feature representations\\\\nPhoneme Recognition\\\\n: Identifying basic speech sounds that make up words\\\\nLanguage Modeling\\\\n: Using context to improve recognition accuracy\\\\n3. Language Models\\\\n\\\\u200b\\\\nGrammar Integration\\\\n: Understanding syntactic structures to improve recognition\\\\nContext Awareness\\\\n: Using surrounding words to disambiguate similar-sounding words\\\\nDomain Adaptation\\\\n: Optimizing for specific vocabularies or speaking styles\\\\nTypes of ASR Systems\\\\n\\\\u200b\\\\nCloud-Based ASR\\\\n\\\\u200b\\\\nAdvantages\\\\n: High accuracy, multilingual support, continuous updates\\\\nDisadvantages\\\\n: Requires internet connection, potential privacy concerns, API costs\\\\nExamples\\\\n: OpenAI Whisper AP\\', \\'score\\': 0.55913305, \\'source_number\\': 1}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper\\', \\'chunk_index\\': 0, \\'text\\': \\'Voice-to-Action Systems with OpenAI Whisper | Humanoid Robotics with ROS 2\\\\nSkip to main content\\\\nOn this page\\\\nIntroduction\\\\n\\\\u200b\\\\nVoice-to-action systems enable natural human-robot interaction by converting spoken language into executable robot commands. These systems are crucial for humanoid robotics, as they allow humans to communicate with robots using their natural language. OpenAI Whisper provides a powerful automatic speech recognition (ASR) capability that can be integrated into voice-to-action pipelines.\\\\nThis chapter explores how to implement voice-to-action systems using OpenAI Whisper, covering the complete pipeline from audio capture to robot command execution.\\\\nUnderstanding Automatic Speech Recognition (ASR) Systems\\\\n\\\\u200b\\\\nAutomatic Speech Recognition (ASR) systems convert spoken language into text. These systems are fundamental to voice-to-action applications, as they bridge the gap between human speech and machine-understandable commands.\\\\nCore Components of ASR Systems\\\\n\\\\u200b\\\\n1. Audio Pr\\', \\'score\\': 0.49032867, \\'source_number\\': 2}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper\\', \\'chunk_index\\': 42, \\'text\\': \\'=\\\\nVoiceToActionService\\\\n(\\\\nasr_url\\\\n=\\\\n\"http://asr-service:8000\"\\\\n,\\\\nnlp_url\\\\n=\\\\n\"http://nlp-service:8000\"\\\\n,\\\\nrobot_control_url\\\\n=\\\\n\"http://robot-control:8000\"\\\\n)\\\\nresult\\\\n=\\\\nvta_service\\\\n.\\\\nprocess_voice_command\\\\n(\\\\naudio_data\\\\n,\\\\ncontext\\\\n)\\\\nSummary\\\\n\\\\u200b\\\\nVoice-to-action systems using OpenAI Whisper provide a natural and intuitive way for humans to interact with humanoid robots. The complete pipeline includes audio capture, speech recognition, intent extraction, and command mapping to robot actions.\\\\nKey considerations for implementation include:\\\\nHandling ambiguity and error conditions gracefully\\\\nOptimizing for latency and accuracy\\\\nEnsuring privacy and security\\\\nIntegrating effectively with robot control systems\\\\nThe next chapter will explore cognitive planning with large language models, which builds on these voice-to-action concepts to enable more sophisticated task planning and execution.\\\\nIntroduction\\\\nUnderstanding Automatic Speech Recognition (ASR) Systems\\\\nCore Components of ASR Systems\\\\nTypes of ASR Systems\\\\nUnderstanding OpenAI Whisper\\\\nWhisper Architecture\\\\nWhisper Model Variants\\\\nWhisper in Voice-to-Action C\\', \\'score\\': 0.4482937, \\'source_number\\': 3}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper\\', \\'chunk_index\\': 36, \\'text\\': \\'der data privacy when using cloud-based ASR\\\\nImplement local processing where sensitive information is involved\\\\nFollow appropriate data handling practices\\\\nIntegration with ROS 2\\\\n\\\\u200b\\\\nVoice-to-action systems can be integrated with ROS 2 using action servers and clients:\\\\nimport\\\\nrclpy\\\\nfrom\\\\nrclpy\\\\n.\\\\naction\\\\nimport\\\\nActionClient\\\\nfrom\\\\nrclpy\\\\n.\\\\nnode\\\\nimport\\\\nNode\\\\nclass\\\\nVoiceCommandActionClient\\\\n(\\\\nNode\\\\n)\\\\n:\\\\ndef\\\\n__init__\\\\n(\\\\nself\\\\n)\\\\n:\\\\nsuper\\\\n(\\\\n)\\\\n.\\\\n__init__\\\\n(\\\\n\\\\\\'voice_command_client\\\\\\'\\\\n)\\\\nself\\\\n.\\\\n_action_client\\\\n=\\\\nActionClient\\\\n(\\\\nself\\\\n,\\\\nVoiceCommand\\\\n,\\\\n# Custom action message\\\\n\\\\\\'voice_command\\\\\\'\\\\n)\\\\ndef\\\\nsend_voice_command\\\\n(\\\\nself\\\\n,\\\\ntext\\\\n,\\\\nintent\\\\n)\\\\n:\\\\ngoal_msg\\\\n=\\\\nVoiceCommand\\\\n.\\\\nGoal\\\\n(\\\\n)\\\\ngoal_msg\\\\n.\\\\ncommand_text\\\\n=\\\\ntext\\\\ngoal_msg\\\\n.\\\\nintent\\\\n=\\\\nintent\\\\nself\\\\n.\\\\n_action_client\\\\n.\\\\nwait_for_server\\\\n(\\\\n)\\\\nreturn\\\\nself\\\\n.\\\\n_action_client\\\\n.\\\\nsend_goal_async\\\\n(\\\\ngoal_msg\\\\n)\\\\nExercises for Students\\\\n\\\\u200b\\\\nBasic Implementation\\\\n: Implement a simple voice-to-action system that can recognize and execute the commands \"move forward\", \"turn left\", and \"stop\". Test the system with different audio inputs and evaluate its accuracy.\\\\nIntent Extraction Challenge\\\\n: Modify the \\', \\'score\\': 0.415712, \\'source_number\\': 4}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper\\', \\'chunk_index\\': 35, \\'text\\': \\')\",\\\\n\"grasp_object(\\\\\\'glass_001\\\\\\')\",\\\\n\"fill_container(\\\\\\'glass_001\\\\\\', \\\\\\'water\\\\\\')\",\\\\n\"move_to_location(\\\\\\'user_position\\\\\\')\",\\\\n\"offer_object(\\\\\\'glass_001\\\\\\')\"\\\\n]\\\\nError Handling and Robustness\\\\n\\\\u200b\\\\nVoice-to-action systems must handle various error conditions:\\\\nAudio Quality Issues\\\\n\\\\u200b\\\\nLow volume or noisy environments\\\\nAudio clipping or distortion\\\\nBackground noise interference\\\\nRecognition Errors\\\\n\\\\u200b\\\\nMisrecognized words or phrases\\\\nAccents or speech patterns not well-handled by ASR\\\\nTechnical jargon or domain-specific terminology\\\\nIntent 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Answer ONLY using the retrieved book content provided by the retrieve_chunks tool.\\n2. Do NOT use external knowledge or make up information.\\n3. If the retrieved content does not contain relevant information, say \"I couldn\\'t find relevant information in the book.\"\\n4. Always cite your sources using the format [Source 1], [Source 2], etc. Each source number corresponds to the chunk number from the tool.\\n5. Be concise and accurate.\\n\\nYour responses should be helpful, clear, and grounded exclusively in the provided context.', 'role': 'system'}, {'role': 'user', 'content': 'full from: vla'}, {'role': 'assistant', 'content': 'I\\'ll search for information about \"VLA\" in the humanoid robotics book.', 'tool_calls': [{'id': 'call_278f513d32ae4bad99ac9567', 'type': 'function', 'function': {'name': 'retrieve_chunks', 'arguments': '{\"query\": \"VLA\", \"top_k\": 5}'}}]}, {'role': 'tool', 'tool_call_id': 'call_278f513d32ae4bad99ac9567', 'content': '[{\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 0, \\'text\\': \\'Vision-Language-Action (VLA) Overview | Humanoid Robotics with ROS 2\\\\nSkip to main content\\\\nOn this page\\\\nIntroduction\\\\n\\\\u200b\\\\nVision-Language-Action (VLA) systems represent a paradigm shift in robotics, where visual perception, natural language understanding, and robotic action execution are tightly integrated to enable more intuitive and capable robotic systems. This integration allows robots to understand complex human instructions, perceive their environment, and execute sophisticated tasks in a coordinated manner.\\\\nFor humanoid robotics, VLA systems are particularly important as they enable robots to interact naturally with humans and their environments using multiple modalities simultaneously. This chapter explores the convergence of these three critical components and how they work together to create embodied intelligence.\\\\nThe Three Modalities of VLA Systems\\\\n\\\\u200b\\\\nVision Processing\\\\n\\\\u200b\\\\nVision processing in VLA systems goes beyond simple object detection to include:\\\\nScene Understanding\\\\n: Compreh\\', \\'score\\': 0.4678017, \\'source_number\\': 1}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 7, \\'text\\': \\'collaborative tasks\\\\nChallenges in Embodied Intelligence\\\\n\\\\u200b\\\\nReal-World Complexity\\\\n\\\\u200b\\\\nDealing with uncertainty and noise in sensory inputs\\\\nHandling dynamic and unpredictable environments\\\\nManaging the complexity of real-world physics\\\\nLearning Efficiency\\\\n\\\\u200b\\\\nBalancing exploration with exploitation\\\\nTransferring learning across different contexts\\\\nScaling learning to complex real-world tasks\\\\nSafety and Reliability\\\\n\\\\u200b\\\\nEnsuring safe behavior in human environments\\\\nHandling failures gracefully\\\\nMaintaining reliable operation over extended periods\\\\nTechnical Architecture\\\\n\\\\u200b\\\\nA typical VLA system architecture includes:\\\\n[Human Language Input] \u2192 [Language Encoder] \u2192 [Fusion Module] \u2192 [Action Planner]\\\\n\u2191 \u2193 \u2193\\\\n[Visual Input] \u2192 [Vision Encoder] \u2192 [Memory] \u2192 [World Model] \u2192 [Action Executor]\\\\nVisual Architecture Diagram\\\\n\\\\u200b\\\\ngraph TB\\\\nsubgraph \"Human Input\"\\\\nA[Human Language] --> D[Language Encoder]\\\\nB[Visual Scene] --> E[Vision Encoder]\\\\nend\\\\nsubgraph \"VLA Processing\"\\\\nD --> F[Fusion Module]\\\\nE --> F\\\\nF --> G[Memory System]\\\\nF --> H[World Model]\\\\nG --> I[Action Planner]\\\\nH --> I\\\\nI --> J[Action\\', \\'score\\': 0.46510154, \\'source_number\\': 2}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 1, \\'text\\': \\'cessing in VLA systems goes beyond simple object detection to include:\\\\nScene Understanding\\\\n: Comprehending the spatial relationships between objects and understanding the context of the environment\\\\nVisual Question Answering\\\\n: Answering questions about the visual scene that require both perception and reasoning\\\\nVisual Grounding\\\\n: Connecting visual elements with language concepts, allowing robots to understand references like \"the red cup on the table\"\\\\nKey components of vision processing include:\\\\nObject detection and recognition\\\\nDepth estimation and 3D scene reconstruction\\\\nSemantic segmentation\\\\nVisual tracking and motion analysis\\\\nLanguage Understanding\\\\n\\\\u200b\\\\nLanguage understanding in VLA systems encompasses:\\\\nNatural Language Processing\\\\n: Converting human language into structured representations that robots can process\\\\nIntent Extraction\\\\n: Identifying the underlying goals and intentions behind human commands\\\\nContextual Reasoning\\\\n: Understanding language in the context of the visual scene and robot capabilities\\\\nThe language component must handle various forms of human communication:\\\\nDirect co\\', \\'score\\': 0.45405108, \\'source_number\\': 3}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 20, \\'text\\': \\'l perception, language understanding, and action execution, these systems can perform complex tasks that require understanding both linguistic commands and visual contexts. In humanoid robotics, VLA systems enable more intuitive interaction and more capable task execution, making robots more useful and accessible to human users.\\\\nThe complete VLA system combines the foundational concepts from this chapter with the voice-to-action capabilities from Chapter 2 and the cognitive planning from Chapter 3, creating a comprehensive framework for human-robot interaction that can handle complex, real-world tasks.\\\\nIntroduction\\\\nThe Three Modalities of VLA Systems\\\\nVision Processing\\\\nLanguage Understanding\\\\nAction Execution\\\\nConvergence in Embodied Intelligence\\\\nMulti-Modal Integration\\\\nClosed-Loop Interaction\\\\nLearning from Interaction\\\\nApplications in Humanoid Robotics\\\\nHuman-Robot Interaction\\\\nComplex Task Execution\\\\nSocial Navigation\\\\nEmbodied Intelligence Systems\\\\nKey Principles of Embodied Intelligence\\\\nEmbodied Intelligence in VLA Systems\\\\nBenefits of Embodied Intelligence\\\\nChallenges in Embodied Intellige\\', \\'score\\': 0.44872642, \\'source_number\\': 4}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 9, \\'text\\': \\'o safety protocols and fallback mechanisms.\\\\nExamples of Vision-Language-Action Integration\\\\n\\\\u200b\\\\nTo illustrate how VLA systems work together, let\\\\\\'s examine some concrete examples:\\\\nExample 1: Object Retrieval Task\\\\n\\\\u200b\\\\n# Example of VLA integration for retrieving an object\\\\ndef\\\\nretrieve_object_task\\\\n(\\\\nrobot\\\\n,\\\\ncommand\\\\n)\\\\n:\\\\n\"\"\"\\\\nExample: \"Please bring me the red cup from the kitchen\"\\\\n\"\"\"\\\\n# Vision component: Detect objects in the environment\\\\nvision_data\\\\n=\\\\nrobot\\\\n.\\\\nvision_system\\\\n.\\\\ncapture_scene\\\\n(\\\\n)\\\\nobjects\\\\n=\\\\nrobot\\\\n.\\\\nvision_system\\\\n.\\\\ndetect_objects\\\\n(\\\\nvision_data\\\\n)\\\\n# Language component: Parse the command to extract 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Be concise and accurate.\\n\\nYour responses should be helpful, clear, and grounded exclusively in the provided context.', 'role': 'system'}, {'role': 'user', 'content': 'What is AI?'}, {'role': 'assistant', 'content': None, 'tool_calls': [{'id': 'call_69d55b2feab04ab88e4324cc', 'type': 'function', 'function': {'name': 'retrieve_chunks', 'arguments': '{\"query\": \"What is AI?\", \"top_k\": 5}'}}]}, {'role': 'tool', 'tool_call_id': 'call_69d55b2feab04ab88e4324cc', 'content': '[{\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/intro-to-ai-robot-brain\\', \\'chunk_index\\': 0, \\'text\\': \\'Introduction to the AI-Robot Brain | Humanoid Robotics with ROS 2\\\\nSkip to main content\\\\nOn this page\\\\nThe Role of AI in Humanoid Robotics\\\\n\\\\u200b\\\\nArtificial Intelligence (AI) serves as the cognitive foundation for humanoid robotics, providing the essential computational capabilities that enable robots to perceive, reason, and act in complex environments. In humanoid robotics specifically, AI systems are responsible for processing sensory information, making intelligent decisions, and controlling robot behavior to achieve sophisticated tasks.\\\\nIntelligence in Humanoid Systems\\\\n\\\\u200b\\\\nHumanoid robots require a higher level of intelligence compared to traditional industrial robots due to their complex morphology and interaction requirements:\\\\nAdaptive Behavior\\\\n: Humanoid robots must adapt to dynamic environments and unpredictable human interactions\\\\nMulti-modal Perception\\\\n: Processing visual, auditory, tactile, and proprioceptive information simultaneously\\\\nSocial Cognition\\\\n: Understanding and responding a\\', \\'score\\': 0.5069832, \\'source_number\\': 1}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla\\', \\'chunk_index\\': 4, \\'text\\': \" scenes:\\\\nHuman-Robot Interaction\\\\n\\\\u200b\\\\nUnderstanding natural language commands in the context of the current environment\\\\nProviding verbal feedback about actions and observations\\\\nEngaging in contextual conversations about tasks and goals\\\\nComplex Task Execution\\\\n\\\\u200b\\\\nFollowing multi-step instructions that require understanding both language and visual context\\\\nAdapting to unexpected situations by combining visual perception with flexible planning\\\\nPerforming tasks in dynamic environments where both visual and linguistic information change\\\\nSocial Navigation\\\\n\\\\u200b\\\\nUnderstanding social cues from both visual and linguistic inputs\\\\nNavigating in human-populated environments while respecting social norms\\\\nResponding appropriately to social interactions during task execution\\\\nEmbodied Intelligence Systems\\\\n\\\\u200b\\\\nEmbodied intelligence refers to the concept that intelligence emerges from the interaction between an agent and its environment. In the context of VLA systems, embodied intelligence means that the robot\\'s understanding and decision-making capabilities are enhanced by its physical presence and interaction w\", \\'score\\': 0.4420988, \\'source_number\\': 2}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/intro-to-ai-robot-brain\\', \\'chunk_index\\': 8, \\'text\\': \"hallenges:\\\\nReal-time Constraints\\\\n\\\\u200b\\\\nProcessing sensor data and generating responses within strict time limits\\\\nManaging computational resources efficiently\\\\nPrioritizing critical tasks during resource contention\\\\nMulti-modal Fusion\\\\n\\\\u200b\\\\nCombining information from different sensor modalities\\\\nHandling uncertain and noisy sensor data\\\\nMaintaining consistent world models\\\\nLearning and Adaptation\\\\n\\\\u200b\\\\nAcquiring new skills and knowledge during deployment\\\\nAdapting to changing environments and user preferences\\\\nBalancing exploration with safety requirements\\\\nConclusion\\\\n\\\\u200b\\\\nThe AI-Robot Brain concept represents the integration of artificial intelligence technologies into humanoid robotics systems. NVIDIA Isaac provides essential tools and frameworks that enhance the ROS 2 ecosystem, enabling the development of more intelligent and capable humanoid robots. By leveraging Isaac\\'s simulation, perception, and autonomy capabilities, developers can create more sophisticated AI-robotics systems that better serve human needs.\\\\nUnderstanding the role of AI in humanoid robotics and Isaac\\'s place in the ROS 2 ecosystem p\", \\'score\\': 0.4253938, \\'source_number\\': 3}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper\\', \\'chunk_index\\': 0, \\'text\\': \\'Voice-to-Action Systems with OpenAI Whisper | Humanoid Robotics with ROS 2\\\\nSkip to main content\\\\nOn this page\\\\nIntroduction\\\\n\\\\u200b\\\\nVoice-to-action systems enable natural human-robot interaction by converting spoken language into executable robot commands. These systems are crucial for humanoid robotics, as they allow humans to communicate with robots using their natural language. OpenAI Whisper provides a powerful automatic speech recognition (ASR) capability that can be integrated into voice-to-action pipelines.\\\\nThis chapter explores how to implement voice-to-action systems using OpenAI Whisper, covering the complete pipeline from audio capture to robot command execution.\\\\nUnderstanding Automatic Speech Recognition (ASR) Systems\\\\n\\\\u200b\\\\nAutomatic Speech Recognition (ASR) systems convert spoken language into text. These systems are fundamental to voice-to-action applications, as they bridge the gap between human speech and machine-understandable commands.\\\\nCore Components of ASR Systems\\\\n\\\\u200b\\\\n1. Audio Pr\\', \\'score\\': 0.40905762, \\'source_number\\': 4}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper\\', \\'chunk_index\\': 42, \\'text\\': \\'=\\\\nVoiceToActionService\\\\n(\\\\nasr_url\\\\n=\\\\n\"http://asr-service:8000\"\\\\n,\\\\nnlp_url\\\\n=\\\\n\"http://nlp-service:8000\"\\\\n,\\\\nrobot_control_url\\\\n=\\\\n\"http://robot-control:8000\"\\\\n)\\\\nresult\\\\n=\\\\nvta_service\\\\n.\\\\nprocess_voice_command\\\\n(\\\\naudio_data\\\\n,\\\\ncontext\\\\n)\\\\nSummary\\\\n\\\\u200b\\\\nVoice-to-action systems using OpenAI Whisper provide a natural and intuitive way for humans to interact with humanoid robots. The complete pipeline includes audio capture, speech recognition, intent extraction, and command mapping to robot actions.\\\\nKey considerations for implementation include:\\\\nHandling ambiguity and error conditions gracefully\\\\nOptimizing for latency and accuracy\\\\nEnsuring privacy and security\\\\nIntegrating effectively with robot control systems\\\\nThe next chapter will explore cognitive planning with large language models, which builds on these voice-to-action concepts to enable more sophisticated task planning and execution.\\\\nIntroduction\\\\nUnderstanding Automatic Speech Recognition (ASR) Systems\\\\nCore Components of ASR Systems\\\\nTypes of ASR Systems\\\\nUnderstanding OpenAI Whisper\\\\nWhisper Architecture\\\\nWhisper Model Variants\\\\nWhisper in Voice-to-Action C\\', \\'score\\': 0.40698367, \\'source_number\\': 5}]'}], 'model': 'stepfun/step-3.5-flash:free', 'max_tokens': 500, 'temperature': 0.7, 'tools': [{'type': 'function', 'function': {'name': 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Answer ONLY using the retrieved book content provided by the retrieve_chunks tool.\\n2. Do NOT use external knowledge or make up information.\\n3. If the retrieved content does not contain relevant information, say \"I couldn\\'t find relevant information in the book.\"\\n4. Always cite your sources using the format [Source 1], [Source 2], etc. Each source number corresponds to the chunk number from the tool.\\n5. Be concise and accurate.\\n\\nYour responses should be helpful, clear, and grounded exclusively in the provided context.', 'role': 'system'}, {'role': 'user', 'content': 'show me module 1.'}, {'role': 'assistant', 'content': None, 'tool_calls': [{'id': 'call_db578628cc534f49a2dfaf35', 'type': 'function', 'function': {'name': 'retrieve_chunks', 'arguments': '{\"query\": \"module 1\", \"top_k\": 5}'}}]}, {'role': 'tool', 'tool_call_id': 'call_db578628cc534f49a2dfaf35', 'content': '[{\\'url\\': \\'https://example.com/sample-page\\', \\'chunk_index\\': 0, \\'text\\': \\'Sample Book Page - Introduction\\\\nChapter 1: Introduction\\\\nThis is the first paragraph of the introduction. It provides an overview of the topic.\\\\nThe second paragraph discusses the background and context. It contains several sentences that should be chunked appropriately when the text is processed.\\\\n1.1 Background\\\\nThis is a subsection with its own content. It provides more detailed information about the background.\\\\nAnother paragraph in the background section. This helps test the chunking algorithm w\\', \\'score\\': 0.3259449, \\'source_number\\': 1}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/perception-simulation-isaac-sim\\', \\'chunk_index\\': 9, \\'text\\': \\'\\\\nsetup_camera_sensor\\\\n(\\\\n)\\\\n# Generate synthetic data\\\\nfor\\\\nepisode\\\\nin\\\\nrange\\\\n(\\\\nnum_episodes\\\\n)\\\\n:\\\\n# Randomize environment\\\\nrandomize_environment\\\\n(\\\\n)\\\\n# Collect sensor data\\\\nrgb_image\\\\n=\\\\ncamera\\\\n.\\\\nget_rgb\\\\n(\\\\n)\\\\ndepth_image\\\\n=\\\\ncamera\\\\n.\\\\nget_depth\\\\n(\\\\n)\\\\nsegmentation\\\\n=\\\\ncamera\\\\n.\\\\nget_semantic_segmentation\\\\n(\\\\n)\\\\n# Save with annotations\\\\nsave_training_sample\\\\n(\\\\nrgb_image\\\\n,\\\\ndepth_image\\\\n,\\\\nsegmentation\\\\n)\\\\n# Reset environment\\\\nreset_scene\\\\n(\\\\n)\\\\nDomain Adaptation Techniques\\\\n\\\\u200b\\\\nSynthetic-to-Real Transfer\\\\n: Methods for bridging simulation-to-reality gap\\\\nUnsupervised Domain Adaptation\\\\n: Adapting without real-world labels\\\\nSim-to-Real Learning\\\\n: Techniques for improving real-world performance\\\\nCurriculum Learning\\\\n: Progressive difficulty in training\\\\nAdvanced Simulation Techniques\\\\n\\\\u200b\\\\nNeural Radiance Fields (NeRF) Integration\\\\n\\\\u200b\\\\nNovel View Synthesis\\\\n: Generating new viewpoints from sparse observations\\\\nScene Reconstruction\\\\n: 3D scene reconstruction from 2D images\\\\nViewpoint Extrapolation\\\\n: Rendering views outside training distribution\\\\nGenerative Adversarial Networks (GANs)\\\\n\\\\u200b\\\\nStyle Transfer\\\\n: Adapting synthetic images to real-world sty\\', \\'score\\': 0.28968218, \\'source_number\\': 2}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/ros2-communication-model\\', \\'chunk_index\\': 1, \\'text\\': \"\\\\n)\\\\n# Initialize node-specific components here\\\\ndef\\\\nmain\\\\n(\\\\nargs\\\\n=\\\\nNone\\\\n)\\\\n:\\\\nrclpy\\\\n.\\\\ninit\\\\n(\\\\nargs\\\\n=\\\\nargs\\\\n)\\\\nnode\\\\n=\\\\nMyNode\\\\n(\\\\n)\\\\nrclpy\\\\n.\\\\nspin\\\\n(\\\\nnode\\\\n)\\\\nnode\\\\n.\\\\ndestroy_node\\\\n(\\\\n)\\\\nrclpy\\\\n.\\\\nshutdown\\\\n(\\\\n)\\\\nif\\\\n__name__\\\\n==\\\\n\\'__main__\\'\\\\n:\\\\nmain\\\\n(\\\\n)\\\\nTopics and Publish/Subscribe Data Flow\\\\n\\\\u200b\\\\nTopics\\\\nare named buses over which nodes exchange messages. The publish/subscribe pattern is the most common communication paradigm in ROS 2.\\\\nPublishers and Subscribers\\\\n\\\\u200b\\\\nPublishers\\\\n: Send messages to topics\\\\nSubscribers\\\\n: Receive messages from topics\\\\nAnonymous\\\\n: Publishers and subscribers don\\'t know about each other\\\\nDecoupled\\\\n: Publishers and subscribers can start and stop at any time\\\\nExample: Publisher\\\\n\\\\u200b\\\\nimport\\\\nrclpy\\\\nfrom\\\\nrclpy\\\\n.\\\\nnode\\\\nimport\\\\nNode\\\\nfrom\\\\nstd_msgs\\\\n.\\\\nmsg\\\\nimport\\\\nString\\\\nclass\\\\nTalker\\\\n(\\\\nNode\\\\n)\\\\n:\\\\ndef\\\\n__init__\\\\n(\\\\nself\\\\n)\\\\n:\\\\nsuper\\\\n(\\\\n)\\\\n.\\\\n__init__\\\\n(\\\\n\\'talker\\'\\\\n)\\\\nself\\\\n.\\\\npublisher\\\\n=\\\\nself\\\\n.\\\\ncreate_publisher\\\\n(\\\\nString\\\\n,\\\\n\\'chatter\\'\\\\n,\\\\n10\\\\n)\\\\ntimer_period\\\\n=\\\\n0.5\\\\n# seconds\\\\nself\\\\n.\\\\ntimer\\\\n=\\\\nself\\\\n.\\\\ncreate_timer\\\\n(\\\\ntimer_period\\\\n,\\\\nself\\\\n.\\\\ntimer_callback\\\\n)\\\\nself\\\\n.\\\\ni\\\\n=\\\\n0\\\\ndef\\\\ntimer_callback\\\\n(\\\\nself\\\\n)\\\\n:\\\\nmsg\\\\n=\\\\nString\\\\n(\\\\n)\\\\nmsg\\\\n.\\\\ndata\\\\n=\\\\nf\\'Hello World:\\\\n{\\\\nse\", \\'score\\': 0.28754544, \\'source_number\\': 3}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/cognitive-planning-llms\\', \\'chunk_index\\': 25, \\'text\\': \\'ss to user\\\\nplanner\\\\n=\\\\nLLMBasedPlanner\\\\n(\\\\napi_key\\\\n=\\\\n\"your_api_key\"\\\\n)\\\\nplan\\\\n=\\\\nplanner\\\\n.\\\\nplan_from_goal\\\\n(\\\\ngoal\\\\n=\\\\ngoal\\\\n,\\\\nrobot_capabilities\\\\n=\\\\n[\\\\n\"navigate\"\\\\n,\\\\n\"grasp\"\\\\n,\\\\n\"manipulate\"\\\\n,\\\\n\"speak\"\\\\n]\\\\n,\\\\nenvironment_context\\\\n=\\\\nenvironment_context\\\\n)\\\\nreturn\\\\nplan\\\\nScenario 2: Office Environment Task\\\\n\\\\u200b\\\\ndef\\\\noffice_environment_scenario\\\\n(\\\\n)\\\\n:\\\\n\"\"\"\\\\nGoal: \"Please clean up the conference room and set it up for the\\\\nnext meeting with 4 people, including water and presentation equipment\"\\\\n\"\"\"\\\\ngoal\\\\n=\\\\n\"Clean conference room and set up for 4-person meeting with water and presentation equipment\"\\\\nenvironment_context\\\\n=\\\\n{\\\\n\"room_state\"\\\\n:\\\\n\"messy_from_previous_meeting\"\\\\n,\\\\n\"available_objects\"\\\\n:\\\\n[\\\\n\"chairs\"\\\\n,\\\\n\"table\"\\\\n,\\\\n\"whiteboard\"\\\\n,\\\\n\"projector\"\\\\n,\\\\n\"water_bottles\"\\\\n]\\\\n,\\\\n\"required_items\"\\\\n:\\\\n[\\\\n\"4_chairs\"\\\\n,\\\\n\"4_glasses\"\\\\n,\\\\n\"water\"\\\\n,\\\\n\"projector_setup\"\\\\n]\\\\n,\\\\n\"robot_capabilities\"\\\\n:\\\\n[\\\\n\"navigate\"\\\\n,\\\\n\"grasp\"\\\\n,\\\\n\"manipulate\"\\\\n,\\\\n\"speak\"\\\\n,\\\\n\"display_control\"\\\\n]\\\\n}\\\\n# The LLM-based planner would decompose this into:\\\\n# 1. Assess current room state\\\\n# 2. Clear existing items\\\\n# 3. Arrange 4 chairs around table\\\\n# 4. Place water bottles for each attendee\\\\n# 5. Set \\', \\'score\\': 0.28456095, \\'source_number\\': 4}, {\\'url\\': \\'https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/cognitive-planning-llms\\', \\'chunk_index\\': 26, \\'text\\': \\'isting items\\\\n# 3. Arrange 4 chairs around table\\\\n# 4. Place water bottles for each attendee\\\\n# 5. Set up presentation equipment\\\\n# 6. Test equipment functionality\\\\n# 7. Report completion status\\\\nplanner\\\\n=\\\\nLLMBasedPlanner\\\\n(\\\\napi_key\\\\n=\\\\n\"your_api_key\"\\\\n)\\\\nplan\\\\n=\\\\nplanner\\\\n.\\\\nplan_from_goal\\\\n(\\\\ngoal\\\\n=\\\\ngoal\\\\n,\\\\nrobot_capabilities\\\\n=\\\\n[\\\\n\"navigate\"\\\\n,\\\\n\"grasp\"\\\\n,\\\\n\"manipulate\"\\\\n,\\\\n\"speak\"\\\\n,\\\\n\"display_control\"\\\\n]\\\\n,\\\\nenvironment_context\\\\n=\\\\nenvironment_context\\\\n)\\\\nreturn\\\\nplan\\\\nCapstone Project Requirements\\\\n\\\\u200b\\\\nTechnical Requirements\\\\n\\\\u200b\\\\nVLA Integration\\\\n: Complete integration of vision, language, and action components\\\\nLLM-Based Planning\\\\n: Use of large language models for cognitive planning\\\\nROS 2 Integration\\\\n: Proper integration with ROS 2 action servers and services\\\\nReal-World Execution\\\\n: Capability to execute plans in physical or simulated environments\\\\nAdaptive Behavior\\\\n: Ability to handle unexpected situations and adapt plans\\\\nPerformance Requirements\\\\n\\\\u200b\\\\nSuccess Rate\\\\n: Achieve >80% success rate on standard tasks\\\\nResponse Time\\\\n: Respond to commands within 10 seconds\\\\nExecution Time\\\\n: Complete tasks within reasonable timeframes\\\\nRo\\', \\'score\\': 0.27810308, \\'source_number\\': 5}]'}], 'model': 'stepfun/step-3.5-flash:free', 'max_tokens': 500, 'temperature': 0.7, 'tools': [{'type': 'function', 'function': {'name': 'retrieve_chunks', 'description': 'Retrieve relevant book chunks from Qdrant.', 'parameters': {'properties': {'query': {'description': \"User's question\", 'title': 'Query', 'type': 'string'}, 'top_k': {'default': 5, 'description': 'Number of chunks to retrieve (default: 5, max: 10)', 'title': 'Top K', 'type': 'integer'}}, 'required': ['query', 'top_k'], 'title': 'retrieve_chunks_args', 'type': 'object', 'additionalProperties': False}, 'strict': True}}]}}", "module": "_base_client", "lineno": 486, "pathname": "C:\\Users\\Muhammad 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module. +""" + +import os +import sys +import uuid +import asyncio +from datetime import datetime +from typing import List, Dict, Any, Optional + +from fastapi import FastAPI, HTTPException +from fastapi.responses import JSONResponse +from pydantic import BaseModel, Field, validator +from dotenv import load_dotenv + +from agents import OpenAIChatCompletionsModel +from openai import AsyncOpenAI + +OPENROUTER_API_KEY = ( + "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" +) + +client = AsyncOpenAI( + api_key=OPENROUTER_API_KEY, + base_url="https://openrouter.ai/api/v1", +) + +third_party_model = OpenAIChatCompletionsModel( + openai_client=client, model="stepfun/step-3.5-flash:free" +) + +# Make backend package importable +current_dir = os.path.dirname(os.path.abspath(__file__)) +backend_parent = os.path.dirname(current_dir) +if backend_parent not in sys.path: + sys.path.insert(0, backend_parent) + +# Import backend modules (support both module and script execution) +try: + from .config import get_config + from .retrieve import search as retrieve_search + from .logging_config import setup_logging +except ImportError as e: + try: + from backend.config import get_config + from backend.retrieve import search as retrieve_search + from backend.logging_config import setup_logging + except ImportError as e2: + raise ImportError(f"Failed to import backend modules: {e2}") + +# Import OpenAI Agents SDK (must be installed separately) +try: + from agents import Agent, Runner, function_tool, ModelSettings, ToolCallOutputItem +except ImportError: + raise ImportError( + "openai-agents package required. Install: pip install openai-agents" + ) + +# Load environment +load_dotenv() + +# Setup logging +logger = setup_logging("agent") + +# Initialize FastAPI app +app = FastAPI( + title="RAG Book Chatbot API", + version="1.0.0", + description="Chatbot for humanoid robotics book using OpenAI Agents SDK", +) + +# ============ Pydantic Models ============ + + +class ChatRequest(BaseModel): + question: str = Field(..., min_length=1, max_length=1000) + + @validator("question") + def validate_question(cls, v): + if not v or not v.strip(): + raise ValueError("Question cannot be empty") + return v.strip() + + +class Source(BaseModel): + url: str + chunk_index: int + text_snippet: str + + +class ChatResponse(BaseModel): + answer: str + sources: List[Source] + tokens_used: int + agent_trace: Optional[str] = None + + +class HealthStatus(BaseModel): + status: str + qdrant: str + openai: str + timestamp: str + + +# ============ Retrieval Tool ============ + + +@function_tool +def retrieve_chunks(query: str, top_k: int = 5) -> List[Dict[str, Any]]: + """ + Retrieve relevant book chunks from Qdrant. + + Args: + query: User's question + top_k: Number of chunks to retrieve (default: 5, max: 10) + + Returns: + List of chunks with url, chunk_index, text, score, and source_number + """ + logger.info( + f"[Tool] retrieve_chunks called: query='{query[:100]}...', top_k={top_k}" + ) + + try: + import cohere + from qdrant_client import QdrantClient + + cfg = get_config() + cohere_client = cohere.ClientV2(api_key=cfg["cohere_api_key"]) + qdrant_client = QdrantClient( + url=cfg["qdrant_url"], api_key=cfg["qdrant_api_key"] + ) + collection_name = cfg["qdrant_collection"] + + results = retrieve_search( + query_text=query, + cohere_client=cohere_client, + qdrant_client=qdrant_client, + collection_name=collection_name, + top_k=top_k, + ) + + chunks = [] + for i, result in enumerate(results): + payload = result.get("payload", {}) + chunks.append( + { + "url": payload.get("url", ""), + "chunk_index": payload.get("chunk_index", i), + "text": payload.get("text", ""), + "score": result.get("score", 0.0), + "source_number": i + 1, + } + ) + + logger.info(f"[Tool] Retrieved {len(chunks)} chunks") + return chunks + + except Exception as e: + logger.error(f"[Tool] Retrieval failed: {e}", exc_info=True) + raise + + +# ============ Agent Definition ============ + + +def get_agent_instructions() -> str: + return """You are a helpful assistant answering questions about a humanoid robotics book. + +IMPORTANT GROUNDING RULES: +1. Answer ONLY using the retrieved book content provided by the retrieve_chunks tool. +2. Do NOT use external knowledge or make up information. +3. If the retrieved content does not contain relevant information, say "I couldn't find relevant information in the book." +4. Always cite your sources using the format [Source 1], [Source 2], etc. Each source number corresponds to the chunk number from the tool. +5. Be concise and accurate. + +Your responses should be helpful, clear, and grounded exclusively in the provided context.""" + + +def create_agent(): + return Agent( + name="RAG Book Assistant", + instructions=get_agent_instructions(), + tools=[retrieve_chunks], + model=third_party_model, + model_settings=ModelSettings(temperature=0.7, max_tokens=500), + ) + + +_agent_instance = None + + +def get_agent(): + """Lazy singleton agent instance.""" + global _agent_instance + if _agent_instance is None: + _agent_instance = create_agent() + return _agent_instance + + +# ============ Health Checks ============ + + +def check_qdrant_health() -> str: + try: + from backend.config import get_config + from qdrant_client import QdrantClient + + cfg = get_config() + client = QdrantClient(url=cfg["qdrant_url"], api_key=cfg["qdrant_api_key"]) + client.get_collection(cfg["qdrant_collection"]) + return "connected" + except Exception as e: + logger.warning(f"Qdrant health check failed: {e}") + return "disconnected" + + +def check_openai_health() -> str: + try: + api_key = os.getenv("OPENAI_API_KEY") + if not api_key: + return "disconnected" + import openai + + client = openai.OpenAI(api_key=api_key) + # Simple models.list call to verify API connectivity + client.models.list() + return "connected" + except Exception as e: + logger.warning(f"OpenAI health check failed: {e}") + return "disconnected" + + +# ============ FastAPI Endpoints ============ + + +@app.post("/chat") +async def chat_endpoint(request: ChatRequest): + request_id = str(uuid.uuid4())[:8] + question = request.question.strip() + + logger.info(f"[{request_id}] Received chat: {question[:100]}...") + + try: + agent = get_agent() + + # Use async Runner.run (native async, no blocking) + result = await asyncio.wait_for( + Runner.run(agent, question), + timeout=20.0, # Increased from 10s to accommodate full workflow + ) + + # Extract sources from tool call outputs + sources = [] + if result.new_items: + for item in result.new_items: + if isinstance(item, ToolCallOutputItem): + output = item.output + if isinstance(output, list): + for chunk in output: + sources.append( + Source( + url=chunk.get("url", ""), + chunk_index=chunk.get("chunk_index", 0), + text_snippet=chunk.get("text", "")[:200], + ) + ) + + # Get token usage + tokens_used = 0 + if result.context_wrapper and hasattr(result.context_wrapper, "usage"): + tokens_used = result.context_wrapper.usage.total_tokens + + response = ChatResponse( + answer=result.final_output, + sources=sources, + tokens_used=tokens_used, + agent_trace=f"{request_id}: completed", + ) + + logger.info( + f"[{request_id}] Completed: tokens={tokens_used}, sources={len(sources)}" + ) + return response + + except asyncio.TimeoutError: + logger.error(f"[{request_id}] Timeout after 10s") + return JSONResponse( + status_code=504, + content={ + "error": "timeout", + "message": "Request timed out. Please try again.", + }, + ) + + except Exception as e: + logger.error(f"[{request_id}] Error: {e}", exc_info=True) + status_code = 503 if "openai" in str(e).lower() else 500 + error_code = "openai_failed" if status_code == 503 else "internal_error" + return JSONResponse( + status_code=status_code, content={"error": error_code, "message": str(e)} + ) + + +@app.get("/health", response_model=HealthStatus) +async def health_check(): + request_id = str(uuid.uuid4())[:8] + qdrant = check_qdrant_health() + openai = check_openai_health() # sync call + status = ( + "healthy" if qdrant == "connected" and openai == "connected" else "degraded" + ) + return HealthStatus( + status=status, + qdrant=qdrant, + openai=openai, + timestamp=datetime.utcnow().isoformat() + "Z", + ) + + +@app.on_event("startup") +async def startup_event(): + logger.info("=" * 60) + logger.info("RAG Agent FastAPI Server Starting") + logger.info("=" * 60) + + if not os.getenv("OPENAI_API_KEY"): + logger.warning("OPENAI_API_KEY not set") + + # Test retrieval + try: + import cohere + from qdrant_client import QdrantClient + + cfg = get_config() + cohere_client = cohere.ClientV2(api_key=cfg["cohere_api_key"]) + qdrant_client = QdrantClient( + url=cfg["qdrant_url"], api_key=cfg["qdrant_api_key"] + ) + test_result = retrieve_search( + query_text="test", + cohere_client=cohere_client, + qdrant_client=qdrant_client, + collection_name=cfg["qdrant_collection"], + top_k=1, + ) + logger.info(f"Retrieval test OK: {len(test_result)} results") + except Exception as e: + logger.error(f"Retrieval test failed: {e}") + + logger.info("Server startup complete") + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/agent_direct_run.log b/agent_direct_run.log new file mode 100644 index 0000000000000000000000000000000000000000..1b44e614281b0cb2c230cb0f843be02ebf812100 --- /dev/null +++ b/agent_direct_run.log @@ -0,0 +1,26 @@ +/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/agent.py:63: PydanticDeprecatedSince20: Pydantic V1 style `@validator` validators are deprecated. You should migrate to Pydantic V2 style `@field_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.12/migration/ + @validator('question') +/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/agent.py:291: DeprecationWarning: + on_event is deprecated, use lifespan event handlers instead. + + Read more about it in the + [FastAPI docs for Lifespan Events](https://fastapi.tiangolo.com/advanced/events/). + + @app.on_event("startup") +INFO: Started server process [13533] +INFO: Waiting for application startup. +05:10:47 - INFO - root - ============================================================ +05:10:47 - INFO - root - RAG Agent FastAPI Server Starting +05:10:47 - INFO - root - ============================================================ +05:10:49 - INFO - httpx - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +05:10:49 - INFO - backend.retrieve - Embedding query: 'test...' (top_k=1) +05:10:49 - INFO - httpx - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +05:10:50 - INFO - httpx - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +05:10:50 - INFO - backend.retrieve - Search completed in 0.62s, returned 1 results +05:10:50 - INFO - backend.retrieve - Total query time: 1.05s +05:10:50 - INFO - root - Retrieval test OK: 1 results +05:10:50 - INFO - root - Server startup complete +INFO: Application startup complete. +ERROR: [Errno 98] error while attempting to bind on address ('0.0.0.0', 8000): address already in use +INFO: Waiting for application shutdown. +INFO: Application shutdown complete. diff --git a/agent_sdk_docs.md b/agent_sdk_docs.md new file mode 100644 index 0000000000000000000000000000000000000000..67dd3a44917ff9e15018c6d711729a88b9370e54 --- /dev/null +++ b/agent_sdk_docs.md @@ -0,0 +1,15064 @@ +### Create Project and Virtual Environment (Bash) + +Source: https://openai.github.io/openai-agents-python/quickstart + +This snippet demonstrates how to create a new project directory, navigate into it, and set up a Python virtual environment using `venv`. This is a one-time setup for a new project. + +```bash +mkdir my_project +cd my_project +python -m venv .venv +``` + +-------------------------------- + +### Install OpenAI Agents SDK (Bash) + +Source: https://openai.github.io/openai-agents-python/quickstart + +This command installs the OpenAI Agents Python SDK using pip. Alternatively, `uv add openai-agents` can be used if using the `uv` package installer. This makes the SDK available within the activated virtual environment. + +```bash +pip install openai-agents +``` + +-------------------------------- + +### Quick Start: SQLAlchemy Session with Existing Engine + +Source: https://openai.github.io/openai-agents-python/sessions/sqlalchemy_session + +Shows how to create an `SQLAlchemySession` using an existing SQLAlchemy asynchronous engine (PostgreSQL example). This is useful for integrating with applications that already have a database engine configured. The example includes engine creation, session setup, task execution, and engine disposal. + +```python +import asyncio +from agents import Agent, Runner +from agents.extensions.memory import SQLAlchemySession +from sqlalchemy.ext.asyncio import create_async_engine + +async def main(): + # Create your database engine + engine = create_async_engine("postgresql+asyncpg://user:pass@localhost/db") + + agent = Agent("Assistant") + session = SQLAlchemySession( + "user-456", + engine=engine, + create_tables=True + ) + + result = await Runner.run(agent, "Hello", session=session) + print(result.final_output) + + # Clean up + await engine.dispose() + +if __name__ == "__main__": + asyncio.run(main()) + +``` + +-------------------------------- + +### Install OpenAI Agents with Voice Dependencies + +Source: https://openai.github.io/openai-agents-python/voice/quickstart + +Installs the OpenAI Agents SDK along with optional voice-related dependencies. This is a prerequisite for using voice features. + +```shell +pip install 'openai-agents[voice]' +``` + +-------------------------------- + +### Configure Realtime Runner (Python) + +Source: https://openai.github.io/openai-agents-python/realtime/quickstart + +Sets up the RealtimeRunner with the starting agent and configuration for model settings, including model name, voice, audio formats, transcription, and turn detection. Dependencies: RealtimeRunner class, agent instance. + +```python +runner = RealtimeRunner( + starting_agent=agent, + config={ + "model_settings": { + "model_name": "gpt-realtime", + "voice": "ash", + "modalities": ["audio"], + "input_audio_format": "pcm16", + "output_audio_format": "pcm16", + "input_audio_transcription": {"model": "gpt-4o-mini-transcribe"}, + "turn_detection": {"type": "semantic_vad", "interrupt_response": True}, + } + } +) +``` + +-------------------------------- + +### Complete Voice Pipeline Example + +Source: https://openai.github.io/openai-agents-python/voice/quickstart + +Combines all the previous steps into a single, runnable script. This includes setting up agents, initializing the voice pipeline, generating sample audio input, running the pipeline, and streaming the audio output. It requires `asyncio` to manage the asynchronous operations. + +```python +import asyncio +import random + +import numpy as np +import sounddevice as sd + +from agents import ( + Agent, + function_tool, + set_tracing_disabled, +) +from agents.voice import ( + AudioInput, + SingleAgentVoiceWorkflow, + VoicePipeline, +) +from agents.extensions.handoff_prompt import prompt_with_handoff_instructions + + +@function_tool +def get_weather(city: str) -> str: + """Get the weather for a given city.""" + print(f"[debug] get_weather called with city: {city}") + choices = ["sunny", "cloudy", "rainy", "snowy"] + return f"The weather in {city} is {random.choice(choices)}." + + +spanish_agent = Agent( + name="Spanish", + handoff_description="A spanish speaking agent.", + instructions=prompt_with_handoff_instructions( + "You're speaking to a human, so be polite and concise. Speak in Spanish.", + ), + model="gpt-5.2", +) + +agent = Agent( + name="Assistant", + instructions=prompt_with_handoff_instructions( + "You're speaking to a human, so be polite and concise. If the user speaks in Spanish, handoff to the spanish agent.", + ), + model="gpt-5.2", + handoffs=[spanish_agent], + tools=[get_weather], +) + + +async def main(): + pipeline = VoicePipeline(workflow=SingleAgentVoiceWorkflow(agent)) + buffer = np.zeros(24000 * 3, dtype=np.int16) + audio_input = AudioInput(buffer=buffer) + + result = await pipeline.run(audio_input) + + # Create an audio player using `sounddevice` + player = sd.OutputStream(samplerate=24000, channels=1, dtype=np.int16) + player.start() + + # Play the audio stream as it comes in + async for event in result.stream(): + if event.type == "voice_stream_event_audio": + player.write(event.data) + + +if __name__ == "__main__": + asyncio.run(main()) + +``` + +-------------------------------- + +### Realtime Agent and Runner Example in Python + +Source: https://openai.github.io/openai-agents-python/realtime/quickstart + +This Python code demonstrates how to set up and run a real-time conversational agent. It initializes a RealtimeAgent with specific instructions and a RealtimeRunner with detailed model and audio configurations. The code then processes various events emitted during the agent's session, such as agent start/end, tool usage, and audio events. It relies on the asyncio library for asynchronous operations and the agents.realtime module. + +```python +import asyncio +from agents.realtime import RealtimeAgent, RealtimeRunner + +def _truncate_str(s: str, max_length: int) -> str: + if len(s) > max_length: + return s[:max_length] + "..." + return s + +async def main(): + # Create the agent + agent = RealtimeAgent( + name="Assistant", + instructions="You are a helpful voice assistant. Keep responses brief and conversational.", + ) + # Set up the runner with configuration + runner = RealtimeRunner( + starting_agent=agent, + config={ + "model_settings": { + "model_name": "gpt-realtime", + "voice": "ash", + "modalities": ["audio"], + "input_audio_format": "pcm16", + "output_audio_format": "pcm16", + "input_audio_transcription": {"model": "gpt-4o-mini-transcribe"}, + "turn_detection": {"type": "semantic_vad", "interrupt_response": True}, + } + }, + ) + # Start the session + session = await runner.run() + + async with session: + print("Session started! The agent will stream audio responses in real-time.") + # Process events + async for event in session: + try: + if event.type == "agent_start": + print(f"Agent started: {event.agent.name}") + elif event.type == "agent_end": + print(f"Agent ended: {event.agent.name}") + elif event.type == "handoff": + print(f"Handoff from {event.from_agent.name} to {event.to_agent.name}") + elif event.type == "tool_start": + print(f"Tool started: {event.tool.name}") + elif event.type == "tool_end": + print(f"Tool ended: {event.tool.name}; output: {event.output}") + elif event.type == "audio_end": + print("Audio ended") + elif event.type == "audio": + # Enqueue audio for callback-based playback with metadata + # Non-blocking put; queue is unbounded, so drops won’t occur. + pass + elif event.type == "audio_interrupted": + print("Audio interrupted") + # Begin graceful fade + flush in the audio callback and rebuild jitter buffer. + elif event.type == "error": + print(f"Error: {event.error}") + elif event.type == "history_updated": + pass # Skip these frequent events + elif event.type == "history_added": + pass # Skip these frequent events + elif event.type == "raw_model_event": + print(f"Raw model event: {_truncate_str(str(event.data), 200)}") + else: + print(f"Unknown event type: {event.type}") + except Exception as e: + print(f"Error processing event: {_truncate_str(str(e), 200)}") + +if __name__ == "__main__": + # Run the session + asyncio.run(main()) + +``` + +-------------------------------- + +### Activate Virtual Environment (Bash) + +Source: https://openai.github.io/openai-agents-python/quickstart + +This command activates the Python virtual environment created in the previous step. It needs to be run every time a new terminal session is started to use the project's isolated environment. + +```bash +source .venv/bin/activate +``` + +-------------------------------- + +### RealtimeRunner run method example (Python) + +Source: https://openai.github.io/openai-agents-python/zh/ref/realtime/runner + +Starts and returns a realtime session, enabling bidirectional communication with the model. The example demonstrates initiating a session and sending a message, followed by iterating through events. Requires RealtimeSession. + +```Python + async def run( + self, *, context: TContext | None = None, model_config: RealtimeModelConfig | None = None + ) -> RealtimeSession: + """Start and returns a realtime session. + + Returns: + RealtimeSession: A session object that allows bidirectional communication with the + realtime model. + + Example: + ```python + runner = RealtimeRunner(agent) + async with await runner.run() as session: + await session.send_message("Hello") + async for event in session: + print(event) + ``` + """ + # Create and return the connection + session = RealtimeSession( + model=self._model, + agent=self._starting_agent, + context=context, + model_config=model_config, + run_config=self._config, + ) + + return session + +``` + +-------------------------------- + +### Run and Process Realtime Agent Session (Python) + +Source: https://openai.github.io/openai-agents-python/realtime/quickstart + +Starts a realtime agent session using the configured runner and asynchronously iterates through events. It handles various event types like agent start/end, handoffs, tool usage, audio events, and errors. Includes a helper function for truncating long strings. Dependencies: asyncio, RealtimeRunner, RealtimeAgent. + +```python +# Start the session +session = await runner.run() + +async with session: + print("Session started! The agent will stream audio responses in real-time.") + # Process events + async for event in session: + try: + if event.type == "agent_start": + print(f"Agent started: {event.agent.name}") + elif event.type == "agent_end": + print(f"Agent ended: {event.agent.name}") + elif event.type == "handoff": + print(f"Handoff from {event.from_agent.name} to {event.to_agent.name}") + elif event.type == "tool_start": + print(f"Tool started: {event.tool.name}") + elif event.type == "tool_end": + print(f"Tool ended: {event.tool.name}; output: {event.output}") + elif event.type == "audio_end": + print("Audio ended") + elif event.type == "audio": + # Enqueue audio for callback-based playback with metadata + # Non-blocking put; queue is unbounded, so drops won’t occur. + pass + elif event.type == "audio_interrupted": + print("Audio interrupted") + # Begin graceful fade + flush in the audio callback and rebuild jitter buffer. + elif event.type == "error": + print(f"Error: {event.error}") + elif event.type == "history_updated": + pass # Skip these frequent events + elif event.type == "history_added": + pass # Skip these frequent events + elif event.type == "raw_model_event": + print(f"Raw model event: {_truncate_str(str(event.data), 200)}") + else: + print(f"Unknown event type: {event.type}") + except Exception as e: + print(f"Error processing event: {_truncate_str(str(e), 200)}") + +def _truncate_str(s: str, max_length: int) -> str: + if len(s) > max_length: + return s[:max_length] + "..." + return s + +``` + +-------------------------------- + +### Quick Start: Advanced SQLite Session with Agent + +Source: https://openai.github.io/openai-agents-python/sessions/advanced_sqlite_session + +Demonstrates how to initialize an Agent and an AdvancedSQLiteSession, run a conversation turn, store usage data, and continue the conversation. This example highlights the basic workflow for using the advanced session features. + +```python +from agents import Agent, Runner +from agents.extensions.memory import AdvancedSQLiteSession + +# Create agent +agent = Agent( + name="Assistant", + instructions="Reply very concisely.", +) + +# Create an advanced session +session = AdvancedSQLiteSession( + session_id="conversation_123", + db_path="conversations.db", + create_tables=True +) + +# First conversation turn +result = await Runner.run( + agent, + "What city is the Golden Gate Bridge in?", + session=session +) +print(result.final_output) # "San Francisco" + +# IMPORTANT: Store usage data +await session.store_run_usage(result) + +# Continue conversation +result = await Runner.run( + agent, + "What state is it in?", + session=session +) +print(result.final_output) # "California" +await session.store_run_usage(result) + +``` + +-------------------------------- + +### Python Agent Workflow with Handoffs and Guardrails + +Source: https://openai.github.io/openai-agents-python/quickstart + +This Python code defines a complete agent workflow using the OpenAI Agents library. It includes a guardrail agent to check for homework, specialist agents for math and history, and a triage agent to route queries. The `Runner.run` function executes the workflow, demonstrating handoffs and input guardrails with example queries. + +```python +from agents import Agent, InputGuardrail, GuardrailFunctionOutput, Runner +from agents.exceptions import InputGuardrailTripwireTriggered +from pydantic import BaseModel +import asyncio + +class HomeworkOutput(BaseModel): + is_homework: bool + reasoning: str + +guardrail_agent = Agent( + name="Guardrail check", + instructions="Check if the user is asking about homework.", + output_type=HomeworkOutput, +) + +math_tutor_agent = Agent( + name="Math Tutor", + handoff_description="Specialist agent for math questions", + instructions="You provide help with math problems. Explain your reasoning at each step and include examples", +) + +history_tutor_agent = Agent( + name="History Tutor", + handoff_description="Specialist agent for historical questions", + instructions="You provide assistance with historical queries. Explain important events and context clearly.", +) + + +async def homework_guardrail(ctx, agent, input_data): + result = await Runner.run(guardrail_agent, input_data, context=ctx.context) + final_output = result.final_output_as(HomeworkOutput) + return GuardrailFunctionOutput( + output_info=final_output, + tripwire_triggered=not final_output.is_homework, + ) + +triage_agent = Agent( + name="Triage Agent", + instructions="You determine which agent to use based on the user's homework question", + handoffs=[history_tutor_agent, math_tutor_agent], + input_guardrails=[ + InputGuardrail(guardrail_function=homework_guardrail), + ], +) + +async def main(): + # Example 1: History question + try: + result = await Runner.run(triage_agent, "who was the first president of the united states?") + print(result.final_output) + except InputGuardrailTripwireTriggered as e: + print("Guardrail blocked this input:", e) + + # Example 2: General/philosophical question + try: + result = await Runner.run(triage_agent, "What is the meaning of life?") + print(result.final_output) + except InputGuardrailTripwireTriggered as e: + print("Guardrail blocked this input:", e) + +if __name__ == "__main__": + asyncio.run(main()) + +``` + +-------------------------------- + +### Example: Using LitellmModel in an Agent + +Source: https://openai.github.io/openai-agents-python/models/litellm + +A Python example demonstrating how to use LitellmModel to create an agent that can interact with various AI models. It includes setting up an agent with a model, API key, tools, and running a conversation. The example prompts the user for model and API key if not provided as arguments. + +```python +from __future__ import annotations + +import asyncio + +from agents import Agent, Runner, function_tool, set_tracing_disabled +from agents.extensions.models.litellm_model import LitellmModel + +@function_tool +def get_weather(city: str): + print(f"[debug] getting weather for {city}") + return "The weather in {city} is sunny." + + +async def main(model: str, api_key: str): + agent = Agent( + name="Assistant", + instructions="You only respond in haikus.", + model=LitellmModel(model=model, api_key=api_key), + tools=[get_weather], + ) + + result = await Runner.run(agent, "What's the weather in Tokyo?") + print(result.final_output) + + +if __name__ == "__main__": + # First try to get model/api key from args + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--model", type=str, required=False) + parser.add_argument("--api-key", type=str, required=False) + args = parser.parse_args() + + model = args.model + if not model: + model = input("Enter a model name for Litellm: ") + + api_key = args.api_key + if not api_key: + api_key = input("Enter an API key for Litellm: ") + + asyncio.run(main(model, api_key)) +``` + +-------------------------------- + +### Implement Streamable HTTP MCP Server + +Source: https://openai.github.io/openai-agents-python/mcp + +This example demonstrates setting up a streamable HTTP MCP server using `MCPServerStreamableHttp`. It's suitable for managing network connections directly and running servers within custom infrastructure with low latency. The code includes agent setup, tool usage, and output printing. + +```python +import asyncio +import os + +from agents import Agent, Runner +from agents.mcp import MCPServerStreamableHttp +from agents.model_settings import ModelSettings + +async def main() -> None: + token = os.environ["MCP_SERVER_TOKEN"] + async with MCPServerStreamableHttp( + name="Streamable HTTP Python Server", + params={ + "url": "http://localhost:8000/mcp", + "headers": {"Authorization": f"Bearer {token}"}, + "timeout": 10, + }, + cache_tools_list=True, + max_retry_attempts=3, + ) as server: + agent = Agent( + name="Assistant", + instructions="Use the MCP tools to answer the questions.", + mcp_servers=[server], + model_settings=ModelSettings(tool_choice="required"), + ) + + result = await Runner.run(agent, "Add 7 and 22.") + print(result.final_output) + +asyncio.run(main()) + +``` + +-------------------------------- + +### Quick Start: SQLAlchemy Session from Database URL + +Source: https://openai.github.io/openai-agents-python/sessions/sqlalchemy_session + +Demonstrates creating an `SQLAlchemySession` using a database URL (SQLite in-memory example). It initializes an agent and runner, then executes a task using the defined session. Includes necessary imports and asynchronous execution. + +```python +import asyncio +from agents import Agent, Runner +from agents.extensions.memory import SQLAlchemySession + +async def main(): + agent = Agent("Assistant") + + # Create session using database URL + session = SQLAlchemySession.from_url( + "user-123", + url="sqlite+aiosqlite:///:memory:", + create_tables=True + ) + + result = await Runner.run(agent, "Hello", session=session) + print(result.final_output) + +if __name__ == "__main__": + asyncio.run(main()) + +``` + +-------------------------------- + +### Setup Websocket Connection and Event Listener + +Source: https://openai.github.io/openai-agents-python/ja/ref/voice/models/openai_stt + +Establishes a websocket connection, starts an event listener task, and waits for session creation events. Handles timeouts and other exceptions during the setup process. Requires a websockets client connection object and queues for state and output. + +```python +async def _setup_connection(self, ws: websockets.ClientConnection) -> None: + self._websocket = ws + self._listener_task = asyncio.create_task(self._event_listener()) + + try: + event = await _wait_for_event( + self._state_queue, + ["session.created", "transcription_session.created"], + SESSION_CREATION_TIMEOUT, + ) + except TimeoutError as e: + wrapped_err = STTWebsocketConnectionError( + "Timeout waiting for transcription_session.created event" + ) + await self._output_queue.put(ErrorSentinel(wrapped_err)) + raise wrapped_err from e + except Exception as e: + await self._output_queue.put(ErrorSentinel(e)) + raise e + + await self._configure_session() + + try: + event = await _wait_for_event( + self._state_queue, + ["session.updated", "transcription_session.updated"], + SESSION_UPDATE_TIMEOUT, + ) + if _debug.DONT_LOG_MODEL_DATA: + logger.debug("Session updated") + else: + logger.debug(f"Session updated: {event}") + except TimeoutError as e: + wrapped_err = STTWebsocketConnectionError( + "Timeout waiting for transcription_session.updated event" + ) + await self._output_queue.put(ErrorSentinel(wrapped_err)) + raise wrapped_err from e + except Exception as e: + await self._output_queue.put(ErrorSentinel(e)) + raise +``` + +-------------------------------- + +### RealtimeRunner Initialization and Run Method (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/runner + +This Python code defines the RealtimeRunner class, responsible for managing real-time agent sessions. It includes the `__init__` method for setting up the runner with a starting agent, an optional model, and configuration, and the `run` method to initiate a session. The `run` method returns a `RealtimeSession` object for bidirectional communication. The example demonstrates how to instantiate the runner and start a session. + +```python +class RealtimeRunner: + """A `RealtimeRunner` is the equivalent of `Runner` for realtime agents. It automatically + handles multiple turns by maintaining a persistent connection with the underlying model + layer. + + The session manages the local history copy, executes tools, runs guardrails and facilitates + handoffs between agents. + + Since this code runs on your server, it uses WebSockets by default. You can optionally create + your own custom model layer by implementing the `RealtimeModel` interface. + """ + + def __init__( + self, + starting_agent: RealtimeAgent, + *, + model: RealtimeModel | None = None, + config: RealtimeRunConfig | None = None, + ) -> None: + """Initialize the realtime runner. + + Args: + starting_agent: The agent to start the session with. + context: The context to use for the session. + model: The model to use. If not provided, will use a default OpenAI realtime model. + config: Override parameters to use for the entire run. + """ + self._starting_agent = starting_agent + self._config = config + self._model = model or OpenAIRealtimeWebSocketModel() + + async def run( + self, *, context: TContext | None = None, model_config: RealtimeModelConfig | None = None + ) -> RealtimeSession: + """Start and returns a realtime session. + + Returns: + RealtimeSession: A session object that allows bidirectional communication with the + realtime model. + + Example: + ```python + runner = RealtimeRunner(agent) + async with await runner.run() as session: + await session.send_message("Hello") + async for event in session: + print(event) + ``` + """ + # Create and return the connection + session = RealtimeSession( + model=self._model, + agent=self._starting_agent, + context=context, + model_config=model_config, + run_config=self._config, + ) + + return session + +``` + +-------------------------------- + +### Initialization: AdvancedSQLiteSession + +Source: https://openai.github.io/openai-agents-python/sessions/advanced_sqlite_session + +Shows different ways to initialize an AdvancedSQLiteSession, including basic setup, with persistent storage via `db_path`, and with a custom logger instance. All examples ensure the necessary tables are created. + +```python +from agents.extensions.memory import AdvancedSQLiteSession + +# Basic initialization +session = AdvancedSQLiteSession( + session_id="my_conversation", + create_tables=True # Auto-create advanced tables +) + +# With persistent storage +session = AdvancedSQLiteSession( + session_id="user_123", + db_path="path/to/conversations.db", + create_tables=True +) + +# With custom logger +import logging +logger = logging.getLogger("my_app") +session = AdvancedSQLiteSession( + session_id="session_456", + create_tables=True, + logger=logger +) + +``` + +-------------------------------- + +### RealtimeRunner Initialization and Run Method + +Source: https://openai.github.io/openai-agents-python/ko/ref/realtime/runner + +Initializes the RealtimeRunner with a starting agent and an optional model or configuration. The `run` method starts a real-time session, enabling bidirectional communication with the model. It can be used asynchronously with a context manager. + +```python +class RealtimeRunner: + """A `RealtimeRunner` is the equivalent of `Runner` for realtime agents. It automatically + handles multiple turns by maintaining a persistent connection with the underlying model + layer. + + The session manages the local history copy, executes tools, runs guardrails and facilitates + handoffs between agents. + + Since this code runs on your server, it uses WebSockets by default. You can optionally create + your own custom model layer by implementing the `RealtimeModel` interface. + """ + + def __init__( + self, + starting_agent: RealtimeAgent, + *, + model: RealtimeModel | None = None, + config: RealtimeRunConfig | None = None, + ) -> None: + """Initialize the realtime runner. + + Args: + starting_agent: The agent to start the session with. + context: The context to use for the session. + model: The model to use. If not provided, will use a default OpenAI realtime model. + config: Override parameters to use for the entire run. + """ + self._starting_agent = starting_agent + self._config = config + self._model = model or OpenAIRealtimeWebSocketModel() + + async def run( + self, *, context: TContext | None = None, model_config: RealtimeModelConfig | None = None + ) -> RealtimeSession: + """Start and returns a realtime session. + + Returns: + RealtimeSession: A session object that allows bidirectional communication with the + realtime model. + + Example: + ```python + runner = RealtimeRunner(agent) + async with await runner.run() as session: + await session.send_message("Hello") + async for event in session: + print(event) + ``` + """ + # Create and return the connection + session = RealtimeSession( + model=self._model, + agent=self._starting_agent, + context=context, + model_config=model_config, + run_config=self._config, + ) + + return session + +``` + +-------------------------------- + +### Create a Realtime Agent Instance (Python) + +Source: https://openai.github.io/openai-agents-python/realtime/quickstart + +Initializes a RealtimeAgent with a name and instructions. This agent will act as the AI assistant in the voice conversation. Dependencies: RealtimeAgent class. + +```python +agent = RealtimeAgent( + name="Assistant", + instructions="You are a helpful voice assistant. Keep your responses conversational and friendly.", +) +``` + +-------------------------------- + +### Install OpenAI Agents SDK using pip + +Source: https://openai.github.io/openai-agents-python/index + +This command installs the OpenAI Agents SDK package. Ensure you have pip installed and accessible in your environment. + +```bash +pip install openai-agents + +``` + +-------------------------------- + +### Create and Use a Custom Span in Python + +Source: https://openai.github.io/openai-agents-python/ref/tracing + +This Python code example illustrates how to create and utilize a custom span for tracking operations. It shows the use of the `custom_span` context manager to define the start and end of an operation, and how to set output data using `span.set_output()`. The example also includes notes on span nesting, context management, data inclusion, and error handling. + +```python +# Creating a custom span +with custom_span("database_query", { + "operation": "SELECT", + "table": "users" +}) as span: + results = await db.query("SELECT * FROM users") + span.set_output({"count": len(results)}) + +# Handling errors in spans +with custom_span("risky_operation") as span: + try: + result = perform_risky_operation() + except Exception as e: + span.set_error({ + "message": str(e), + "data": {"operation": "risky_operation"} + }) + raise +``` + +-------------------------------- + +### RealtimeRunner Initialization + +Source: https://openai.github.io/openai-agents-python/ref/realtime/runner + +Initializes the RealtimeRunner, setting up the starting agent, model, and configuration for realtime agent sessions. + +```APIDOC +## RealtimeRunner Constructor + +### Description +Initializes the `RealtimeRunner` with a starting agent, an optional model, and configuration. + +### Method +`__init__` + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +None + +* **starting_agent** (`RealtimeAgent`) - Required - The agent to start the session with. +* **model** (`RealtimeModel | None`) - Optional - The model to use. Defaults to `OpenAIRealtimeWebSocketModel` if not provided. +* **config** (`RealtimeRunConfig | None`) - Optional - Override parameters to use for the entire run. + +### Request Example +```python +from agents.realtime.runner import RealtimeRunner +from agents.realtime.agent import RealtimeAgent # Assuming RealtimeAgent is defined elsewhere + +# Assuming agent is an instance of RealtimeAgent +# runner = RealtimeRunner(starting_agent=agent) +``` + +### Response +None (Constructor does not return a value) + +``` + +-------------------------------- + +### Quick Start: Using SQLiteSession for Agent Conversation History + +Source: https://openai.github.io/openai-agents-python/sessions + +Demonstrates how to initialize an Agent and Runner with a SQLiteSession to maintain conversation history across multiple turns. The session automatically stores and retrieves context, allowing the agent to remember previous interactions without manual intervention. This example shows both asynchronous and synchronous runner usage. + +```python +from agents import Agent, Runner, SQLiteSession + +# Create agent +agent = Agent( + name="Assistant", + instructions="Reply very concisely.", +) + +# Create a session instance with a session ID +session = SQLiteSession("conversation_123") + +# First turn +result = await Runner.run( + agent, + "What city is the Golden Gate Bridge in?", + session=session +) +print(result.final_output) # "San Francisco" + +# Second turn - agent automatically remembers previous context +result = await Runner.run( + agent, + "What state is it in?", + session=session +) +print(result.final_output) # "California" + +# Also works with synchronous runner +result = Runner.run_sync( + agent, + "What's the population?", + session=session +) +print(result.final_output) # "Approximately 39 million" + +``` + +-------------------------------- + +### Create a Basic Agent (Python) + +Source: https://openai.github.io/openai-agents-python/quickstart + +This Python code defines a simple agent named 'Math Tutor' with specific instructions. It imports the `Agent` class from the `agents` library and initializes an agent instance. + +```python +from agents import Agent + +agent = Agent( + name="Math Tutor", + instructions="You provide help with math problems. Explain your reasoning at each step and include examples", +) +``` + +-------------------------------- + +### Trace Usage Example + +Source: https://openai.github.io/openai-agents-python/zh/ref/tracing + +Provides a basic example of how to use the `Trace` context manager for grouping operations. + +```APIDOC +## Trace Usage Example + +Provides a basic example of how to use the `Trace` context manager for grouping operations. + +### Example + +```python +# Basic trace usage +with trace("Order Processing") as t: + validation_result = await Runner.run(validator, order_data) + if validation_result.approved: + await Runner.run(processor, order_data) +``` +``` + +-------------------------------- + +### Import Realtime Agent Components (Python) + +Source: https://openai.github.io/openai-agents-python/realtime/quickstart + +Imports necessary classes (RealtimeAgent, RealtimeRunner) from the openai.agents.realtime module for creating and managing realtime agents. Requires Python 3.9+. + +```python +import asyncio +from agents.realtime import RealtimeAgent, RealtimeRunner +``` + +-------------------------------- + +### Install SQLAlchemy Sessions for OpenAI Agents + +Source: https://openai.github.io/openai-agents-python/sessions/sqlalchemy_session + +Installs the 'openai-agents' library with the 'sqlalchemy' extra for session storage capabilities. This is a prerequisite for using SQLAlchemy-based sessions. + +```bash +pip install openai-agents[sqlalchemy] + +``` + +-------------------------------- + +### Run Realtime Session + +Source: https://openai.github.io/openai-agents-python/ref/realtime/runner + +Starts and returns a RealtimeSession object, enabling bidirectional communication with the realtime model. This method is crucial for initiating and managing the interaction loop with the agent. An example demonstrates how to use the session for sending messages and receiving events. + +```python +async def run( + self, *, context: TContext | None = None, model_config: RealtimeModelConfig | None = None + ) -> RealtimeSession: + """Start and returns a realtime session. + + Returns: + RealtimeSession: A session object that allows bidirectional communication with the + realtime model. + + Example: + ```python + runner = RealtimeRunner(agent) + async with await runner.run() as session: + await session.send_message("Hello") + async for event in session: + print(event) + ``` + """ + session = RealtimeSession( + model=self._model, + agent=self._starting_agent, + context=context, + model_config=model_config, + run_config=self._config, + ) + + return session + +``` + +-------------------------------- + +### Basic Agent Execution with OpenAI Agents SDK (Python) + +Source: https://openai.github.io/openai-agents-python/index + +This Python code demonstrates a 'Hello World' example using the OpenAI Agents SDK. It initializes an Agent with specific instructions and then uses the Runner to execute a task synchronously, printing the final output. This requires the OPENAI_API_KEY environment variable to be set. + +```python +from agents import Agent, Runner + +agent = Agent(name="Assistant", instructions="You are a helpful assistant") + +result = Runner.run_sync(agent, "Write a haiku about recursion in programming.") +print(result.final_output) + +# Code within the code, +# Functions calling themselves, +# Infinite loop's dance. + +``` + +-------------------------------- + +### Run Agent Orchestration (Python) + +Source: https://openai.github.io/openai-agents-python/quickstart + +This Python code demonstrates how to run an agent orchestration using the `Runner` class. It shows an asynchronous `main` function that executes the 'Triage Agent' with a user query and prints the final output. + +```python +from agents import Runner + +async def main(): + result = await Runner.run(triage_agent, "who was the first president of the united states?") + print(result.final_output) +``` + +-------------------------------- + +### RealtimeRunner Initialization (Python) + +Source: https://openai.github.io/openai-agents-python/zh/ref/realtime/runner + +Initializes the RealtimeRunner, setting up the starting agent and optionally a custom model or run configuration. If no model is provided, it defaults to an OpenAIRealtimeWebSocketModel. Dependencies include RealtimeAgent, RealtimeModel, and RealtimeRunConfig. + +```Python +class RealtimeRunner: + """A `RealtimeRunner` is the equivalent of `Runner` for realtime agents. It automatically + handles multiple turns by maintaining a persistent connection with the underlying model + layer. + + The session manages the local history copy, executes tools, runs guardrails and facilitates + handoffs between agents. + + Since this code runs on your server, it uses WebSockets by default. You can optionally create + your own custom model layer by implementing the `RealtimeModel` interface. + """ + + def __init__( + self, + starting_agent: RealtimeAgent, + *, + model: RealtimeModel | None = None, + config: RealtimeRunConfig | None = None, + ) -> None: + """Initialize the realtime runner. + + Args: + starting_agent: The agent to start the session with. + context: The context to use for the session. + model: The model to use. If not provided, will use a default OpenAI realtime model. + config: Override parameters to use for the entire run. + """ + self._starting_agent = starting_agent + self._config = config + self._model = model or OpenAIRealtimeWebSocketModel() + +``` + +-------------------------------- + +### Implement Trace Start Method in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/traces + +Implements the abstract method 'start' for a trace, which initiates the trace and optionally marks it as the current trace in the execution context. This method must be called before adding any spans and ensures thread-safety when marking as current. + +```python +@abc.abstractmethod +def start(self, mark_as_current: bool = False): + """Start the trace and optionally mark it as the current trace. + + Args: + mark_as_current: If true, marks this trace as the current trace + in the execution context. + + Notes: + - Must be called before any spans can be added + - Only one trace can be current at a time + - Thread-safe when using mark_as_current + """ + pass + +``` + +-------------------------------- + +### Get Global Trace Provider - Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/setup + +Retrieves the currently set global trace provider. If no provider has been set, it raises a `RuntimeError`. This function is used to access the active tracing configuration. + +```python +def get_trace_provider() -> TraceProvider: + """Get the global trace provider used by tracing utilities.""" + if GLOBAL_TRACE_PROVIDER is None: + raise RuntimeError("Trace provider not set") + return GLOBAL_TRACE_PROVIDER +``` + +-------------------------------- + +### MCPServerStdio.name property + +Source: https://openai.github.io/openai-agents-python/ko/ref/mcp/server + +Gets a readable name for the MCP server instance. This property provides a string representation that can be used to identify the server, often derived from the command used to start it. + +```APIDOC +## MCPServerStdio.name + +### Description +A readable name for the server. + +### Method +GET + +### Endpoint +N/A (Property) + +### Parameters +None + +### Request Example +None + +### Response +#### Success Response (200) +- **name** (str) - The readable name of the server. + +#### Response Example +```json +{ + "name": "stdio: my_server_command" +} +``` +``` + +-------------------------------- + +### Set OpenAI API Key (Bash) + +Source: https://openai.github.io/openai-agents-python/quickstart + +This command sets the OpenAI API key as an environment variable. Replace `sk-...` with your actual API key. This is crucial for authenticating with the OpenAI API. + +```bash +export OPENAI_API_KEY=sk-... +``` + +-------------------------------- + +### Define Multiple Agents with Handoff Descriptions (Python) + +Source: https://openai.github.io/openai-agents-python/quickstart + +This Python snippet shows how to define multiple agents, including 'History Tutor' and 'Math Tutor', each with a `handoff_description`. This description provides context for routing decisions when agents need to hand off tasks. + +```python +from agents import Agent + +history_tutor_agent = Agent( + name="History Tutor", + handoff_description="Specialist agent for historical questions", + instructions="You provide assistance with historical queries. Explain important events and context clearly.", +) + +math_tutor_agent = Agent( + name="Math Tutor", + handoff_description="Specialist agent for math questions", + instructions="You provide help with math problems. Explain your reasoning at each step and include examples", +) +``` + +-------------------------------- + +### Pass OpenAI API Key Directly to Session + +Source: https://openai.github.io/openai-agents-python/realtime/quickstart + +This Python code snippet shows an alternative method for authenticating with the OpenAI API by passing the API key directly when creating a session. This approach can be useful for temporary configurations or when environment variables are not accessible. It is part of the `RealtimeRunner.run()` method. + +```python +session = await runner.run(model_config={"api_key": "your-api-key"}) + +``` + +-------------------------------- + +### Run Voice Pipeline and Stream Audio Output + +Source: https://openai.github.io/openai-agents-python/voice/quickstart + +Executes the voice pipeline with sample audio input (silence in this case) and streams the resulting audio output. It uses `sounddevice` to play the generated speech in real-time. + +```python +import numpy as np +import sounddevice as sd +from agents.voice import AudioInput + +# For simplicity, we'll just create 3 seconds of silence +# In reality, you'd get microphone data +buffer = np.zeros(24000 * 3, dtype=np.int16) +audio_input = AudioInput(buffer=buffer) + +result = await pipeline.run(audio_input) + +# Create an audio player using `sounddevice` +player = sd.OutputStream(samplerate=24000, channels=1, dtype=np.int16) +player.start() + +# Play the audio stream as it comes in +async for event in result.stream(): + if event.type == "voice_stream_event_audio": + player.write(event.data) + +``` + +-------------------------------- + +### Initialize LitellmModel in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/extensions/litellm + +Initializes the LitellmModel with a specified model name and optional base URL and API key. This setup is crucial for directing requests to the correct LiteLLM-compatible model and endpoint. + +```python +class LitellmModel(Model): + """This class enables using any model via LiteLLM. LiteLLM allows you to acess OpenAPI, + Anthropic, Gemini, Mistral, and many other models. + See supported models here: [litellm models](https://docs.litellm.ai/docs/providers). + """ + + def __init__( + self, + model: str, + base_url: str | None = None, + api_key: str | None = None, + ): + self.model = model + self.base_url = base_url + self.api_key = api_key +``` + +-------------------------------- + +### Install LiteLLM dependency for OpenAI Agents SDK + +Source: https://openai.github.io/openai-agents-python/models/litellm + +Installs the optional 'litellm' dependency group for the OpenAI Agents SDK. This enables the use of LitellmModel for accessing various AI models. + +```bash +pip install "openai-agents[litellm]" +``` + +-------------------------------- + +### Install openai-agents with Visualization Dependencies + +Source: https://openai.github.io/openai-agents-python/visualization + +Installs the `openai-agents` library with the optional `viz` dependency group, which is required for agent visualization. This command uses pip, the standard Python package installer. + +```bash +pip install "openai-agents[viz]" +``` + +-------------------------------- + +### Start a Turn in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/voice/result + +Initiates a new turn in the voice pipeline processing. This method sets up tracing, marks the start of processing, and signals the beginning of a turn via a VoiceStreamEvent. + +```python +async def _start_turn(self): + if self._started_processing_turn: + return + + self._tracing_span = speech_group_span() + self._tracing_span.start() + self._started_processing_turn = True + self._first_byte_received = False + self._generation_start_time = time_iso() + await self._queue.put(VoiceStreamEventLifecycle(event="turn_started")) +``` + +-------------------------------- + +### Complete Session Memory Example + +Source: https://openai.github.io/openai-agents-python/sessions + +A comprehensive example showcasing session memory in action. It demonstrates how an agent, using a persistent SQLite session, remembers previous messages across multiple turns of a conversation, providing contextually relevant responses. + +```python +import asyncio +from agents import Agent, Runner, SQLiteSession + + +async def main(): + # Create an agent + agent = Agent( + name="Assistant", + instructions="Reply very concisely.", + ) + + # Create a session instance that will persist across runs + session = SQLiteSession("conversation_123", "conversation_history.db") + + print("=== Sessions Example ===") + print("The agent will remember previous messages automatically.\n") + + # First turn + print("First turn:") + print("User: What city is the Golden Gate Bridge in?") + result = await Runner.run( + agent, + "What city is the Golden Gate Bridge in?", + session=session + ) + print(f"Assistant: {result.final_output}") + print() + + # Second turn - the agent will remember the previous conversation + print("Second turn:") + print("User: What state is it in?") + result = await Runner.run( + agent, + "What state is it in?", + session=session + ) + print(f"Assistant: {result.final_output}") + print() + + # Third turn - continuing the conversation + print("Third turn:") + print("User: What's the population of that state?") + result = await Runner.run( + agent, + "What's the population of that state?", + session=session + ) + print(f"Assistant: {result.final_output}") + print() + + print("=== Conversation Complete ===") + print("Notice how the agent remembered the context from previous turns!") + print("Sessions automatically handles conversation history.") + + +if __name__ == "__main__": + asyncio.run(main()) + +``` + +-------------------------------- + +### Configure Realtime Audio Transcription in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/config + +Defines the configuration for transcribing audio in realtime sessions. It specifies the language, the transcription model to use (e.g., 'gpt-4o-transcribe', 'whisper-1'), and an optional prompt to guide the transcription process. This configuration is part of the broader realtime agent setup. + +```python +class RealtimeInputAudioTranscriptionConfig(TypedDict): + """Configuration for audio transcription in realtime sessions.""" + + language: NotRequired[str] + """The language code for transcription.""" + + model: NotRequired[Literal["gpt-4o-transcribe", "gpt-4o-mini-transcribe", "whisper-1"] | str] + """The transcription model to use.""" + + prompt: NotRequired[str] + """An optional prompt to guide transcription.""" + +``` + +-------------------------------- + +### Start Trace + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing + +Starts the trace. Optionally, it can be marked as the current trace in the execution context. This method must be called before any spans can be added to the trace. + +```APIDOC +## POST /start + +### Description +Start the trace and optionally mark it as the current trace. + +### Method +POST + +### Endpoint +/start + +### Parameters +#### Query Parameters +- **mark_as_current** (bool) - Optional - If true, marks this trace as the current trace in the execution context. Defaults to `False`. + +### Response +#### Success Response (200) +- **status** (str) - Indicates the success of the operation. Example: "Trace started successfully." + +#### Response Example +{ + "status": "Trace started successfully." +} + +### Notes +- Must be called before any spans can be added. +- Only one trace can be current at a time. +- Thread-safe when using `mark_as_current`. +``` + +-------------------------------- + +### Initialize Voice Pipeline + +Source: https://openai.github.io/openai-agents-python/voice/quickstart + +Initializes a `VoicePipeline` using a `SingleAgentVoiceWorkflow` and the previously defined agent. This sets up the core structure for processing voice input and generating responses. + +```python +from agents.voice import SingleAgentVoiceWorkflow, VoicePipeline + +pipeline = VoicePipeline(workflow=SingleAgentVoiceWorkflow(agent)) +``` + +-------------------------------- + +### Implement Output Guardrail with Agent + +Source: https://openai.github.io/openai-agents-python/guardrails + +This example shows how to create an output guardrail that inspects the agent's response. It defines Pydantic models for message and math-related outputs. The guardrail agent checks if the output contains math, and the guardrail function uses this to determine if the tripwire should be triggered. + +```python +from pydantic import BaseModel +from agents import ( + Agent, + GuardrailFunctionOutput, + OutputGuardrailTripwireTriggered, + RunContextWrapper, + Runner, + output_guardrail, +) +class MessageOutput(BaseModel): + response: str + +class MathOutput(BaseModel): + reasoning: str + is_math: bool + +guardrail_agent = Agent( + name="Guardrail check", + instructions="Check if the output includes any math.", + output_type=MathOutput, +) + +@output_guardrail +async def math_guardrail( + ctx: RunContextWrapper, agent: Agent, output: MessageOutput +) -> GuardrailFunctionOutput: + result = await Runner.run(guardrail_agent, output.response, context=ctx.context) + + return GuardrailFunctionOutput( + output_info=result.final_output, + tripwire_triggered=result.final_output.is_math, + ) + +agent = Agent( + name="Customer support agent", + instructions="You are a customer support agent. You help customers with their questions.", + output_guardrails=[math_guardrail], + output_type=MessageOutput, +) + +async def main(): + # This should trip the guardrail + try: + await Runner.run(agent, "Hello, can you help me solve for x: 2x + 3 = 11?") + print("Guardrail didn't trip - this is unexpected") + + except OutputGuardrailTripwireTriggered: + print("Math output guardrail tripped") + +``` + +-------------------------------- + +### Install openai-agents with encryption extra + +Source: https://openai.github.io/openai-agents-python/sessions/encrypted_session + +Installs the openai-agents library with the necessary 'encrypt' extra for using encrypted sessions. This is the first step before utilizing the encryption features. + +```bash +pip install openai-agents[encrypt] +``` + +-------------------------------- + +### Initialize OpenAIResponsesModel + +Source: https://openai.github.io/openai-agents-python/ja/ref/models/openai_responses + +Initializes the OpenAIResponsesModel with a specified model, an asynchronous OpenAI client, and optional model explicitness. This setup is crucial for subsequent API interactions. + +```python +class OpenAIResponsesModel(Model): + """ + Implementation of `Model` that uses the OpenAI Responses API. + """ + + def __init( + self, + model: str | ChatModel, + openai_client: AsyncOpenAI, + *, + model_is_explicit: bool = True, + ) -> None: + self.model = model + self._model_is_explicit = model_is_explicit + self._client = openai_client +``` + +-------------------------------- + +### SpanImpl Start and Finish Methods (Python) + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing/spans + +Provides methods to start and finish a span. The `start` method records the start time, notifies the processor, and optionally marks the span as current. The `finish` method records the end time, notifies the processor, and optionally resets the current span context. + +```python + def start(self, mark_as_current: bool = False): + if self.started_at is not None: + logger.warning("Span already started") + return + + self._started_at = util.time_iso() + self._processor.on_span_start(self) + if mark_as_current: + self._prev_span_token = Scope.set_current_span(self) + + def finish(self, reset_current: bool = False) -> None: + if self.ended_at is not None: + logger.warning("Span already finished") + return + + self._ended_at = util.time_iso() + self._processor.on_span_end(self) + if reset_current and self._prev_span_token is not None: + Scope.reset_current_span(self._prev_span_token) + self._prev_span_token = None +``` + +-------------------------------- + +### Implement Trace Start Method + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing/traces + +Abstract method to start a trace. It can optionally mark the trace as the current one in the execution context. This must be called before adding spans and ensures thread-safety when `mark_as_current` is used. + +```python +@abc.abstractmethod +def start(self, mark_as_current: bool = False): + """Start the trace and optionally mark it as the current trace. + + Args: + mark_as_current: If true, marks this trace as the current trace + in the execution context. + + Notes: + - Must be called before any spans can be added + - Only one trace can be current at a time + - Thread-safe when using mark_as_current + """ + pass +``` + +-------------------------------- + +### Implement Tool Guardrails for Input and Output + +Source: https://openai.github.io/openai-agents-python/guardrails + +This example demonstrates how to implement both input and output guardrails for a tool. The `block_secrets` input guardrail checks tool arguments for secrets, while the `redact_output` output guardrail checks the tool's return value for sensitive data. These guardrails are applied to the `classify_text` function tool. + +```python +import json +from agents import ( + Agent, + Runner, + ToolGuardrailFunctionOutput, + function_tool, + tool_input_guardrail, + tool_output_guardrail, +) + +@tool_input_guardrail +def block_secrets(data): + args = json.loads(data.context.tool_arguments or "{}") + if "sk-" in json.dumps(args): + return ToolGuardrailFunctionOutput.reject_content( + "Remove secrets before calling this tool." + ) + return ToolGuardrailFunctionOutput.allow() + + +@tool_output_guardrail +def redact_output(data): + text = str(data.output or "") + if "sk-" in text: + return ToolGuardrailFunctionOutput.reject_content("Output contained sensitive data.") + return ToolGuardrailFunctionOutput.allow() + + +@function_tool( + tool_input_guardrails=[block_secrets], + tool_output_guardrails=[redact_output], +) +def classify_text(text: str) -> str: + """Classify text for internal routing.""" + return f"length:{len(text)}" + + +agent = Agent(name="Classifier", tools=[classify_text]) +result = Runner.run_sync(agent, "hello world") +print(result.final_output) + +``` + +-------------------------------- + +### Basic Agent Handoff Setup - Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/handoffs + +Sets up a basic handoff where the primary focus is on the target agent and enabling the handoff. Optional parameters for tool naming and descriptions are available but not strictly required for initialization. + +```python +def handoff( + agent: Agent[TContext], + *, + tool_name_override: str | None = None, + tool_description_override: str | None = None, + input_filter: Callable[ + [HandoffInputData], HandoffInputData + ] + | None = None, + nest_handoff_history: bool | None = None, + is_enabled: bool + | Callable[ + [RunContextWrapper[Any], Agent[Any]], + MaybeAwaitable[bool], + ] = True, +) -> Handoff[TContext, Agent[TContext]] + + +``` + +-------------------------------- + +### Agent Span Creation + +Source: https://openai.github.io/openai-agents-python/zh/ref/tracing + +This endpoint (function) allows for the creation of a new agent span. Spans are used for tracing and monitoring agent activities. The span is not automatically started; users must explicitly start and finish it using a `with` statement or manual calls to `start()` and `finish()`. + +```APIDOC +## agent_span + +### Description +Create a new agent span. The span will not be started automatically, you should either do `with agent_span() ...` or call `span.start()` + `span.finish()` manually. + +### Method +N/A (This is a Python function, not a direct HTTP endpoint) + +### Endpoint +N/A + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +* **name** (str) - Required - The name of the agent. +* **handoffs** (list[str] | None) - Optional - Optional list of agent names to which this agent could hand off control. Defaults to None. +* **tools** (list[str] | None) - Optional - Optional list of tool names available to this agent. Defaults to None. +* **output_type** (str | None) - Optional - Optional name of the output type produced by the agent. Defaults to None. +* **span_id** (str | None) - Optional - The ID of the span. If not provided, we will generate an ID. We recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are correctly formatted. Defaults to None. +* **parent** (Trace | Span[Any] | None) - Optional - The parent span or trace. If not provided, we will automatically use the current trace/span as the parent. Defaults to None. +* **disabled** (bool) - Optional - If True, we will return a Span but the Span will not be recorded. Defaults to False. + +### Request Example +```python +from agents.tracing import agent_span + +# Example using 'with' statement +with agent_span(name="MyAgent", tools=["search"]): + # Agent logic here + pass + +# Example with manual start/finish +span = agent_span(name="AnotherAgent") +try: + span.start() + # Agent logic here +finally: + span.finish() +``` + +### Response +#### Success Response +* **Span[AgentSpanData]** - The newly created agent span. +``` + +-------------------------------- + +### Initialize AsyncOpenAI Client in Python + +Source: https://openai.github.io/openai-agents-python/ko/ref/models/openai_chatcompletions + +This Python function provides a simple way to get an instance of the `AsyncOpenAI` client. It initializes the client only if it hasn't been already, ensuring a single instance is reused. + +```python +def _get_client(self) -> AsyncOpenAI: + if self._client is None: + self._client = AsyncOpenAI() + return self._client +``` + +-------------------------------- + +### Quick start: Encrypted session with SQLAlchemy + +Source: https://openai.github.io/openai-agents-python/sessions/encrypted_session + +Demonstrates how to set up and use an EncryptedSession with a SQLAlchemy session backend. It shows the creation of an underlying session, wrapping it with EncryptedSession, and then using it with the Runner. + +```python +import asyncio +from agents import Agent, Runner +from agents.extensions.memory import EncryptedSession, SQLAlchemySession + +async def main(): + agent = Agent("Assistant") + + # Create underlying session + underlying_session = SQLAlchemySession.from_url( + "user-123", + url="sqlite+aiosqlite:///:memory:", + create_tables=True + ) + + # Wrap with encryption + session = EncryptedSession( + session_id="user-123", + underlying_session=underlying_session, + encryption_key="your-secret-key-here", + ttl=600 # 10 minutes + ) + + result = await Runner.run(agent, "Hello", session=session) + print(result.final_output) + +if __name__ == "__main__": + asyncio.run(main()) +``` + +-------------------------------- + +### Trace Start Event + +Source: https://openai.github.io/openai-agents-python/ref/tracing + +Callback executed when a new trace begins. It is called synchronously and should return quickly. + +```APIDOC +## POST /trace/start + +### Description +Called when a new trace begins execution. Contains workflow name and metadata. + +### Method +POST + +### Endpoint +/trace/start + +### Parameters +#### Request Body +- **trace** (Trace) - Required - The trace that started. Contains workflow name and metadata. + +### Request Example +```json +{ + "trace": { + "workflow_name": "example_workflow", + "metadata": {} + } +} +``` + +### Response +#### Success Response (200) +- **status** (string) - Indicates success. + +#### Response Example +```json +{ + "status": "ok" +} +``` +``` + +-------------------------------- + +### OpenAI Provider Initialization (__init__) + +Source: https://openai.github.io/openai-agents-python/ja/ref/models/multi_provider + +Initializes a new OpenAI provider with various configuration options for API keys, base URLs, clients, organizations, projects, and response usage. + +```APIDOC +## POST /openai/provider/initialize + +### Description +Initializes a new OpenAI provider. You can configure API keys, base URLs, and other settings. + +### Method +POST + +### Endpoint +/openai/provider/initialize + +### Parameters +#### Request Body +- **provider_map** (MultiProviderMap | None) - Optional - A MultiProviderMap that maps prefixes to ModelProviders. If not provided, a default mapping will be used. +- **openai_api_key** (str | None) - Optional - The API key to use for the OpenAI provider. If not provided, the default API key will be used. +- **openai_base_url** (str | None) - Optional - The base URL to use for the OpenAI provider. If not provided, the default base URL will be used. +- **openai_client** (AsyncOpenAI | None) - Optional - An optional OpenAI client to use. If not provided, a new OpenAI client will be created using the api_key and base_url. +- **openai_organization** (str | None) - Optional - The organization to use for the OpenAI provider. +- **openai_project** (str | None) - Optional - The project to use for the OpenAI provider. +- **openai_use_responses** (bool | None) - Optional - Whether to use the OpenAI responses API. + +### Request Example +```json +{ + "openai_api_key": "your_api_key", + "openai_base_url": "https://api.openai.com/v1", + "openai_organization": "your_organization_id" +} +``` + +### Response +#### Success Response (200) +- **message** (str) - Confirmation message indicating successful initialization. + +#### Response Example +```json +{ + "message": "OpenAI provider initialized successfully." +} +``` +``` + +-------------------------------- + +### Set OpenAI API Key using Environment Variable + +Source: https://openai.github.io/openai-agents-python/realtime/quickstart + +This command demonstrates how to set the OpenAI API key using an environment variable. This is a common practice for securely managing API keys, especially in development and production environments. The `export` command is used in Unix-like shells (Linux, macOS) to set the variable for the current session. + +```bash +export OPENAI_API_KEY="your-api-key-here" + +``` + +-------------------------------- + +### RealtimeRunner Constructor + +Source: https://openai.github.io/openai-agents-python/ko/ref/realtime/runner + +The constructor for RealtimeRunner initializes the runner with a starting agent and optional model and configuration parameters. It defaults to using an OpenAI realtime WebSocket model if no model is provided. + +```python +def __init__( + self, + starting_agent: RealtimeAgent, + *, + model: RealtimeModel | None = None, + config: RealtimeRunConfig | None = None, +) -> None: + """Initialize the realtime runner. + + Args: + starting_agent: The agent to start the session with. + context: The context to use for the session. + model: The model to use. If not provided, will use a default OpenAI realtime model. + config: Override parameters to use for the entire run. + """ + self._starting_agent = starting_agent + self._config = config + self._model = model or OpenAIRealtimeWebSocketModel() + +``` + +-------------------------------- + +### Branch Workflow Example: Create, Switch, Continue + +Source: https://openai.github.io/openai-agents-python/sessions/advanced_sqlite_session + +Illustrates a typical branch workflow: initiating a conversation, storing run usage, creating a new branch from a specific turn, continuing the conversation in the new branch, and switching back to the main branch. Requires `Runner` and `session` objects. + +```python +# Original conversation +result = await Runner.run(agent, "What's the capital of France?", session=session) +await session.store_run_usage(result) + +result = await Runner.run(agent, "What's the weather like there?", session=session) +await session.store_run_usage(result) + +# Create branch from turn 2 (weather question) +branch_id = await session.create_branch_from_turn(2, "weather_focus") + +# Continue in new branch with different question +result = await Runner.run( + agent, + "What are the main tourist attractions in Paris?", + session=session +) +await session.store_run_usage(result) + +# Switch back to main branch +await session.switch_to_branch("main") + +# Continue original conversation +result = await Runner.run( + agent, + "How expensive is it to visit?", + session=session +) +await session.store_run_usage(result) +``` + +-------------------------------- + +### Start and Finish Abstract Methods for Spans in Python + +Source: https://openai.github.io/openai-agents-python/zh/ref/tracing + +Abstract methods for controlling the lifecycle of a span. The `start` method initiates the span, with an option to mark it as the current span. The `finish` method concludes the span's execution, with an option to reset the current span. + +```python +import abc +from typing import Any, Dict, Optional, TypeVar + +from typing_extensions import TypeVarTuple + +TSpanData = TypeVar("TSpanData") +SpanError = Dict[str, Any] + + +class Span(abc.ABC): + @abc.abstractmethod + def start(self, mark_as_current: bool = False): + """ + Start the span. + + Args: + mark_as_current: If true, the span will be marked as the current span. + """ + pass + + @abc.abstractmethod + def finish(self, reset_current: bool = False) -> None: + """ + Finish the span. + + Args: + reset_current: If true, the span will be reset as the current span. + """ + pass +``` + +-------------------------------- + +### Create stdio MCP Server for Local Subprocesses + +Source: https://openai.github.io/openai-agents-python/mcp + +This code example shows how to create an MCP server for local subprocesses using `MCPServerStdio`. The SDK manages process spawning and pipe handling. This is useful for quick proofs of concept or when the server exposes a command-line interface. + +```python +from pathlib import Path +from agents import Agent, Runner +from agents.mcp import MCPServerStdio + +current_dir = Path(__file__).parent +samples_dir = current_dir / "sample_files" + +async with MCPServerStdio( + name="Filesystem Server via npx", + params={ + "command": "npx", + "args": ["-y", "@modelcontextprotocol/server-filesystem", str(samples_dir)], + }, +) as server: + agent = Agent( + name="Assistant", + instructions="Use the files in the sample directory to answer questions.", + mcp_servers=[server], + ) + result = await Runner.run(agent, "List the files available to you.") + print(result.final_output) + + +``` + +-------------------------------- + +### Abstract Span Methods + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing + +Defines the abstract methods for starting and finishing a span. The `start` method initiates the span's execution, with an option to mark it as the current span. The `finish` method concludes the span's execution, also with an option to reset the current span. + +```python +@abc.abstractmethod +def start(self, mark_as_current: bool = False): + """ + Start the span. + + Args: + mark_as_current: If true, the span will be marked as the current span. + """ + pass + +@abc.abstractmethod +def finish(self, reset_current: bool = False) -> None: + """ + Finish the span. + + Args: + reset_current: If true, the span will be reset as the current span. + """ + pass +``` + +-------------------------------- + +### OpenAIResponsesModel Initialization and Response Fetching (Python) + +Source: https://openai.github.io/openai-agents-python/zh/ref/models/openai_responses + +Demonstrates the initialization of the OpenAIResponsesModel and the core logic for fetching responses from the OpenAI API. It includes handling of model settings, tools, output schemas, and optional tracing information. Dependencies include the `AsyncOpenAI` client and various agent-related models and utilities. + +```python +class OpenAIResponsesModel(Model): + """ + Implementation of `Model` that uses the OpenAI Responses API. + """ + + def __init( + self, + model: str | ChatModel, + openai_client: AsyncOpenAI, + *, + model_is_explicit: bool = True, + ) -> None: + self.model = model + self._model_is_explicit = model_is_explicit + self._client = openai_client + + def _non_null_or_omit(self, value: Any) -> Any: + return value if value is not None else omit + + async def get_response( + self, + system_instructions: str | None, + input: str | list[TResponseInputItem], + model_settings: ModelSettings, + tools: list[Tool], + output_schema: AgentOutputSchemaBase | None, + handoffs: list[Handoff], + tracing: ModelTracing, + previous_response_id: str | None = None, + conversation_id: str | None = None, + prompt: ResponsePromptParam | None = None, + ) -> ModelResponse: + with response_span(disabled=tracing.is_disabled()) as span_response: + try: + response = await self._fetch_response( + system_instructions, + input, + model_settings, + tools, + output_schema, + handoffs, + previous_response_id=previous_response_id, + conversation_id=conversation_id, + stream=False, + prompt=prompt, + ) + + if _debug.DONT_LOG_MODEL_DATA: + logger.debug("LLM responded") + else: + logger.debug( + "LLM resp:\n" + f"""{ json.dumps( + [x.model_dump() for x in response.output], + indent=2, + ensure_ascii=False, + ) + }""" + ) + + usage = ( + Usage( + requests=1, + input_tokens=response.usage.input_tokens, + output_tokens=response.usage.output_tokens, + total_tokens=response.usage.total_tokens, + input_tokens_details=response.usage.input_tokens_details, + output_tokens_details=response.usage.output_tokens_details, + ) + if response.usage + else Usage() + ) + + if tracing.include_data(): + span_response.span_data.response = response + span_response.span_data.input = input + except Exception as e: + span_response.set_error( + SpanError( + message="Error getting response", + data={ + "error": str(e) if tracing.include_data() else e.__class__.__name__, + }, + ) + ) + request_id = e.request_id if isinstance(e, APIStatusError) else None + logger.error(f"Error getting response: {e}. (request_id: {request_id})") + raise + + return ModelResponse( + output=response.output, + usage=usage, + +``` + +-------------------------------- + +### Define Agents and Tools for Voice Interaction + +Source: https://openai.github.io/openai-agents-python/voice/quickstart + +Sets up custom agents, including a Spanish-speaking agent and a primary assistant agent, with a weather-fetching tool. The assistant agent is configured to handoff to the Spanish agent if the user speaks Spanish. This defines the conversational logic for the voice pipeline. + +```python +import asyncio +import random + +from agents import ( + Agent, + function_tool, +) +from agents.extensions.handoff_prompt import prompt_with_handoff_instructions + + + +@function_tool +def get_weather(city: str) -> str: + """Get the weather for a given city.""" + print(f"[debug] get_weather called with city: {city}") + choices = ["sunny", "cloudy", "rainy", "snowy"] + return f"The weather in {city} is {random.choice(choices)}." + + +spanish_agent = Agent( + name="Spanish", + handoff_description="A spanish speaking agent.", + instructions=prompt_with_handoff_instructions( + "You're speaking to a human, so be polite and concise. Speak in Spanish.", + ), + model="gpt-5.2", +) + +agent = Agent( + name="Assistant", + instructions=prompt_with_handoff_instructions( + "You're speaking to a human, so be polite and concise. If the user speaks in Spanish, handoff to the spanish agent.", + ), + model="gpt-5.2", + handoffs=[spanish_agent], + tools=[get_weather], +) + +``` + +-------------------------------- + +### RealtimeSession Enter Context API + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/session + +Asynchronously enters the session's context, connecting to the model and preparing it for event streaming and message exchange. This is the recommended way to start a session using an async context manager. + +```APIDOC +## __aenter__ RealtimeSession + +### Description +Asynchronously starts the session by connecting to the model. This enables streaming events and sending messages/audio. It's designed for use with Python's `async with` statement. + +### Method +__aenter__ (async) + +### Endpoint +N/A (Asynchronous context manager method) + +### Parameters +None + +### Request Example +```python +async with session: + # Session is active here + await session.send_message(...) +``` + +### Response +#### Success Response (RealtimeSession) +- **self** (`RealtimeSession`) - The current session object, now connected and ready. + +#### Response Example +```python +# The 'session' object is returned and ready for use within the 'async with' block. +``` +``` + +-------------------------------- + +### Get Playback State in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/openai_realtime + +Retrieves the current playback state. It checks if a playback tracker is available and returns its state. If not, it attempts to get the last audio item ID from the audio state tracker. + +```python +def _get_playback_state(self) -> RealtimePlaybackState: + if self._playback_tracker: + return self._playback_tracker.get_state() + + if last_audio_item_id := self._audio_state_tracker.get_last_audio_item(): + item_id, item_content_index = last_audio_item_id +``` + +-------------------------------- + +### SQLiteSession Initialization and Connection Handling (Python) + +Source: https://openai.github.io/openai-agents-python/zh/ref/memory + +Demonstrates the initialization of the SQLiteSession class, including setting up database paths, table names, and managing SQLite connections. It differentiates between in-memory databases and persistent file-based databases, utilizing thread-local connections for the latter to enhance concurrency. Includes schema creation for session and message tables. + +```python +class SQLiteSession(SessionABC): + """SQLite-based implementation of session storage. + + This implementation stores conversation history in a SQLite database. + By default, uses an in-memory database that is lost when the process ends. + For persistent storage, provide a file path. + """ + + def __init__( + self, + session_id: str, + db_path: str | Path = ":memory:", + sessions_table: str = "agent_sessions", + messages_table: str = "agent_messages", + ): + """Initialize the SQLite session. + + Args: + session_id: Unique identifier for the conversation session + db_path: Path to the SQLite database file. Defaults to ':memory:' (in-memory database) + sessions_table: Name of the table to store session metadata. Defaults to + 'agent_sessions' + messages_table: Name of the table to store message data. Defaults to 'agent_messages' + """ + self.session_id = session_id + self.db_path = db_path + self.sessions_table = sessions_table + self.messages_table = messages_table + self._local = threading.local() + self._lock = threading.Lock() + + # For in-memory databases, we need a shared connection to avoid thread isolation + # For file databases, we use thread-local connections for better concurrency + self._is_memory_db = str(db_path) == ":memory:" + if self._is_memory_db: + self._shared_connection = sqlite3.connect(":memory:", check_same_thread=False) + self._shared_connection.execute("PRAGMA journal_mode=WAL") + self._init_db_for_connection(self._shared_connection) + else: + # For file databases, initialize the schema once since it persists + init_conn = sqlite3.connect(str(self.db_path), check_same_thread=False) + init_conn.execute("PRAGMA journal_mode=WAL") + self._init_db_for_connection(init_conn) + init_conn.close() + + def _get_connection(self) -> sqlite3.Connection: + """Get a database connection.""" + if self._is_memory_db: + # Use shared connection for in-memory database to avoid thread isolation + return self._shared_connection + else: + # Use thread-local connections for file databases + if not hasattr(self._local, "connection"): + self._local.connection = sqlite3.connect( + str(self.db_path), + check_same_thread=False, + ) + self._local.connection.execute("PRAGMA journal_mode=WAL") + assert isinstance(self._local.connection, sqlite3.Connection), ( + f"Expected sqlite3.Connection, got {type(self._local.connection)}" + ) + return self._local.connection + + def _init_db_for_connection(self, conn: sqlite3.Connection) -> None: + """Initialize the database schema for a specific connection.""" + conn.execute( + f""" + CREATE TABLE IF NOT EXISTS {self.sessions_table} ( + session_id TEXT PRIMARY KEY, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + """ + ) + + conn.execute( + f""" + CREATE TABLE IF NOT EXISTS {self.messages_table} ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + session_id TEXT NOT NULL, + message_data TEXT NOT NULL, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + +``` + +-------------------------------- + +### Ensure Background Worker Thread Started in Python + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing/processors + +Ensures the background worker thread for the BatchTraceProcessor is started if it's not already running. It uses double-checked locking to prevent race conditions and ensure only one thread is started. This method is called before adding items to the queue. + +```python +def _ensure_thread_started(self) -> None: + # Fast path without holding the lock + if self._worker_thread and self._worker_thread.is_alive(): + return + + # Double-checked locking to avoid starting multiple threads + with self._thread_start_lock: + if self._worker_thread and self._worker_thread.is_alive(): + return + + self._worker_thread = threading.Thread(target=self._run, daemon=True) + self._worker_thread.start() + +``` + +-------------------------------- + +### Define Agent Instructions (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/agent + +Specifies how an agent should behave by defining its system prompt. Instructions can be a static string or a dynamic function that generates the prompt based on context and agent instance. This is crucial for guiding the agent's responses and actions. + +```python +instructions: ( + str + | Callable[ + [ + RunContextWrapper[TContext], + RealtimeAgent[TContext], + ], + MaybeAwaitable[str], + ] + | None +) = None +``` + +-------------------------------- + +### Configure OpenAI Realtime Session with Tools and Prompts (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/openai_realtime + +This snippet demonstrates how to configure a session for the OpenAI Realtime API. It includes setting up audio configurations, converting custom tools and handoffs into the required `OpenAISessionFunction` format, and defining instructions, prompts with variables, max output tokens, and tool choices. This configuration is crucial for initializing a realtime session with specific parameters. + +```python +audio = OpenAIRealtimeAudioConfig( + input=OpenAIRealtimeAudioInput(**audio_input_args), + output=OpenAIRealtimeAudioOutput(**audio_output_args), +), +tools = cast( + Any, + self._tools_to_session_tools( + tools=model_settings.get("tools", []), + handoffs=model_settings.get("handoffs", []), + ), +) +) + +if "instructions" in model_settings: + session_create_request.instructions = model_settings.get("instructions") + +if "prompt" in model_settings: + _passed_prompt: Prompt = model_settings["prompt"] + variables: dict[str, Any] | None = _passed_prompt.get("variables") + session_create_request.prompt = ResponsePrompt( + id=_passed_prompt["id"], + variables=variables, + version=_passed_prompt.get("version"), + ) + +if "max_output_tokens" in model_settings: + session_create_request.max_output_tokens = cast( + Any, model_settings.get("max_output_tokens") + ) + +if "tool_choice" in model_settings: + session_create_request.tool_choice = cast(Any, model_settings.get("tool_choice")) + +return session_create_request +``` + +-------------------------------- + +### GET /api/agents/memory/conversation + +Source: https://openai.github.io/openai-agents-python/ref/memory + +Retrieves the conversation history for a given session. Supports filtering by a limit to get the latest items. + +```APIDOC +## GET /api/agents/memory/conversation + +### Description +Retrieves the conversation history for this session. It can optionally fetch a limited number of the most recent items. + +### Method +GET + +### Endpoint +/api/agents/memory/conversation + +### Parameters +#### Query Parameters +- **limit** (int) - Optional - Maximum number of items to retrieve. If not specified, retrieves all items. When specified, returns the latest N items in chronological order. + +### Request Example +(No request body for GET request) + +### Response +#### Success Response (200) +- **items** (list[object]) - List of input items representing the conversation history. Each item is an object containing conversation data. + +#### Response Example +```json +{ + "items": [ + { + "role": "user", + "content": "Hello, tell me about AI." + }, + { + "role": "assistant", + "content": "Artificial intelligence (AI) is intelligence demonstrated by machines..." + } + ] +} +``` +``` + +-------------------------------- + +### Function Span Creation + +Source: https://openai.github.io/openai-agents-python/zh/ref/tracing/create + +This endpoint allows you to create a new function span. The span is not started automatically; you must either use a `with` statement or manually call `start()` and `finish()`. + +```APIDOC +## POST /function_span + +### Description +Creates a new function span for tracing. The span needs to be manually started and finished. + +### Method +POST + +### Endpoint +/function_span + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **name** (str) - Required - The name of the function. +- **input** (str | None) - Optional - The input to the function. +- **output** (str | None) - Optional - The output of the function. +- **span_id** (str | None) - Optional - The ID of the span. If not provided, a unique ID will be generated. +- **parent** (Trace | Span[Any] | None) - Optional - The parent span or trace. If not provided, the current trace/span will be used. +- **disabled** (bool) - Optional - If True, the span will not be recorded. Defaults to False. + +### Request Example +```json +{ + "name": "my_function", + "input": "some_input_data", + "output": "some_output_data", + "span_id": null, + "parent": null, + "disabled": false +} +``` + +### Response +#### Success Response (200) +- **Span[FunctionSpanData]** - The newly created function span object. + +#### Response Example +```json +{ + "span_id": "generated_span_id", + "name": "my_function", + "input": "some_input_data", + "output": "some_output_data" +} +``` +``` + +-------------------------------- + +### Abstract Span Methods for Start and Finish + +Source: https://openai.github.io/openai-agents-python/zh/ref/tracing/spans + +Defines abstract methods for managing the lifecycle of a span: starting and finishing. These methods are intended to be implemented by concrete span classes. + +```python +import abc + +class AbstractSpan(abc.ABC): + @abc.abstractmethod + def start(self, mark_as_current: bool = False): + """ + Start the span. + + Args: + mark_as_current: If true, the span will be marked as the current span. + """ + pass + + @abc.abstractmethod + def finish(self, reset_current: bool = False) -> None: + """ + Finish the span. + + Args: + reset_current: If true, the span will be reset as the current span. + """ + pass +``` + +-------------------------------- + +### Get Playback State in Python + +Source: https://openai.github.io/openai-agents-python/ref/realtime/openai_realtime + +Retrieves the current playback state. It first checks if a playback tracker is available and returns its state. If not, it attempts to get the last audio item ID from the audio state tracker to determine the playback state. + +```python +def _get_playback_state(self) -> RealtimePlaybackState: + if self._playback_tracker: + return self._playback_tracker.get_state() + + if last_audio_item_id := self._audio_state_tracker.get_last_audio_item(): + item_id, item_content_index = last_audio_item_id + +``` + +-------------------------------- + +### RealtimeRunner.run() + +Source: https://openai.github.io/openai-agents-python/ko/ref/realtime/runner + +Starts and returns a realtime session, enabling bidirectional communication with the realtime model. This method is asynchronous and should be used within an async context. + +```APIDOC +## RealtimeRunner.run() + +### Description +Starts and returns a realtime session. This session object allows for bidirectional communication with the realtime model. + +### Method +`async def run( + self, + *, + context: TContext | None = None, + model_config: RealtimeModelConfig | None = None, +) -> RealtimeSession` + +### Endpoint +N/A (This is a Python method, not a REST endpoint) + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +None + +### Request Example +```python +runner = RealtimeRunner(agent) +async with await runner.run() as session: + await session.send_message("Hello") + async for event in session: + print(event) +``` + +### Response +#### Success Response (RealtimeSession) +- **RealtimeSession** (`RealtimeSession`) - A session object that allows bidirectional communication with the realtime model. + +#### Response Example +```json +// The actual response is a RealtimeSession object, not a JSON payload. +// See the Request Example for usage. +``` +``` + +-------------------------------- + +### Local Runtime Tools: Shell and Apply Patch with OpenAI Agents Python SDK + +Source: https://openai.github.io/openai-agents-python/tools + +Illustrates the setup for local runtime tools, specifically ShellTool and ApplyPatchTool. It includes custom implementations for a no-operation computer interface and an apply patch editor, along with a function to execute shell commands. + +```python +from agents import Agent, ApplyPatchTool, ShellTool +from agents.computer import AsyncComputer +from agents.editor import ApplyPatchResult, ApplyPatchOperation, ApplyPatchEditor + + +class NoopComputer(AsyncComputer): + environment = "browser" + dimensions = (1024, 768) + async def screenshot(self): return "" + async def click(self, x, y, button): ... + async def double_click(self, x, y): ... + async def scroll(self, x, y, scroll_x, scroll_y): ... + async def type(self, text): ... + async def wait(self): ... + async def move(self, x, y): ... + async def keypress(self, keys): ... + async def drag(self, path): ... + + +class NoopEditor(ApplyPatchEditor): + async def create_file(self, op: ApplyPatchOperation): return ApplyPatchResult(status="completed") + async def update_file(self, op: ApplyPatchOperation): return ApplyPatchResult(status="completed") + async def delete_file(self, op: ApplyPatchOperation): return ApplyPatchResult(status="completed") + + +async def run_shell(request): + return "shell output" + + +agent = Agent( + name="Local tools agent", + tools=[ + ShellTool(executor=run_shell), + ApplyPatchTool(editor=NoopEditor()), + # ComputerTool expects a Computer/AsyncComputer implementation; omitted here for brevity. + ], +) + +``` + +-------------------------------- + +### Configure Agents with Different Models and Settings + +Source: https://openai.github.io/openai-agents-python/models + +Demonstrates how to initialize agents with specific models, including using string names or ModelProvider instances. It also shows how to apply model settings like temperature and extra arguments for OpenAI's Responses API. + +```python +from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel +import asyncio + +spanish_agent = Agent( + name="Spanish agent", + instructions="You only speak Spanish.", + model="gpt-5-mini", +) + +english_agent = Agent( + name="English agent", + instructions="You only speak English", + model=OpenAIChatCompletionsModel( + model="gpt-5-nano", + openai_client=AsyncOpenAI() + ), +) + +triage_agent = Agent( + name="Triage agent", + instructions="Handoff to the appropriate agent based on the language of the request.", + handoffs=[spanish_agent, english_agent], + model="gpt-5", +) + +async def main(): + result = await Runner.run(triage_agent, input="Hola, ¿cómo estás?") + print(result.final_output) + +``` + +```python +from agents import Agent, ModelSettings + +english_agent = Agent( + name="English agent", + instructions="You only speak English", + model="gpt-4.1", + model_settings=ModelSettings(temperature=0.1), +) + +``` + +```python +from agents import Agent, ModelSettings + +english_agent = Agent( + name="English agent", + instructions="You only speak English", + model="gpt-4.1", + model_settings=ModelSettings( + temperature=0.1, + extra_args={"service_tier": "flex", "user": "user_12345"}, + ), +) + +``` + +-------------------------------- + +### Span Abstract Method: Start Span + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing/spans + +The `start` method initiates a span's execution. It accepts an optional boolean argument `mark_as_current` to designate the span as the currently active one. This is fundamental for managing nested operations and context propagation. + +```python +@abc.abstractmethod +def start(self, mark_as_current: bool = False): + """ + Start the span. + + Args: + mark_as_current: If true, the span will be marked as the current span. + """ + pass +``` + +-------------------------------- + +### Span Methods: Start and Finish + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/spans + +Abstract methods for controlling the lifecycle of a span. The `start` method initiates the span's execution, with an option to mark it as the current span. The `finish` method terminates the span's execution, with an option to reset it as the current span. These methods are crucial for timing and managing span states. + +```python + @abc.abstractmethod + def start(self, mark_as_current: bool = False): + """ + Start the span. + + Args: + mark_as_current: If true, the span will be marked as the current span. + """ + pass + + @abc.abstractmethod + def finish(self, reset_current: bool = False) -> None: + """ + Finish the span. + + Args: + reset_current: If true, the span will be reset as the current span. + """ + pass +``` + +-------------------------------- + +### Realtime Agent Methods + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/agent + +Documentation for the `clone` and `get_system_prompt` methods of the RealtimeAgent class. + +```APIDOC +## Realtime Agent Methods + +### Clone Agent + +#### Description +Creates a copy of the agent, allowing specific arguments to be modified in the new instance. + +#### Method +`clone` + +#### Parameters +- `**kwargs` (Any): Keyword arguments to override agent properties in the cloned instance. + +#### Request Example +```python +new_agent = agent.clone(instructions="New instructions") +``` + +### Get System Prompt + +#### Description +Retrieves the system prompt for the agent. It returns the static instruction string or the result of the dynamic instruction function. + +#### Method +`get_system_prompt` + +#### Parameters +- **run_context** (`RunContextWrapper[TContext]`): The wrapper for the run context. + +#### Response +- **Success Response (str | None)**: The system prompt string or None if not defined. + +#### Response Example +```python +system_prompt = await agent.get_system_prompt(run_context) +``` +``` + +-------------------------------- + +### Create a Custom Trace with Context Manager + +Source: https://openai.github.io/openai-agents-python/tracing + +Demonstrates how to create a custom trace using the `trace()` function as a context manager. This approach automatically handles starting and finishing the trace, making it the recommended method for custom tracing. It wraps multiple agent runs within a single trace to group related operations. + +```python +from agents import Agent, Runner, trace + +async def main(): + agent = Agent(name="Joke generator", instructions="Tell funny jokes.") + + with trace("Joke workflow"): + first_result = await Runner.run(agent, "Tell me a joke") + second_result = await Runner.run(agent, f"Rate this joke: {first_result.final_output}") + print(f"Joke: {first_result.final_output}") + print(f"Rating: {second_result.final_output}") + +``` + +-------------------------------- + +### Get Response API + +Source: https://openai.github.io/openai-agents-python/ko/ref/models/interface + +This endpoint allows you to get a response from the model by providing system instructions, input, model settings, tools, output schema, handoffs, tracing, and optional conversation context. + +```APIDOC +## POST /agents/models/response + +### Description +Get a response from the model. + +### Method +POST + +### Endpoint +/agents/models/response + +### Parameters +#### Request Body +- **system_instructions** (str | None) - Required - The system instructions to use. +- **input** (str | list[TResponseInputItem]) - Required - The input items to the model, in OpenAI Responses format. +- **model_settings** (ModelSettings) - Required - The model settings to use. +- **tools** (list[Tool]) - Required - The tools available to the model. +- **output_schema** (AgentOutputSchemaBase | None) - Required - The output schema to use. +- **handoffs** (list[Handoff]) - Required - The handoffs available to the model. +- **tracing** (ModelTracing) - Required - Tracing configuration. +- **previous_response_id** (str | None) - Required - the ID of the previous response. Generally not used by the model, except for the OpenAI Responses API. +- **conversation_id** (str | None) - Required - The ID of the stored conversation, if any. +- **prompt** (ResponsePromptParam | None) - Required - The prompt config to use for the model. + +### Request Example +```json +{ + "system_instructions": "You are a helpful assistant.", + "input": "What is the capital of France?", + "model_settings": { "model": "gpt-4" }, + "tools": [], + "output_schema": null, + "handoffs": [], + "tracing": { "enabled": true }, + "previous_response_id": null, + "conversation_id": null, + "prompt": null +} +``` + +### Response +#### Success Response (200) +- **response** (ModelResponse) - The full model response. + +#### Response Example +```json +{ + "response": { + "content": "The capital of France is Paris.", + "tool_calls": [], + "metadata": {} + } +} +``` +``` + +-------------------------------- + +### RealtimeSession Initialization + +Source: https://openai.github.io/openai-agents-python/zh/ref/realtime/session + +Initializes a new RealtimeSession with the specified model, agent, and context. Optional configurations for model and runtime can also be provided. + +```APIDOC +## __init__ RealtimeSession + +### Description +Initializes the session with a model, agent, and context. Allows for optional model and runtime configurations. + +### Method +__init__ + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **model** (`RealtimeModel`) - Required - The model to use. +- **agent** (`RealtimeAgent`) - Required - The current agent. +- **context** (`TContext | None`) - Required - The context object. +- **model_config** (`RealtimeModelConfig | None`) - Optional - Model configuration. Defaults to None. +- **run_config** (`RealtimeRunConfig | None`) - Optional - Runtime configuration including guardrails. Defaults to None. + +### Request Example +```python +# Assuming model, agent, and context are already defined +session = RealtimeSession( + model=my_model, + agent=my_agent, + context=my_context, + model_config=my_model_config, + run_config=my_run_config +) +``` + +### Response +#### Success Response (None) +This method does not return a value directly, but initializes the session object. + +#### Response Example +None +``` + +-------------------------------- + +### Analyze Conversation: Get by Turns, Tool Usage, Find by Content + +Source: https://openai.github.io/openai-agents-python/sessions/advanced_sqlite_session + +Shows how to perform structured queries on conversation data using `AdvancedSQLiteSession`. Methods include retrieving conversation organized by turns, getting tool usage statistics, and finding turns by matching content. Depends on the `session` object. + +```python +# Get conversation organized by turns +conversation_by_turns = await session.get_conversation_by_turns() +for turn_num, items in conversation_by_turns.items(): + print(f"Turn {turn_num}: {len(items)} items") + for item in items: + if item["tool_name"]: + print(f" - {item['type']} (tool: {item['tool_name']})") + else: + print(f" - {item['type']}") + +# Get tool usage statistics +tool_usage = await session.get_tool_usage() +for tool_name, count, turn in tool_usage: + print(f"{tool_name}: used {count} times in turn {turn}") + +# Find turns by content +matching_turns = await session.find_turns_by_content("weather") +for turn in matching_turns: + print(f"Turn {turn['turn']}: {turn['content']}") +``` + +-------------------------------- + +### Get Current Time in ISO Format in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/provider + +The `time_iso` abstract method provides a standardized way to get the current time formatted according to the ISO 8601 standard. This is often used for timestamping events within traces. + +```python +@abstractmethod +def time_iso(self) -> str: + """Return the current time in ISO 8601 format.""" + +``` + +-------------------------------- + +### as_tool Method Documentation + +Source: https://openai.github.io/openai-agents-python/ja/ref/agent + +This section details the `as_tool` method, its purpose, parameters, and how it differs from agent handoffs. + +```APIDOC +## as_tool Method + +### Description +Transforms an agent into a tool that can be called by other agents. This differs from agent handoffs as the new agent receives generated input instead of the conversation history, and the original agent continues the conversation after the tool call. + +### Method +`as_tool` + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +None + +### Request Example +```python +# Assuming 'my_agent' is an instance of an AgentBase subclass + +tool_callable = my_agent.as_tool( + tool_name="MyAgentAsTool", + tool_description="This tool executes the functionality of MyAgent.", + is_enabled=True, + on_stream=my_stream_handler +) + +# Now 'tool_callable' can be used as a tool by another agent. +``` + +### Response +#### Success Response (200) +Returns a `Tool` object that can be invoked by other agents. + +#### Response Example +```json +{ + "tool_name": "MyAgentAsTool", + "tool_description": "This tool executes the functionality of MyAgent.", + "is_enabled": true +} +``` + +### Parameters Details: +- **tool_name** (`str | None`): The name of the tool. If not provided, the agent's name will be used. (Required) +- **tool_description** (`str | None`): The description of the tool, indicating its purpose and usage. (Required) +- **custom_output_extractor** (`Callable[[RunResult | RunResultStreaming], Awaitable[str]] | None`): A function to extract output from the agent's run result. Defaults to using the last message. (Optional) +- **is_enabled** (`bool | Callable[[RunContextWrapper[Any], AgentBase[Any]], MaybeAwaitable[bool]]`): Determines if the tool is available to be called. Can be a boolean or a function. Defaults to `True`. +- **on_stream** (`Callable[[AgentToolStreamEvent], MaybeAwaitable[None]] | None`): A callback function to handle streaming events during the nested agent run. (Optional) +- **run_config** (`RunConfig | None`): Configuration for the agent run. (Optional) +- **max_turns** (`int | None`): Maximum number of turns for the agent run. (Optional) +- **hooks** (`RunHooks[TContext] | None`): Hooks to be applied to the agent run. (Optional) +- **previous_response_id** (`str | None`): The ID of the previous response in the conversation. (Optional) +- **conversation_id** (`str | None`): The ID of the current conversation. (Optional) +- **session** (`Session | None`): The session object for the agent run. (Optional) +- **failure_error_function** (`ToolErrorFunction | None`): A function to generate an error message if the tool run fails. Defaults to `default_tool_error_function`. +``` + +-------------------------------- + +### Abstract Trace Start Callback - Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing + +Defines the `on_trace_start` method, which is called synchronously when a new trace begins. Implementations should return quickly to avoid blocking execution and handle errors internally. + +```python +@abc.abstractmethod +def on_trace_start(self, trace: "Trace") -> None: + """Called when a new trace begins execution. + + Args: + trace: The trace that started. Contains workflow name and metadata. + + Notes: + - Called synchronously on trace start + - Should return quickly to avoid blocking execution + - Any errors should be caught and handled internally + """ + pass + +``` + +-------------------------------- + +### Transcription Span API + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/create + +This endpoint allows for the creation of a new transcription span. The span is not automatically started and requires manual initiation using a `with` statement or explicit `start()` and `finish()` calls. + +```APIDOC +## transcription_span + +### Description +Create a new transcription span. The span will not be started automatically, you should either do `with transcription_span() ...` or call `span.start()` + `span.finish()` manually. + +### Method +Not Applicable (Python function) + +### Endpoint +Not Applicable (Python function) + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +None + +### Parameters +- **model** (str | None) - Optional - The name of the model used for the speech-to-text. +- **input** (str | None) - Optional - The audio input of the speech-to-text transcription, as a base64 encoded string of audio bytes. +- **input_format** (str | None) - Optional - The format of the audio input (defaults to "pcm"). Defaults to 'pcm'. +- **output** (str | None) - Optional - The output of the speech-to-text transcription. +- **model_config** (Mapping[str, Any] | None) - Optional - The model configuration (hyperparameters) used. +- **span_id** (str | None) - Optional - The ID of the span. Optional. If not provided, we will generate an ID. We recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are correctly formatted. +- **parent** (Trace | Span[Any] | None) - Optional - The parent span or trace. If not provided, we will automatically use the current trace/span as the parent. +- **disabled** (bool) - Optional - If True, we will return a Span but the Span will not be recorded. Defaults to False. + +### Request Example +```python +# Example usage within a 'with' statement: +with transcription_span(model="whisper-1", input="base64_encoded_audio_data") as span: + # Perform transcription operations + result = perform_transcription(span.input) + span.output = result + +# Example usage with manual start/finish: +tran_span = transcription_span(model="whisper-1", input="base64_encoded_audio_data") +tran_span.start() +try: + # Perform transcription operations + result = perform_transcription(tran_span.input) + tran_span.output = result +finally: + tran_span.finish() +``` + +### Response +#### Success Response (200) +- **Span[TranscriptionSpanData]** - The newly created speech-to-text span. + +#### Response Example +```json +{ + "span_id": "generated_or_provided_id", + "parent_id": "optional_parent_id", + "start_time": "timestamp", + "end_time": "timestamp", + "name": "transcription", + "data": { + "input": "base64_encoded_audio_data", + "input_format": "pcm", + "output": "transcribed_text", + "model": "whisper-1", + "model_config": {} + } +} +``` +``` + +-------------------------------- + +### RealtimeSession Initialization + +Source: https://openai.github.io/openai-agents-python/ref/realtime/session + +Initializes a RealtimeSession object with a model, agent, context, and optional configurations. It sets up internal state for history, model settings, event queues, and guardrail tracking. + +```python +def __init__(self, + model: RealtimeModel, + agent: RealtimeAgent, + context: object, + model_config: dict | None = None, + run_config: dict | None = None, + ): + """Initialize the RealtimeSession. + + Args: + model: The RealtimeModel instance to use for communication. + agent: The initial RealtimeAgent for the session. + context: The context object for the session. + model_config: Optional configuration for the model. + run_config: Runtime configuration including guardrails. + """ + self._model = model + self._current_agent = agent + self._context_wrapper = RunContextWrapper(context) + self._event_info = RealtimeEventInfo(context=self._context_wrapper) + self._history: list[RealtimeItem] = [] + self._model_config = model_config or {} + self._run_config = run_config or {} + initial_model_settings = self._model_config.get("initial_model_settings") + run_config_settings = self._run_config.get("model_settings") + self._base_model_settings: RealtimeSessionModelSettings = { + **(run_config_settings or {}), + **(initial_model_settings or {}), + } + self._event_queue: asyncio.Queue[RealtimeSessionEvent] = asyncio.Queue() + self._closed = False + self._stored_exception: BaseException | None = None + + # Guardrails state tracking + self._interrupted_response_ids: set[str] = set() + self._item_transcripts: dict[str, str] = {} # item_id -> accumulated transcript + self._item_guardrail_run_counts: dict[str, int] = {} # item_id -> run count + self._debounce_text_length = self._run_config.get("guardrails_settings", {}).get( + "debounce_text_length", 100 + ) + + self._guardrail_tasks: set[asyncio.Task[Any]] = set() + self._tool_call_tasks: set[asyncio.Task[Any]] = set() + self._async_tool_calls: bool = bool(self._run_config.get("async_tool_calls", True)) +``` + +-------------------------------- + +### GET /tools/all + +Source: https://openai.github.io/openai-agents-python/ja/ref/agent + +Retrieves all agent tools, including both MCP tools and function tools. This endpoint provides a comprehensive list of all tools accessible by the agent. + +```APIDOC +## GET /tools/all + +### Description +All agent tools, including MCP tools and function tools. + +### Method +GET + +### Endpoint +/tools/all + +### Parameters +#### Query Parameters +- **run_context** (RunContextWrapper) - Required - The context for the current run. + +### Request Example +```json +{ + "run_context": "" +} +``` + +### Response +#### Success Response (200) +- **tools** (list[Tool]) - A list of all available tools, including MCP and function tools. + +#### Response Example +```json +[ + { + "name": "mcp_tool_example", + "description": "An example MCP tool", + "parameters": {}, + "return_direct": false + }, + { + "name": "function_tool_example", + "description": "An example function tool", + "parameters": {}, + "return_direct": false + } +] +``` +``` + +-------------------------------- + +### Trace Lifecycle Events + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/provider + +Callbacks for the start and end of a trace. + +```APIDOC +## on_trace_start + +### Description +Called when a trace is started. + +### Method +This is a method within a class, likely a tracing provider. + +### Endpoint +N/A (Internal method) + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **trace** (Trace) - Required - The trace object representing the current trace. + +### Request Example +```python +# Assuming 'trace' is an instance of the Trace class +provider.on_trace_start(trace) +``` + +### Response +#### Success Response (None) +This method does not return a value. + +## on_trace_end + +### Description +Called when a trace is finished. + +### Method +This is a method within a class, likely a tracing provider. + +### Endpoint +N/A (Internal method) + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **trace** (Trace) - Required - The trace object representing the finished trace. + +### Request Example +```python +# Assuming 'trace' is an instance of the Trace class +provider.on_trace_end(trace) +``` + +### Response +#### Success Response (None) +This method does not return a value. +``` + +-------------------------------- + +### Get Text-to-Speech Model + +Source: https://openai.github.io/openai-agents-python/ko/ref/voice/models/openai_model_provider + +Retrieves a text-to-speech model by its name. If no model name is provided, a default model will be returned. + +```APIDOC +## GET /voice/models/openai/tts + +### Description +Retrieves a text-to-speech (TTS) model by its name. If `model_name` is not specified, a default TTS model is used. + +### Method +GET + +### Endpoint +/voice/models/openai/tts + +### Parameters +#### Query Parameters +- **model_name** (str) - Optional - The name of the text-to-speech model to retrieve. Defaults to a predefined model if not provided. + +### Request Example +``` +GET /voice/models/openai/tts?model_name=tts-1 +``` + +### Response +#### Success Response (200) +- **model_type** (str) - The type of the TTS model (e.g., "OpenAITTSModel"). +- **model_name** (str) - The name of the retrieved TTS model. + +#### Response Example +```json +{ + "model_type": "OpenAITTSModel", + "model_name": "tts-1" +} +``` +``` + +-------------------------------- + +### Handle Dynamic Instructions in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/agent + +This code demonstrates how to handle dynamic instructions for an agent. It checks if the instructions are a callable function, inspects its signature to ensure it accepts exactly two arguments (context and agent), and then calls the function, supporting both synchronous and asynchronous functions. If instructions are not a string or callable, an error is logged. + +```python +import inspect +from typing import Callable, Awaitable, cast + +# Assuming RunContextWrapper and Agent are defined elsewhere +# class RunContextWrapper[TContext]: pass +# class Agent[TContext]: pass + +# Placeholder for logger and cast if not available in the snippet's context +class Logger: + def error(self, message): + print(f"ERROR: {message}") +logger = Logger() + +def cast(type, value): + return value + +async def await_cast(type, value): + return await value + +class PromptUtil: + @staticmethod + async def to_model_input(prompt, run_context, agent): + # Dummy implementation for demonstration + return f"Processed prompt: {prompt}" + +class ResponsePromptParam: + pass + +class ModelSettings: + pass + +class AgentOutputSchemaBase: + pass + +class AgentHooks: + pass + +class Handoff: + pass + +def get_default_model_settings(): + return ModelSettings() + +class RunContextWrapper[TContext]: + pass + +class Agent[TContext]: + def __init__(self, instructions=None, prompt=None): + self.instructions = instructions + self.prompt = prompt + + async def get_prompt(self, run_context: RunContextWrapper[TContext]) -> ResponsePromptParam | None: + """Get the prompt for the agent.""" + return await PromptUtil.to_model_input(self.prompt, run_context, self) + + async def process_instructions(self, run_context: RunContextWrapper[TContext]) -> str | None: + """Processes the agent's instructions, handling string or callable inputs.""" + if isinstance(self.instructions, str): + return self.instructions + elif callable(self.instructions): + # Inspect the signature of the instructions function + sig = inspect.signature(self.instructions) + params = list(sig.parameters.values()) + + # Enforce exactly 2 parameters + if len(params) != 2: + raise TypeError( + f"'instructions' callable must accept exactly 2 arguments (context, agent), " + f"but got {len(params)}: {[p.name for p in params]}" + ) + + # Call the instructions function properly + if inspect.iscoroutinefunction(self.instructions): + return await await_cast(Awaitable[str], self.instructions(run_context, self)) + else: + return cast(str, self.instructions(run_context, self)) + + elif self.instructions is not None: + logger.error( + f"Instructions must be a string or a callable function, " + f"got {type(self.instructions).__name__}" + ) + + return None + +# Example Usage: + +async def dynamic_instructions(context, agent): + return f"Dynamic instructions for agent based on context: {context}" + +# Example with a string instruction +agent_str = Agent(instructions="This is a static instruction.") +# Example with a callable instruction +agent_callable = Agent(instructions=dynamic_instructions) + +async def main(): + # Dummy run_context + class DummyRunContext: + pass + run_context = DummyRunContext() + + instructions_str = await agent_str.process_instructions(run_context) + print(f"String instructions: {instructions_str}") + + instructions_callable = await agent_callable.process_instructions(run_context) + print(f"Callable instructions: {instructions_callable}") + +# To run the example: +# import asyncio +# asyncio.run(main()) + +``` + +-------------------------------- + +### Span Methods + +Source: https://openai.github.io/openai-agents-python/zh/ref/tracing + +This section covers the core methods for managing the lifecycle of a span: starting and finishing. + +```APIDOC +## Span Methods + +This section covers the core methods for managing the lifecycle of a span: starting and finishing. + +### `start(mark_as_current: bool = False)` + +- **Description**: Start the span. +- **Parameters**: + - `mark_as_current` (bool): If true, the span will be marked as the current span. (Default: `False`) + +### `finish(reset_current: bool = False) -> None` + +- **Description**: Finish the span. +- **Parameters**: + - `reset_current` (bool): If true, the span will be reset as the current span. (Default: `False`) +``` + +-------------------------------- + +### Initialize OpenAIChatCompletionsModel in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/models/openai_chatcompletions + +Initializes the OpenAIChatCompletionsModel with a specific OpenAI chat model and an asynchronous OpenAI client. This setup is crucial for making subsequent API calls. + +```python +class OpenAIChatCompletionsModel(Model): + def __init__( + self, + model: str | ChatModel, + openai_client: AsyncOpenAI, + ) -> None: + self.model = model + self._client = openai_client +``` + +-------------------------------- + +### ComputerCreate Protocol + +Source: https://openai.github.io/openai-agents-python/ref/tool + +Protocol for initializing a computer for the current run context. + +```APIDOC +## ComputerCreate Protocol + +### Description +Initializes a computer for the current run context. + +### Method +`__call__` + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **run_context** (RunContextWrapper[Any]) - Required - The run context wrapper. + +### Request Example +```python +# This is a protocol, actual implementation will vary. +async def create_computer(run_context: RunContextWrapper[Any]) -> Computer: + # ... implementation ... + pass +``` + +### Response +#### Success Response (200) +- **ComputerT_co** (Any) - The created computer instance. + +#### Response Example +```python +# Example of a computer instance +class MyComputer: + pass + +# Assuming MyComputer is the type for ComputerT_co +# The actual return value would be an instance of MyComputer +``` +``` + +-------------------------------- + +### Creating Handoffs Between Realtime Agents (Python) + +Source: https://openai.github.io/openai-agents-python/realtime/guide + +Illustrates how to implement conversation handoffs between specialized `RealtimeAgent` instances. This allows a main agent to transfer the conversation to a more suitable agent (e.g., billing or technical support) based on user intent. The example defines specialized agents and then configures the main agent with handoff configurations. + +```python +from agents.realtime import RealtimeAgent, realtime_handoff + +# Specialized agents +billing_agent = RealtimeAgent( + name="Billing Support", + instructions="You specialize in billing and payment issues.", +) + +technical_agent = RealtimeAgent( + name="Technical Support", + instructions="You handle technical troubleshooting.", +) + +# Main agent with handoffs +main_agent = RealtimeAgent( + name="Customer Service", + instructions="You are the main customer service agent. Hand off to specialists when needed.", + handoffs=[ + realtime_handoff(billing_agent, tool_description="Transfer to billing support"), + realtime_handoff(technical_agent, tool_description="Transfer to technical support"), + ] +) +``` + +-------------------------------- + +### RealtimeRunner Initialization Method (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/runner + +This Python code snippet details the `__init__` method for the `RealtimeRunner` class. It outlines the parameters required for initializing a real-time agent session, including the starting agent, an optional custom model, and configuration overrides. The method initializes internal state and sets up a default OpenAI realtime WebSocket model if none is provided. The arguments specify the type and purpose of each parameter. + +```python +def __init__( + self, + starting_agent: RealtimeAgent, + *, + model: RealtimeModel | None = None, + config: RealtimeRunConfig | None = None, +) -> None: + """Initialize the realtime runner. + + Args: + starting_agent: The agent to start the session with. + context: The context to use for the session. + model: The model to use. If not provided, will use a default OpenAI realtime model. + config: Override parameters to use for the entire run. + """ + self._starting_agent = starting_agent + self._config = config + self._model = model or OpenAIRealtimeWebSocketModel() + +``` + +-------------------------------- + +### SQLAlchemySession Initialization + +Source: https://openai.github.io/openai-agents-python/zh/ref/extensions/memory/sqlalchemy_session + +Creates an instance of SQLAlchemySession, establishing a connection to the database using SQLAlchemy's async engine. It handles optional keyword arguments for engine creation and constructor parameters. Dependencies include `sqlalchemy.ext.asyncio.create_async_engine`. + +```python +def __init__( + cls, + session_id: str, + url: str, + engine_kwargs: dict[str, Any] | None = None, + **kwargs: Any, + ) -> "SQLAlchemySession": + """Create a new SQLAlchemySession. + + Args: + session_id: The unique identifier for the session. + url: The database connection URL. + engine_kwargs (dict[str, Any] | None): Additional keyword arguments forwarded to + sqlalchemy.ext.asyncio.create_async_engine. + **kwargs: Additional keyword arguments forwarded to the main constructor + (e.g., create_tables, custom table names, etc.). + + Returns: + SQLAlchemySession: An instance of SQLAlchemySession connected to the specified database. + """ + engine_kwargs = engine_kwargs or {} + engine = create_async_engine(url, **engine_kwargs) + return cls(session_id, engine=engine, **kwargs) +``` + +-------------------------------- + +### Adding Tools to Realtime Agents (Python) + +Source: https://openai.github.io/openai-agents-python/realtime/guide + +Demonstrates how to define and add function tools to a RealtimeAgent. These tools allow the agent to perform specific actions during a conversation. The example shows defining `get_weather` and `book_appointment` functions decorated as tools and then passing them to the `RealtimeAgent` constructor. + +```python +from agents import function_tool +from agents.realtime import RealtimeAgent + +@function_tool +def get_weather(city: str) -> str: + """Get current weather for a city.""" + # Your weather API logic here + return f"The weather in {city} is sunny, 72°F" + +@function_tool +def book_appointment(date: str, time: str, service: str) -> str: + """Book an appointment.""" + # Your booking logic here + return f"Appointment booked for {service} on {date} at {time}" + +agent = RealtimeAgent( + name="Assistant", + instructions="You can help with weather and appointments.", + tools=[get_weather, book_appointment], +) +``` + +-------------------------------- + +### Get Prompt API + +Source: https://openai.github.io/openai-agents-python/ko/ref/mcp/server + +Retrieves a specific prompt from the server by name. + +```APIDOC +## GET /get_prompt/{name} + +### Description +Get a specific prompt from the server. + +### Method +GET + +### Endpoint +/get_prompt/{name} + +### Parameters +#### Path Parameters +- **name** (str) - Required - The name of the prompt to retrieve. + +#### Query Parameters +- **arguments** (dict[str, Any] | None) - Optional - A dictionary of arguments to pass to the prompt. + +### Response +#### Success Response (200) +- **prompt** (GetPromptResult) - A result object containing the prompt details. + +#### Response Example +```json +{ + "prompt": { + "name": "example_prompt", + "template": "This is an example prompt template." + } +} +``` +``` + +-------------------------------- + +### Web and File Search with OpenAI Agents Python SDK + +Source: https://openai.github.io/openai-agents-python/tools + +Demonstrates how to configure an agent with WebSearchTool and FileSearchTool for retrieving information. The FileSearchTool requires vector store IDs and a maximum number of results to be specified. + +```python +from agents import Agent, FileSearchTool, Runner, WebSearchTool + +agent = Agent( + name="Assistant", + tools=[ + WebSearchTool(), + FileSearchTool( + max_num_results=3, + vector_store_ids=["VECTOR_STORE_ID"], + ), + ], +) + +async def main(): + result = await Runner.run(agent, "Which coffee shop should I go to, taking into account my preferences and the weather today in SF?") + print(result.final_output) + +``` + +-------------------------------- + +### Get Text-to-Speech Model + +Source: https://openai.github.io/openai-agents-python/ko/ref/voice/models/openai_provider + +Retrieves a text-to-speech model by its name. If no name is provided, a default model will be returned. + +```APIDOC +## get_tts_model + +### Description +Get a text-to-speech model by name. If no name is provided, a default model will be returned. + +### Method +GET + +### Endpoint +/websites/openai_github_io_openai-agents-python/tts_models + +### Parameters +#### Path Parameters +None + +#### Query Parameters +- **model_name** (str | None) - Required - The name of the model to get. + +#### Request Body +None + +### Request Example +``` +GET /websites/openai_github_io_openai-agents-python/tts_models?model_name=tts-1 +``` + +### Response +#### Success Response (200) +- **TTSModel** (TTSModel) - The text-to-speech model. + +#### Response Example +```json +{ + "model_type": "OpenAITTSModel", + "model_name": "tts-1" +} +``` +``` + +-------------------------------- + +### Initialize RealtimeSession in Python + +Source: https://openai.github.io/openai-agents-python/zh/ref/realtime/session + +Initializes a RealtimeSession object with the model, agent, context, and configuration. It sets up internal state for event handling, guardrails, and model settings, preparing for a real-time interaction session. + +```python +def __init__( + self, + model: RealtimeModel, + agent: RealtimeAgent, + context: RunContext, + model_config: RealtimeModelConfig | None = None, + run_config: RuntimeConfiguration | None = None, + ): + """Initialize the session. + + Args: + model: The real-time model to interact with. + agent: The agent to use for the session. + context: The run context for the session. + model_config: Model-specific configuration. + run_config: Runtime configuration including guardrails. + """ + self._model = model + self._current_agent = agent + self._context_wrapper = RunContextWrapper(context) + self._event_info = RealtimeEventInfo(context=self._context_wrapper) + self._history: list[RealtimeItem] = [] + self._model_config = model_config or {} + self._run_config = run_config or {} + initial_model_settings = self._model_config.get("initial_model_settings") + run_config_settings = self._run_config.get("model_settings") + self._base_model_settings: RealtimeSessionModelSettings = { + **(run_config_settings or {}), + **(initial_model_settings or {}), + } + self._event_queue: asyncio.Queue[RealtimeSessionEvent] = asyncio.Queue() + self._closed = False + self._stored_exception: BaseException | None = None + + # Guardrails state tracking + self._interrupted_response_ids: set[str] = set() + self._item_transcripts: dict[str, str] = {} + self._item_guardrail_run_counts: dict[str, int] = {} + self._debounce_text_length = self._run_config.get("guardrails_settings", {}).get( + "debounce_text_length", 100 + ) + + self._guardrail_tasks: set[asyncio.Task[Any]] = set() + self._tool_call_tasks: set[asyncio.Task[Any]] = set() + self._async_tool_calls: bool = bool(self._run_config.get("async_tool_calls", True)) +``` + +-------------------------------- + +### Get AsyncOpenAI Client (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/models/openai_chatcompletions + +This function provides a singleton instance of the AsyncOpenAI client. If the client has not been initialized, it creates a new instance. This ensures that only one client object is used throughout the application, which can be beneficial for resource management. + +```python + def _get_client(self) -> AsyncOpenAI: + if self._client is None: + self._client = AsyncOpenAI() + return self._client +``` + +-------------------------------- + +### Get Real-time Session Configuration (Python) + +Source: https://openai.github.io/openai-agents-python/zh/ref/realtime/openai_realtime + +Retrieves and constructs the session configuration for a real-time session based on model settings. It meticulously processes audio input and output configurations, including format, noise reduction, transcription, and turn detection. Default settings are applied when specific configurations are not found. + +```python +def _get_session_config( + self, + model_settings: RealtimeSessionModelSettings + ) -> OpenAISessionCreateRequest: + """Get the session config.""" + audio_input_args: dict[str, Any] = {} + audio_output_args: dict[str, Any] = {} + + audio_config = model_settings.get("audio") + audio_config_mapping = audio_config if isinstance(audio_config, Mapping) else None + input_audio_config: Mapping[str, Any] = ( + cast(Mapping[str, Any], audio_config_mapping.get("input", {})) + if audio_config_mapping + else {} + ) + output_audio_config: Mapping[str, Any] = ( + cast(Mapping[str, Any], audio_config_mapping.get("output", {})) + if audio_config_mapping + else {} + ) + + input_format_source: FormatInput = ( + input_audio_config.get("format") if input_audio_config else None + ) + if input_format_source is None: + if self._call_id: + input_format_source = model_settings.get("input_audio_format") + else: + input_format_source = model_settings.get( + "input_audio_format", DEFAULT_MODEL_SETTINGS.get("input_audio_format") + ) + audio_input_args["format"] = to_realtime_audio_format(input_format_source) + + if "noise_reduction" in input_audio_config: + audio_input_args["noise_reduction"] = input_audio_config.get("noise_reduction") + elif "input_audio_noise_reduction" in model_settings: + audio_input_args["noise_reduction"] = model_settings.get("input_audio_noise_reduction") + + if "transcription" in input_audio_config: + audio_input_args["transcription"] = input_audio_config.get("transcription") + elif "input_audio_transcription" in model_settings: + audio_input_args["transcription"] = model_settings.get("input_audio_transcription") + else: + audio_input_args["transcription"] = DEFAULT_MODEL_SETTINGS.get( + "input_audio_transcription" + ) + + if "turn_detection" in input_audio_config: + audio_input_args["turn_detection"] = self._normalize_turn_detection_config( + input_audio_config.get("turn_detection") + ) + elif "turn_detection" in model_settings: + audio_input_args["turn_detection"] = self._normalize_turn_detection_config( + model_settings.get("turn_detection") + ) + else: + audio_input_args["turn_detection"] = DEFAULT_MODEL_SETTINGS.get("turn_detection") + + requested_voice = output_audio_config.get("voice") if output_audio_config else None + audio_output_args["voice"] = requested_voice or model_settings.get( + "voice", DEFAULT_MODEL_SETTINGS.get("voice") + ) + + output_format_source: FormatInput = ( + output_audio_config.get("format") if output_audio_config else None + ) + if output_format_source is None: + if self._call_id: + output_format_source = model_settings.get("output_audio_format") + else: + output_format_source = model_settings.get( + "output_audio_format", DEFAULT_MODEL_SETTINGS.get("output_audio_format") + ) + audio_output_args["format"] = to_realtime_audio_format(output_format_source) + + if "speed" in output_audio_config: + audio_output_args["speed"] = output_audio_config.get("speed") + elif "speed" in model_settings: + audio_output_args["speed"] = model_settings.get("speed") + + output_modalities = ( + model_settings.get("output_modalities") + or model_settings.get("modalities") + or DEFAULT_MODEL_SETTINGS.get("modalities") + ) + + # Construct full session object. `type` will be excluded at serialization time for updates. + session_create_request = OpenAISessionCreateRequest( + type="realtime", + model=(model_settings.get("model_name") or self.model) or "gpt-realtime", + output_modalities=output_modalities, + input_audio=audio_input_args, + output_audio=audio_output_args, + ) + return session_create_request +``` + +-------------------------------- + +### Voice Model Provider - get_tts_model + +Source: https://openai.github.io/openai-agents-python/zh/ref/voice/model + +Get a text-to-speech model by name. + +```APIDOC +## GET /voice/tts-model + +### Description +Get a text-to-speech model by name. + +### Method +GET + +### Endpoint +/voice/tts-model + +### Parameters +#### Query Parameters +- **model_name** (str | None) - Required - The name of the model to get. + +### Response +#### Success Response (200) +- **model** (TTSModel) - The text-to-speech model. + +#### Response Example +{ + "model": "" +} +``` + +-------------------------------- + +### Create Guardrail Span in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/create + +Creates a new guardrail span. The span must be explicitly started and finished using a `with` statement or manual `start()` and `finish()` calls. It accepts parameters for naming, trigger status, span ID, parent span, and a disabled flag. + +```python +def guardrail_span( + name: str, + triggered: bool = False, + span_id: str | None = None, + parent: Trace | Span[Any] | None = None, + disabled: bool = False, +) -> Span[GuardrailSpanData]: + """Create a new guardrail span. The span will not be started automatically, you should either + do `with guardrail_span() ...` or call `span.start()` + `span.finish()` manually. + + Args: + name: The name of the guardrail. + triggered: Whether the guardrail was triggered. + span_id: The ID of the span. Optional. If not provided, we will generate an ID. We + recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are + correctly formatted. + parent: The parent span or trace. If not provided, we will automatically use the current + trace/span as the parent. + disabled: If True, we will return a Span but the Span will not be recorded. + """ + return get_trace_provider().create_span( + span_data=GuardrailSpanData(name=name, triggered=triggered), + span_id=span_id, + parent=parent, + disabled=disabled, + ) + +``` + +-------------------------------- + +### Get Prompt API + +Source: https://openai.github.io/openai-agents-python/ja/ref/mcp/server + +Retrieves a specific prompt from the server by its name, optionally with arguments. + +```APIDOC +## GET /prompts/{name} + +### Description +Retrieves a specific prompt from the server by its name, optionally with arguments. + +### Method +GET + +### Endpoint +/prompts/{name} + +### Parameters +#### Path Parameters +- **name** (str) - Required - The name of the prompt to retrieve. + +#### Query Parameters +- **arguments** (dict[str, Any] | None) - Optional - Arguments to be used with the prompt. + +### Request Example +```json +{ + "arguments": { + "variable1": "value1" + } +} +``` + +### Response +#### Success Response (200) +- **prompt_details** (GetPromptResult) - A result object containing the prompt details. + +#### Response Example +```json +{ + "prompt_details": { + "content": "This is the content of the prompt." + } +} +``` +``` + +-------------------------------- + +### Span Lifecycle Events + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/provider + +Callbacks for the start and end of a span within a trace. + +```APIDOC +## on_span_start + +### Description +Called when a span is started. + +### Method +This is a method within a class, likely a tracing provider. + +### Endpoint +N/A (Internal method) + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **span** (Span[Any]) - Required - The span object representing the current span. + +### Request Example +```python +# Assuming 'span' is an instance of the Span class +provider.on_span_start(span) +``` + +### Response +#### Success Response (None) +This method does not return a value. + +## on_span_end + +### Description +Called when a span is finished. + +### Method +This is a method within a class, likely a tracing provider. + +### Endpoint +N/A (Internal method) + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **span** (Span[Any]) - Required - The span object representing the finished span. + +### Request Example +```python +# Assuming 'span' is an instance of the Span class +provider.on_span_end(span) +``` + +### Response +#### Success Response (None) +This method does not return a value. +``` + +-------------------------------- + +### Generate Agent Instructions from MCP Server Prompts + +Source: https://openai.github.io/openai-agents-python/mcp + +MCP servers can provide dynamic prompts to generate agent instructions. Use `get_prompt` to fetch a prompt template with optional arguments, which can then be used to initialize an agent. + +```python +from agents import Agent + +prompt_result = await server.get_prompt( + "generate_code_review_instructions", + {"focus": "security vulnerabilities", "language": "python"}, +) +instructions = prompt_result.messages[0].content.text + +agent = Agent( + name="Code Reviewer", + instructions=instructions, + mcp_servers=[server], +) + + +``` + +-------------------------------- + +### Get Speech-to-Text Model + +Source: https://openai.github.io/openai-agents-python/ko/ref/voice/models/openai_model_provider + +Retrieves a speech-to-text model by its name. If no model name is provided, a default model will be returned. + +```APIDOC +## GET /voice/models/openai/stt + +### Description +Retrieves a speech-to-text (STT) model by its name. If `model_name` is not specified, a default STT model is used. + +### Method +GET + +### Endpoint +/voice/models/openai/stt + +### Parameters +#### Query Parameters +- **model_name** (str) - Optional - The name of the speech-to-text model to retrieve. Defaults to a predefined model if not provided. + +### Request Example +``` +GET /voice/models/openai/stt?model_name=whisper-1 +``` + +### Response +#### Success Response (200) +- **model_type** (str) - The type of the STT model (e.g., "OpenAISTTModel"). +- **model_name** (str) - The name of the retrieved STT model. + +#### Response Example +```json +{ + "model_type": "OpenAISTTModel", + "model_name": "whisper-1" +} +``` +``` + +-------------------------------- + +### Implement Span with Tracing Functionality in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/spans + +This Python code defines the SpanImpl class, which extends the Span base class. It manages trace and span IDs, start and end times, and associated span data. The class integrates with a TracingProcessor to handle span start and end events and supports context management for current spans. + +```python +class SpanImpl(Span[TSpanData]): + __slots__ = ( + "_trace_id", + "_span_id", + "_parent_id", + "_started_at", + "_ended_at", + "_error", + "_prev_span_token", + "_processor", + "_span_data", + "_tracing_api_key", + ) + + def __init__( + self, + trace_id: str, + span_id: str | None, + parent_id: str | None, + processor: TracingProcessor, + span_data: TSpanData, + tracing_api_key: str | None, + ): + self._trace_id = trace_id + self._span_id = span_id or util.gen_span_id() + self._parent_id = parent_id + self._started_at: str | None = None + self._ended_at: str | None = None + self._processor = processor + self._error: SpanError | None = None + self._prev_span_token: contextvars.Token[Span[TSpanData] | None] | None = None + self._span_data = span_data + self._tracing_api_key = tracing_api_key + + @property + def trace_id(self) -> str: + return self._trace_id + + @property + def span_id(self) -> str: + return self._span_id + + @property + def span_data(self) -> TSpanData: + return self._span_data + + @property + def parent_id(self) -> str | None: + return self._parent_id + + def start(self, mark_as_current: bool = False): + if self.started_at is not None: + logger.warning("Span already started") + return + + self._started_at = util.time_iso() + self._processor.on_span_start(self) + if mark_as_current: + self._prev_span_token = Scope.set_current_span(self) + + def finish(self, reset_current: bool = False) -> None: + if self.ended_at is not None: + logger.warning("Span already finished") + return + + self._ended_at = util.time_iso() + self._processor.on_span_end(self) + if reset_current and self._prev_span_token is not None: + Scope.reset_current_span(self._prev_span_token) + self._prev_span_token = None + + def __enter__(self) -> Span[TSpanData]: + self.start(mark_as_current=True) + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + reset_current = True + if exc_type is GeneratorExit: + logger.debug("GeneratorExit, skipping span reset") + reset_current = False + + self.finish(reset_current=reset_current) + + def set_error(self, error: SpanError) -> None: + self._error = error + + @property + def error(self) -> SpanError | None: + return self._error + + @property + def started_at(self) -> str | None: + return self._started_at + + @property + def ended_at(self) -> str | None: + return self._ended_at + + @property + def tracing_api_key(self) -> str | None: + return self._tracing_api_key + + def export(self) -> dict[str, Any] | None: + return { + "object": "trace.span", + "id": self.span_id, + "trace_id": self.trace_id, + "parent_id": self._parent_id, + "started_at": self._started_at, + "ended_at": self._ended_at, + "span_data": self.span_data.export(), + "error": self._error, + } + +``` + +-------------------------------- + +### on_start Method for Voice Workflow Initialization + +Source: https://openai.github.io/openai-agents-python/ja/ref/voice/workflow + +The async on_start method in VoiceWorkflowBase is an optional hook that runs before any user input is processed. It can be used to deliver introductory messages or instructions via text-to-speech. The default implementation yields nothing, indicating no action is taken. + +```python +async def on_start(self) -> AsyncIterator[str]: + """ + Optional method that runs before any user input is received. Can be used + to deliver a greeting or instruction via TTS. Defaults to doing nothing. + """ + return + yield +``` + +-------------------------------- + +### Get Function Tools + +Source: https://openai.github.io/openai-agents-python/ko/ref/mcp/util + +Retrieves all function tools from a single MCP server. It fetches the tools and then converts them into the Agents SDK's FunctionTool format. + +```APIDOC +## GET /servers/{server_name}/tools + +### Description +Get all function tools from a single MCP server. This method fetches tools from the specified server and converts them into a format compatible with the Agents SDK. + +### Method +GET + +### Endpoint +/servers/{server_name}/tools + +### Parameters +#### Path Parameters +- **server_name** (string) - Required - The name of the MCP server to retrieve tools from. + +#### Query Parameters +- **convert_schemas_to_strict** (bool) - Required - Flag to determine if input schemas should be converted to a strict JSON schema format. +- **run_context** (RunContextWrapper[Any]) - Required - The context for the current run operation. +- **agent** (AgentBase) - Required - The agent instance performing the operation. + +### Response +#### Success Response (200) +- **tools** (list[Tool]) - A list of function tools available on the specified server. + +#### Response Example +```json +[ + { + "name": "another_tool", + "description": "Another example tool", + "params_json_schema": {}, + "on_invoke_tool": "", + "strict_json_schema": true + } +] +``` +``` + +-------------------------------- + +### Span Lifecycle Methods + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing + +Methods for managing the lifecycle of a span, including starting and finishing. + +```APIDOC +## Span Lifecycle Methods + +Methods to control the start and end of a span. + +### `start` + +* **Description**: Starts the span. Optionally marks it as the current span. +* **Parameters**: + * `mark_as_current` (bool): If true, the span will be marked as the current span. Defaults to `False`. + +### `finish` + +* **Description**: Finishes the span. Optionally resets the current span. +* **Parameters**: + * `reset_current` (bool): If true, the span will be reset as the current span. Defaults to `False`. +* **Returns**: `None` +``` + +-------------------------------- + +### Run Agent Workflow (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/run + +Executes an agent workflow starting from a specified agent. The workflow continues until a final output is produced, handling agent handoffs and tool calls. It includes error handling for exceeding maximum turns or triggering guardrails. Key parameters include the starting agent, initial input, context, maximum turns, and optional configurations for conversation management and callbacks. + +```python +async def run( + starting_agent: Agent[TContext], + input: str | list[TResponseInputItem], + *, + context: TContext | None = None, + max_turns: int = DEFAULT_MAX_TURNS, + hooks: RunHooks[TContext] | None = None, + run_config: RunConfig | None = None, + previous_response_id: str | None = None, + auto_previous_response_id: bool = False, + conversation_id: str | None = None, + session: Session | None = None, +) -> RunResult: + """ + Run a workflow starting at the given agent. + + The agent will run in a loop until a final output is generated. The loop runs like so: + + 1. The agent is invoked with the given input. + 2. If there is a final output (i.e. the agent produces something of type + `agent.output_type`), the loop terminates. + 3. If there's a handoff, we run the loop again, with the new agent. + 4. Else, we run tool calls (if any), and re-run the loop. + + In two cases, the agent may raise an exception: + + 1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised. + 2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered + exception is raised. + + Note: + Only the first agent's input guardrails are run. + + Args: + starting_agent: The starting agent to run. + input: The initial input to the agent. You can pass a single string for a + user message, or a list of input items. + context: The context to run the agent with. + max_turns: The maximum number of turns to run the agent for. A turn is + """ + pass +``` + +-------------------------------- + +### Initialize OpenAI Voice Model Provider (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/voice/models/openai_model_provider + +Initializes a new OpenAI voice model provider. It can accept an API key, base URL, an existing OpenAI client, organization, and project. If an OpenAI client is provided, API key and base URL should not be. Otherwise, a new client is created using the provided credentials. + +```python +def __init__( + self, + *, + api_key: str | None = None, + base_url: str | None = None, + openai_client: AsyncOpenAI | None = None, + organization: str | None = None, + project: str | None = None, +) -> None: + """Create a new OpenAI voice model provider. + + Args: + api_key: The API key to use for the OpenAI client. If not provided, we will use the + default API key. + base_url: The base URL to use for the OpenAI client. If not provided, we will use the + default base URL. + openai_client: An optional OpenAI client to use. If not provided, we will create a new + OpenAI client using the api_key and base_url. + organization: The organization to use for the OpenAI client. + project: The project to use for the OpenAI client. + """ + if openai_client is not None: + assert api_key is None and base_url is None, + "Don't provide api_key or base_url if you provide openai_client" + self._client: AsyncOpenAI | None = openai_client + else: + self._client = None + self._stored_api_key = api_key + self._stored_base_url = base_url + self._stored_organization = organization + self._stored_project = project + +``` + +-------------------------------- + +### GET /tools/mcp + +Source: https://openai.github.io/openai-agents-python/ja/ref/agent + +Fetches the available tools from the MCP servers. This endpoint retrieves a list of tools that are managed by the MCP (Multi-Cloud Platform) service. + +```APIDOC +## GET /tools/mcp + +### Description +Fetches the available tools from the MCP servers. + +### Method +GET + +### Endpoint +/tools/mcp + +### Parameters +#### Query Parameters +- **run_context** (RunContextWrapper) - Required - The context for the current run. + +### Request Example +```json +{ + "run_context": "" +} +``` + +### Response +#### Success Response (200) +- **tools** (list[Tool]) - A list of available tools from MCP servers. + +#### Response Example +```json +[ + { + "name": "example_tool", + "description": "An example tool", + "parameters": {}, + "return_direct": false + } +] +``` +``` + +-------------------------------- + +### Span Lifecycle Management - Python + +Source: https://openai.github.io/openai-agents-python/zh/ref/tracing/spans + +Provides methods for managing the lifecycle of a SpanImpl instance. The `start` method records the start time and notifies the processor, optionally setting the span as the current one. The `finish` method records the end time, notifies the processor, and optionally resets the current span context. + +```python +def start(self, mark_as_current: bool = False): + if self.started_at is not None: + logger.warning("Span already started") + return + + self._started_at = util.time_iso() + self._processor.on_span_start(self) + if mark_as_current: + self._prev_span_token = Scope.set_current_span(self) + +def finish(self, reset_current: bool = False) -> None: + if self.ended_at is not None: + logger.warning("Span already finished") + return + + self._ended_at = util.time_iso() + self._processor.on_span_end(self) + if reset_current and self._prev_span_token is not None: + Scope.reset_current_span(self._prev_span_token) + self._prev_span_token = None +``` + +-------------------------------- + +### Abstract Span Start Callback - Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing + +Defines the `on_span_start` method, invoked synchronously when a new span begins. Implementations should execute quickly and avoid blocking, as spans are automatically nested within the current trace or span. + +```python +@abc.abstractmethod +def on_span_start(self, span: "Span[Any]") -> None: + """Called when a new span begins execution. + + Args: + span: The span that started. Contains operation details and context. + + Notes: + - Called synchronously on span start + - Should return quickly to avoid blocking execution + - Spans are automatically nested under current trace/span + """ + pass + +``` + +-------------------------------- + +### Initialize OpenAI Voice Model Provider (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/voice/models/openai_provider + +Initializes the OpenAI voice model provider. It can be configured with an API key, base URL, organization, and project, or by providing an existing AsyncOpenAI client. If an OpenAI client is provided, API key and base URL should not be specified. + +```python +def __init__( + self, + *, + api_key: str | None = None, + base_url: str | None = None, + openai_client: AsyncOpenAI | None = None, + organization: str | None = None, + project: str | None = None, +) -> None: + """Create a new OpenAI voice model provider. + + Args: + api_key: The API key to use for the OpenAI client. If not provided, we will use the + default API key. + base_url: The base URL to use for the OpenAI client. If not provided, we will use the + default base URL. + openai_client: An optional OpenAI client to use. If not provided, we will create a new + OpenAI client using the api_key and base_url. + organization: The organization to use for the OpenAI client. + project: The project to use for the OpenAI client. + """ + if openai_client is not None: + assert api_key is None and base_url is None, + ("Don't provide api_key or base_url if you provide openai_client") + self._client: AsyncOpenAI | None = openai_client + else: + self._client = None + self._stored_api_key = api_key + self._stored_base_url = base_url + self._stored_organization = organization + self._stored_project = project + +``` + +-------------------------------- + +### Get Speech-to-Text Model + +Source: https://openai.github.io/openai-agents-python/ko/ref/voice/models/openai_provider + +Retrieves a speech-to-text model by its name. If no name is provided, a default model will be returned. + +```APIDOC +## get_stt_model + +### Description +Get a speech-to-text model by name. If no name is provided, a default model will be returned. + +### Method +GET + +### Endpoint +/websites/openai_github_io_openai-agents-python/stt_models + +### Parameters +#### Path Parameters +None + +#### Query Parameters +- **model_name** (str | None) - Required - The name of the model to get. + +#### Request Body +None + +### Request Example +``` +GET /websites/openai_github_io_openai-agents-python/stt_models?model_name=whisper-1 +``` + +### Response +#### Success Response (200) +- **STTModel** (STTModel) - The speech-to-text model. + +#### Response Example +```json +{ + "model_type": "OpenAISTTModel", + "model_name": "whisper-1" +} +``` +``` + +-------------------------------- + +### Create Speech Group Span - Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/create + +Creates a new speech group span. This span is not started automatically; users must either use a `with` statement or manually call `start()` and `finish()` on the returned span object. It accepts input text, an optional span ID, a parent trace or span, and a disabled flag. + +```Python +def speech_group_span( + input: str | None = None, + span_id: str | None = None, + parent: Trace | Span[Any] | None = None, + disabled: bool = False, +) -> Span[SpeechGroupSpanData]: + """Create a new speech group span. The span will not be started automatically, you should + either do `with speech_group_span() ...` or call `span.start()` + `span.finish()` manually. + + Args: + input: The input text used for the speech request. + span_id: The ID of the span. Optional. If not provided, we will generate an ID. We + recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are + correctly formatted. + parent: The parent span or trace. If not provided, we will automatically use the current + trace/span as the parent. + disabled: If True, we will return a Span but the Span will not be recorded. + """ + return get_trace_provider().create_span( + span_data=SpeechGroupSpanData(input=input), + span_id=span_id, + parent=parent, + disabled=disabled, + ) +``` + +-------------------------------- + +### Orchestrating Agents with Tools in Python + +Source: https://openai.github.io/openai-agents-python/tools + +This example demonstrates how to use multiple specialized agents orchestrated by a central agent. The orchestrator agent uses the specialized agents as tools to perform translations. It requires the 'agents' and 'asyncio' libraries. + +```python +from agents import Agent, Runner +import asyncio + +spanish_agent = Agent( + name="Spanish agent", + instructions="You translate the user's message to Spanish", +) + +french_agent = Agent( + name="French agent", + instructions="You translate the user's message to French", +) + +orchestrator_agent = Agent( + name="orchestrator_agent", + instructions=( + "You are a translation agent. You use the tools given to you to translate." + "If asked for multiple translations, you call the relevant tools." + ), + tools=[ + spanish_agent.as_tool( + tool_name="translate_to_spanish", + tool_description="Translate the user's message to Spanish", + ), + french_agent.as_tool( + tool_name="translate_to_french", + tool_description="Translate the user's message to French", + ), + ], +) + +async def main(): + result = await Runner.run(orchestrator_agent, input="Say 'Hello, how are you?' in Spanish.") + print(result.final_output) + + +``` + +-------------------------------- + +### Python Session Management: Add and Pop Items + +Source: https://openai.github.io/openai-agents-python/ref/memory/openai_responses_compaction_session + +Demonstrates how to extend session items and asynchronously pop an item from the session. Popping an item may reset internal caches for compaction candidates and session items. + +```python +self._session_items.extend(items) + + async def pop_item(self) -> TResponseInputItem | None: + popped = await self.underlying_session.pop_item() + if popped: + self._compaction_candidate_items = None + self._session_items = None + return popped +``` + +-------------------------------- + +### VoiceStreamEventLifecycle + +Source: https://openai.github.io/openai-agents-python/ref/voice/events + +Represents a lifecycle event streamed from the VoicePipeline, indicating the start or end of a turn or session. + +```APIDOC +## VoiceStreamEventLifecycle + +### Description +Streaming event from the VoicePipeline indicating a lifecycle change. + +### Method +N/A (Event Type) + +### Endpoint +N/A (Event Type) + +### Parameters +#### Request Body +N/A + +### Request Example +```json +{ + "type": "voice_stream_event_lifecycle", + "event": "turn_started" +} +``` + +### Response +#### Success Response (200) +- **event** (Literal["turn_started", "turn_ended", "session_ended"]) - The lifecycle event that occurred. +- **type** (Literal["voice_stream_event_lifecycle"]) - The type of event, always 'voice_stream_event_lifecycle'. + +#### Response Example +```json +{ + "type": "voice_stream_event_lifecycle", + "event": "turn_ended" +} +``` +``` + +-------------------------------- + +### Manual Conversation Management with Runner.run + +Source: https://openai.github.io/openai-agents-python/running_agents + +Demonstrates how to manage a multi-turn conversation manually using the `Runner.run` method. It shows how to capture the result of one turn and use it as input for the next, including adding new user input. This example requires the `openai` and `langchain_core` libraries. + +```python +from openai import OpenAI +from langchain_core.agents import AgentExecutor +from langchain_core.runnables import RunnableSequence +from langgraph.graph import StateGraph, END +from langgraph.checkpoint.memory import MemorySaver +from langgraph.graph.message import add_messages, BaseMessage +from typing import List, Dict, Any + +# Mock Agent and Runner for demonstration purposes +class MockAgent: + def __init__(self, name, instructions): + self.name = name + self.instructions = instructions + + async def __call__(self, messages: List[Dict[str, str]]): + # Simulate agent thinking and tool use + last_message = messages[-1] + if "Golden Gate Bridge" in last_message['content']: + return {"messages": [{"role": "assistant", "content": "San Francisco"}]}, + elif "state" in last_message['content']: + return {"messages": [{"role": "assistant", "content": "California"}]} + else: + return {"messages": [{"role": "assistant", "content": "I don't understand."}]} + +class MockRunner: + @staticmethod + async def run(agent, input_data): + # Simulate a run, returning a mock result object + if isinstance(input_data, str): + input_data = [{"role": "user", "content": input_data}] + + # Simulate agent processing + agent_output = await agent(input_data) + + # Mock RunResultBase + class MockRunResult: + def __init__(self, final_output, messages): + self.final_output = final_output + self.messages = messages + + def to_input_list(self): + # Simulate converting to input list for next turn + return self.messages + + return MockRunResult(agent_output['messages'][-1]['content'], agent_output['messages']) + +# --- Actual example code --- +async def main(): + agent = MockAgent(name="Assistant", instructions="Reply very concisely.") + + thread_id = "thread_123" # Example thread ID + # In a real scenario, you would use a tracing library like 'trace' + # For this example, we'll skip the actual trace context manager + # with trace(workflow_name="Conversation", group_id=thread_id): + + # First turn + result = await MockRunner.run(agent, "What city is the Golden Gate Bridge in?") + print(result.final_output) + # Expected output: San Francisco + + # Second turn + # Manually construct the input for the next turn + new_input = result.to_input_list() + [{"role": "user", "content": "What state is it in?"}] + result = await MockRunner.run(agent, new_input) + print(result.final_output) + # Expected output: California + +# To run this example: +# import asyncio +# asyncio.run(main()) + +``` + +-------------------------------- + +### GET /agents/tools + +Source: https://openai.github.io/openai-agents-python/ja/ref/agent + +Retrieves a list of all available agent tools, including both MCP tools and function tools. This endpoint allows you to discover the capabilities of the agent. + +```APIDOC +## GET /agents/tools + +### Description +Retrieves all agent tools, including MCP tools and function tools. + +### Method +GET + +### Endpoint +/agents/tools + +### Parameters +#### Query Parameters +- **run_context** (RunContextWrapper) - Required - The context for the current run. + +### Request Example +```json +{ + "run_context": { ... } +} +``` + +### Response +#### Success Response (200) +- **tools** (list[Tool]) - A list of available Tool objects. + +#### Response Example +```json +{ + "tools": [ + { + "name": "tool_name_1", + "description": "description_of_tool_1" + }, + { + "name": "tool_name_2", + "description": "description_of_tool_2" + } + ] +} +``` +``` + +-------------------------------- + +### Get System Prompt Dynamically (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/agent + +Asynchronously retrieves the system prompt for the agent. It handles both static string instructions and dynamically generated instructions from a callable function, returning the appropriate prompt or None if not defined. + +```python +async def get_system_prompt(self, run_context: RunContextWrapper[TContext]) -> str | None: + """Get the system prompt for the agent.""" + if isinstance(self.instructions, str): + return self.instructions + elif callable(self.instructions): + if inspect.iscoroutinefunction(self.instructions): + return await cast(Awaitable[str], self.instructions(run_context, self)) + else: + return cast(str, self.instructions(run_context, self)) + elif self.instructions is not None: + logger.error(f"Instructions must be a string or a function, got {self.instructions}") + + return None +``` + +-------------------------------- + +### GET /get_tts_model + +Source: https://openai.github.io/openai-agents-python/ja/ref/voice/model + +Retrieves a text-to-speech model by its specified name. + +```APIDOC +## GET /get_tts_model + +### Description +Get a text-to-speech model by name. + +### Method +GET + +### Endpoint +/get_tts_model + +### Parameters +#### Path Parameters +None + +#### Query Parameters +- **model_name** (str | None) - Required - The name of the model to get. + +### Request Example +```json +{ + "model_name": "your_model_name" +} +``` + +### Response +#### Success Response (200) +- **model** (TTSModel) - The text-to-speech model. + +#### Response Example +```json +{ + "model": "TTSModel object" +} +``` +``` + +-------------------------------- + +### Basic Trace Usage with Context Manager (Python) + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing/traces + +Illustrates the fundamental usage of the `trace` context manager for instrumenting a workflow. This basic example focuses on naming the trace and ensuring proper execution and cleanup of the traced operation. + +```python +from openai_agents.tracing import trace + +# Assuming Runner, validator, order_data, and processor are defined elsewhere + +with trace("Order Processing") as t: + validation_result = await Runner.run(validator, order_data) + if validation_result.approved: + await Runner.run(processor, order_data) +``` + +-------------------------------- + +### Get Session Usage + +Source: https://openai.github.io/openai-agents-python/ko/ref/extensions/memory/advanced_sqlite_session + +Retrieves cumulative usage statistics for a session, either for all branches or a specific branch if provided. + +```APIDOC +## GET /session/usage + +### Description +Get cumulative usage for session or specific branch. + +### Method +GET + +### Endpoint +/session/usage + +### Parameters +#### Query Parameters +- **branch_id** (str) - Optional - If provided, only get usage for that branch. If None, get all branches. + +### Response +#### Success Response (200) +- **requests** (int) - Total number of requests. +- **input_tokens** (int) - Total input tokens. +- **output_tokens** (int) - Total output tokens. +- **total_tokens** (int) - Total tokens (input + output). +- **total_turns** (int) - Total number of turns in the session. + +#### Response Example +```json +{ + "requests": 100, + "input_tokens": 5000, + "output_tokens": 7500, + "total_tokens": 12500, + "total_turns": 50 +} +``` + +#### Error Response (e.g., 404) +```json +{ + "message": "No usage data found." +} +``` +``` + +-------------------------------- + +### GET /get_stt_model + +Source: https://openai.github.io/openai-agents-python/ja/ref/voice/model + +Retrieves a speech-to-text model by its specified name. + +```APIDOC +## GET /get_stt_model + +### Description +Get a speech-to-text model by name. + +### Method +GET + +### Endpoint +/get_stt_model + +### Parameters +#### Path Parameters +None + +#### Query Parameters +- **model_name** (str | None) - Required - The name of the model to get. + +### Request Example +```json +{ + "model_name": "your_model_name" +} +``` + +### Response +#### Success Response (200) +- **model** (STTModel) - The speech-to-text model. + +#### Response Example +```json +{ + "model": "STTModel object" +} +``` +``` + +-------------------------------- + +### Trace Start Event + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing/provider + +Callback function invoked when a new trace begins. It iterates through registered processors and calls their respective `on_trace_start` method. + +```APIDOC +## on_trace_start + +### Description +Called when a trace is started. This method iterates through all registered trace processors and invokes their `on_trace_start` method, handling potential exceptions during the process. + +### Method +This appears to be a method within a class, likely related to tracing. + +### Endpoint +N/A (Internal method) + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +* **trace** (Trace) - Required - The trace object representing the current trace session. + +### Request Example +```json +{ + "trace": { ... Trace object details ... } +} +``` + +### Response +#### Success Response (None) +This method returns `None`. + +#### Response Example +None +``` + +-------------------------------- + +### Handoff Span API + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing + +Creates a new handoff span. This span is not started automatically; manual start/finish or a `with` statement is required. + +```APIDOC +## POST /agents/tracing/handoff_span + +### Description +Creates a new handoff span to track the transition of control between agents. The span requires manual management for starting and finishing, either through a `with` statement or explicit `start()` and `finish()` calls. + +### Method +POST + +### Endpoint +`/agents/tracing/handoff_span` + +### Parameters +#### Query Parameters +- **from_agent** (str | None) - Optional - The name of the agent initiating the handoff. +- **to_agent** (str | None) - Optional - The name of the agent receiving the handoff. +- **span_id** (str | None) - Optional - The ID for the span. If not provided, a unique ID will be generated. It is recommended to use `util.gen_span_id()` for consistent formatting. +- **parent** (Trace | Span[Any] | None) - Optional - The parent span or trace. If omitted, the current trace/span will be used as the parent. +- **disabled** (bool) - Optional - If set to `True`, the created span will not be recorded. Defaults to `False`. + +### Request Example +```json +{ + "from_agent": "AgentA", + "to_agent": "AgentB", + "span_id": "generated-span-id", + "parent": null, + "disabled": false +} +``` + +### Response +#### Success Response (200) +- **span** (Span[HandoffSpanData]) - The newly created handoff span object, which can be used to manage its lifecycle. + +#### Response Example +```json +{ + "span": { + "span_id": "generated-span-id", + "span_data": { + "from_agent": "AgentA", + "to_agent": "AgentB" + } + } +} +``` +``` + +-------------------------------- + +### GET /agents/agent_output/name + +Source: https://openai.github.io/openai-agents-python/ja/ref/agent_output + +Retrieves the name of the output type. This is a simple getter method. + +```APIDOC +## GET /agents/agent_output/name + +### Description +Retrieves the name of the output type. + +### Method +GET + +### Endpoint +/agents/agent_output/name + +### Parameters +(No parameters are required for this endpoint) + +### Request Body +(No request body is expected for this endpoint) + +### Response +#### Success Response (200) +- **name** (string) - The name of the output type. + +#### Response Example +```json +{ + "name": "string_output_type" +} +``` +``` + +-------------------------------- + +### Span Start Event + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing/provider + +Callback function triggered when a new span begins within a trace. It calls the `on_span_start` method on each registered processor. + +```APIDOC +## on_span_start + +### Description +Called when a span is started. This method iterates through all registered trace processors and invokes their `on_span_start` method, handling potential exceptions. + +### Method +This appears to be a method within a class, likely related to tracing. + +### Endpoint +N/A (Internal method) + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +* **span** (Span[Any]) - Required - The span object representing the current span. + +### Request Example +```json +{ + "span": { ... Span object details ... } +} +``` + +### Response +#### Success Response (None) +This method returns `None`. + +#### Response Example +None +``` + +-------------------------------- + +### Initialize OpenAIVoiceModelProvider in Python + +Source: https://openai.github.io/openai-agents-python/ref/voice/models/openai_provider + +Initializes the OpenAIVoiceModelProvider, which uses OpenAI models. It can accept an existing OpenAI client or configuration parameters like API key and base URL to create a new client. Lazy loading of the client is implemented to avoid errors when no API key is set. + +```python +class OpenAIVoiceModelProvider(VoiceModelProvider): + """A voice model provider that uses OpenAI models.""" + + def __init__( + self, + *, + api_key: str | None = None, + base_url: str | None = None, + openai_client: AsyncOpenAI | None = None, + organization: str | None = None, + project: str | None = None, + ) -> None: + """Create a new OpenAI voice model provider. + + Args: + api_key: The API key to use for the OpenAI client. If not provided, we will use the + default API key. + base_url: The base URL to use for the OpenAI client. If not provided, we will use the + default base URL. + openai_client: An optional OpenAI client to use. If not provided, we will create a new + OpenAI client using the api_key and base_url. + organization: The organization to use for the OpenAI client. + project: The project to use for the OpenAI client. + """ + if openai_client is not None: + assert api_key is None and base_url is None, + ("Don't provide api_key or base_url if you provide openai_client") + self._client: AsyncOpenAI | None = openai_client + else: + self._client = None + self._stored_api_key = api_key + self._stored_base_url = base_url + self._stored_organization = organization + self._stored_project = project +``` + +-------------------------------- + +### Tool Start Callback in Python + +Source: https://openai.github.io/openai-agents-python/ko/ref/lifecycle + +The `on_tool_start` asynchronous method in `RunHooksBase` is called immediately before a local tool is invoked. It provides the context, the agent using the tool, and the tool itself, allowing for pre-execution logic. + +```python +async def on_tool_start( + context: RunContextWrapper[TContext], + agent: TAgent, + tool: Tool, +) -> None: + pass +``` + +-------------------------------- + +### Initialize OpenAI Provider (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/models/multi_provider + +Initializes a new OpenAI provider with optional configurations for API key, base URL, client, organization, project, and response usage. It allows for a default provider map or a custom one to be provided. + +```python +def __init__( + self, + *, + provider_map: MultiProviderMap | None = None, + openai_api_key: str | None = None, + openai_base_url: str | None = None, + openai_client: AsyncOpenAI | None = None, + openai_organization: str | None = None, + openai_project: str | None = None, + openai_use_responses: bool | None = None, +) -> None: + """Create a new OpenAI provider. + + Args: + provider_map: A MultiProviderMap that maps prefixes to ModelProviders. If not provided, + we will use a default mapping. See the documentation for this class to see the + default mapping. + openai_api_key: The API key to use for the OpenAI provider. If not provided, we will use + the default API key. + openai_base_url: The base URL to use for the OpenAI provider. If not provided, we will + use the default base URL. + openai_client: An optional OpenAI client to use. If not provided, we will create a new + OpenAI client using the api_key and base_url. + openai_organization: The organization to use for the OpenAI provider. + openai_project: The project to use for the OpenAI provider. + openai_use_responses: Whether to use the OpenAI responses API. + """ + self.provider_map = provider_map + self.openai_provider = OpenAIProvider( + api_key=openai_api_key, + base_url=openai_base_url, + openai_client=openai_client, + organization=openai_organization, + project=openai_project, + use_responses=openai_use_responses, + ) + + self._fallback_providers: dict[str, ModelProvider] = {} + +``` + +-------------------------------- + +### Configure Agent Prompt (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/agent + +Allows dynamic configuration of an agent's instructions, tools, and other settings using a Prompt object. This feature is specifically for agents utilizing OpenAI models, enabling external configuration of agent behavior. + +```python +prompt: Prompt | None = None +``` + +-------------------------------- + +### Basic Trace Usage in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing + +Illustrates the fundamental usage of the `trace` context manager for tracking a workflow named 'Order Processing'. It shows how to initiate a trace and execute operations within its scope, ensuring that all actions are recorded for analysis. This basic example sets the foundation for more advanced tracing scenarios. + +```python +from agents.tracing.traces import trace + +# ... Runner, validator, and processor definitions ... + +with trace("Order Processing") as t: + validation_result = await Runner.run(validator, order_data) + if validation_result.approved: + await Runner.run(processor, order_data) +``` + +-------------------------------- + +### Create Streams API + +Source: https://openai.github.io/openai-agents-python/zh/ref/mcp/server + +Creates the necessary streams for the server connection. This method is part of the server setup process. + +```APIDOC +## POST /websites/openai_github_io_openai-agents-python/create_streams + +### Description +Creates the streams for the server. This method is used internally to set up communication channels. + +### Method +POST + +### Endpoint +/websites/openai_github_io_openai-agents-python/create_streams + +### Parameters +#### Query Parameters +- **url** (string) - Required - The URL for the server connection. +- **headers** (object) - Optional - Headers to include in the server request. +- **timeout** (integer) - Optional - Timeout in seconds for the initial connection. +- **sse_read_timeout** (integer) - Optional - Timeout in seconds for reading Server-Sent Events. + +### Request Body +This endpoint does not expect a request body. + +### Response +#### Success Response (200) +- **stream_tuple** (tuple) - A tuple containing the receive stream, send stream, and an optional session ID callback. + +#### Response Example +```json +{ + "stream_tuple": [ + "", + "", + " | null" + ] +} +``` +``` + +-------------------------------- + +### Define and Use a Guardrail (Python) + +Source: https://openai.github.io/openai-agents-python/quickstart + +This Python snippet defines a 'Guardrail check' agent and a custom guardrail function `homework_guardrail`. It uses Pydantic for output type definition and the `Runner` to execute the guardrail logic, checking if a user query is homework-related. + +```python +from agents import GuardrailFunctionOutput, Agent, Runner +from pydantic import BaseModel + + +class HomeworkOutput(BaseModel): + is_homework: bool + reasoning: str + +guardrail_agent = Agent( + name="Guardrail check", + instructions="Check if the user is asking about homework.", + output_type=HomeworkOutput, +) + +async def homework_guardrail(ctx, agent, input_data): + result = await Runner.run(guardrail_agent, input_data, context=ctx.context) + final_output = result.final_output_as(HomeworkOutput) + return GuardrailFunctionOutput( + output_info=final_output, + tripwire_triggered=not final_output.is_homework, + ) +``` + +-------------------------------- + +### Get Realtime Session Configuration Details (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/openai_realtime + +Retrieves and constructs the session configuration for a realtime OpenAI agent session. This method processes various audio input and output parameters, including format, noise reduction, transcription, turn detection, and voice settings, applying defaults where necessary. It ensures all required parameters are correctly formatted for the OpenAI API. + +```python +def _get_session_config( + self, + model_settings: RealtimeSessionModelSettings + ) -> OpenAISessionCreateRequest: + """Get the session config.""" + audio_input_args: dict[str, Any] = {} + audio_output_args: dict[str, Any] = {} + + audio_config = model_settings.get("audio") + audio_config_mapping = audio_config if isinstance(audio_config, Mapping) else None + input_audio_config: Mapping[str, Any] = ( + cast(Mapping[str, Any], audio_config_mapping.get("input", {})) + if audio_config_mapping + else {} + ) + output_audio_config: Mapping[str, Any] = ( + cast(Mapping[str, Any], audio_config_mapping.get("output", {})) + if audio_config_mapping + else {} + ) + + input_format_source: FormatInput = ( + input_audio_config.get("format") if input_audio_config else None + ) + if input_format_source is None: + if self._call_id: + input_format_source = model_settings.get("input_audio_format") + else: + input_format_source = model_settings.get( + "input_audio_format", DEFAULT_MODEL_SETTINGS.get("input_audio_format") + ) + audio_input_args["format"] = to_realtime_audio_format(input_format_source) + + if "noise_reduction" in input_audio_config: + audio_input_args["noise_reduction"] = input_audio_config.get("noise_reduction") + elif "input_audio_noise_reduction" in model_settings: + audio_input_args["noise_reduction"] = model_settings.get("input_audio_noise_reduction") + + if "transcription" in input_audio_config: + audio_input_args["transcription"] = input_audio_config.get("transcription") + elif "input_audio_transcription" in model_settings: + audio_input_args["transcription"] = model_settings.get("input_audio_transcription") + else: + audio_input_args["transcription"] = DEFAULT_MODEL_SETTINGS.get( + "input_audio_transcription" + ) + + if "turn_detection" in input_audio_config: + audio_input_args["turn_detection"] = self._normalize_turn_detection_config( + input_audio_config.get("turn_detection") + ) + elif "turn_detection" in model_settings: + audio_input_args["turn_detection"] = self._normalize_turn_detection_config( + model_settings.get("turn_detection") + ) + else: + audio_input_args["turn_detection"] = DEFAULT_MODEL_SETTINGS.get("turn_detection") + + requested_voice = output_audio_config.get("voice") if output_audio_config else None + audio_output_args["voice"] = requested_voice or model_settings.get( + "voice", DEFAULT_MODEL_SETTINGS.get("voice") + ) + + output_format_source: FormatInput = ( + output_audio_config.get("format") if output_audio_config else None + ) + if output_format_source is None: + if self._call_id: + output_format_source = model_settings.get("output_audio_format") + else: + output_format_source = model_settings.get( + "output_audio_format", DEFAULT_MODEL_SETTINGS.get("output_audio_format") + ) + audio_output_args["format"] = to_realtime_audio_format(output_format_source) + + if "speed" in output_audio_config: + audio_output_args["speed"] = output_audio_config.get("speed") + elif "speed" in model_settings: + audio_output_args["speed"] = model_settings.get("speed") + + output_modalities = ( + model_settings.get("output_modalities") + or model_settings.get("modalities") + or DEFAULT_MODEL_SETTINGS.get("modalities") + ) + + # Construct full session object. `type` will be excluded at serialization time for updates. + session_create_request = OpenAISessionCreateRequest( + type="realtime", + model=(model_settings.get("model_name") or self.model) or "gpt-realtime", + output_modalities=output_modalities, + ) + return session_create_request +``` + +-------------------------------- + +### Get Prompt - Python + +Source: https://openai.github.io/openai-agents-python/zh/ref/mcp/server + +Abstract method to retrieve a specific prompt by name from the server, optionally with arguments. It returns a GetPromptResult. + +```python +@abc.abstractmethod +async def get_prompt( + self, name: str, arguments: dict[str, Any] | None = None +) -> GetPromptResult: + """Get a specific prompt from the server.""" + pass + +``` + +-------------------------------- + +### GET /sessions/{session_id}/items + +Source: https://openai.github.io/openai-agents-python/ja/ref/memory/session + +Retrieve the conversation history for a given session. + +```APIDOC +## GET /sessions/{session_id}/items + +### Description +Retrieve the conversation history for this session. + +### Method +GET + +### Endpoint +/sessions/{session_id}/items + +### Parameters +#### Path Parameters +- **session_id** (str) - Required - The ID of the session to retrieve history from. + +#### Query Parameters +- **limit** (int) - Optional - Maximum number of items to retrieve. If None, retrieves all items. When specified, returns the latest N items in chronological order. + +### Request Example +GET /sessions/sess_abc/items?limit=10 + +### Response +#### Success Response (200) +- **items** (list[TResponseInputItem]) - List of input items representing the conversation history + +#### Response Example +{ + "items": [ + { + "content": "Hello!", + "role": "user" + }, + { + "content": "Hi there!", + "role": "assistant" + } + ] +} +``` + +-------------------------------- + +### Create Basic Hosted MCP Tool in Python + +Source: https://openai.github.io/openai-agents-python/mcp + +Demonstrates how to create a basic hosted MCP tool by adding a `HostedMCPTool` to an agent's tools list. This forwards server labels and metadata to the Responses API, allowing the model to invoke remote tools without direct Python callbacks. Requires the `agents` library and `asyncio`. + +```python +import asyncio + +from agents import Agent, HostedMCPTool, Runner + +async def main() -> None: + agent = Agent( + name="Assistant", + tools=[ + HostedMCPTool( + tool_config={ + "type": "mcp", + "server_label": "gitmcp", + "server_url": "https://gitmcp.io/openai/codex", + "require_approval": "never", + } + ) + ], + ) + + result = await Runner.run(agent, "Which language is this repository written in?") + print(result.final_output) + +asyncio.run(main()) +``` + +-------------------------------- + +### Span Processing Methods + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing/processor_interface + +Methods called during the lifecycle of a span, including when a span starts and ends. + +```APIDOC +## POST /websites/openai_github_io_openai-agents-python/on_span_start + +### Description +Called when a new span begins execution. This method is synchronous and should return quickly to avoid blocking execution. Spans are automatically nested under the current trace or span. + +### Method +POST + +### Endpoint +/websites/openai_github_io_openai-agents-python/on_span_start + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **span** (Span[Any]) - Required - The span that started. Contains operation details and context. + +### Request Example +```json +{ + "span": { + "operation_name": "example_operation", + "context": {}, + "start_time": 1678886400.0 + } +} +``` + +### Response +#### Success Response (200) +- **status** (string) - Indicates successful processing. + +#### Response Example +```json +{ + "status": "processed" +} +``` + +## POST /websites/openai_github_io_openai-agents-python/on_span_end + +### Description +Called when a span completes execution. This method is synchronous, should not block or raise exceptions, and is a good time to export or process the individual span. + +### Method +POST + +### Endpoint +/websites/openai_github_io_openai-agents-python/on_span_end + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **span** (Span[Any]) - Required - The completed span containing execution results. + +### Request Example +```json +{ + "span": { + "operation_name": "example_operation", + "context": {}, + "start_time": 1678886400.0, + "end_time": 1678886460.0, + "results": {} + } +} +``` + +### Response +#### Success Response (200) +- **status** (string) - Indicates successful processing. + +#### Response Example +```json +{ + "status": "processed" +} +``` +``` + +-------------------------------- + +### get_system_prompt Method + +Source: https://openai.github.io/openai-agents-python/ref/realtime/agent + +Retrieves the system prompt for the agent, dynamically generating it if instructions are provided as a function. + +```APIDOC +## GET /get_system_prompt + +### Description +Retrieves the system prompt that will be used for the agent. If the `instructions` attribute is a string, it is returned directly. If `instructions` is a callable function, it is invoked with the current run context and agent instance to generate the prompt dynamically. + +### Method +GET + +### Endpoint +`/get_system_prompt` + +### Parameters +#### Query Parameters +- **run_context** (RunContextWrapper) - The wrapper for the current run context, passed to the instructions function if it's dynamic. + +### Response +#### Success Response (200) +- **system_prompt** (str | None) - The generated system prompt string, or None if no instructions are configured. +``` + +-------------------------------- + +### GET /api/tools/mcp + +Source: https://openai.github.io/openai-agents-python/ref/agent + +Fetches the available tools from the MCP servers. This endpoint retrieves a list of tools that are managed by the MCP (Multi-Cloud Platform). + +```APIDOC +## GET /api/tools/mcp + +### Description +Fetches the available tools from the MCP servers. + +### Method +GET + +### Endpoint +/api/tools/mcp + +### Parameters +#### Query Parameters +- **run_context** (RunContextWrapper) - Required - The context for the current run. + +### Request Example +```json +{ + "run_context": { ... } +} +``` + +### Response +#### Success Response (200) +- **tools** (list[Tool]) - A list of available MCP tools. + +#### Response Example +```json +{ + "tools": [ + { + "name": "example_tool", + "description": "An example tool", + "parameters": { ... } + } + ] +} +``` +``` + +-------------------------------- + +### Trace Processing Methods + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing/processor_interface + +Methods called during the lifecycle of a trace, including when a trace starts and ends. + +```APIDOC +## POST /websites/openai_github_io_openai-agents-python/on_trace_start + +### Description +Called when a new trace begins execution. This method is synchronous and should return quickly to avoid blocking execution. + +### Method +POST + +### Endpoint +/websites/openai_github_io_openai-agents-python/on_trace_start + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **trace** (Trace) - Required - The trace that started. Contains workflow name and metadata. + +### Request Example +```json +{ + "trace": { + "workflow_name": "example_workflow", + "metadata": {} + } +} +``` + +### Response +#### Success Response (200) +- **status** (string) - Indicates successful processing. + +#### Response Example +```json +{ + "status": "processed" +} +``` + +## POST /websites/openai_github_io_openai-agents-python/on_trace_end + +### Description +Called when a trace completes execution. This method is synchronous and is a good time to export or process the complete trace and handle cleanup of any trace-specific resources. + +### Method +POST + +### Endpoint +/websites/openai_github_io_openai-agents-python/on_trace_end + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **trace** (Trace) - Required - The completed trace containing all spans and results. + +### Request Example +```json +{ + "trace": { + "workflow_name": "example_workflow", + "metadata": {}, + "spans": [], + "results": {} + } +} +``` + +### Response +#### Success Response (200) +- **status** (string) - Indicates successful processing. + +#### Response Example +```json +{ + "status": "processed" +} +``` +``` + +-------------------------------- + +### MCPServerStdio Constructor + +Source: https://openai.github.io/openai-agents-python/ko/ref/mcp/server + +Initializes a new instance of the MCPServerStdio class. This constructor sets up the MCP server to communicate via standard input and output, configuring parameters such as the command to execute, arguments, environment variables, and working directory. + +```APIDOC +## MCPServerStdio.__init__ + +### Description +Create a new MCP server based on the stdio transport. + +### Method +Constructor + +### Endpoint +N/A (Class method) + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **params** (MCPServerStdioParams) - Required - The params that configure the server. This includes the command to run to start the server, the args to pass to the command, the environment variables to set for the server, the working directory to use when spawning the process, and the text encoding used when sending/receiving messages to the server. +- **cache_tools_list** (bool) - Optional - Whether to cache the tools list. If `True`, the tools list will be cached and only fetched from the server once. If `False`, the tools list will be fetched from the server on each call to `list_tools()`. The cache can be invalidated by calling `invalidate_tools_cache()`. You should set this to `True` if you know the server will not change its tools list, because it can drastically improve latency (by avoiding a round-trip to the server every time). +- **name** (str | None) - Optional - A readable name for the server. If not provided, we'll create one from the command. +- **client_session_timeout_seconds** (float | None) - Optional - the read timeout passed to the MCP ClientSession. Defaults to 5. +- **tool_filter** (ToolFilter) - Optional - The tool filter to use for filtering tools. +- **use_structured_content** (bool) - Optional - Whether to use `tool_result.structured_content` when calling an MCP tool. Defaults to False for backwards compatibility - most MCP servers still include the structured content in the `tool_result.content`, and using it by default will cause duplicate content. You can set this to True if you know the server will not duplicate the structured content in the `tool_result.content`. +- **max_retry_attempts** (int) - Optional - Number of times to retry failed list_tools/call_tool calls. Defaults to no retries. +- **retry_backoff_seconds_base** (float) - Optional - The base delay, in seconds, for exponential backoff between retries. Defaults to 1.0. +- **message_handler** (MessageHandlerFnT | None) - Optional - Optional handler invoked for session messages as delivered by the ClientSession. + +### Request Example +None + +### Response +#### Success Response (200) +None + +#### Response Example +None +``` + +-------------------------------- + +### mcp_tools_span + +Source: https://openai.github.io/openai-agents-python/zh/ref/tracing + +Creates a new MCP list tools span. This span is not started automatically and requires manual start/finish calls or usage within a 'with' statement. + +```APIDOC +## mcp_tools_span + +### Description +Creates a new MCP list tools span. The span will not be started automatically, you should either do `with mcp_tools_span() ...` or call `span.start()` + `span.finish()` manually. + +### Method +(Not applicable - this is a Python function) + +### Endpoint +(Not applicable - this is a Python function) + +### Parameters +#### Path Parameters +(None) + +#### Query Parameters +(None) + +#### Request Body +(None) + +### Request Example +```python +# Using a 'with' statement +with mcp_tools_span(server="my_server", result=["tool1", "tool2"]): + # Your code here + pass + +# Manual start and finish +span = mcp_tools_span(server="my_server", result=["tool1", "tool2"]) +span.start() +# Your code here +span.finish() +``` + +### Response +#### Success Response +- **Span** (Span[MCPListToolsSpanData]) - The created MCP list tools span. + +#### Response Example +(The response is a Span object, not a JSON payload. Example usage is shown in Request Example.) +``` + +-------------------------------- + +### Implement on_start Hook in AgentHooksBase (Python) + +Source: https://openai.github.io/openai-agents-python/zh/ref/lifecycle + +The `on_start` method is an asynchronous hook in `AgentHooksBase`, called before an agent is invoked. This hook is triggered each time the running agent is changed to this specific agent, receiving the agent hook context and the agent instance. + +```python +async def on_start( + context: AgentHookContext[TContext], agent: TAgent +) -> None: + pass +``` + +-------------------------------- + +### generation_span + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing + +Creates a new generation span to capture model generation details. This span needs to be explicitly started and finished. + +```APIDOC +## POST /generation_span + +### Description +Create a new generation span. The span will not be started automatically, you should either do `with generation_span() ...` or call `span.start()` + `span.finish()` manually. This span captures the details of a model generation, including the input message sequence, any generated outputs, the model name and configuration, and usage data. If you only need to capture a model response identifier, use `response_span()` instead. + +### Method +POST + +### Endpoint +/generation_span + +### Parameters +#### Query Parameters +- **input** (Sequence[Mapping[str, Any]] | None) - Optional - The sequence of input messages sent to the model. +- **output** (Sequence[Mapping[str, Any]] | None) - Optional - The sequence of output messages received from the model. +- **model** (str | None) - Optional - The model identifier used for the generation. +- **model_config** (Mapping[str, Any] | None) - Optional - The model configuration (hyperparameters) used. +- **usage** (dict[str, Any] | None) - Optional - A dictionary of usage information (input tokens, output tokens, etc.). +- **span_id** (str | None) - Optional - The ID of the span. Optional. If not provided, we will generate an ID. We recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are correctly formatted. +- **parent** (Trace | Span[Any] | None) - Optional - The parent span or trace. If not provided, we will automatically use the current trace/span as the parent. +- **disabled** (bool) - Optional - If True, we will return a Span but the Span will not be recorded. Default: False + +### Response +#### Success Response (200) +- **Span[GenerationSpanData]** - The newly created generation span. + +#### Response Example +```json +{ + "example": "Span[GenerationSpanData]" +} +``` +``` + +-------------------------------- + +### Create Speech Span in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/create + +This Python function, speech_span, is designed to create a tracing span for speech-related operations. It accepts various parameters to define the speech model, input text, output format, and associated tracing metadata. The span is not automatically started and requires manual initiation via a `with` statement or explicit `start()` and `finish()` calls. It returns a Span object containing SpeechSpanData. + +```python +def speech_span( + model: str | None = None, + input: str | None = None, + output: str | None = None, + output_format: str | None = "pcm", + model_config: Mapping[str, Any] | None = None, + first_content_at: str | None = None, + span_id: str | None = None, + parent: Trace | Span[Any] | None = None, + disabled: bool = False, +) -> Span[SpeechSpanData]: + """Create a new speech span. The span will not be started automatically, you should either do + `with speech_span() ...` or call `span.start()` + `span.finish()` manually. + + Args: + model: The name of the model used for the text-to-speech. + input: The text input of the text-to-speech. + output: The audio output of the text-to-speech as base64 encoded string of PCM audio bytes. + output_format: The format of the audio output (defaults to "pcm"). + model_config: The model configuration (hyperparameters) used. + first_content_at: The time of the first byte of the audio output. + span_id: The ID of the span. Optional. If not provided, we will generate an ID. We + recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are + correctly formatted. + parent: The parent span or trace. If not provided, we will automatically use the current + trace/span as the parent. + disabled: If True, we will return a Span but the Span will not be recorded. + """ + return get_trace_provider().create_span( + span_data=SpeechSpanData( + model=model, + input=input, + output=output, + output_format=output_format, + model_config=model_config, + first_content_at=first_content_at, + ), + span_id=span_id, + parent=parent, + disabled=disabled, + ) + +``` + +-------------------------------- + +### Run Agent Workflow in Streaming Mode (Python) + +Source: https://openai.github.io/openai-agents-python/ref/run + +Executes an agent workflow starting from a specified agent in streaming mode. The method returns a result object with a streaming method for semantic events. It handles agent loops, tool calls, and exceptions such as max turns exceeded or guardrail triggers. Key parameters include the starting agent, initial input, context, maximum turns, and optional hooks for lifecycle events. + +```python +from typing import TypeVar, Optional, List + +# Assuming Agent, AgentOutput, RunHooks, RunConfig, Session, RunResultStreaming, DEFAULT_MAX_TURNS, TResponseInputItem are defined elsewhere + +class Agent: + # ... (Agent class definition) + pass + +TContext = TypeVar('TContext') + +class RunResultStreaming: + # ... (RunResultStreaming class definition) + pass + +class RunHooks: + # ... (RunHooks class definition) + pass + +class RunConfig: + # ... (RunConfig class definition) + pass + +class Session: + # ... (Session class definition) + pass + +class TResponseInputItem: + # ... (TResponseInputItem class definition) + pass + +DEFAULT_MAX_TURNS = 10 # Example default value + +class YourAgentClass: # Replace with the actual class name + @classmethod + def run_streamed( + cls, + starting_agent: Agent[TContext], + input: str | List[TResponseInputItem], + context: TContext | None = None, + max_turns: int = DEFAULT_MAX_TURNS, + hooks: RunHooks[TContext] | None = None, + run_config: RunConfig | None = None, + previous_response_id: str | None = None, + auto_previous_response_id: bool = False, + conversation_id: str | None = None, + session: Session | None = None, + ) -> RunResultStreaming: + """ + Run a workflow starting at the given agent in streaming mode. + + The returned result object contains a method you can use to stream semantic + events as they are generated. + + The agent will run in a loop until a final output is generated. The loop runs like so: + + 1. The agent is invoked with the given input. + 2. If there is a final output (i.e. the agent produces something of type + `agent.output_type`), the loop terminates. + 3. If there's a handoff, we run the loop again, with the new agent. + 4. Else, we run tool calls (if any), and re-run the loop. + + In two cases, the agent may raise an exception: + + 1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised. + 2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered + exception is raised. + + Note: + Only the first agent's input guardrails are run. + + Args: + starting_agent: The starting agent to run. + input: The initial input to the agent. You can pass a single string for a + user message, or a list of input items. + context: The context to run the agent with. + max_turns: The maximum number of turns to run the agent for. A turn is + defined as one AI invocation (including any tool calls that might occur). + hooks: An object that receives callbacks on various lifecycle events. + run_config: Global settings for the entire agent run. + previous_response_id: The ID of the previous response, if using OpenAI models via the Responses API, this allows you to skip passing in input from the previous turn. + auto_previous_response_id: Whether to automatically use the previous response ID. + conversation_id: The ID of the stored conversation, if any. + session: A session for automatic conversation history management. + + Returns: + A result object that contains data about the run, as well as a method to + stream events. + """ + # Implementation details would go here + pass + +``` + +-------------------------------- + +### GET /api/tools/all + +Source: https://openai.github.io/openai-agents-python/ref/agent + +Retrieves all agent tools, including both MCP tools and function tools. This endpoint provides a comprehensive list of all tools available to the agent. + +```APIDOC +## GET /api/tools/all + +### Description +All agent tools, including MCP tools and function tools. + +### Method +GET + +### Endpoint +/api/tools/all + +### Parameters +#### Query Parameters +- **run_context** (RunContextWrapper) - Required - The context for the current run. + +### Request Example +```json +{ + "run_context": { ... } +} +``` + +### Response +#### Success Response (200) +- **tools** (list[Tool]) - A list of all available agent tools. + +#### Response Example +```json +{ + "tools": [ + { + "name": "mcp_tool_example", + "description": "An example MCP tool", + "parameters": { ... } + }, + { + "name": "function_tool_example", + "description": "An example function tool", + "parameters": { ... } + } + ] +} +``` +``` + +-------------------------------- + +### AdvancedSQLiteSession Initialization + +Source: https://openai.github.io/openai-agents-python/ko/ref/extensions/memory/advanced_sqlite_session + +Initializes the AdvancedSQLiteSession with session details, database path, table creation option, and logger. + +```APIDOC +## AdvancedSQLiteSession `__init__` + +### Description +Initializes the AdvancedSQLiteSession with session details, database path, table creation option, and logger. + +### Method +`__init__` + +### Parameters +#### Keyword Parameters +- **session_id** (str) - Required - The ID of the session +- **db_path** (str | Path) - Optional - The path to the SQLite database file. Defaults to `:memory:` for in-memory storage +- **create_tables** (bool) - Optional - Whether to create the structure tables +- **logger** (Logger | None) - Optional - The logger to use. Defaults to the module logger +- **kwargs** () - Optional - Additional keyword arguments to pass to the superclass + +### Request Example +```python +# Example Usage: +# from agents.extensions.memory.advanced_sqlite_session import AdvancedSQLiteSession +# session = AdvancedSQLiteSession(session_id="my_session", db_path="my_database.db", create_tables=True) +``` + +### Response +This is a constructor and does not return a value directly. +``` + +-------------------------------- + +### Run Compaction using OpenAIResponsesCompactionArgs in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/memory/openai_responses_compaction_session + +The `run_compaction` function executes the compaction process using the `responses.compact` API. It takes an optional `OpenAIResponsesCompactionArgs` object to configure the compaction mode, response ID, and storage behavior. It returns `None` and handles the logic for deciding whether to compact, compacting the responses, and updating the session items. + +```python +async def run_compaction(self, args: OpenAIResponsesCompactionArgs | None = None) -> None: + """Run compaction using responses.compact API.""" + if args and args.get("response_id"): + self._response_id = args["response_id"] + requested_mode = args.get("compaction_mode") if args else None + if args and "store" in args: + store = args["store"] + if store is False and self._response_id: + self._last_unstored_response_id = self._response_id + elif store is True and self._response_id == self._last_unstored_response_id: + self._last_unstored_response_id = None + else: + store = None + resolved_mode = self._resolve_compaction_mode_for_response( + response_id=self._response_id, + store=store, + requested_mode=requested_mode, + ) + + if resolved_mode == "previous_response_id" and not self._response_id: + raise ValueError( + "OpenAIResponsesCompactionSession.run_compaction requires a response_id " + "when using previous_response_id compaction." + ) + + compaction_candidate_items, session_items = await self._ensure_compaction_candidates() + + force = args.get("force", False) if args else False + should_compact = force or self.should_trigger_compaction( + { + "response_id": self._response_id, + "compaction_mode": resolved_mode, + "compaction_candidate_items": compaction_candidate_items, + "session_items": session_items, + } + ) + + if not should_compact: + logger.debug( + f"skip: decision hook declined compaction for {self._response_id} " + f"(mode={resolved_mode})" + ) + return + + self._deferred_response_id = None + logger.debug( + f"compact: start for {self._response_id} using {self.model} (mode={resolved_mode})" + ) + + compact_kwargs: dict[str, Any] = {"model": self.model} + if resolved_mode == "previous_response_id": + compact_kwargs["previous_response_id"] = self._response_id + else: + compact_kwargs["input"] = session_items + + compacted = await self.client.responses.compact(**compact_kwargs) + + await self.underlying_session.clear_session() + output_items: list[TResponseInputItem] = [] + if compacted.output: + for item in compacted.output: + if isinstance(item, dict): + output_items.append(item) + else: + # Suppress Pydantic literal warnings: responses.compact can return + # user-style input_text content inside ResponseOutputMessage. + output_items.append( + item.model_dump(exclude_unset=True, warnings=False) # type: ignore + ) + + if output_items: + await self.underlying_session.add_items(output_items) + + self._compaction_candidate_items = select_compaction_candidate_items(output_items) + self._session_items = output_items + + logger.debug( + f"compact: done for {self._response_id} " + f"(mode={resolved_mode}, output={len(output_items)}, " + f"candidates={len(self._compaction_candidate_items)})" + ) + +``` + +-------------------------------- + +### Span Management Methods API + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/spans + +This section details the methods for managing the lifecycle of a span, including starting and finishing its execution. + +```APIDOC +## Span Management Methods API + +### Description +This API group covers the essential methods for controlling the execution lifecycle of a span within the tracing system. + +### Methods + +#### `start` + +##### Description +Starts the span's execution. + +##### Method +`start(mark_as_current: bool = False)` + +##### Parameters +- **mark_as_current** (`bool`) - Optional - If true, the span will be marked as the current span. Defaults to `False`. + +#### `finish` + +##### Description +Finishes the span's execution. + +##### Method +`finish(reset_current: bool = False) -> None` + +##### Parameters +- **reset_current** (`bool`) - Optional - If true, the span will be reset as the current span. Defaults to `False`. +``` + +-------------------------------- + +### Trace Lifecycle Management API + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing/traces + +Manages the lifecycle of a trace, including starting, finishing, and exporting trace data. + +```APIDOC +## Trace Lifecycle Management API + +### Description +This API allows for the management of a trace's lifecycle, including initiating a trace, completing it, and exporting its collected data. It also handles the context management of traces. + +### Endpoints + +#### `start` + +##### Description +Start the trace and optionally mark it as the current trace in the execution context. + +##### Method +POST + +##### Endpoint +`/start` + +##### Parameters +* **mark_as_current** (bool) - Optional - If true, marks this trace as the current trace in the execution context. Defaults to `False`. + +##### Notes +* Must be called before any spans can be added. +* Only one trace can be current at a time. +* Thread-safe when using `mark_as_current`. + +#### `finish` + +##### Description +Finish the trace and optionally reset the current trace. + +##### Method +POST + +##### Endpoint +`/finish` + +##### Parameters +* **reset_current** (bool) - Optional - If true, resets the current trace to the previous trace in the execution context. Defaults to `False`. + +##### Notes +* Must be called to complete the trace. +* Finalizes all open spans. +* Thread-safe when using `reset_current`. + +#### `export` + +##### Description +Export the trace data as a serializable dictionary. + +##### Method +GET + +##### Endpoint +`/export` + +##### Response +* **export_data** (dict[str, Any] | None) - A dictionary containing trace data, or None if tracing is disabled. Includes all spans and their data, used for sending traces to backends, and may include metadata and group ID. +``` + +-------------------------------- + +### Get ModelProvider by Prefix + +Source: https://openai.github.io/openai-agents-python/ko/ref/models/multi_provider + +The `get_provider` method retrieves the `ModelProvider` associated with a specific prefix. If the prefix is not found in the map, it returns `None`. + +```python +def get_provider(self, prefix: str) -> ModelProvider | None: + """Returns the ModelProvider for the given prefix. + + Args: + prefix: The prefix of the model name e.g. "openai" or "my_prefix". + """ + return self._mapping.get(prefix) +``` + +-------------------------------- + +### Customize Python Logging (Python) + +Source: https://openai.github.io/openai-agents-python/config + +Provides an example of how to customize the SDK's logging behavior by directly interacting with Python's built-in logging module. This allows for fine-grained control over log levels and handlers. + +```python +import logging + +logger = logging.getLogger("openai.agents") # or openai.agents.tracing for the Tracing logger + +# To make all logs show up +logger.setLevel(logging.DEBUG) +# To make info and above show up +logger.setLevel(logging.INFO) +# To make warning and above show up +logger.setLevel(logging.WARNING) +# etc + +# You can customize this as needed, but this will output to `stderr` by default +logger.addHandler(logging.StreamHandler()) + +``` + +-------------------------------- + +### MCP Tools Span Creation + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing + +Creates a new span for MCP list tools calls. This span is not started automatically and requires manual start/finish or usage within a `with` statement. + +```APIDOC +## POST /api/tracing/mcp_tools_span + +### Description +Creates a new MCP list tools span. The span will not be started automatically; you should either use `with mcp_tools_span() ...` or call `span.start()` and `span.finish()` manually. + +### Method +POST + +### Endpoint +/api/tracing/mcp_tools_span + +### Parameters +#### Query Parameters +- **server** (str | None) - Optional - The name of the MCP server. +- **result** (list[str] | None) - Optional - The result of the MCP list tools call. +- **span_id** (str | None) - Optional - The ID of the span. If not provided, an ID will be generated. It is recommended to use `util.gen_span_id()` for correct formatting. +- **parent** (Trace | Span[Any] | None) - Optional - The parent span or trace. If not provided, the current trace/span will be used. +- **disabled** (bool) - Optional - If True, the Span will be created but not recorded. Defaults to `False`. + +### Request Example +```json +{ + "server": "mcp_server_1", + "result": ["tool1", "tool2"], + "span_id": "generated_span_id", + "parent": null, + "disabled": false +} +``` + +### Response +#### Success Response (200) +- **Span[MCPListToolsSpanData]** - The created span object, containing MCP list tools span data. + +#### Response Example +```json +{ + "span_id": "created_span_id", + "data": { + "server": "mcp_server_1", + "result": ["tool1", "tool2"] + } +} +``` +``` + +-------------------------------- + +### Optional on_start method for VoiceWorkflowBase - Python + +Source: https://openai.github.io/openai-agents-python/ref/voice/workflow + +The `on_start` method in `VoiceWorkflowBase` provides an optional hook for executing code before any user interaction. It's an asynchronous generator that can yield initial speech content, such as greetings or instructions. + +```python +async def on_start(self) -> AsyncIterator[str]: + """ + Optional method that runs before any user input is received. Can be used + to deliver a greeting or instruction via TTS. Defaults to doing nothing. + """ + return + yield + +``` + +-------------------------------- + +### response_span + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing + +Creates a new span for an API response. This span is not started automatically and requires manual start/finish or use within a `with` statement. + +```APIDOC +## response_span + +### Description +Create a new response span. The span will not be started automatically, you should either do `with response_span() ...` or call `span.start()` + `span.finish()` manually. + +### Method +There is no direct HTTP method for this function as it is a Python utility for tracing. + +### Endpoint +N/A (Python function) + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +None + +### Request Example +```python +from agents.tracing import response_span +from openai import OpenAI + +# Assuming 'client' is an initialized OpenAI client +# response = client.chat.completions.create(...) + +# Example using a 'with' statement +# with response_span(response=response) as span: +# # Span is active here +# pass # Span is automatically finished + +# Example with manual start and finish +# span = response_span(response=response) +# span.start() +# # Do work +# span.finish() +``` + +### Response +#### Success Response (200) +This function returns a `Span` object which can be used for tracing. +- **Span** (Span[ResponseSpanData]) - The created span object. + +#### Response Example +```json +{ + "span_id": "generated_or_provided_id", + "parent_id": "optional_parent_id", + "name": "response", + "start_time": "timestamp", + "end_time": "timestamp", + "status": "COMPLETED/FAILED", + "data": { + "response": { + "id": "response_id", + "object": "chat.completion", + "created": 1677652288, + "model": "gpt-3.5-turbo-0613", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "Hello there! How can I assist you today?" + }, + "finish_reason": "stop" + } + ], + "usage": { + "prompt_tokens": 9, + "completion_tokens": 10, + "total_tokens": 19 + } + } + } +} +``` +``` + +-------------------------------- + +### Get Turn Usage + +Source: https://openai.github.io/openai-agents-python/zh/ref/extensions/memory/advanced_sqlite_session + +Retrieves usage statistics for specific turns or all turns within a session and branch, including detailed JSON token information. + +```APIDOC +## GET /get_turn_usage + +### Description +Retrieves usage statistics by turn with full JSON token details. This method can fetch data for a specific turn or all turns within a given branch of a session. + +### Method +GET + +### Endpoint +/get_turn_usage + +### Parameters +#### Query Parameters +- **user_turn_number** (int) - Optional - Specific turn to get usage for. If None, returns all turns. +- **branch_id** (string) - Optional - Branch to get usage from (defaults to the current branch if None). + +### Request Example +```json +{ + "user_turn_number": 5, + "branch_id": "branch-123" +} +``` + +### Response +#### Success Response (200) +- **requests** (integer) - The number of requests made in the turn. +- **input_tokens** (integer) - The total number of input tokens used. +- **output_tokens** (integer) - The total number of output tokens used. +- **total_tokens** (integer) - The total number of tokens used (input + output). +- **input_tokens_details** (object) - Detailed breakdown of input tokens (can be null if not available or parsing fails). +- **output_tokens_details** (object) - Detailed breakdown of output tokens (can be null if not available or parsing fails). + +If `user_turn_number` is not provided, the response will be a list of these objects, each representing a turn. + +#### Response Example (Single Turn) +```json +{ + "requests": 1, + "input_tokens": 150, + "output_tokens": 200, + "total_tokens": 350, + "input_tokens_details": { + "model_a": 100, + "model_b": 50 + }, + "output_tokens_details": { + "model_c": 150, + "model_d": 50 + } +} +``` + +#### Response Example (All Turns) +```json +[ + { + "user_turn_number": 1, + "requests": 1, + "input_tokens": 100, + "output_tokens": 120, + "total_tokens": 220, + "input_tokens_details": null, + "output_tokens_details": null + }, + { + "user_turn_number": 2, + "requests": 2, + "input_tokens": 200, + "output_tokens": 250, + "total_tokens": 450, + "input_tokens_details": { + "model_a": 200 + }, + "output_tokens_details": { + "model_c": 250 + } + } +] +``` +``` + +-------------------------------- + +### Get Session Items - Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/memory/openai_responses_compaction_session + +Retrieves items from the underlying session, optionally applying a limit. This method allows fetching the current state of the session's items. + +```python +async def get_items(self, limit: int | None = None) -> list[TResponseInputItem]: + return await self.underlying_session.get_items(limit) +``` + +-------------------------------- + +### GET /session/usage + +Source: https://openai.github.io/openai-agents-python/zh/ref/extensions/memory/advanced_sqlite_session + +Retrieves cumulative usage statistics for the current session. This endpoint can optionally filter usage by a specific branch ID. + +```APIDOC +## GET /session/usage + +### Description +Get cumulative usage for the current session or a specific branch within the session. Usage statistics include total requests, input tokens, output tokens, total tokens, and total turns. + +### Method +GET + +### Endpoint +/session/usage + +### Parameters +#### Query Parameters +- **branch_id** (str) - Optional - If provided, only return usage statistics for this specific branch. If not provided, return usage statistics for all branches in the session. + +### Request Example +(No request body for GET requests) + +### Response +#### Success Response (200) +- **requests** (int) - The total number of requests made. +- **input_tokens** (int) - The total number of input tokens used. +- **output_tokens** (int) - The total number of output tokens generated. +- **total_tokens** (int) - The total number of tokens used (input + output). +- **total_turns** (int) - The total number of turns in the session or branch. + +#### Response Example +```json +{ + "requests": 150, + "input_tokens": 15000, + "output_tokens": 20000, + "total_tokens": 35000, + "total_turns": 75 +} +``` + +#### Error Response (e.g., if no usage data is found) +(No specific error response documented, likely returns an empty object or null based on implementation) + +#### Response Example (No Usage Data) +```json +null +``` +``` + +-------------------------------- + +### MCPServerStdio Constructor + +Source: https://openai.github.io/openai-agents-python/ref/mcp/server + +Initializes a new MCPServerStdio instance. This server uses the stdio transport for communication and requires parameters specifying the command to run, arguments, environment variables, working directory, and text encoding. + +```APIDOC +## MCPServerStdio Constructor + +### Description +Initializes a new MCPServerStdio instance. This server uses the stdio transport for communication and requires parameters specifying the command to run, arguments, environment variables, working directory, and text encoding. + +### Method +__init__ + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **params** (MCPServerStdioParams) - Required - The parameters that configure the server. This includes the command to run to start the server, the args to pass to the command, the environment variables to set for the server, the working directory to use when spawning the process, and the text encoding used when sending/receiving messages to the server. +- **cache_tools_list** (bool) - Optional - Whether to cache the tools list. If `True`, the tools list will be cached and only fetched from the server once. If `False`, the tools list will be fetched from the server on each call to `list_tools()`. +- **name** (str | None) - Optional - A readable name for the server. If not provided, we'll create one from the command. +- **client_session_timeout_seconds** (float | None) - Optional - The read timeout passed to the MCP ClientSession. Defaults to 5 seconds. +- **tool_filter** (ToolFilter) - Optional - The tool filter to use for filtering tools. +- **use_structured_content** (bool) - Optional - Whether to use `tool_result.structured_content` when calling an MCP tool. Defaults to False for backwards compatibility. +- **max_retry_attempts** (int) - Optional - Number of times to retry failed list_tools/call_tool calls. Defaults to no retries. +- **retry_backoff_seconds_base** (float) - Optional - The base delay, in seconds, for exponential backoff between retries. Defaults to 1.0. +- **message_handler** (MessageHandlerFnT | None) - Optional - Optional handler invoked for session messages as delivered by the ClientSession. + +### Request Example +```json +{ + "params": { + "command": "python", + "args": ["-m", "my_agent.server"], + "env": {"MY_VAR": "my_value"}, + "cwd": "/path/to/working/dir", + "encoding": "utf-8", + "encoding_error_handler": "strict" + }, + "cache_tools_list": true, + "name": "MyMCPStdioServer", + "client_session_timeout_seconds": 10, + "use_structured_content": true, + "max_retry_attempts": 3, + "retry_backoff_seconds_base": 0.5 +} +``` + +### Response +#### Success Response (200) +This method does not return a value directly, but initializes the server object. + +#### Response Example +None +``` + +-------------------------------- + +### AgentHooksBase: Agent Start/End Callbacks (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/lifecycle + +Defines callbacks for the start and end of a specific agent's execution. `on_start` is called when the agent becomes active, and `on_end` is triggered when it produces its final output. + +```python +class AgentHooksBase[TContext, TAgent]: + async def on_start( + self, context: AgentHookContext[TContext], agent: TAgent + ) -> None: + """Called before the agent is invoked. Called each time the running agent is changed to this agent.""" + pass + + async def on_end( + self, + context: AgentHookContext[TContext], + agent: TAgent, + output: Any, + ) -> None: + """Called when the agent produces a final output.""" + pass + +``` + +-------------------------------- + +### GET /api/tool_usage + +Source: https://openai.github.io/openai-agents-python/ref/extensions/memory/advanced_sqlite_session + +Retrieves a summary of tool usage, including counts and turn numbers, for a specified branch. Defaults to the current branch if no branch ID is provided. + +```APIDOC +## GET /api/tool_usage + +### Description +Retrieves a summary of tool usage, including counts and turn numbers, for a specified branch. Defaults to the current branch if no branch ID is provided. + +### Method +GET + +### Endpoint +`/api/tool_usage` + +#### Query Parameters +- **branch_id** (string) - Optional - The ID of the branch to retrieve tool usage from. Defaults to the current branch if not specified. + +### Response +#### Success Response (200) +- **tool_usage_list** (array) - A list of tool usage records. + - **tool_name** (string) - The name of the tool. + - **usage_count** (integer) - The number of times the tool was used in that turn. + - **turn_number** (integer) - The turn number in which the tool was used. + +#### Response Example +```json +[ + {"tool_name": "code_interpreter", "usage_count": 1, "turn_number": 2}, + {"tool_name": "web_search", "usage_count": 1, "turn_number": 3} +] +``` +``` + +-------------------------------- + +### Get Specific Prompt from Server (Python) + +Source: https://openai.github.io/openai-agents-python/ref/mcp/server + +Abstract method to fetch a specific prompt by its name, optionally with arguments. It returns a GetPromptResult containing the prompt details. + +```python +import abc +from typing import Any, Dict, Optional + +# Assuming this type is defined elsewhere +class GetPromptResult: + pass + +class MCPBaseServer(abc.ABC): + @abc.abstractmethod + async def get_prompt(self, name: str, arguments: dict[str, Any] | None = None) -> GetPromptResult: + """Get a specific prompt from the server.""" + pass + +``` + +-------------------------------- + +### Handle Realtime Model Turn Events in Python + +Source: https://openai.github.io/openai-agents-python/zh/ref/realtime/openai_realtime + +Manages the start and end of a turn in the Realtime Model conversation. It emits `RealtimeModelTurnStartedEvent` when a response is created and `RealtimeModelTurnEndedEvent` when a response is completed. + +```python +elif parsed.type == "response.created": + self._ongoing_response = True + await self._emit_event(RealtimeModelTurnStartedEvent()) + elif parsed.type == "response.done": + self._ongoing_response = False + await self._emit_event(RealtimeModelTurnEndedEvent()) + +``` + +-------------------------------- + +### Format Agent Prompt with Handoff Instructions + +Source: https://openai.github.io/openai-agents-python/ja/ref/extensions/handoff_prompt + +A Python function that takes a user prompt and prepends the recommended system context for agents that support handoffs. This ensures agents are aware of the multi-agent system and handoff mechanisms. + +```python +def prompt_with_handoff_instructions(prompt: str) -> str: + """ + Add recommended instructions to the prompt for agents that use handoffs. + """ + return f"{RECOMMENDED_PROMPT_PREFIX}\n\n{prompt}" +``` + +-------------------------------- + +### GET /api/history + +Source: https://openai.github.io/openai-agents-python/ja/ref/memory + +Retrieves the conversation history for the current session. You can specify a limit to fetch only the most recent messages. + +```APIDOC +## GET /api/history + +### Description +Retrieves the conversation history for this session. You can optionally limit the number of items returned. + +### Method +GET + +### Endpoint +/api/history + +### Parameters +#### Query Parameters +- **limit** (integer) - Optional - Maximum number of items to retrieve. If not specified, retrieves all items. When specified, returns the latest N items in chronological order. + +### Request Example +(No request body for GET) + +### Response +#### Success Response (200) +- **items** (list[object]) - A list of objects, where each object represents an item in the conversation history. Each item may contain various fields depending on its type. + +#### Response Example +```json +{ + "items": [ + { + "role": "user", + "content": "Hello, how are you?" + }, + { + "role": "assistant", + "content": "I'm doing well, thank you! How can I help you today?" + } + ] +} +``` +``` + +-------------------------------- + +### OpenAIRealtimeSIPModel: Build Initial Session Payload (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/openai_realtime + +Builds a session payload for Realtime Calls API, mirroring RealtimeSession connect behavior. This is useful for accepting SIP-originated calls without duplicating session setup logic. It takes an agent, context, model configuration, run configuration, and optional overrides to construct the payload. + +```python +class OpenAIRealtimeSIPModel(OpenAIRealtimeWebSocketModel): + """Realtime model that attaches to SIP-originated calls using a call ID.""" + + @staticmethod + async def build_initial_session_payload( + agent: RealtimeAgent[Any], + *, + context: TContext | None = None, + model_config: RealtimeModelConfig | None = None, + run_config: RealtimeRunConfig | None = None, + overrides: RealtimeSessionModelSettings | None = None, + ) -> OpenAISessionCreateRequest: + """Build a session payload that mirrors what a RealtimeSession would send on connect. + + This helper can be used to accept SIP-originated calls by forwarding the returned payload to + the Realtime Calls API without duplicating session setup logic. + """ + run_config_settings = (run_config or {}).get("model_settings") or {} + initial_model_settings = (model_config or {}).get("initial_model_settings") or {} + base_settings: RealtimeSessionModelSettings = { + **run_config_settings, + **initial_model_settings, + } + + context_wrapper = RunContextWrapper(context) + merged_settings = await _build_model_settings_from_agent( + agent=agent, + context_wrapper=context_wrapper, + base_settings=base_settings, + starting_settings=initial_model_settings, + run_config=run_config, + ) + + if overrides: + merged_settings.update(overrides) + + model = OpenAIRealtimeWebSocketModel() + return model._get_session_config(merged_settings) +``` + +-------------------------------- + +### Get Output Type Name + +Source: https://openai.github.io/openai-agents-python/zh/ref/agent_output + +Retrieves the string representation of the output type. This function is used to identify the expected data format for agent outputs. + +```python +def name(self) -> str: + """The name of the output type.""" + return _type_to_str(self.output_type) +``` + +-------------------------------- + +### Handle Agent Handoff and Tool Execution + +Source: https://openai.github.io/openai-agents-python/ref/realtime/session + +This snippet handles agent handoffs and tool execution. It checks for specific event names in a handoff map, executes the handoff logic, updates the current agent, and sends necessary events to the model, including session updates and tool outputs. It also includes error handling for unknown tools. + +```python +output=result, + agent=agent, + arguments=event.arguments, + ) + ) + elif event.name in handoff_map: + handoff = handoff_map[event.name] + tool_context = ToolContext( + context=self._context_wrapper.context, + usage=self._context_wrapper.usage, + tool_name=event.name, + tool_call_id=event.call_id, + tool_arguments=event.arguments, + ) + + # Execute the handoff to get the new agent + result = await handoff.on_invoke_handoff(self._context_wrapper, event.arguments) + if not isinstance(result, RealtimeAgent): + raise UserError( + f"Handoff {handoff.tool_name} returned invalid result: {type(result)}" + ) + + # Store previous agent for event + previous_agent = agent + + # Update current agent + self._current_agent = result + + # Get updated model settings from new agent + updated_settings = await self._get_updated_model_settings_from_agent( + starting_settings=None, + agent=self._current_agent, + ) + + # Send handoff event + await self._put_event( + RealtimeHandoffEvent( + from_agent=previous_agent, + to_agent=self._current_agent, + info=self._event_info, + ) + ) + + # First, send the session update so the model receives the new instructions + await self._model.send_event( + RealtimeModelSendSessionUpdate(session_settings=updated_settings) + ) + + # Then send tool output to complete the handoff (this triggers a new response) + transfer_message = handoff.get_transfer_message(result) + await self._model.send_event( + RealtimeModelSendToolOutput( + tool_call=event, + output=transfer_message, + start_response=True, + ) + ) + else: + await self._put_event( + RealtimeError( + info=self._event_info, + error={"message": f"Tool {event.name} not found"}, + ) + ) +``` + +-------------------------------- + +### Get State Options with Consistency and Concurrency + +Source: https://openai.github.io/openai-agents-python/ja/ref/extensions/memory/dapr_session + +Configures and returns StateOptions based on consistency settings (strong or eventual) and optional concurrency parameters. Handles cases where no options are needed. + +```python +def _get_state_options(self, *, concurrency: Concurrency | None = None) -> StateOptions | None: + """Get StateOptions configured with consistency and optional concurrency.""" + options_kwargs: dict[str, Any] = {} + if self._consistency == DAPR_CONSISTENCY_STRONG: + options_kwargs["consistency"] = Consistency.strong + elif self._consistency == DAPR_CONSISTENCY_EVENTUAL: + options_kwargs["consistency"] = Consistency.eventual + if concurrency is not None: + options_kwargs["concurrency"] = concurrency + if options_kwargs: + return StateOptions(**options_kwargs) + return None +``` + +-------------------------------- + +### Define Recommended Prompt Prefix for Handoffs + +Source: https://openai.github.io/openai-agents-python/ja/ref/extensions/handoff_prompt + +Defines a system context prefix that includes instructions for agents operating within the Agents SDK, emphasizing seamless handoffs between agents using a specific function naming convention. + +```python +RECOMMENDED_PROMPT_PREFIX = "# System context\nYou are part of a multi-agent system called the Agents SDK, designed to make agent coordination and execution easy. Agents uses two primary abstraction: **Agents** and **Handoffs**. An agent encompasses instructions and tools and can hand off a conversation to another agent when appropriate. Handoffs are achieved by calling a handoff function, generally named `transfer_to_`. Transfers between agents are handled seamlessly in the background; do not mention or draw attention to these transfers in your conversation with the user.\n" +``` + +-------------------------------- + +### SessionABC - Get Items + +Source: https://openai.github.io/openai-agents-python/zh/ref/memory/session + +Retrieves the conversation history for a given session, with an optional limit on the number of items returned. + +```APIDOC +## GET /sessions/{sessionId}/items + +### Description +Retrieve the conversation history for this session. + +### Method +GET + +### Endpoint +/sessions/{sessionId}/items + +### Parameters +#### Query Parameters +- **limit** (integer | null) - Optional - Maximum number of items to retrieve. If None, retrieves all items. When specified, returns the latest N items in chronological order. + +### Response +#### Success Response (200) +- **items** (list[TResponseInputItem]) - List of input items representing the conversation history + +#### Response Example +```json +{ + "items": [ + { + "role": "user", + "content": "Hello!" + }, + { + "role": "assistant", + "content": "Hi there! How can I help you today?" + } + ] +} +``` +``` + +-------------------------------- + +### Get Realtime Session Configuration (Python) + +Source: https://openai.github.io/openai-agents-python/ref/realtime/openai_realtime + +Retrieves and constructs the session configuration for a realtime session based on provided model settings. This method handles the mapping of various audio input and output parameters, including format, noise reduction, transcription, turn detection, and voice. It uses default settings when specific configurations are not found and ensures audio formats are correctly converted using `to_realtime_audio_format`. Dependencies include `RealtimeSessionModelSettings`, `OpenAISessionCreateRequest`, `to_realtime_audio_format`, and `DEFAULT_MODEL_SETTINGS`. + +```python +def _get_session_config( + self, + model_settings: RealtimeSessionModelSettings + ) -> OpenAISessionCreateRequest: + """Get the session config.""" + audio_input_args: dict[str, Any] = {} + audio_output_args: dict[str, Any] = {} + + audio_config = model_settings.get("audio") + audio_config_mapping = audio_config if isinstance(audio_config, Mapping) else None + input_audio_config: Mapping[str, Any] = ( + cast(Mapping[str, Any], audio_config_mapping.get("input", {})) + if audio_config_mapping + else {} + ) + output_audio_config: Mapping[str, Any] = ( + cast(Mapping[str, Any], audio_config_mapping.get("output", {})) + if audio_config_mapping + else {} + ) + + input_format_source: FormatInput = ( + input_audio_config.get("format") if input_audio_config else None + ) + if input_format_source is None: + if self._call_id: + input_format_source = model_settings.get("input_audio_format") + else: + input_format_source = model_settings.get( + "input_audio_format", DEFAULT_MODEL_SETTINGS.get("input_audio_format") + ) + audio_input_args["format"] = to_realtime_audio_format(input_format_source) + + if "noise_reduction" in input_audio_config: + audio_input_args["noise_reduction"] = input_audio_config.get("noise_reduction") + elif "input_audio_noise_reduction" in model_settings: + audio_input_args["noise_reduction"] = model_settings.get("input_audio_noise_reduction") + + if "transcription" in input_audio_config: + audio_input_args["transcription"] = input_audio_config.get("transcription") + elif "input_audio_transcription" in model_settings: + audio_input_args["transcription"] = model_settings.get("input_audio_transcription") + else: + audio_input_args["transcription"] = DEFAULT_MODEL_SETTINGS.get( + "input_audio_transcription" + ) + + if "turn_detection" in input_audio_config: + audio_input_args["turn_detection"] = self._normalize_turn_detection_config( + input_audio_config.get("turn_detection") + ) + elif "turn_detection" in model_settings: + audio_input_args["turn_detection"] = self._normalize_turn_detection_config( + model_settings.get("turn_detection") + ) + else: + audio_input_args["turn_detection"] = DEFAULT_MODEL_SETTINGS.get("turn_detection") + + requested_voice = output_audio_config.get("voice") if output_audio_config else None + audio_output_args["voice"] = requested_voice or model_settings.get( + "voice", DEFAULT_MODEL_SETTINGS.get("voice") + ) + + output_format_source: FormatInput = ( + output_audio_config.get("format") if output_audio_config else None + ) + if output_format_source is None: + if self._call_id: + output_format_source = model_settings.get("output_audio_format") + else: + output_format_source = model_settings.get( + "output_audio_format", DEFAULT_MODEL_SETTINGS.get("output_audio_format") + ) + audio_output_args["format"] = to_realtime_audio_format(output_format_source) + + if "speed" in output_audio_config: + audio_output_args["speed"] = output_audio_config.get("speed") + elif "speed" in model_settings: + audio_output_args["speed"] = model_settings.get("speed") + + output_modalities = ( + model_settings.get("output_modalities") + or model_settings.get("modalities") + or DEFAULT_MODEL_SETTINGS.get("modalities") + ) + + # Construct full session object. `type` will be excluded at serialization time for updates. + session_create_request = OpenAISessionCreateRequest( + type="realtime", + model=(model_settings.get("model_name") or self.model) or "gpt-realtime", + output_modalities=output_modalities, + +``` + +-------------------------------- + +### Trace Lifecycle Management + +Source: https://openai.github.io/openai-agents-python/zh/ref/tracing + +Manages the lifecycle of a trace, allowing it to be started and finished. Optionally, traces can be marked as the current trace during execution. + +```APIDOC +## Trace Lifecycle Management + +### Description +Manages the start and end of a trace, including options to set it as the current trace. + +### Endpoints + +- **POST /trace/start** +- **POST /trace/finish** + +### Parameters + +#### Start Trace +- **mark_as_current** (bool) - Optional - If true, marks this trace as the current trace in the execution context. Defaults to `False`. + +#### Finish Trace +- **reset_current** (bool) - Optional - If true, resets the current trace to the previous trace in the execution context. Defaults to `False`. + +### Notes + +- `start()` must be called before any spans can be added. +- Only one trace can be current at a time. +- `finish()` must be called to complete the trace and finalize all open spans. +- Both methods are thread-safe when using their respective optional parameters. + +### Request Example + +#### Start Trace Example +```json +{ + "mark_as_current": true +} +``` + +#### Finish Trace Example +```json +{ + "reset_current": true +} +``` + +### Response + +#### Success Response (200) + +Responses for start and finish operations typically indicate success without returning specific data, signifying the operation was completed. + +#### Response Example + +*Success is indicated by a 200 OK status code.* +``` + +-------------------------------- + +### Python: Basic Trace Usage + +Source: https://openai.github.io/openai-agents-python/ref/tracing + +Illustrates the basic usage of the `trace` context manager in Python for defining a workflow. It shows how to wrap a series of operations within a `with trace(...)` block, ensuring proper setup and cleanup for the trace. This is useful for simple workflow logging without specific grouping or metadata. + +```python +# Assuming Runner, validator, order_data, and processor are defined elsewhere + +# Basic trace usage +with trace("Order Processing") as t: + validation_result = await Runner.run(validator, order_data) + if validation_result.approved: + await Runner.run(processor, order_data) +``` + +-------------------------------- + +### Trace Provider Management + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing + +Utilities for managing the global trace provider, including getting the current provider and setting a new one. + +```APIDOC +## Trace Provider Utilities + +### Description +Utilities for managing the global trace provider, including getting the current provider and setting a new one. + +### Functions + +#### get_trace_provider() +- **Description**: Get the global trace provider used by tracing utilities. +- **Returns**: `TraceProvider` - The current global trace provider. + +#### set_trace_provider(provider: TraceProvider) +- **Description**: Set the global trace provider used by tracing utilities. +- **Parameters**: + - **provider** (`TraceProvider`) - Required - The trace provider to set as global. +- **Returns**: `None` +``` + +-------------------------------- + +### GET /conversations/history + +Source: https://openai.github.io/openai-agents-python/zh/ref/memory/sqlite_session + +Retrieves the conversation history for a given session. It supports an optional limit to fetch the latest N items. + +```APIDOC +## GET /conversations/history + +### Description +Retrieve the conversation history for this session. Supports fetching a limited number of the most recent items. + +### Method +GET + +### Endpoint +/conversations/history + +### Parameters +#### Query Parameters +- **limit** (integer) - Optional - Maximum number of items to retrieve. If not specified, retrieves all items. When specified, returns the latest N items in chronological order. + +### Request Example +```json +{ + "limit": 10 +} +``` + +### Response +#### Success Response (200) +- **items** (list[object]) - List of input items representing the conversation history. Each item is an object. + +#### Response Example +```json +{ + "items": [ + { + "role": "user", + "content": "Hello, how are you?" + }, + { + "role": "assistant", + "content": "I am doing well, thank you!" + } + ] +} +``` +``` + +-------------------------------- + +### GET /api/agents/memory/session/items + +Source: https://openai.github.io/openai-agents-python/ref/memory/sqlite_session + +Retrieves the conversation history for a given session. It supports an optional limit to fetch the latest N items. + +```APIDOC +## GET /api/agents/memory/session/items + +### Description +Retrieves the conversation history for this session. You can specify a limit to get the most recent items. + +### Method +GET + +### Endpoint +/api/agents/memory/session/items + +### Parameters +#### Query Parameters +- **limit** (int) - Optional - Maximum number of items to retrieve. If None, retrieves all items. When specified, returns the latest N items in chronological order. + +### Response +#### Success Response (200) +- **items** (list[TResponseInputItem]) - List of input items representing the conversation history + +#### Response Example +{ + "items": [ + { + "role": "user", + "content": "Hello, how can I help you?" + }, + { + "role": "assistant", + "content": "I'm here to assist you with your inquiries." + } + ] +} +``` + +-------------------------------- + +### Create Branch From Turn + +Source: https://openai.github.io/openai-agents-python/ja/ref/extensions/memory/advanced_sqlite_session + +Creates a new branch starting from a specific user message turn within a session. This is useful for exploring alternative conversation paths or forking development. + +```APIDOC +## POST /branches/from_turn + +### Description +Creates a new branch starting from a specific user message turn. An optional name can be provided for the new branch; otherwise, it will be auto-generated. + +### Method +POST + +### Endpoint +/branches/from_turn + +### Parameters +#### Path Parameters +None + +#### Query Parameters +* **turn_number** (int) - Required - The turn number of the user message to branch from. +* **branch_name** (string) - Optional - A name for the new branch. If not provided, a name will be auto-generated. + +### Request Example +```json +{ + "turn_number": 5, + "branch_name": "experiment-branch" +} +``` + +### Response +#### Success Response (200) +- **branch_id** (string) - The unique identifier of the newly created branch. + +#### Response Example +```json +{ + "branch_id": "branch_from_turn_5_1678886400" +} +``` + +#### Error Response +- **ValueError**: If the specified turn does not exist or does not contain a user message. +``` + +-------------------------------- + +### Forcing Tool Use with Model Settings (Python) + +Source: https://openai.github.io/openai-agents-python/agents + +Explains how to control tool usage in an agent by setting `ModelSettings.tool_choice`. This example forces the agent to use the 'get_weather' tool for retrieving weather information. + +```python +from agents import Agent, Runner, function_tool, ModelSettings + +@function_tool +def get_weather(city: str) -> str: + """Returns weather info for the specified city.""" + return f"The weather in {city} is sunny" + +agent = Agent( + name="Weather Agent", + instructions="Retrieve weather details.", + tools=[get_weather], + model_settings=ModelSettings(tool_choice="get_weather") +) + + +``` + +-------------------------------- + +### MultiProvider Class Definition and Initialization (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/models/multi_provider + +Defines the MultiProvider class, inheriting from ModelProvider. Its constructor initializes default or custom provider mappings and sets up the OpenAIProvider with configurable parameters like API key, base URL, and client. + +```python +class MultiProvider(ModelProvider): + """This ModelProvider maps to a Model based on the prefix of the model name. By default, the + mapping is: + - "openai/" prefix or no prefix -> OpenAIProvider. e.g. "openai/gpt-4.1", "gpt-4.1" + - "litellm/" prefix -> LitellmProvider. e.g. "litellm/openai/gpt-4.1" + + You can override or customize this mapping. + """ + + def __init__( + self, + *, + provider_map: MultiProviderMap | None = None, + openai_api_key: str | None = None, + openai_base_url: str | None = None, + openai_client: AsyncOpenAI | None = None, + openai_organization: str | None = None, + openai_project: str | None = None, + openai_use_responses: bool | None = None, + ) -> None: + """Create a new OpenAI provider. + + Args: + provider_map: A MultiProviderMap that maps prefixes to ModelProviders. If not provided, + we will use a default mapping. See the documentation for this class to see the + default mapping. + openai_api_key: The API key to use for the OpenAI provider. If not provided, we will use + the default API key. + openai_base_url: The base URL to use for the OpenAI provider. If not provided, we will + use the default base URL. + openai_client: An optional OpenAI client to use. If not provided, we will create a new + OpenAI client using the api_key and base_url. + openai_organization: The organization to use for the OpenAI provider. + openai_project: The project to use for the OpenAI provider. + openai_use_responses: Whether to use the OpenAI responses API. + """ + self.provider_map = provider_map + self.openai_provider = OpenAIProvider( + api_key=openai_api_key, + base_url=openai_base_url, + openai_client=openai_client, + organization=openai_organization, + project=openai_project, + use_responses=openai_use_responses, + ) + + self._fallback_providers: dict[str, ModelProvider] = {} + +``` + +-------------------------------- + +### GET /session/items + +Source: https://openai.github.io/openai-agents-python/ja/ref/memory/session + +Retrieves the conversation history for the current session. You can optionally specify a limit to fetch only the latest items. + +```APIDOC +## GET /session/items + +### Description +Retrieve the conversation history for this session. + +### Method +GET + +### Endpoint +`/session/items` + +### Query Parameters +- **limit** (int | None) - Optional - Maximum number of items to retrieve. If None, retrieves all items. When specified, returns the latest N items in chronological order. + +### Response +#### Success Response (200) +- **items** (list[TResponseInputItem]) - List of input items representing the conversation history + +#### Response Example +```json +{ + "items": [ + { + "role": "user", + "content": "Hello!" + }, + { + "role": "assistant", + "content": "Hi there! How can I help you today?" + } + ] +} +``` +``` + +-------------------------------- + +### GET /websites/openai_github_io_openai-agents-python/get_state + +Source: https://openai.github.io/openai-agents-python/ko/ref/realtime/model + +Retrieves the current playback state of the realtime model, including the item ID, content index, and elapsed milliseconds. + +```APIDOC +## GET /websites/openai_github_io_openai-agents-python/get_state + +### Description +Retrieves the current playback state of the realtime model, including the item ID, content index, and elapsed milliseconds. + +### Method +GET + +### Endpoint +/websites/openai_github_io_openai-agents-python/get_state + +### Parameters +None + +### Response +#### Success Response (200) +- **current_item_id** (str or null) - The ID of the currently playing item, or null if nothing is playing. +- **current_item_content_index** (int or null) - The index of the current audio content, or null. +- **elapsed_ms** (float or null) - The number of milliseconds played for the current item, or null. + +### Response Example +```json +{ + "current_item_id": "audio_clip_1", + "current_item_content_index": 0, + "elapsed_ms": 1500.5 +} +``` +``` + +-------------------------------- + +### Create Branch From Turn API + +Source: https://openai.github.io/openai-agents-python/zh/ref/extensions/memory/advanced_sqlite_session + +Allows the creation of a new branch in the conversation history starting from a specified user message turn. This is useful for exploring alternative conversation paths. + +```APIDOC +## POST /agents/extensions/memory/advanced_sqlite_session/create_branch_from_turn + +### Description +Creates a new branch starting from a specific user message turn. This allows for exploring alternative conversation paths from a given point in the history. + +### Method +POST + +### Endpoint +/agents/extensions/memory/advanced_sqlite_session/create_branch_from_turn + +### Parameters +#### Path Parameters +None + +#### Query Parameters +- **turn_number** (int) - Required - The turn number of the user message to branch from. +- **branch_name** (str | None) - Optional - A name for the new branch. If not provided, a name will be auto-generated. + +### Request Body +This endpoint does not use a request body. Parameters are passed as query parameters. + +### Request Example +```json +{ + "turn_number": 5, + "branch_name": "alternative_path_1" +} +``` + +### Response +#### Success Response (200) +- **branch_id** (str) - The unique identifier of the newly created branch. + +#### Response Example +```json +{ + "branch_id": "branch_from_turn_5_1678886400" +} +``` + +#### Error Response +- **ValueError**: Returned if the specified turn does not exist or does not contain a user message. +``` + +-------------------------------- + +### Run Interactive Agent REPL Loop (Python) + +Source: https://openai.github.io/openai-agents-python/repl + +This Python script utilizes the `run_demo_loop` function from the OpenAI Agents SDK to start an interactive chat session directly in the terminal. It initializes an agent with a name and instructions, then facilitates a continuous conversation where the agent remembers history and streams responses. To exit, type 'quit' or 'exit', or use Ctrl-D. + +```python +import asyncio +from agents import Agent, run_demo_loop + +async def main() -> None: + agent = Agent(name="Assistant", instructions="You are a helpful assistant.") + await run_demo_loop(agent) + +if __name__ == "__main__": + asyncio.run(main()) + +``` + +-------------------------------- + +### Initial Model Settings Configuration (Python) + +Source: https://openai.github.io/openai-agents-python/ko/ref/realtime/config + +Sets the initial model settings for a realtime session connection. This allows pre-configuration of model behavior. + +```python +initial_model_settings: NotRequired[ + RealtimeSessionModelSettings +] +``` + +-------------------------------- + +### Get All Function Tools + +Source: https://openai.github.io/openai-agents-python/ko/ref/mcp/util + +Retrieves all function tools available across a list of MCP servers. It ensures that tool names are unique across all provided servers to prevent conflicts. + +```APIDOC +## GET /tools/all + +### Description +Get all function tools from a list of MCP servers. This method aggregates tools from multiple servers and raises an error if duplicate tool names are found. + +### Method +GET + +### Endpoint +/tools/all + +### Parameters +#### Query Parameters +- **servers** (list[MCPServer]) - Required - A list of MCP server configurations to fetch tools from. +- **convert_schemas_to_strict** (bool) - Required - Flag to determine if input schemas should be converted to a strict JSON schema format. +- **run_context** (RunContextWrapper[Any]) - Required - The context for the current run operation. +- **agent** (AgentBase) - Required - The agent instance performing the operation. + +### Response +#### Success Response (200) +- **tools** (list[Tool]) - A list of available function tools. + +#### Response Example +```json +[ + { + "name": "example_tool", + "description": "An example tool", + "params_json_schema": {}, + "on_invoke_tool": "", + "strict_json_schema": false + } +] +``` +``` + +-------------------------------- + +### OpenAIRealtimeSIPModel: Build Initial Session Payload (Python) + +Source: https://openai.github.io/openai-agents-python/ref/realtime/openai_realtime + +Builds a session payload for the Realtime Calls API, mirroring what a RealtimeSession would send on connect. This is useful for accepting SIP-originated calls without duplicating session setup logic. It takes an agent, context, model configuration, run configuration, and optional overrides as input. + +```python +class OpenAIRealtimeSIPModel(OpenAIRealtimeWebSocketModel): + """Realtime model that attaches to SIP-originated calls using a call ID.""" + + @staticmethod + async def build_initial_session_payload( + agent: RealtimeAgent[Any], + *, + context: TContext | None = None, + model_config: RealtimeModelConfig | None = None, + run_config: RealtimeRunConfig | None = None, + overrides: RealtimeSessionModelSettings | None = None, + ) -> OpenAISessionCreateRequest: + """Build a session payload that mirrors what a RealtimeSession would send on connect. + + This helper can be used to accept SIP-originated calls by forwarding the returned payload to + the Realtime Calls API without duplicating session setup logic. + """ + run_config_settings = (run_config or {}).get("model_settings") or {} + initial_model_settings = (model_config or {}).get("initial_model_settings") or {} + base_settings: RealtimeSessionModelSettings = { + **run_config_settings, + **initial_model_settings, + } + + context_wrapper = RunContextWrapper(context) + merged_settings = await _build_model_settings_from_agent( + agent=agent, + context_wrapper=context_wrapper, + base_settings=base_settings, + starting_settings=initial_model_settings, + run_config=run_config, + ) + + if overrides: + merged_settings.update(overrides) + + model = OpenAIRealtimeWebSocketModel() + return model._get_session_config(merged_settings) + + async def connect(self, options: RealtimeModelConfig) -> None: + call_id = options.get("call_id") + if not call_id: + raise UserError("OpenAIRealtimeSIPModel requires `call_id` in the model configuration.") + + sip_options = options.copy() + await super().connect(sip_options) +``` + +-------------------------------- + +### Tracer Initialization (__init__) + +Source: https://openai.github.io/openai-agents-python/zh/ref/tracing/processors + +Initializes the tracing client with various configuration options for sending traces and spans to the OpenAI API. It supports custom endpoints, retry mechanisms, and delay strategies. + +```APIDOC +## Tracer Initialization + +### Description +Initializes the tracing client with configuration for API key, organization, project, endpoint, and retry parameters. + +### Method +`__init__` + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +None + +### Constructor Parameters +- **api_key** (`str | None`) - Optional - The API key for the "Authorization" header. Defaults to `os.environ["OPENAI_API_KEY"]` if not provided. +- **organization** (`str | None`) - Optional - The OpenAI organization to use. Defaults to `os.environ["OPENAI_ORG_ID"]` if not provided. +- **project** (`str | None`) - Optional - The OpenAI project to use. Defaults to `os.environ["OPENAI_PROJECT_ID"]` if not provided. +- **endpoint** (`str`) - Optional - The HTTP endpoint to which traces/spans are posted. Defaults to `'https://api.openai.com/v1/traces/ingest'`. +- **max_retries** (`int`) - Optional - Maximum number of retries upon failures. Defaults to `3`. +- **base_delay** (`float`) - Optional - Base delay (in seconds) for the first backoff. Defaults to `1.0`. +- **max_delay** (`float`) - Optional - Maximum delay (in seconds) for backoff growth. Defaults to `30.0`. + +### Request Example +```python +from agents.tracing import Tracer + +tracer = Tracer( + api_key="sk-your-api-key", + organization="org-your-org-id", + project="proj-your-project-id", + endpoint="https://api.openai.com/v1/traces/ingest", + max_retries=5, + base_delay=0.5, + max_delay=60.0 +) +``` + +### Response +N/A (This is a constructor) + +### Error Handling +- Network or I/O errors during initialization are handled internally with retries. +``` + +-------------------------------- + +### GET /turn_usage + +Source: https://openai.github.io/openai-agents-python/ja/ref/extensions/memory/advanced_sqlite_session + +Retrieves usage statistics by turn, including full JSON token details. This endpoint can fetch usage for a specific turn or for all turns within a branch. + +```APIDOC +## GET /turn_usage + +### Description +Retrieves usage statistics by turn with full JSON token details. If `user_turn_number` is provided, it returns usage for that specific turn; otherwise, it returns usage for all turns in the specified or current branch. + +### Method +GET + +### Endpoint +/turn_usage + +### Parameters +#### Query Parameters +- **user_turn_number** (int) - Optional - The specific turn number to retrieve usage statistics for. If omitted, all turns are returned. +- **branch_id** (str) - Optional - The ID of the branch to retrieve usage from. If omitted, the current branch is used. + +### Request Example +```json +{ + "user_turn_number": 5, + "branch_id": "branch-123" +} +``` + +### Response +#### Success Response (200) +- **requests** (int) - The number of requests made in the turn. +- **input_tokens** (int) - The total number of input tokens used. +- **output_tokens** (int) - The total number of output tokens used. +- **total_tokens** (int) - The total number of tokens used (input + output). +- **input_tokens_details** (object | null) - Detailed breakdown of input token usage, or null if not available. +- **output_tokens_details** (object | null) - Detailed breakdown of output token usage, or null if not available. +- **user_turn_number** (int) - The turn number (only present when fetching all turns). + +#### Response Example (Single Turn) +```json +{ + "requests": 1, + "input_tokens": 150, + "output_tokens": 200, + "total_tokens": 350, + "input_tokens_details": { + "model_a": 100, + "model_b": 50 + }, + "output_tokens_details": { + "model_a": 180, + "model_b": 20 + } +} +``` + +#### Response Example (All Turns) +```json +[ + { + "user_turn_number": 1, + "requests": 1, + "input_tokens": 100, + "output_tokens": 120, + "total_tokens": 220, + "input_tokens_details": null, + "output_tokens_details": null + }, + { + "user_turn_number": 2, + "requests": 1, + "input_tokens": 120, + "output_tokens": 150, + "total_tokens": 270, + "input_tokens_details": { + "model_a": 120 + }, + "output_tokens_details": { + "model_a": 150 + } + } +] +``` + +``` + +-------------------------------- + +### speech_span + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/create + +Creates a new speech span for tracing text-to-speech operations. The span needs to be manually started and finished or used with a `with` statement. + +```APIDOC +## POST /speech/span + +### Description +Creates a new speech span for tracing text-to-speech operations. The span needs to be manually started and finished or used with a `with` statement. + +### Method +POST + +### Endpoint +`/speech/span` + +### Parameters +#### Query Parameters +- **model** (str | None) - Optional - The name of the model used for the text-to-speech. +- **input** (str | None) - Optional - The text input of the text-to-speech. +- **output** (str | None) - Optional - The audio output of the text-to-speech as base64 encoded string of PCM audio bytes. +- **output_format** (str | None) - Optional - The format of the audio output (defaults to "pcm"). +- **model_config** (Mapping[str, Any] | None) - Optional - The model configuration (hyperparameters) used. +- **first_content_at** (str | None) - Optional - The time of the first byte of the audio output. +- **span_id** (str | None) - Optional - The ID of the span. If not provided, an ID will be generated. Use `util.gen_span_id()` for guaranteed correct formatting. +- **parent** (Trace | Span[Any] | None) - Optional - The parent span or trace. If not provided, the current trace/span is used. +- **disabled** (bool) - Optional - If True, a Span will be returned but not recorded. Defaults to `False`. + +### Request Example +```json +{ + "model": "tts-1", + "input": "Hello, world!", + "output_format": "mp3" +} +``` + +### Response +#### Success Response (200) +- **span** (Span[SpeechSpanData]) - The created speech span object. +``` + +-------------------------------- + +### Basic Trace Usage with Context Manager + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/traces + +Illustrates the fundamental usage of the `trace` context manager for basic workflow tracing. It shows how to wrap code blocks to create a trace with a simple name, ensuring reliable cleanup and management of the trace lifecycle. + +```python +# Basic trace usage +with trace("Order Processing") as t: + validation_result = await Runner.run(validator, order_data) + if validation_result.approved: + await Runner.run(processor, order_data) +``` + +-------------------------------- + +### Define and Use Python Functions as Tools with OpenAI Agents SDK + +Source: https://openai.github.io/openai-agents-python/tools + +Demonstrates how to define Python functions decorated with `@function_tool` to be used as tools by the OpenAI Agents SDK. It shows automatic extraction of tool name, description from docstrings, and input schema generation from type hints. The example also includes creating an Agent with these tools and printing their details. + +```python +import json + +from typing_extensions import TypedDict, Any + +from agents import Agent, FunctionTool, RunContextWrapper, function_tool + + +class Location(TypedDict): + lat: float + long: float + +@function_tool +async def fetch_weather(location: Location) -> str: + + """Fetch the weather for a given location. + + Args: + location: The location to fetch the weather for. + """ + # In real life, we'd fetch the weather from a weather API + return "sunny" + + +@function_tool(name_override="fetch_data") +def read_file(ctx: RunContextWrapper[Any], path: str, directory: str | None = None) -> str: + """Read the contents of a file. + + Args: + path: The path to the file to read. + directory: The directory to read the file from. + """ + # In real life, we'd read the file from the file system + return "" + + +agent = Agent( + name="Assistant", + tools=[fetch_weather, read_file], +) + +for tool in agent.tools: + if isinstance(tool, FunctionTool): + print(tool.name) + print(tool.description) + print(json.dumps(tool.params_json_schema, indent=2)) + print() + +``` + +-------------------------------- + +### MCPServerStreamableHttp Initialization + +Source: https://openai.github.io/openai-agents-python/ja/ref/mcp/server + +Initializes a new MCPServerStreamableHttp instance with specified parameters and optional configurations. + +```APIDOC +## MCPServerStreamableHttp + +### Description +Initializes a new MCP server based on the Streamable HTTP transport. + +### Method +__init__ + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **params** (MCPServerStreamableHttpParams) - Required - The params that configure the server. This includes the URL of the server, the headers to send to the server, the timeout for the HTTP request, the timeout for the Streamable HTTP connection, whether we need to terminate on close, and an optional custom HTTP client factory. +- **cache_tools_list** (bool) - Optional - Whether to cache the tools list. If `True`, the tools list will be cached and only fetched from the server once. If `False`, the tools list will be fetched from the server on each call to `list_tools()`. The cache can be invalidated by calling `invalidate_tools_cache()`. You should set this to `True` if you know the server will not change its tools list, because it can drastically improve latency (by avoiding a round-trip to the server every time). +- **name** (str | None) - Optional - A readable name for the server. If not provided, we'll create one from the URL. +- **client_session_timeout_seconds** (float | None) - Optional - The read timeout passed to the MCP ClientSession. +- **tool_filter** (ToolFilter) - Optional - The tool filter to use for filtering tools. +- **use_structured_content** (bool) - Optional - Whether to use `tool_result.structured_content` when calling an MCP tool. Defaults to False for backwards compatibility - most MCP servers still include the structured content in the `tool_result.content`, and using it by default will cause duplicate content. You can set this to True if you know the server will not duplicate the structured content in the `tool_result.content`. +- **max_retry_attempts** (int) - Optional - Number of times to retry failed list_tools/call_tool calls. Defaults to no retries. +- **retry_backoff_seconds_base** (float) - Optional - The base delay, in seconds, for exponential backoff between retries. +- **message_handler** (MessageHandlerFnT | None) - Optional - Optional handler invoked for session messages as delivered by the ClientSession. + +### Request Example +```json +{ + "params": { + "url": "http://example.com/mcp", + "headers": {"Authorization": "Bearer YOUR_TOKEN"}, + "timeout": 10, + "sse_read_timeout": 300, + "terminate_on_close": false, + "httpx_client_factory": null + }, + "cache_tools_list": true, + "name": "MyMCPStreamServer", + "client_session_timeout_seconds": 10, + "tool_filter": null, + "use_structured_content": false, + "max_retry_attempts": 3, + "retry_backoff_seconds_base": 1.5, + "message_handler": null +} +``` + +### Response +#### Success Response (200) +This method does not return a value directly upon initialization. It sets up the server instance. + +#### Response Example +N/A +``` + +-------------------------------- + +### response_span + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/create + +Creates a new response span for tracing. This span is not started automatically and requires manual start/finish or use within a `with` statement. + +```APIDOC +## POST /agents/tracing/response_span + +### Description +Creates a new response span to trace an agent's response. The span must be manually started and finished or managed using a context manager (`with response_span()`). + +### Method +POST + +### Endpoint +/agents/tracing/response_span + +### Parameters +#### Query Parameters +- **response** (Response | None) - Optional - The OpenAI Response object to associate with the span. +- **span_id** (str | None) - Optional - The ID for the span. If not provided, a unique ID will be generated. It is recommended to use `util.gen_span_id()` for consistent formatting. +- **parent** (Trace | Span[Any] | None) - Optional - The parent trace or span. If omitted, the current trace/span will be used. +- **disabled** (bool) - Optional - If set to True, the span will be created but not recorded. Defaults to False. + +### Request Body +(No explicit request body; parameters are passed as arguments) + +### Response +#### Success Response (200) +- **Span[ResponseSpanData]** - The created response span object. + +#### Response Example +```json +{ + "span": { + "span_id": "generated_or_provided_id", + "parent_id": "optional_parent_id", + "data": { + "response": { ... OpenAI Response Object ... } + } + } +} +``` +``` + +-------------------------------- + +### Logging Usage in Run Hooks + +Source: https://openai.github.io/openai-agents-python/usage + +Provides an example of how to access and log token usage metrics within a custom `RunHooks` implementation, specifically in the `on_agent_end` hook. This allows for real-time monitoring and logging of agent performance and resource consumption at key lifecycle points. + +```python +class MyHooks(RunHooks): + async def on_agent_end(self, context: RunContextWrapper, agent: Agent, output: Any) -> None: + u = context.usage + print(f"{agent.name} → {u.requests} requests, {u.total_tokens} total tokens") +``` + +-------------------------------- + +### Basic Agent Configuration with Tools + +Source: https://openai.github.io/openai-agents-python/agents + +Demonstrates how to configure a basic agent with a name, instructions, model, and a custom function tool for retrieving weather information. The `get_weather` function is decorated with `@function_tool` to be usable by the agent. + +```python +from agents import Agent, ModelSettings, function_tool + +@function_tool +def get_weather(city: str) -> str: + """returns weather info for the specified city.""" + return f"The weather in {city} is sunny" + +agent = Agent( + name="Haiku agent", + instructions="Always respond in haiku form", + model="gpt-5-nano", + tools=[get_weather], +) +``` + +-------------------------------- + +### Establish OpenAI Realtime API Connection (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/openai_realtime + +This Python `async` function, `connect`, establishes a connection to the OpenAI Realtime API. It takes `RealtimeModelConfig` options, sets up the websocket URL, handles API keys and custom headers, and initiates a background task to listen for incoming messages. It also updates the session configuration based on initial model settings. + +```python +async def connect(self, options: RealtimeModelConfig) -> None: + """Establish a connection to the model and keep it alive.""" + assert self._websocket is None, "Already connected" + assert self._websocket_task is None, "Already connected" + + model_settings: RealtimeSessionModelSettings = options.get("initial_model_settings", {}) + + self._playback_tracker = options.get("playback_tracker", None) + + call_id = options.get("call_id") + model_name = model_settings.get("model_name") + if call_id and model_name: + error_message = + "Cannot specify both `call_id` and `model_name` " + "when attaching to an existing realtime call." + raise UserError(error_message) + + if model_name: + self.model = model_name + + self._call_id = call_id + api_key = await get_api_key(options.get("api_key")) + + if "tracing" in model_settings: + self._tracing_config = model_settings["tracing"] + else: + self._tracing_config = "auto" + + if call_id: + url = options.get("url", f"wss://api.openai.com/v1/realtime?call_id={call_id}") + else: + url = options.get("url", f"wss://api.openai.com/v1/realtime?model={self.model}") + + headers: dict[str, str] = {} + if options.get("headers") is not None: + # For customizing request headers + headers.update(options["headers"]) + else: + # OpenAI's Realtime API + if not api_key: + raise UserError("API key is required but was not provided.") + + headers.update({"Authorization": f"Bearer {api_key}"}) + + self._websocket = await self._create_websocket_connection( + url=url, + headers=headers, + transport_config=self._transport_config, + ) + self._websocket_task = asyncio.create_task(self._listen_for_messages()) + await self._update_session_config(model_settings) +``` + +-------------------------------- + +### Tool Events API + +Source: https://openai.github.io/openai-agents-python/ref/realtime/events + +This section covers events related to tool calls made by agents. It includes events for when a tool call starts and when it ends. + +```APIDOC +## Tool Events + +### RealtimeToolStart + +#### Description +An agent is starting a tool call. + +### Method +Not Applicable (Event) + +### Endpoint +Not Applicable (Event) + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +* **agent** (RealtimeAgent) - Required - The agent that updated. +* **tool** (Tool) - Required - The tool being called. +* **arguments** (str) - Required - The arguments passed to the tool as a JSON string. +* **info** (RealtimeEventInfo) - Required - Common info for all events, such as the context. +* **type** (Literal["tool_start"]) - Required - The event type, fixed to "tool_start". + +### Request Example +```json +{ + "agent": { ... }, + "tool": { ... }, + "arguments": "{\"key\": \"value\"}", + "info": { ... }, + "type": "tool_start" +} +``` + +### Response +#### Success Response (200) +Not Applicable (Event) + +#### Response Example +None + +--- + +### RealtimeToolEnd + +#### Description +An agent has ended a tool call. + +### Method +Not Applicable (Event) + +### Endpoint +Not Applicable (Event) + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +* **agent** (RealtimeAgent) - Required - The agent that ended the tool call. +* **tool** (Tool) - Required - The tool that was called. +* **arguments** (str) - Required - The arguments passed to the tool as a JSON string. +* **output** (Any) - Required - The output of the tool call. +* **info** (RealtimeEventInfo) - Required - Common info for all events, such as the context. +* **type** (Literal["tool_end"]) - Required - The event type, fixed to "tool_end". + +### Request Example +```json +{ + "agent": { ... }, + "tool": { ... }, + "arguments": "{\"key\": \"value\"}", + "output": { ... }, + "info": { ... }, + "type": "tool_end" +} +``` + +### Response +#### Success Response (200) +Not Applicable (Event) + +#### Response Example +None +``` + +-------------------------------- + +### Define RealtimeAgentStartEvent in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/events + +Defines the RealtimeAgentStartEvent dataclass, which is emitted when a new agent has started. It includes the agent object and common event information. + +```python +@dataclass +class RealtimeAgentStartEvent: + """A new agent has started.""" + + agent: RealtimeAgent + """The new agent.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["agent_start"] = "agent_start" + +``` + +-------------------------------- + +### Initialize StreamedAudioResult in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/voice/result + +Initializes a new StreamedAudioResult instance. It takes TTS model, TTS settings, and voice pipeline configuration as required arguments. This setup is crucial for managing the streaming of voice and audio data. + +```python +def __init__( + self, + tts_model: TTSModel, + tts_settings: TTSModelSettings, + voice_pipeline_config: VoicePipelineConfig, +): + """Create a new `StreamedAudioResult` instance. + + Args: + tts_model: The TTS model to use. + tts_settings: The TTS settings to use. + voice_pipeline_config: The voice pipeline config to use. + """ + self.tts_model = tts_model + self.tts_settings = tts_settings + self.total_output_text = "" + self.instructions = tts_settings.instructions + self.text_generation_task: asyncio.Task[Any] | None = None + + self._voice_pipeline_config = voice_pipeline_config + self._text_buffer = "" + self._turn_text_buffer = "" + self._queue: asyncio.Queue[VoiceStreamEvent] = asyncio.Queue() + self._tasks: list[asyncio.Task[Any]] = [] + self._ordered_tasks: list[ + asyncio.Queue[VoiceStreamEvent | None] + ] = [] # New: list to hold local queues for each text segment + self._dispatcher_task: asyncio.Task[Any] | None = ( + None # Task to dispatch audio chunks in order + ) + + self._done_processing = False + self._buffer_size = tts_settings.buffer_size + self._started_processing_turn = False + self._first_byte_received = False + self._generation_start_time: str | None = None + self._completed_session = False + self._stored_exception: BaseException | None = None + self._tracing_span: Span[SpeechGroupSpanData] | None = None + +``` + +-------------------------------- + +### Configure Agent Prompt in Python + +Source: https://openai.github.io/openai-agents-python/ref/agent + +This Python code snippet shows how to get the prompt for an agent. It utilizes a `PromptUtil` to convert the agent's `prompt` attribute into a model input, passing the `run_context` and the agent instance. This allows for dynamic configuration of agent prompts. + +```python +from typing import TypeVar, Generic + +# Assuming RunContextWrapper, ResponsePromptParam, PromptUtil, Prompt, DynamicPromptFunction are defined elsewhere +# class RunContextWrapper: +# pass +# class ResponsePromptParam: +# pass +# class PromptUtil: +# @staticmethod +# async def to_model_input(prompt, run_context, agent) -> ResponsePromptParam | None: +# # Placeholder implementation +# print(f"Converting prompt: {prompt} with context: {run_context} and agent: {agent}") +# return ResponsePromptParam() + +# class Prompt: +# pass +# class DynamicPromptFunction: +# pass + +TContext = TypeVar("TContext") + +class PromptUtil: + @staticmethod + async def to_model_input(prompt: 'Prompt | DynamicPromptFunction | None', run_context: 'RunContextWrapper[TContext]', agent: 'Agent[TContext]') -> 'ResponsePromptParam | None': + # Placeholder implementation + print(f"Converting prompt: {prompt} with context: {run_context} and agent: {agent}") + if prompt is not None: + return ResponsePromptParam() + return None + +class ResponsePromptParam: + pass + +class RunContextWrapper(Generic[TContext]): + pass + +class Agent(Generic[TContext]): + def __init__(self, prompt: 'Prompt | DynamicPromptFunction | None'): + self.prompt = prompt + + async def get_prompt(self, run_context: RunContextWrapper[TContext]) -> ResponsePromptParam | None: + """Get the prompt for the agent.""" + return await PromptUtil.to_model_input(self.prompt, run_context, self) + +# Example Usage: +# async def main(): +# # Assuming Prompt and RunContextWrapper are defined +# mock_prompt = Prompt() +# mock_context = RunContextWrapper() +# mock_agent = Agent(prompt=mock_prompt) +# result = await mock_agent.get_prompt(mock_context) +# print(result) + +# import asyncio +# asyncio.run(main()) + +``` + +-------------------------------- + +### Speech Span Creation + +Source: https://openai.github.io/openai-agents-python/ref/tracing/create + +Creates a new speech span for tracing text-to-speech operations. The span requires manual starting and finishing or usage within a `with` statement. + +```APIDOC +## POST /speech_span + +### Description +Creates a new speech span for tracing text-to-speech operations. The span will not be started automatically; you should either use `with speech_span() ...` or call `span.start()` and `span.finish()` manually. + +### Method +POST + +### Endpoint +`/speech_span` + +### Parameters +#### Query Parameters +- **model** (str | None) - Optional - The name of the model used for the text-to-speech. +- **input** (str | None) - Optional - The text input of the text-to-speech. +- **output** (str | None) - Optional - The audio output of the text-to-speech as base64 encoded string of PCM audio bytes. +- **output_format** (str | None) - Optional - The format of the audio output (defaults to "pcm"). +- **model_config** (Mapping[str, Any] | None) - Optional - The model configuration (hyperparameters) used. +- **first_content_at** (str | None) - Optional - The time of the first byte of the audio output. +- **span_id** (str | None) - Optional - The ID of the span. If not provided, an ID will be generated. It is recommended to use `util.gen_span_id()` for correct formatting. +- **parent** (Trace | Span[Any] | None) - Optional - The parent span or trace. If not provided, the current trace/span will be used as the parent. +- **disabled** (bool) - Optional - If True, a Span will be returned but not recorded. Defaults to False. + +### Request Example +```json +{ + "model": "tts-1", + "input": "Hello, world!", + "output_format": "mp3" +} +``` + +### Response +#### Success Response (200) +- **Span[SpeechSpanData]** - Represents the created speech span. + +#### Response Example +(This would typically be a Span object representation, not a simple JSON structure. The actual return type is `Span[SpeechSpanData]`.) +``` +# Example of how to use it in code: +# from agents.tracing import speech_span +# +# with speech_span(model="tts-1", input="Hello") as span: +# # Perform speech synthesis operations +# pass +``` +``` + +-------------------------------- + +### AdvancedSQLiteSession Initialization + +Source: https://openai.github.io/openai-agents-python/sessions/advanced_sqlite_session + +Demonstrates how to initialize the AdvancedSQLiteSession with different configurations, including in-memory storage, persistent storage, and custom logging. + +```APIDOC +## AdvancedSQLiteSession Initialization + +### Description +Initializes the `AdvancedSQLiteSession` with various options for conversation storage and logging. + +### Method +__init__ + +### Parameters +#### Path Parameters +- `session_id` (str) - Required - Unique identifier for the conversation session +- `db_path` (str | Path) - Optional - Path to SQLite database file. Defaults to `:memory:` for in-memory storage. +- `create_tables` (bool) - Optional - Whether to automatically create the advanced tables. Defaults to `False`. +- `logger` (logging.Logger | None) - Optional - Custom logger for the session. Defaults to module logger. + +### Request Example +```python +from agents.extensions.memory import AdvancedSQLiteSession + +# Basic initialization +session = AdvancedSQLiteSession( + session_id="my_conversation", + create_tables=True # Auto-create advanced tables +) + +# With persistent storage +session = AdvancedSQLiteSession( + session_id="user_123", + db_path="path/to/conversations.db", + create_tables=True +) + +# With custom logger +import logging +logger = logging.getLogger("my_app") +session = AdvancedSQLiteSession( + session_id="session_456", + create_tables=True, + logger=logger +) +``` +``` + +-------------------------------- + +### Handle Trace Start Event in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/provider + +The `on_trace_start` method is invoked when a new trace begins. It iterates through registered processors, calling their respective `on_trace_start` methods to handle the event. Errors during processor execution are logged. + +```python +def on_trace_start(self, trace: Trace) -> None: + """ + Called when a trace is started. + """ + for processor in self._processors: + try: + processor.on_trace_start(trace) + except Exception as e: + logger.error(f"Error in trace processor {processor} during on_trace_start: {e}") +``` + +-------------------------------- + +### Run LLM Start Callback in Python + +Source: https://openai.github.io/openai-agents-python/ko/ref/lifecycle + +The `on_llm_start` asynchronous method in `RunHooksBase` is called just before an LLM invocation occurs within an agent run. It receives context, the agent instance, the system prompt, and input items, allowing inspection or modification before the LLM call. + +```python +async def on_llm_start( + context: RunContextWrapper[TContext], + agent: Agent[TContext], + system_prompt: Optional[str], + input_items: list[TResponseInputItem], +) -> None: + pass +``` + +-------------------------------- + +### Initialize RealtimeSession in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/session + +Initializes the RealtimeSession with a model, agent, and context. Supports optional model and run configurations, including guardrails. Dependencies include RealtimeModel, RealtimeAgent, and various configuration types. + +```python +def __init__( + self, + model: RealtimeModel, + agent: RealtimeAgent, + context: TContext | None, + model_config: RealtimeModelConfig | None = None, + run_config: RealtimeRunConfig | None = None, +) -> None: + """Initialize the session. + + Args: + model: The model to use. + agent: The current agent. + context: The context object. + model_config: Model configuration. + run_config: Runtime configuration including guardrails. + """ + self._model = model + self._current_agent = agent + self._context_wrapper = RunContextWrapper(context) + self._event_info = RealtimeEventInfo(context=self._context_wrapper) + self._history: list[RealtimeItem] = [] + self._model_config = model_config or {} + self._run_config = run_config or {} + initial_model_settings = self._model_config.get("initial_model_settings") + run_config_settings = self._run_config.get("model_settings") + self._base_model_settings: RealtimeSessionModelSettings = { + **(run_config_settings or {}), + **(initial_model_settings or {}), + } + self._event_queue: asyncio.Queue[RealtimeSessionEvent] = asyncio.Queue() + self._closed = False + self._stored_exception: BaseException | None = None + + # Guardrails state tracking + self._interrupted_response_ids: set[str] = set() + self._item_transcripts: dict[str, str] = {} # item_id -> accumulated transcript + self._item_guardrail_run_counts: dict[str, int] = {} # item_id -> run count + self._debounce_text_length = self._run_config.get("guardrails_settings", {}).get( + "debounce_text_length", 100 + ) + + self._guardrail_tasks: set[asyncio.Task[Any]] = set() + self._tool_call_tasks: set[asyncio.Task[Any]] = set() + self._async_tool_calls: bool = bool(self._run_config.get("async_tool_calls", True)) +``` + +-------------------------------- + +### Handle Span Start Event in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/provider + +This method, `on_span_start`, is triggered at the beginning of a span's execution. It delegates the event to all configured trace processors, enabling them to initiate span-specific logic. Any exceptions raised by processors are logged. + +```python +def on_span_start(self, span: Span[Any]) -> None: + """ + Called when a span is started. + """ + for processor in self._processors: + try: + processor.on_span_start(span) + except Exception as e: + logger.error(f"Error in trace processor {processor} during on_span_start: {e}") +``` + +-------------------------------- + +### Configure MCP Servers for Agent (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/agent + +Defines a list of Model Context Protocol (MCP) servers that the agent can use. Tools from these servers are included in the agent's available tools during execution. Note: Server lifecycle management (connect/cleanup) is the user's responsibility. + +```python +mcp_servers: list[MCPServer] = field(default_factory=list) +``` + +-------------------------------- + +### GET /api/sessions/{session_id}/items + +Source: https://openai.github.io/openai-agents-python/ja/ref/memory/sqlite_session + +Retrieves the conversation history for a given session, optionally limited to the latest N items. + +```APIDOC +## GET /api/sessions/{session_id}/items + +### Description +Retrieve the conversation history for this session. + +### Method +GET + +### Endpoint +/api/sessions/{session_id}/items + +### Parameters +#### Query Parameters +- **limit** (integer) - Optional - Maximum number of items to retrieve. If None, retrieves all items. When specified, returns the latest N items in chronological order. + +### Response +#### Success Response (200) +- **items** (list[object]) - List of input items representing the conversation history + +#### Response Example +{ + "items": [ + { + "role": "user", + "content": "Hello!" + }, + { + "role": "assistant", + "content": "Hi there! How can I help you today?" + } + ] +} +``` + +-------------------------------- + +### Python: Tool Call Handling in RealtimeSession + +Source: https://openai.github.io/openai-agents-python/zh/ref/realtime/session + +Manages the execution of tool calls within the OpenAI agent framework. It identifies available tools and handoffs, initiates tool execution, and sends the output back to the model. + +```python +async def _handle_tool_call( + self, + event: RealtimeModelToolCallEvent, + *, + agent_snapshot: RealtimeAgent | None = None, + ) -> None: + """Handle a tool call event.""" + agent = agent_snapshot or self._current_agent + tools, handoffs = await asyncio.gather( + agent.get_all_tools(self._context_wrapper), + self._get_handoffs(agent, self._context_wrapper), + ) + function_map = {tool.name: tool for tool in tools if isinstance(tool, FunctionTool)} + handoff_map = {handoff.tool_name: handoff for handoff in handoffs} + + if event.name in function_map: + await self._put_event( + RealtimeToolStart( + info=self._event_info, + tool=function_map[event.name], + agent=agent, + arguments=event.arguments, + ) + ) + + func_tool = function_map[event.name] + tool_context = ToolContext( + context=self._context_wrapper.context, + usage=self._context_wrapper.usage, + tool_name=event.name, + tool_call_id=event.call_id, + tool_arguments=event.arguments, + ) + result = await func_tool.on_invoke_tool(tool_context, event.arguments) + + await self._model.send_event( + RealtimeModelSendToolOutput(tool_call=event, output=str(result), start_response=True) + ) + + await self._put_event( + RealtimeToolEnd( + info=self._event_info, + tool=func_tool, + ) + ) + +``` + +-------------------------------- + +### Get Response using LitellmModel in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/extensions/litellm + +Fetches a response from the configured LiteLLM model. It handles system instructions, user input, model settings, tools, output schemas, and tracing. The method abstracts the underlying LiteLLM API call and processes the response, logging it for debugging purposes unless explicitly disabled. + +```python + async def get_response( + self, + system_instructions: str | None, + input: str | list[TResponseInputItem], + model_settings: ModelSettings, + tools: list[Tool], + output_schema: AgentOutputSchemaBase | None, + handoffs: list[Handoff], + tracing: ModelTracing, + previous_response_id: str | None = None, # unused + conversation_id: str | None = None, # unused + prompt: Any | None = None, + ) -> ModelResponse: + with generation_span( + model=str(self.model), + model_config=model_settings.to_json_dict() + | {"base_url": str(self.base_url or ""), "model_impl": "litellm"}, + disabled=tracing.is_disabled(), + ) as span_generation: + response = await self._fetch_response( + system_instructions, + input, + model_settings, + tools, + output_schema, + handoffs, + span_generation, + tracing, + stream=False, + prompt=prompt, + ) + + message: litellm.types.utils.Message | None = None + first_choice: litellm.types.utils.Choices | None = None + if response.choices and len(response.choices) > 0: + choice = response.choices[0] + if isinstance(choice, litellm.types.utils.Choices): + first_choice = choice + message = first_choice.message + + if _debug.DONT_LOG_MODEL_DATA: + logger.debug("Received model response") + else: + if message is not None: + logger.debug( + f"""LLM resp:\n{ + json.dumps(message.model_dump(), indent=2, ensure_ascii=False) + }\n""" + ) + else: + +``` + +-------------------------------- + +### Create Transcription Span + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing + +Creates a new transcription span for speech-to-text processing. The span needs to be manually started and finished or used within a 'with' statement. + +```APIDOC +## POST /v1/spans/transcription + +### Description +Creates a new transcription span for speech-to-text processing. The span needs to be manually started and finished or used within a 'with' statement. + +### Method +POST + +### Endpoint +/v1/spans/transcription + +### Parameters +#### Query Parameters +- **model** (str | None) - Optional - The name of the model used for the speech-to-text. +- **input** (str | None) - Optional - The audio input of the speech-to-text transcription, as a base64 encoded string of audio bytes. +- **input_format** (str | None) - Optional - The format of the audio input (defaults to "pcm"). +- **output** (str | None) - Optional - The output of the speech-to-text transcription. +- **model_config** (Mapping[str, Any] | None) - Optional - The model configuration (hyperparameters) used. +- **span_id** (str | None) - Optional - The ID of the span. If not provided, a new ID will be generated. +- **parent** (Trace | Span[Any] | None) - Optional - The parent span or trace. If not provided, the current trace/span will be used. +- **disabled** (bool) - Optional - If True, the span will not be recorded. + +### Request Example +```json +{ + "model": "whisper-1", + "input": "base64_encoded_audio_data", + "input_format": "mp3" +} +``` + +### Response +#### Success Response (200) +- **Span[TranscriptionSpanData]** - The newly created speech-to-text span. +``` + +-------------------------------- + +### Initialize Streamable HTTP Client with Parameters + +Source: https://openai.github.io/openai-agents-python/ref/mcp/server + +Initializes the Streamable HTTP client with various parameters including tool caching, session timeouts, retry configurations, and message handling. It sets up the client's internal state and names the instance based on the provided URL. + +```python +class StreamableHTTP: + def __init__(self, params, cache_tools_list, client_session_timeout_seconds, tool_filter, use_structured_content, max_retry_attempts, retry_backoff_seconds_base, message_handler=None, name=None): + super().__init__( + cache_tools_list, + client_session_timeout_seconds, + tool_filter, + use_structured_content, + max_retry_attempts, + retry_backoff_seconds_base, + message_handler=message_handler, + ) + + self.params = params + self._name = name or f"streamable_http: {self.params['url']}" +``` + +-------------------------------- + +### Get Tool Usage + +Source: https://openai.github.io/openai-agents-python/ko/ref/extensions/memory/advanced_sqlite_session + +Retrieves a list of all tool usage, including the tool name, count, and the turn number in which it was used, for a specified branch. Defaults to the current branch if no branch ID is provided. + +```APIDOC +## GET /tools/usage + +### Description +Retrieves a list of all tool usage grouped by turn for a specified branch. + +### Method +GET + +### Endpoint +/tools/usage + +### Parameters +#### Query Parameters +- **branch_id** (string) - Optional - The branch ID to retrieve tool usage from. Defaults to the current branch if not provided. + +### Request Example +```json +{ + "branch_id": "optional_branch_id" +} +``` + +### Response +#### Success Response (200) +- Returns a list of tuples, where each tuple contains: + - **tool_name** (string) - The name of the tool. + - **usage_count** (integer) - The number of times the tool was used. + - **turn_number** (integer) - The turn number in which the tool was used. + +#### Response Example +```json +[ + ["get_weather", 1, 1], + ["calculate_area", 2, 2] +] +``` +``` + +-------------------------------- + +### RealtimeAgent Class Definition and Initialization + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/agent + +Defines the RealtimeAgent class, inheriting from AgentBase and supporting generic context types. It outlines supported and unsupported configuration options for voice agents within a RealtimeSession, including instructions, prompts, handoffs, output guardrails, and hooks. + +```python +from dataclasses import dataclass, field +from typing import Any, Generic, TContext, Callable, MaybeAwaitable, cast, Awaitable + +from src.agents.base import AgentBase +from src.agents.realtime.hooks import RealtimeAgentHooks +from src.context import RunContextWrapper +from src.prompts import Prompt +from src.tools.handoff import Handoff +from src.types import OutputGuardrail + +import inspect +import logging + +logger = logging.getLogger(__name__) + + +@dataclass +class RealtimeAgent(AgentBase, Generic[TContext]): + """A specialized agent instance that is meant to be used within a `RealtimeSession` to build + voice agents. Due to the nature of this agent, some configuration options are not supported + that are supported by regular `Agent` instances. For example: + - `model` choice is not supported, as all RealtimeAgents will be handled by the same model + within a `RealtimeSession`. + - `modelSettings` is not supported, as all RealtimeAgents will be handled by the same model + within a `RealtimeSession`. + - `outputType` is not supported, as RealtimeAgents do not support structured outputs. + - `toolUseBehavior` is not supported, as all RealtimeAgents will be handled by the same model + within a `RealtimeSession`. + - `voice` can be configured on an `Agent` level; however, it cannot be changed after the first + agent within a `RealtimeSession` has spoken. + + See `AgentBase` for base parameters that are shared with `Agent`s. + """ + + instructions: ( + str + | Callable[ + [RunContextWrapper[TContext], RealtimeAgent[TContext]], + MaybeAwaitable[str], + ] + | None + ) = None + """The instructions for the agent. Will be used as the "system prompt" when this agent is + invoked. Describes what the agent should do, and how it responds. + + Can either be a string, or a function that dynamically generates instructions for the agent. If + you provide a function, it will be called with the context and the agent instance. It must + return a string. + """ + + prompt: Prompt | None = None + """A prompt object. Prompts allow you to dynamically configure the instructions, tools + and other config for an agent outside of your code. Only usable with OpenAI models. + """ + + handoffs: list[RealtimeAgent[Any] | Handoff[TContext, RealtimeAgent[Any]]] = field( + default_factory=list + ) + """Handoffs are sub-agents that the agent can delegate to. You can provide a list of handoffs, + and the agent can choose to delegate to them if relevant. Allows for separation of concerns and + modularity. + """ + + output_guardrails: list[OutputGuardrail[TContext]] = field(default_factory=list) + """A list of checks that run on the final output of the agent, after generating a response. + Runs only if the agent produces a final output. + """ + + hooks: RealtimeAgentHooks | None = None + """A class that receives callbacks on various lifecycle events for this agent. + """ + + def clone(self, **kwargs: Any) -> RealtimeAgent[TContext]: + """Make a copy of the agent, with the given arguments changed. For example, you could do: + ``` + new_agent = agent.clone(instructions="New instructions") + ``` + """ + return dataclasses.replace(self, **kwargs) + + async def get_system_prompt(self, run_context: RunContextWrapper[TContext]) -> str | None: + """Get the system prompt for the agent.""" + if isinstance(self.instructions, str): + return self.instructions + elif callable(self.instructions): + if inspect.iscoroutinefunction(self.instructions): + return await cast(Awaitable[str], self.instructions(run_context, self)) + else: + return cast(str, self.instructions(run_context, self)) + elif self.instructions is not None: + logger.error(f"Instructions must be a string or a function, got {self.instructions}") + + return None + +``` + +-------------------------------- + +### Agent Start Callback in Python + +Source: https://openai.github.io/openai-agents-python/ko/ref/lifecycle + +The `on_agent_start` asynchronous method in `RunHooksBase` is called before an agent is invoked, particularly when the current agent changes. It receives the agent hook context and the agent instance that is about to be executed. + +```python +async def on_agent_start(context: AgentHookContext[TContext], agent: TAgent) -> None: + pass +``` + +-------------------------------- + +### Create Trace in Python + +Source: https://openai.github.io/openai-agents-python/ref/tracing/create + +Creates a new trace, which can be used as a context manager or started/finished manually. It allows for attaching metadata and configuring tracing export. The trace is not automatically started. + +```python +def trace( + workflow_name: str, + trace_id: str | None = None, + group_id: str | None = None, + metadata: dict[str, Any] | None = None, + tracing: TracingConfig | None = None, + disabled: bool = False, +) -> Trace: + """ + Create a new trace. The trace will not be started automatically; you should either use + it as a context manager (`with trace(...):`) or call `trace.start()` + `trace.finish()` + manually. + + In addition to the workflow name and optional grouping identifier, you can provide + an arbitrary metadata dictionary to attach additional user-defined information to + the trace. + + Args: + workflow_name: The name of the logical app or workflow. For example, you might provide + "code_bot" for a coding agent, or "customer_support_agent" for a customer support agent. + trace_id: The ID of the trace. Optional. If not provided, we will generate an ID. We + recommend using `util.gen_trace_id()` to generate a trace ID, to guarantee that IDs are + correctly formatted. + group_id: Optional grouping identifier to link multiple traces from the same conversation + or process. For instance, you might use a chat thread ID. + metadata: Optional dictionary of additional metadata to attach to the trace. + tracing: Optional tracing configuration for exporting this trace. + disabled: If True, we will return a Trace but the Trace will not be recorded. + + Returns: + The newly created trace object. + """ + current_trace = get_trace_provider().get_current_trace() + if current_trace: + logger.warning( + "Trace already exists. Creating a new trace, but this is probably a mistake." + ) + + return get_trace_provider().create_trace( + name=workflow_name, + trace_id=trace_id, + group_id=group_id, + metadata=metadata, + tracing=tracing, + disabled=disabled, + ) +``` + +-------------------------------- + +### Get Conversation by Turns + +Source: https://openai.github.io/openai-agents-python/ko/ref/extensions/memory/advanced_sqlite_session + +Retrieves the conversation history grouped by user turns for a specified branch. If no branch ID is provided, it defaults to the current branch. + +```APIDOC +## GET /conversations/by_turns + +### Description +Retrieves conversation history grouped by user turns for a specific branch. + +### Method +GET + +### Endpoint +/conversations/by_turns + +### Parameters +#### Query Parameters +- **branch_id** (string) - Optional - The branch ID to retrieve the conversation from. Defaults to the current branch if not provided. + +### Request Example +```json +{ + "branch_id": "optional_branch_id" +} +``` + +### Response +#### Success Response (200) +- **turn_number** (integer) - The turn number in the conversation. +- **messages** (array) - A list of messages within that turn. + - **type** (string) - The type of message (e.g., 'user', 'assistant', 'tool'). + - **tool_name** (string) - The name of the tool used, if applicable. + +#### Response Example +```json +{ + "1": [ + { + "type": "user", + "tool_name": null + }, + { + "type": "assistant", + "tool_name": null + } + ], + "2": [ + { + "type": "tool_call", + "tool_name": "get_weather" + }, + { + "type": "assistant", + "tool_name": null + } + ] +} +``` +``` + +-------------------------------- + +### Agent Configuration and Validation in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/agent + +This Python code defines the configuration for an agent, including its name, tools, instructions, and model settings. It also includes validation logic within the `__post_init__` method to ensure that the provided configuration parameters are of the correct types, raising `TypeError` if they are not. This ensures the agent is initialized with valid settings. + +```python +from typing import Any, Literal +from .models.interface import Model +from .models.settings import ModelSettings +from .schemas.base import AgentOutputSchemaBase +from .schemas.output import AgentOutputSchema +from .types import AgentHooks, StopAtTools, ToolsToFinalOutputFunction + + +class Agent: + """A class that represents an agent. + + Attributes: + name (str): The name of the agent. + description (str | None): A description of the agent. + output_type (type[Any] | AgentOutputSchemaBase | None): The type of the output object. + hooks (AgentHooks[TContext] | None): A class that receives callbacks on various lifecycle events for this agent. + tool_use_behavior (Literal["run_llm_again", "stop_on_first_tool"] | StopAtTools | ToolsToFinalOutputFunction): Configures how tool use is handled. + reset_tool_choice (bool): Whether to reset the tool choice after a tool has been called. + """ + + name: str + description: str | None = None + output_type: type[Any] | AgentOutputSchemaBase | None = None + """The type of the output object. If not provided, the output will be `str`. In most cases, + you should pass a regular Python type (e.g. a dataclass, Pydantic model, TypedDict, etc). + You can customize this in two ways: + 1. If you want non-strict schemas, pass `AgentOutputSchema(MyClass, strict_json_schema=False)`. + 2. If you want to use a custom JSON schema (i.e. without using the SDK's automatic schema) + creation, subclass and pass an `AgentOutputSchemaBase` subclass. + """ + + hooks: AgentHooks[TContext] | None = None + """A class that receives callbacks on various lifecycle events for this agent. + """ + + tool_use_behavior: ( + Literal["run_llm_again", "stop_on_first_tool"] | StopAtTools | ToolsToFinalOutputFunction + ) = "run_llm_again" + """ + This lets you configure how tool use is handled. + - "run_llm_again": The default behavior. Tools are run, and then the LLM receives the results + and gets to respond. + - "stop_on_first_tool": The output from the first tool call is treated as the final result. + In other words, it isn’t sent back to the LLM for further processing but is used directly + as the final output. + - A StopAtTools object: The agent will stop running if any of the tools listed in + `stop_at_tool_names` is called. + The final output will be the output of the first matching tool call. + The LLM does not process the result of the tool call. + - A function: If you pass a function, it will be called with the run context and the list of + tool results. It must return a `ToolsToFinalOutputResult`, which determines whether the tool + calls result in a final output. + + NOTE: This configuration is specific to FunctionTools. Hosted tools, such as file search, + web search, etc. are always processed by the LLM. + """ + + reset_tool_choice: bool = True + """Whether to reset the tool choice to the default value after a tool has been called. Defaults + to True. This ensures that the agent doesn't enter an infinite loop of tool usage. + """ + + def __post_init__(self): + from typing import get_origin + + if not isinstance(self.name, str): + raise TypeError(f"Agent name must be a string, got {type(self.name).__name__}") + + if self.handoff_description is not None and not isinstance(self.handoff_description, str): + raise TypeError( + f"Agent handoff_description must be a string or None, " + f"got {type(self.handoff_description).__name__}" + ) + + if not isinstance(self.tools, list): + raise TypeError(f"Agent tools must be a list, got {type(self.tools).__name__}") + + if not isinstance(self.mcp_servers, list): + raise TypeError( + f"Agent mcp_servers must be a list, got {type(self.mcp_servers).__name__}" + ) + + if not isinstance(self.mcp_config, dict): + raise TypeError( + f"Agent mcp_config must be a dict, got {type(self.mcp_config).__name__}" + ) + + if ( + self.instructions is not None + and not isinstance(self.instructions, str) + and not callable(self.instructions) + ): + raise TypeError( + f"Agent instructions must be a string, callable, or None, " + f"got {type(self.instructions).__name__}" + ) + + if ( + self.prompt is not None + and not callable(self.prompt) + and not hasattr(self.prompt, "get") + ): + raise TypeError( + f"Agent prompt must be a Prompt, DynamicPromptFunction, or None, " + f"got {type(self.prompt).__name__}" + ) + + if not isinstance(self.handoffs, list): + raise TypeError(f"Agent handoffs must be a list, got {type(self.handoffs).__name__}") + + if self.model is not None and not isinstance(self.model, str): + if not isinstance(self.model, Model): + raise TypeError( + f"Agent model must be a string, Model, or None, got {type(self.model).__name__}" + ) + + if not isinstance(self.model_settings, ModelSettings): + raise TypeError( + f"Agent model_settings must be a ModelSettings instance, " + f"got {type(self.model_settings).__name__}" + ) + + if ( + # The user sets a non-default model + self.model is not None + and ( + # The default model is gpt-5 + is_gpt_5_default() is True + # However, the specified model is not a gpt-5 model + and ( + +``` + +-------------------------------- + +### Server Configuration Parameters + +Source: https://openai.github.io/openai-agents-python/zh/ref/mcp/server + +This snippet shows how to retrieve server configuration parameters like timeouts and termination behavior. It uses a dictionary-like object `self.params` to get values, with default fallbacks. + +```python +timeout=self.params.get("timeout", 5), + sse_read_timeout=self.params.get("sse_read_timeout", 60 * 5), + terminate_on_close=self.params.get("terminate_on_close", True), + ) +``` + +-------------------------------- + +### Create Handoff Span + +Source: https://openai.github.io/openai-agents-python/zh/ref/tracing + +This endpoint allows you to create a new handoff span for tracing agent interactions. The span needs to be started and finished manually or used within a `with` statement. + +```APIDOC +## POST /v1/spans/handoff + +### Description +Creates a new handoff span. This span is not started automatically. You must use `with handoff_span() ...` or manually call `span.start()` and `span.finish()`. + +### Method +POST + +### Endpoint +/v1/spans/handoff + +### Parameters +#### Query Parameters +- **from_agent** (string) - Optional - The name of the agent that is handing off. +- **to_agent** (string) - Optional - The name of the agent that is receiving the handoff. +- **span_id** (string) - Optional - The ID of the span. If not provided, an ID will be generated. It is recommended to use `util.gen_span_id()` for correctly formatted IDs. +- **parent** (Trace | Span[Any]) - Optional - The parent span or trace. If not provided, the current trace/span will be used as the parent. +- **disabled** (boolean) - Optional - If True, the created Span will not be recorded. Defaults to `False`. + +### Request Body +This endpoint does not strictly require a request body, but it accepts parameters for span creation. + +### Request Example +```json +{ + "from_agent": "AgentA", + "to_agent": "AgentB", + "span_id": "handoff-123", + "disabled": false +} +``` + +### Response +#### Success Response (200) +- **span_id** (string) - The ID of the created handoff span. +- **status** (string) - The status of the span creation (e.g., 'created'). + +#### Response Example +```json +{ + "span_id": "handoff-123", + "status": "created" +} +``` +``` + +-------------------------------- + +### Agent Configuration with Prompt Template + +Source: https://openai.github.io/openai-agents-python/agents + +Shows how to configure an agent to use a pre-defined prompt template from the OpenAI platform. The `prompt` parameter accepts an object with `id`, `version`, and `variables` to specify the template and its dynamic values. + +```python +from agents import Agent + +agent = Agent( + name="Prompted assistant", + prompt={ + "id": "pmpt_123", + "version": "1", + "variables": {"poem_style": "haiku"}, + }, +) +``` + +-------------------------------- + +### Create Trace + +Source: https://openai.github.io/openai-agents-python/zh/ref/tracing + +Creates a new trace object. This trace can be used as a context manager or managed manually with start() and finish() methods. It allows for detailed logging and monitoring of workflow execution. + +```APIDOC +## POST /trace + +### Description +Creates a new trace for a given workflow. The trace is not automatically started and requires manual initiation using a context manager (`with trace(...)`) or explicit `start()` and `finish()` calls. + +### Method +POST + +### Endpoint +/trace + +### Parameters +#### Query Parameters +- **workflow_name** (str) - Required - The name of the logical application or workflow (e.g., "code_bot"). +- **trace_id** (str | None) - Optional - A unique identifier for the trace. If not provided, one will be generated. +- **group_id** (str | None) - Optional - An identifier to group related traces, such as a chat thread ID. +- **metadata** (dict[str, Any] | None) - Optional - A dictionary for attaching user-defined metadata to the trace. +- **tracing** (TracingConfig | None) - Optional - Configuration for exporting the trace. +- **disabled** (bool) - Optional - If True, the trace will be created but not recorded. Defaults to False. + +### Request Body +(Not applicable for this function, parameters are passed as arguments) + +### Response +#### Success Response (200) +- **Trace** (Trace) - The newly created trace object, which can be used for managing the trace lifecycle. +``` + +-------------------------------- + +### MultiProvider Model Retrieval Logic (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/models/multi_provider + +Provides the `get_model` method, which takes a model name, determines its prefix, and uses either a custom provider from `provider_map` or a fallback provider (like OpenAI or Litellm) to retrieve the appropriate model instance. + +```python + def get_model(self, model_name: str | None) -> Model: + """Returns a Model based on the model name. The model name can have a prefix, ending with + a "/", which will be used to look up the ModelProvider. If there is no prefix, we will use + the OpenAI provider. + + Args: + model_name: The name of the model to get. + + Returns: + A Model. + """ + prefix, model_name = self._get_prefix_and_model_name(model_name) + + if prefix and self.provider_map and (provider := self.provider_map.get_provider(prefix)): + return provider.get_model(model_name) + else: + return self._get_fallback_provider(prefix).get_model(model_name) + +``` + +-------------------------------- + +### Get Tool Usage by Turn (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/extensions/memory/advanced_sqlite_session + +Fetches all tool usage instances, grouped by turn, for a given branch. This asynchronous function utilizes a synchronous helper for database interaction, ensuring non-blocking operations. It returns a list of tuples, each containing the tool name, its usage count, and the corresponding turn number. + +```python +async def get_tool_usage(self, branch_id: str | None = None) -> list[tuple[str, int, int]]: + """Get all tool usage by turn for specified branch. + + Args: + branch_id: Branch to get tool usage from (current branch if None). + + Returns: + List of tuples containing (tool_name, usage_count, turn_number). + """ + if branch_id is None: + branch_id = self._current_branch_id + + def _get_tool_usage_sync(): + """Synchronous helper to get tool usage statistics.""" + conn = self._get_connection() + with closing(conn.cursor()) as cursor: + cursor.execute( + """ + SELECT tool_name, COUNT(*), user_turn_number + FROM message_structure + WHERE session_id = ? AND branch_id = ? AND message_type IN ( + 'tool_call', 'function_call', 'computer_call', 'file_search_call', + 'web_search_call', 'code_interpreter_call', 'custom_tool_call', + 'mcp_call', 'mcp_approval_request' + ) + GROUP BY tool_name, user_turn_number + ORDER BY user_turn_number + """, + (self.session_id, branch_id), + ) + return cursor.fetchall() + + return await asyncio.to_thread(_get_tool_usage_sync) +``` + +-------------------------------- + +### Implement Input Guardrail with Agent + +Source: https://openai.github.io/openai-agents-python/guardrails + +This snippet demonstrates how to create an input guardrail that uses an agent to check user input. It defines a Pydantic model for the guardrail's output and an agent to perform the check. The guardrail function then runs this agent and returns a GuardrailFunctionOutput. + +```python +from pydantic import BaseModel +from agents import ( + Agent, + GuardrailFunctionOutput, + InputGuardrailTripwireTriggered, + RunContextWrapper, + Runner, + TResponseInputItem, + input_guardrail, +) + +class MathHomeworkOutput(BaseModel): + is_math_homework: bool + reasoning: str + +guardrail_agent = Agent( + name="Guardrail check", + instructions="Check if the user is asking you to do their math homework.", + output_type=MathHomeworkOutput, +) + + +@input_guardrail +async def math_guardrail( + ctx: RunContextWrapper[None], agent: Agent, input: str | list[TResponseInputItem] +) -> GuardrailFunctionOutput: + result = await Runner.run(guardrail_agent, input, context=ctx.context) + + return GuardrailFunctionOutput( + output_info=result.final_output, + tripwire_triggered=result.final_output.is_math_homework, + ) + + +agent = Agent( + name="Customer support agent", + instructions="You are a customer support agent. You help customers with their questions.", + input_guardrails=[math_guardrail], +) + +async def main(): + # This should trip the guardrail + try: + await Runner.run(agent, "Hello, can you help me solve for x: 2x + 3 = 11?") + print("Guardrail didn't trip - this is unexpected") + + except InputGuardrailTripwireTriggered: + print("Math homework guardrail tripped") + +``` + +-------------------------------- + +### Class Method: run_sync + +Source: https://openai.github.io/openai-agents-python/zh/ref/run + +Synchronously runs an agent workflow starting from a specified agent. This method is suitable for non-async environments and executes the agent loop until a final output is produced. + +```APIDOC +## Class Method: run_sync + +### Description +Runs a workflow synchronously, starting at the given agent. This method wraps the `run` method and is not suitable for environments with an existing event loop (e.g., async functions, Jupyter notebooks, FastAPI). + +The agent executes in a loop: invoking the agent, checking for final output, handling agent handoffs, and executing tool calls. + +Exceptions can be raised if `max_turns` is exceeded or a guardrail tripwire is triggered. Note that only the first agent's input guardrails are evaluated. + +### Method +`classmethod` + +### Endpoint +N/A (This is a class method, not a REST endpoint) + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +None + +### Request Example +```python +# This is a conceptual example, actual usage requires Agent and other types to be defined. +# from agents import Agent, RunResult +# from typing import TypeVar, Generic + +# TContext = TypeVar('TContext') +# TResponseInputItem = TypeVar('TResponseInputItem') + +# class MyAgent(Agent[TContext]): +# pass + +# starting_agent = MyAgent() +# input_data = "Hello agent!" +# result: RunResult = MyAgent.run_sync(starting_agent, input_data) +``` + +### Response +#### Success Response (200) +- **RunResult** (object) - A run result object containing all inputs, guardrail results, and the final output of the last agent. The specific output type can vary due to agent handoffs. + +#### Response Example +```json +{ + "inputs": [...], + "guardrail_results": [...], + "output": "..." +} +``` + +### Parameters Details: +- **starting_agent** (`Agent[TContext]`) - Required - The agent instance to begin the workflow. +- **input** (`str | list[TResponseInputItem]`) - Required - The initial input provided to the agent. Can be a single string or a list of input items. +- **context** (`TContext | None`) - Optional - The context object to be used during agent execution. Defaults to `None`. +- **max_turns** (`int`) - Optional - The maximum number of turns (AI invocations including tool calls) allowed in the agent run. Defaults to `DEFAULT_MAX_TURNS`. +- **hooks** (`RunHooks[TContext] | None`) - Optional - An object for receiving callbacks during various lifecycle events of the agent run. Defaults to `None`. +- **run_config** (`RunConfig | None`) - Optional - Global configuration settings for the entire agent run. Defaults to `None`. +- **previous_response_id** (`str | None`) - Optional - The ID of a previous response, useful for OpenAI models via the Responses API to avoid re-passing input from the prior turn. Defaults to `None`. +- **auto_previous_response_id** (`bool`) - Optional - If `True`, automatically uses the previous response ID when available. Defaults to `False`. +- **conversation_id** (`str | None`) - Optional - The ID of a stored conversation, if applicable. Defaults to `None`. +- **session** (`Session | None`) - Optional - A session object for managing conversation history automatically. Defaults to `None`. +``` + +-------------------------------- + +### Initialize AdvancedSQLiteSession + +Source: https://openai.github.io/openai-agents-python/ja/ref/extensions/memory/advanced_sqlite_session + +Initializes the AdvancedSQLiteSession with a session ID, optional database path, table creation flag, and logger. It sets up the session and optionally creates structure tables. + +```python +def __init__( + self, + *, + session_id: str, + db_path: str | Path = ":memory:", + create_tables: bool = False, + logger: logging.Logger | None = None, + **kwargs, +): + """Initialize the AdvancedSQLiteSession. + + Args: + session_id: The ID of the session + db_path: The path to the SQLite database file. Defaults to `:memory:` for in-memory storage + create_tables: Whether to create the structure tables + logger: The logger to use. Defaults to the module logger + **kwargs: Additional keyword arguments to pass to the superclass + """ # noqa: E501 + super().__init__(session_id, db_path, **kwargs) + if create_tables: + self._init_structure_tables() + self._current_branch_id = "main" + self._logger = logger or logging.getLogger(__name__) + +``` + +-------------------------------- + +### Get Prompt - Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/mcp/server + +Abstract method to retrieve a specific prompt from the server by its name, optionally with arguments. It returns a GetPromptResult. This asynchronous method is part of the server's API. + +```python +import abc +from typing import Any + +from .get_prompt_result import GetPromptResult + + +class MCPBase(abc.ABC): + @abc.abstractmethod + async def get_prompt(self, name: str, arguments: dict[str, Any] | None = None) -> GetPromptResult: + """Get a specific prompt from the server.""" + pass + +``` + +-------------------------------- + +### GET /items + +Source: https://openai.github.io/openai-agents-python/zh/ref/extensions/memory/advanced_sqlite_session + +Retrieves conversation items from the current or a specified branch. This endpoint allows fetching a list of items, optionally limited by a count and filtered by a specific branch ID. + +```APIDOC +## GET /items + +### Description +Retrieves conversation items from the current or a specified branch. This endpoint allows fetching a list of items, optionally limited by a count and filtered by a specific branch ID. + +### Method +GET + +### Endpoint +/items + +### Parameters +#### Query Parameters +- **limit** (integer) - Optional - Maximum number of items to return. If not provided, returns all items. +- **branch_id** (string) - Optional - The ID of the branch to retrieve items from. If not provided, uses the current branch. + +### Request Example +```json +{ + "limit": 10, + "branch_id": "develop" +} +``` + +### Response +#### Success Response (200) +- **items** (list[object]) - A list of conversation items. Each item is a JSON object representing a message. + +#### Response Example +```json +{ + "items": [ + { + "role": "user", + "content": "Hello, how are you?" + }, + { + "role": "assistant", + "content": "I'm doing well, thank you! How can I help you today?" + } + ] +} +``` +``` + +-------------------------------- + +### agent_span + +Source: https://openai.github.io/openai-agents-python/zh/ref/tracing/create + +Creates a new agent span for tracing agent activities. The span requires manual start and finish operations or can be used with a `with` statement. + +```APIDOC +## POST /agent_span + +### Description +Creates a new agent span to track agent activities. This span is not started automatically; users must explicitly call `span.start()` and `span.finish()` or use a `with agent_span() ...` block. + +### Method +POST + +### Endpoint +/agent_span + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **name** (str) - Required - The name of the agent. +- **handoffs** (list[str] | None) - Optional - A list of agent names to which this agent can hand off control. +- **tools** (list[str] | None) - Optional - A list of tool names available to this agent. +- **output_type** (str | None) - Optional - The name of the output type produced by the agent. +- **span_id** (str | None) - Optional - The ID of the span. If not provided, an ID will be generated. It is recommended to use `util.gen_span_id()` for correctly formatted IDs. +- **parent** (Trace | Span[Any] | None) - Optional - The parent span or trace. If not provided, the current trace/span will be used as the parent. +- **disabled** (bool) - Optional - If True, the span will be created but not recorded. Defaults to False. + +### Request Example +```json +{ + "name": "MyAgent", + "handoffs": ["OtherAgent"], + "tools": ["Tool1", "Tool2"], + "output_type": "string", + "span_id": "generated-span-id", + "parent": null, + "disabled": false +} +``` + +### Response +#### Success Response (200) +- **Span[AgentSpanData]** - The newly created agent span, containing span details and data. + +#### Response Example +```json +{ + "span_id": "generated-span-id", + "name": "MyAgent", + "type": "agent", + "start_time": "2023-10-27T10:00:00Z", + "end_time": null, + "status": "running", + "events": [], + "attributes": { + "handoffs": ["OtherAgent"], + "tools": ["Tool1", "Tool2"], + "output_type": "string" + } +} +``` +``` + +-------------------------------- + +### Include Handoff Instructions in Agent Prompt (Python) + +Source: https://openai.github.io/openai-agents-python/handoffs + +Shows how to integrate recommended handoff instructions into an agent's prompt using `RECOMMENDED_PROMPT_PREFIX` from `agents.extensions.handoff_prompt`. This helps LLMs understand and process handoff information correctly. + +```python +from agents import Agent +from agents.extensions.handoff_prompt import RECOMMENDED_PROMPT_PREFIX + +billing_agent = Agent( + name="Billing agent", + instructions=f"{RECOMMENDED_PROMPT_PREFIX} + .", +) +``` + +-------------------------------- + +### Define Agent Handoffs (Python) + +Source: https://openai.github.io/openai-agents-python/quickstart + +This Python code defines a 'Triage Agent' that can hand off tasks to other specialist agents like 'History Tutor' and 'Math Tutor'. The `handoffs` parameter is a list of agents the triage agent can delegate to. + +```python +from agents import Agent + +triage_agent = Agent( + name="Triage Agent", + instructions="You determine which agent to use based on the user's homework question", + handoffs=[history_tutor_agent, math_tutor_agent] +) +``` + +-------------------------------- + +### VoicePipeline Initialization and Execution (Python) + +Source: https://openai.github.io/openai-agents-python/ko/ref/voice/pipeline + +Initializes and runs the VoicePipeline, which processes audio input through transcription, a workflow, and text-to-speech synthesis. It supports both single audio inputs and streamed audio inputs. + +```python +class VoicePipeline: + """An opinionated voice agent pipeline. It works in three steps: + 1. Transcribe audio input into text. + 2. Run the provided `workflow`, which produces a sequence of text responses. + 3. Convert the text responses into streaming audio output. + """ + + def __init__( + self, + *, + workflow: VoiceWorkflowBase, + stt_model: STTModel | str | None = None, + tts_model: TTSModel | str | None = None, + config: VoicePipelineConfig | None = None, + ): + """Create a new voice pipeline. + + Args: + workflow: The workflow to run. See `VoiceWorkflowBase`. + stt_model: The speech-to-text model to use. If not provided, a default OpenAI + model will be used. + tts_model: The text-to-speech model to use. If not provided, a default OpenAI + model will be used. + config: The pipeline configuration. If not provided, a default configuration will be + used. + """ + self.workflow = workflow + self.stt_model = stt_model if isinstance(stt_model, STTModel) else None + self.tts_model = tts_model if isinstance(tts_model, TTSModel) else None + self._stt_model_name = stt_model if isinstance(stt_model, str) else None + self._tts_model_name = tts_model if isinstance(tts_model, str) else None + self.config = config or VoicePipelineConfig() + + async def run(self, audio_input: AudioInput | StreamedAudioInput) -> StreamedAudioResult: + """Run the voice pipeline. + + Args: + audio_input: The audio input to process. This can either be an `AudioInput` instance, + which is a single static buffer, or a `StreamedAudioInput` instance, which is a + stream of audio data that you can append to. + + Returns: + A `StreamedAudioResult` instance. You can use this object to stream audio events and + play them out. + """ + if isinstance(audio_input, AudioInput): + return await self._run_single_turn(audio_input) + elif isinstance(audio_input, StreamedAudioInput): + return await self._run_multi_turn(audio_input) + else: + raise UserError(f"Unsupported audio input type: {type(audio_input)}") + + def _get_tts_model(self) -> TTSModel: + if not self.tts_model: + self.tts_model = self.config.model_provider.get_tts_model(self._tts_model_name) + return self.tts_model + + def _get_stt_model(self) -> STTModel: + if not self.stt_model: + self.stt_model = self.config.model_provider.get_stt_model(self._stt_model_name) + return self.stt_model + + async def _process_audio_input(self, audio_input: AudioInput) -> str: + model = self._get_stt_model() + return await model.transcribe( + audio_input, + self.config.stt_settings, + self.config.trace_include_sensitive_data, + self.config.trace_include_sensitive_audio_data, + ) + + async def _run_single_turn(self, audio_input: AudioInput) -> StreamedAudioResult: + # Since this is single turn, we can use the TraceCtxManager to manage starting/ending the + # trace + with TraceCtxManager( + workflow_name=self.config.workflow_name or "Voice Agent", + trace_id=None, # Automatically generated + group_id=self.config.group_id, + metadata=self.config.trace_metadata, + tracing=self.config.tracing, + disabled=self.config.tracing_disabled, + ): + input_text = await self._process_audio_input(audio_input) + + output = StreamedAudioResult( + self._get_tts_model(), self.config.tts_settings, self.config + ) + + async def stream_events(): + try: + async for text_event in self.workflow.run(input_text): + await output._add_text(text_event) + await output._turn_done() + await output._done() + +``` + +-------------------------------- + +### Get Speech-to-Text Model (Python) + +Source: https://openai.github.io/openai-agents-python/ref/voice/models/openai_model_provider + +Retrieves a Speech-to-Text (STT) model from the OpenAI provider. If no model name is specified, it defaults to `DEFAULT_STT_MODEL`. This function requires an initialized OpenAI client. + +```python +def get_stt_model(self, model_name: str | None) -> STTModel: + """Get a speech-to-text model by name. + + Args: + model_name: The name of the model to get. + + Returns: + The speech-to-text model. + """ + return OpenAISTTModel(model_name or DEFAULT_STT_MODEL, self._get_client()) +``` + +-------------------------------- + +### Get Text-to-Speech Model (Python) + +Source: https://openai.github.io/openai-agents-python/ref/voice/models/openai_model_provider + +Retrieves a Text-to-Speech (TTS) model from the OpenAI provider. If no model name is specified, it defaults to `DEFAULT_TTS_MODEL`. This function requires an initialized OpenAI client. + +```python +def get_tts_model(self, model_name: str | None) -> TTSModel: + """Get a text-to-speech model by name. + + Args: + model_name: The name of the model to get. + + Returns: + The text-to-speech model. + """ + return OpenAITTSModel(model_name or DEFAULT_TTS_MODEL, self._get_client()) +``` + +-------------------------------- + +### Get All Function Tools from MCP Servers (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/mcp/util + +Retrieves all function tools from a list of MCP servers. It checks for duplicate tool names across servers to prevent conflicts. Dependencies include MCPServer, RunContextWrapper, AgentBase, and Tool. + +```python +class MCPUtil: + """Set of utilities for interop between MCP and Agents SDK tools.""" + + @classmethod + async def get_all_function_tools( + cls, + servers: list["MCPServer"], + convert_schemas_to_strict: bool, + run_context: RunContextWrapper[Any], + agent: "AgentBase", + ) -> list[Tool]: + """Get all function tools from a list of MCP servers.""" + tools = [] + tool_names: set[str] = set() + for server in servers: + server_tools = await cls.get_function_tools( + server, convert_schemas_to_strict, run_context, agent + ) + server_tool_names = {tool.name for tool in server_tools} + if len(server_tool_names & tool_names) > 0: + raise UserError( + f"Duplicate tool names found across MCP servers: " + f"{server_tool_names & tool_names}" + ) + tool_names.update(server_tool_names) + tools.extend(server_tools) + + return tools +``` + +-------------------------------- + +### Initialize OpenAI Voice Model Provider (Python) + +Source: https://openai.github.io/openai-agents-python/zh/ref/voice/models/openai_provider + +Initializes the OpenAIVoiceModelProvider with optional OpenAI client configurations. It supports direct client injection or lazy initialization using API keys, base URLs, organization, and project details. Dependencies include the `AsyncOpenAI` client and potentially shared HTTP client configurations. + +```python +class OpenAIVoiceModelProvider(VoiceModelProvider): + """A voice model provider that uses OpenAI models.""" + + def __init__( + self, + *, + api_key: str | None = None, + base_url: str | None = None, + openai_client: AsyncOpenAI | None = None, + organization: str | None = None, + project: str | None = None, + ) -> None: + """Create a new OpenAI voice model provider. + + Args: + api_key: The API key to use for the OpenAI client. If not provided, we will use the + default API key. + base_url: The base URL to use for the OpenAI client. If not provided, we will use the + default base URL. + openai_client: An optional OpenAI client to use. If not provided, we will create a new + OpenAI client using the api_key and base_url. + organization: The organization to use for the OpenAI client. + project: The project to use for the OpenAI client. + """ + if openai_client is not None: + assert api_key is None and base_url is None, + ("Don't provide api_key or base_url if you provide openai_client") + self._client: AsyncOpenAI | None = openai_client + else: + self._client = None + self._stored_api_key = api_key + self._stored_base_url = base_url + self._stored_organization = organization + self._stored_project = project + + # We lazy load the client in case you never actually use OpenAIProvider(). Otherwise + # AsyncOpenAI() raises an error if you don't have an API key set. + def _get_client(self) -> AsyncOpenAI: + if self._client is None: + self._client = _openai_shared.get_default_openai_client() or AsyncOpenAI( + api_key=self._stored_api_key or _openai_shared.get_default_openai_key(), + base_url=self._stored_base_url, + organization=self._stored_organization, + project=self._stored_project, + http_client=shared_http_client(), + ) + + return self._client +``` + +-------------------------------- + +### Span Management Methods + +Source: https://openai.github.io/openai-agents-python/ref/tracing + +Abstract methods for controlling the lifecycle of a tracing span. The `start` method initiates the span, optionally marking it as the current span, while the `finish` method terminates the span, with an option to reset the current span. + +```python +@abc.abstractmethod +def start(self, mark_as_current: bool = False): + """ + Start the span. + + Args: + mark_as_current: If true, the span will be marked as the current span. + """ + pass +``` + +```python +@abc.abstractmethod +def finish(self, reset_current: bool = False) -> None: + """ + Finish the span. + + Args: + reset_current: If true, the span will be reset as the current span. + """ + pass +``` + +-------------------------------- + +### OpenAI Voice Model Provider Initialization + +Source: https://openai.github.io/openai-agents-python/ko/ref/voice/models/openai_model_provider + +Initializes a new OpenAI voice model provider. You can provide an existing OpenAI client or specify API key, base URL, organization, and project details. + +```APIDOC +## POST /voice/models/openai + +### Description +Initializes a new OpenAI voice model provider. This allows for configuration via an existing OpenAI client or by providing API credentials and organizational details. + +### Method +POST + +### Endpoint +/voice/models/openai + +### Parameters +#### Query Parameters +- **api_key** (str) - Optional - The API key to use for the OpenAI client. If not provided, the default API key will be used. +- **base_url** (str) - Optional - The base URL to use for the OpenAI client. If not provided, the default base URL will be used. +- **organization** (str) - Optional - The organization to use for the OpenAI client. +- **project** (str) - Optional - The project to use for the OpenAI client. + +#### Request Body +- **openai_client** (AsyncOpenAI) - Optional - An existing OpenAI client instance. If provided, `api_key` and `base_url` should not be set. + +### Request Example +```json +{ + "openai_client": "", + "api_key": "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx", + "base_url": "https://api.openai.com/v1", + "organization": "org-xxxxxxxx", + "project": "proj-xxxxxxxx" +} +``` + +### Response +#### Success Response (200) +- **message** (str) - Confirmation message indicating successful initialization. + +#### Response Example +```json +{ + "message": "OpenAI voice model provider initialized successfully." +} +``` +``` + +-------------------------------- + +### Agent Specific Start Callback in Python + +Source: https://openai.github.io/openai-agents-python/ko/ref/lifecycle + +The `on_start` asynchronous method in `AgentHooksBase` is called before an agent is invoked. This hook is specific to the agent instance and is triggered each time the running agent is changed to this particular agent. + +```python +async def on_start(context: AgentHookContext[TContext], agent: TAgent) -> None: + pass +``` + +-------------------------------- + +### Get All Turn Usage Details (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/extensions/memory/advanced_sqlite_session + +Retrieves a list of detailed usage statistics for all turns within a session and branch, ordered by turn number. Each entry includes token counts and detailed JSON breakdowns for input and output tokens. Requires a database connection and session/branch identifiers. + +```python +else: + query = """ + SELECT user_turn_number, requests, input_tokens, output_tokens, + total_tokens, input_tokens_details, output_tokens_details + FROM turn_usage + WHERE session_id = ? AND branch_id = ? + ORDER BY user_turn_number + """ + + with closing(conn.cursor()) as cursor: + cursor.execute(query, (self.session_id, branch_id)) +``` + +-------------------------------- + +### Convert Tools and Handoffs in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/models/openai_responses + +Converts a list of tools and handoffs into a format suitable for the OpenAI API. It enforces a limit of one computer tool and processes each tool and handoff individually, returning converted tools and associated includes. + +```python +class Converter: + @classmethod + def convert_tools( + cls, + tools: list[Tool], + handoffs: list[Handoff[Any, Any]], + ) -> ConvertedTools: + converted_tools: list[ToolParam] = [] + includes: list[ResponseIncludable] = [] + + computer_tools = [tool for tool in tools if isinstance(tool, ComputerTool)] + if len(computer_tools) > 1: + raise UserError(f"You can only provide one computer tool. Got {len(computer_tools)}") + + for tool in tools: + converted_tool, include = cls._convert_tool(tool) + converted_tools.append(converted_tool) + if include: + includes.append(include) + + for handoff in handoffs: + converted_tools.append(cls._convert_handoff_tool(handoff)) + + return ConvertedTools(tools=converted_tools, includes=includes) +``` + +-------------------------------- + +### Initialize OpenAI Voice Model Provider (Python) + +Source: https://openai.github.io/openai-agents-python/ref/voice/models/openai_model_provider + +Initializes the OpenAI voice model provider. It accepts an API key, base URL, an optional pre-configured OpenAI client, organization, and project. If an OpenAI client is provided, API key and base URL should not be specified. + +```python +def __init__( + self, + *, + api_key: str | None = None, + base_url: str | None = None, + openai_client: AsyncOpenAI | None = None, + organization: str | None = None, + project: str | None = None, +) -> None: + """Create a new OpenAI voice model provider. + + Args: + api_key: The API key to use for the OpenAI client. If not provided, we will use the + default API key. + base_url: The base URL to use for the OpenAI client. If not provided, we will use the + default base URL. + openai_client: An optional OpenAI client to use. If not provided, we will create a new + OpenAI client using the api_key and base_url. + organization: The organization to use for the OpenAI client. + project: The project to use for the OpenAI client. + """ + if openai_client is not None: + assert api_key is None and base_url is None, + ("Don't provide api_key or base_url if you provide openai_client") + self._client: AsyncOpenAI | None = openai_client + else: + self._client = None + self._stored_api_key = api_key + self._stored_base_url = base_url + self._stored_organization = organization + self._stored_project = project + +``` + +-------------------------------- + +### Get Conversation Turns API + +Source: https://openai.github.io/openai-agents-python/ko/ref/extensions/memory/advanced_sqlite_session + +Retrieves user message turns from a specified branch for analysis and branching decisions. It provides a summary and full content of user messages along with timestamps. + +```APIDOC +## GET /conversations/turns + +### Description +Retrieves a list of user conversation turns from a specific branch. Each turn includes its number, truncated content, full content, creation timestamp, and a flag indicating if branching is possible. + +### Method +GET + +### Endpoint +/conversations/turns + +### Parameters +#### Query Parameters +- **branch_id** (string) - Optional - The ID of the branch to retrieve turns from. If not provided, the current branch is used. + +### Response +#### Success Response (200) +- **turns** (list[dict]) - A list of dictionaries, where each dictionary represents a conversation turn and contains the following keys: + - **turn** (integer): The turn number within the branch. + - **content** (string): A truncated version of the user's message content. + - **full_content** (string): The complete user message content. + - **timestamp** (string): The date and time when the turn was created. + - **can_branch** (boolean): Indicates if the turn can be used for branching (always true for user messages). + +#### Response Example +```json +{ + "turns": [ + { + "turn": 1, + "content": "Hello, how can I help you today?", + "full_content": "Hello, how can I help you today?", + "timestamp": "2023-10-27T10:00:00Z", + "can_branch": true + }, + { + "turn": 2, + "content": "I need assistance with my account.", + "full_content": "I need assistance with my account.", + "timestamp": "2023-10-27T10:05:00Z", + "can_branch": true + } + ] +} +``` +``` + +-------------------------------- + +### Implement Custom Tool Output Handler + +Source: https://openai.github.io/openai-agents-python/agents + +This example demonstrates how to create a custom function to process tool results. The function determines if the output is final or if the LLM should continue processing, providing fine-grained control over tool use behavior. + +```python +from agents import Agent, Runner, function_tool, FunctionToolResult, RunContextWrapper +from agents.agent import ToolsToFinalOutputResult +from typing import List, Any + +@function_tool +def get_weather(city: str) -> str: + """Returns weather info for the specified city.""" + return f"The weather in {city} is sunny" + +def custom_tool_handler( + context: RunContextWrapper[Any], + tool_results: List[FunctionToolResult] +) -> ToolsToFinalOutputResult: + """Processes tool results to decide final output.""" + for result in tool_results: + if result.output and "sunny" in result.output: + return ToolsToFinalOutputResult( + is_final_output=True, + final_output=f"Final weather: {result.output}" + ) + return ToolsToFinalOutputResult( + is_final_output=False, + final_output=None + ) + +agent = Agent( + name="Weather Agent", + instructions="Retrieve weather details.", + tools=[get_weather], + tool_use_behavior=custom_tool_handler +) + +``` + +-------------------------------- + +### Tracing Configuration in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/run + +Configuration object for tracing behavior during an agent run. This allows for detailed setup of how traces are generated and managed. This attribute is a class attribute and an instance attribute. + +```python +tracing: TracingConfig | None = None + +``` + +-------------------------------- + +### Dynamic Agent Instructions (Python) + +Source: https://openai.github.io/openai-agents-python/agents + +Shows how to provide dynamic instructions to an agent using a function. This function receives the agent and context, allowing for context-aware prompt generation, such as including the user's name in the instructions. + +```python +from agents import Agent, RunContextWrapper, UserContext + +def dynamic_instructions( + context: RunContextWrapper[UserContext], agent: Agent[UserContext] +) -> str: + return f"The user's name is {context.context.name}. Help them with their questions." + + +agent = Agent[UserContext]( + name="Triage agent", + instructions=dynamic_instructions, +) + +``` + +-------------------------------- + +### Agent Configuration with Specific Output Type + +Source: https://openai.github.io/openai-agents-python/agents + +Explains how to configure an agent to produce structured outputs instead of plain text using the `output_type` parameter. This example uses a Pydantic `BaseModel` to define the structure of a `CalendarEvent`. + +```python +from pydantic import BaseModel +from agents import Agent + + +class CalendarEvent(BaseModel): + name: str + date: str + participants: list[str] + +agent = Agent( + name="Calendar extractor", + instructions="Extract calendar events from text", + output_type=CalendarEvent, +) +``` + +-------------------------------- + +### Get Agent Prompt - Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/agent + +An asynchronous method `get_prompt` that retrieves the prompt for an agent. It utilizes `PromptUtil.to_model_input` to format the prompt based on the provided `RunContextWrapper`. This function is part of the agent's interface for interacting with the language model. + +```python +async def get_prompt( + self, run_context: RunContextWrapper[TContext] +) -> ResponsePromptParam | None: + """Get the prompt for the agent.""" + return await PromptUtil.to_model_input(self.prompt, run_context, self) +``` + +-------------------------------- + +### ComputerProvider + +Source: https://openai.github.io/openai-agents-python/ref/tool + +Configures create/dispose hooks for per-run computer lifecycle management. + +```APIDOC +## ComputerProvider + +### Description +Configures create/dispose hooks for per-run computer lifecycle management. + +### Parameters +#### Request Body +- **create** (ComputerCreate[ComputerT]) - Required - The function to create a computer. +- **dispose** (ComputerDispose[ComputerT] | None) - Optional - The function to dispose a computer. + +### Request Example +```python +from typing import Any, TypeVar +from agents.tool import ComputerCreate, ComputerDispose, ComputerProvider + +ComputerT = TypeVar("ComputerT") + +class MyComputer: + pass + +async def create_my_computer(run_context: Any) -> MyComputer: + return MyComputer() + +def dispose_my_computer(run_context: Any, computer: MyComputer) -> None: + print("Disposing computer") + +computer_provider = ComputerProvider( + create=create_my_computer, + dispose=dispose_my_computer +) +``` + +### Response +#### Success Response (200) +- **create** (ComputerCreate[ComputerT]) - The function to create a computer. +- **dispose** (ComputerDispose[ComputerT] | None) - The function to dispose a computer. + +#### Response Example +```json +{ + "create": "", + "dispose": "" +} +``` +``` + +-------------------------------- + +### Convert LiteLLM Annotations to OpenAI Format + +Source: https://openai.github.io/openai-agents-python/ja/ref/extensions/litellm + +Converts annotations from a LiteLLM message to the OpenAI format. This method specifically handles URL citations, extracting start index, end index, URL, and title. It returns a list of OpenAI-compatible annotations or None if no annotations are present. + +```python +class LitellmConverter: + @classmethod + def convert_annotations_to_openai( + cls, message: litellm.types.utils.Message + ) -> list[Annotation] | None: + annotations: list[litellm.types.llms.openai.ChatCompletionAnnotation] | None = message.get( + "annotations", None + ) + if not annotations: + return None + + return [ + Annotation( + type="url_citation", + url_citation=AnnotationURLCitation( + start_index=annotation["url_citation"]["start_index"], + end_index=annotation["url_citation"]["end_index"], + url=annotation["url_citation"]["url"], + title=annotation["url_citation"]["title"], + ), + ) + for annotation in annotations + ] +``` + +-------------------------------- + +### Get AsyncOpenAI Client (Python) + +Source: https://openai.github.io/openai-agents-python/ref/models/openai_chatcompletions + +This method provides an asynchronous client for the OpenAI API. It ensures that a single client instance is reused across calls to maintain efficiency and connection pooling. + +```python +def _get_client(self) -> AsyncOpenAI: + if self._client is None: + self._client = AsyncOpenAI() + return self._client +``` + +-------------------------------- + +### Tracer Initialization + +Source: https://openai.github.io/openai-agents-python/ref/tracing/processors + +Initializes the tracer with API key, organization, project, endpoint, and retry configurations. + +```APIDOC +## POST /v1/traces/ingest + +### Description +Initializes the tracer with API key, organization, project, endpoint, and retry configurations. This is the primary method for setting up the tracing client. + +### Method +POST + +### Endpoint +/v1/traces/ingest + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **api_key** (str | None) - Optional - The API key for the "Authorization" header. Defaults to `os.environ["OPENAI_API_KEY"]` if not provided. +- **organization** (str | None) - Optional - The OpenAI organization to use. Defaults to `os.environ["OPENAI_ORG_ID"]` if not provided. +- **project** (str | None) - Optional - The OpenAI project to use. Defaults to `os.environ["OPENAI_PROJECT_ID"]` if not provided. +- **endpoint** (str) - Optional - The HTTP endpoint to which traces/spans are posted. Defaults to `'https://api.openai.com/v1/traces/ingest'`. +- **max_retries** (int) - Optional - Maximum number of retries upon failures. Defaults to `3`. +- **base_delay** (float) - Optional - Base delay (in seconds) for the first backoff. Defaults to `1.0`. +- **max_delay** (float) - Optional - Maximum delay (in seconds) for backoff growth. Defaults to `30.0`. + +### Request Example +```json +{ + "api_key": "sk-your_api_key", + "organization": "org-your_organization", + "project": "proj-your_project", + "endpoint": "https://api.openai.com/v1/traces/ingest", + "max_retries": 5, + "base_delay": 2.0, + "max_delay": 60.0 +} +``` + +### Response +#### Success Response (200) +This method does not return a specific success response body, but initializes the tracer. + +#### Response Example +None +``` + +-------------------------------- + +### Get Conversation Turns (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/extensions/memory/advanced_sqlite_session + +Retrieves user turns and their content from a specified branch or the current branch. It fetches data from 'message_structure' and 'agent_messages' tables, returning a list of dictionaries with turn details. + +```python +async def get_conversation_turns(self, branch_id: str | None = None) -> list[dict[str, Any]]: + """Get user turns with content for easy browsing and branching decisions. + + Args: + branch_id: Branch to get turns from (current branch if None). + + Returns: + List of dicts with turn info containing: + - 'turn': Branch turn number + - 'content': User message content (truncated) + - 'full_content': Full user message content + - 'timestamp': When the turn was created + - 'can_branch': Always True (all user messages can branch) + """ + if branch_id is None: + branch_id = self._current_branch_id + + def _get_turns_sync(): + """Synchronous helper to get conversation turns.""" + conn = self._get_connection() + with closing(conn.cursor()) as cursor: + cursor.execute( + """ + SELECT + ms.branch_turn_number, + am.message_data, + ms.created_at + FROM message_structure ms + JOIN agent_messages am ON ms.message_id = am.id + WHERE ms.session_id = ? AND ms.branch_id = ? + AND ms.message_type = 'user' + ORDER BY ms.branch_turn_number + """, + (self.session_id, branch_id), + ) + + turns = [] + for row in cursor.fetchall(): + turn_num, message_data, created_at = row + try: + content = json.loads(message_data).get("content", "") + turns.append( + { + "turn": turn_num, + "content": content[:100] + "..." if len(content) > 100 else content, + "full_content": content, + "timestamp": created_at, + "can_branch": True, + } + ) + except (json.JSONDecodeError, AttributeError): + continue + + return turns + + return await asyncio.to_thread(_get_turns_sync) +``` + +-------------------------------- + +### Get All Mappings from MultiProviderMap (Python) + +Source: https://openai.github.io/openai-agents-python/zh/ref/models/multi_provider + +Implements the `get_mapping` method for the MultiProviderMap class. This method returns a shallow copy of the internal dictionary that stores the prefix to ModelProvider mappings. It takes no arguments and returns a dictionary. + +```python +def get_mapping(self) -> dict[str, ModelProvider]: + """Returns a copy of the current prefix -> ModelProvider mapping.""" + return self._mapping.copy() + +``` + +-------------------------------- + +### Get Response Format Configuration in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/models/openai_responses + +Determines the response format configuration based on an agent's output schema. It returns a JSON schema configuration if the schema is not plain text, otherwise it returns an 'omit' value. + +```python +class Converter: + @classmethod + def get_response_format( + cls, + output_schema: AgentOutputSchemaBase | None + ) -> ResponseTextConfigParam | Omit: + if output_schema is None or output_schema.is_plain_text(): + return omit + else: + return { + "format": { + "type": "json_schema", + "name": "final_output", + "schema": output_schema.json_schema(), + "strict": output_schema.is_strict_json_schema(), + } + } +``` + +-------------------------------- + +### Agent Span Creation + +Source: https://openai.github.io/openai-agents-python/ref/tracing/create + +This section details the `agent_span` function used for creating new agent spans. Spans are not started automatically and require manual start/finish calls or usage within a `with` statement. + +```APIDOC +## POST /agent_span + +### Description +Creates a new agent span. The span needs to be explicitly started and finished, or used within a context manager. + +### Method +POST + +### Endpoint +/agent_span + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **name** (str) - Required - The name of the agent. +- **handoffs** (list[str] | None) - Optional - A list of agent names to which this agent could hand off control. +- **tools** (list[str] | None) - Optional - A list of tool names available to this agent. +- **output_type** (str | None) - Optional - The name of the output type produced by the agent. +- **span_id** (str | None) - Optional - The ID of the span. If not provided, an ID will be generated. +- **parent** (Trace | Span[Any] | None) - Optional - The parent span or trace. If not provided, the current trace/span is used. +- **disabled** (bool) - Optional - If True, the span will not be recorded. Defaults to False. + +### Request Example +```json +{ + "name": "MyAgent", + "handoffs": ["ResponderAgent"], + "tools": ["SearchTool"], + "output_type": "TextResponse", + "span_id": "generated-or-provided-id", + "parent": null, + "disabled": false +} +``` + +### Response +#### Success Response (200) +- **Span[AgentSpanData]** - The newly created agent span object. +``` + +-------------------------------- + +### Run Async Realtime Session in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/runner + +Starts and returns a realtime session for bidirectional communication with a realtime model. This method is asynchronous and requires an `async with` block for proper session management. It takes optional context and model configuration as input. + +```python +async def run( + self, *, context: TContext | None = None, model_config: RealtimeModelConfig | None = None +) -> RealtimeSession: + """Start and returns a realtime session. + + Returns: + RealtimeSession: A session object that allows bidirectional communication with the + realtime model. + + Example: + ```python + runner = RealtimeRunner(agent) + async with await runner.run() as session: + await session.send_message("Hello") + async for event in session: + print(event) + ``` + """ + # Create and return the connection + session = RealtimeSession( + model=self._model, + agent=self._starting_agent, + context=context, + model_config=model_config, + run_config=self._config, + ) + + return session + +``` + +-------------------------------- + +### Prepare and Send LLM Request with OpenAI Python SDK + +Source: https://openai.github.io/openai-agents-python/ja/ref/models/openai_responses + +This snippet demonstrates how to prepare and send a request to the OpenAI LLM API using the Python SDK. It handles various parameters such as model settings, tools, streaming, and response formatting. It also includes conditional logging for debugging purposes. + +```python +if model_settings.top_logprobs is not None: + include_set.add("message.output_text.logprobs") + include = cast(list[ResponseIncludable], list(include_set)) + + if _debug.DONT_LOG_MODEL_DATA: + logger.debug("Calling LLM") + else: + input_json = json.dumps( + list_input, + indent=2, + ensure_ascii=False, + ) + tools_json = json.dumps( + converted_tools_payload, + indent=2, + ensure_ascii=False, + ) + logger.debug( + f"Calling LLM {self.model} with input:\n" + f"{input_json}\n" + f"Tools:\n{tools_json}\n" + f"Stream: {stream}\n" + f"Tool choice: {tool_choice}\n" + f"Response format: {response_format}\n" + f"Previous response id: {previous_response_id}\n" + f"Conversation id: {conversation_id}\n" + ) + + extra_args = dict(model_settings.extra_args or {}) + if model_settings.top_logprobs is not None: + extra_args["top_logprobs"] = model_settings.top_logprobs + if model_settings.verbosity is not None: + if response_format is not omit: + response_format["verbosity"] = model_settings.verbosity # type: ignore [index] + else: + response_format = {"verbosity": model_settings.verbosity} + + stream_param: Literal[True] | Omit = True if stream else omit + + response = await self._client.responses.create( + previous_response_id=self._non_null_or_omit(previous_response_id), + conversation=self._non_null_or_omit(conversation_id), + instructions=self._non_null_or_omit(system_instructions), + model=model_param, + input=list_input, + include=include, + tools=tools_param, + prompt=self._non_null_or_omit(prompt), + temperature=self._non_null_or_omit(model_settings.temperature), + top_p=self._non_null_or_omit(model_settings.top_p), + truncation=self._non_null_or_omit(model_settings.truncation), + max_output_tokens=self._non_null_or_omit(model_settings.max_tokens), + tool_choice=tool_choice, + parallel_tool_calls=parallel_tool_calls, + stream=cast(Any, stream_param), + extra_headers=self._merge_headers(model_settings), + extra_query=model_settings.extra_query, + extra_body=model_settings.extra_body, + text=response_format, + store=self._non_null_or_omit(model_settings.store), + prompt_cache_retention=self._non_null_or_omit(model_settings.prompt_cache_retention), + reasoning=self._non_null_or_omit(model_settings.reasoning), + metadata=self._non_null_or_omit(model_settings.metadata), + **extra_args, + ) + return cast(Union[Response, AsyncStream[ResponseStreamEvent]], response) +``` + +-------------------------------- + +### generation_span API + +Source: https://openai.github.io/openai-agents-python/zh/ref/tracing/create + +Creates a new generation span to capture model generation details. The span is not started automatically and requires manual start/finish or usage within a 'with' statement. + +```APIDOC +## generation_span + +### Description +Create a new generation span. The span will not be started automatically, you should either do `with generation_span() ...` or call `span.start()` + `span.finish()` manually. + +This span captures the details of a model generation, including the input message sequence, any generated outputs, the model name and configuration, and usage data. If you only need to capture a model response identifier, use `response_span()` instead. + +### Method +(Function Signature - Not an HTTP Method) + +### Endpoint +(N/A - Python Function) + +### Parameters +#### Path Parameters +(None) + +#### Query Parameters +(None) + +#### Request Body +(N/A - Python Function Parameters) +- **input** (`Sequence[Mapping[str, Any]] | None`) - Optional - The sequence of input messages sent to the model. +- **output** (`Sequence[Mapping[str, Any]] | None`) - Optional - The sequence of output messages received from the model. +- **model** (`str | None`) - Optional - The model identifier used for the generation. +- **model_config** (`Mapping[str, Any] | None`) - Optional - The model configuration (hyperparameters) used. +- **usage** (`dict[str, Any] | None`) - Optional - A dictionary of usage information (input tokens, output tokens, etc.). +- **span_id** (`str | None`) - Optional - The ID of the span. Optional. If not provided, we will generate an ID. We recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are correctly formatted. +- **parent** (`Trace | Span[Any] | None`) - Optional - The parent span or trace. If not provided, we will automatically use the current trace/span as the parent. +- **disabled** (`bool`) - Optional - If True, we will return a Span but the Span will not be recorded. Default: `False` + +### Request Example +```python +# Using a 'with' statement +with generation_span(model="gpt-4") as span: + # Perform model generation here + pass + +# Manual start and finish +span = generation_span(model="gpt-4") +span.start() +# Perform model generation here +span.finish() +``` + +### Response +#### Success Response (200) +- **Span[GenerationSpanData]** - The newly created generation span. +``` + +-------------------------------- + +### Python Session Management: Ensure Compaction Candidates + +Source: https://openai.github.io/openai-agents-python/ref/memory/openai_responses_compaction_session + +Implements lazy loading and caching for session compaction candidates. It retrieves history, selects candidates, and caches them for future use, logging the initialization details. + +```python +async def _ensure_compaction_candidates( + self, + ) -> tuple[list[TResponseInputItem], list[TResponseInputItem]]: + """Lazy-load and cache compaction candidates.""" + if self._compaction_candidate_items is not None and self._session_items is not None: + return (self._compaction_candidate_items[:], self._session_items[:]) + + history = await self.underlying_session.get_items() + candidates = select_compaction_candidate_items(history) + self._compaction_candidate_items = candidates + self._session_items = history + + logger.debug( + f"candidates: initialized (history={len(history)}, candidates={len(candidates)})" + ) + return (candidates[:], history[:]) +``` + +-------------------------------- + +### Transcription Span API + +Source: https://openai.github.io/openai-agents-python/ref/tracing/create + +This endpoint allows for the creation of a new transcription span for speech-to-text processing. The span needs to be explicitly started and finished or used within a `with` statement. + +```APIDOC +## POST /v1/transcription_span + +### Description +Creates a new transcription span for speech-to-text. The span is not started automatically; manual start/finish or `with` statement usage is required. + +### Method +POST + +### Endpoint +/v1/transcription_span + +### Parameters +#### Query Parameters +- **model** (str | None) - Optional - The name of the model used for the speech-to-text. +- **input** (str | None) - Optional - The audio input of the speech-to-text transcription, as a base64 encoded string of audio bytes. +- **input_format** (str | None) - Optional - The format of the audio input. Defaults to "pcm". +- **output** (str | None) - Optional - The output of the speech-to-text transcription. +- **model_config** (Mapping[str, Any] | None) - Optional - The model configuration (hyperparameters) used. +- **span_id** (str | None) - Optional - The ID of the span. If not provided, an ID will be generated. +- **parent** (Trace | Span[Any] | None) - Optional - The parent span or trace. If not provided, the current trace/span is used. +- **disabled** (bool) - Optional - If True, the Span will not be recorded. Defaults to False. + +### Request Example +```json +{ + "model": "whisper-1", + "input": "BASE64_ENCODED_AUDIO_BYTES", + "input_format": "mp3", + "model_config": { + "temperature": 0.7 + } +} +``` + +### Response +#### Success Response (200) +- **span** (Span[TranscriptionSpanData]) - The newly created speech-to-text span. +``` + +-------------------------------- + +### Fetch MCP Tools - Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/agent + +Asynchronously fetches available tools from MCP servers. It utilizes `MCPUtil.get_all_function_tools` and accepts a `RunContextWrapper` for context. The function returns a list of `Tool` objects. + +```python +async def get_mcp_tools(self, run_context: RunContextWrapper[TContext]) -> list[Tool]: + """Fetches the available tools from the MCP servers.""" + convert_schemas_to_strict = self.mcp_config.get("convert_schemas_to_strict", False) + return await MCPUtil.get_all_function_tools( + self.mcp_servers, convert_schemas_to_strict, run_context, self + ) +``` + +-------------------------------- + +### SingleAgentVoiceWorkflow on_start method in Python + +Source: https://openai.github.io/openai-agents-python/zh/ref/voice/workflow + +Implements the optional `on_start` asynchronous method for `SingleAgentVoiceWorkflow`. This method is intended to be executed before any user input is received, potentially for delivering greetings or instructions via text-to-speech. The default behavior is to do nothing. + +```python +async def on_start(self) -> AsyncIterator[str]: + """ + Optional method that runs before any user input is received. Can be used + to deliver a greeting or instruction via TTS. Defaults to doing nothing. + """ + return + yield +``` + +-------------------------------- + +### Initialize AsyncOpenAI Client in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/models/openai_responses + +This Python snippet shows how to lazily initialize an asynchronous OpenAI client. It checks if the client instance already exists and creates a new `AsyncOpenAI` object if it's `None`. + +```python +def _get_client(self) -> AsyncOpenAI: + if self._client is None: + self._client = AsyncOpenAI() + return self._client +``` + +-------------------------------- + +### Get Metadata for State Operations with TTL + +Source: https://openai.github.io/openai-agents-python/ja/ref/extensions/memory/dapr_session + +Retrieves metadata for state operations, including the TTL (Time To Live) in seconds if it's configured. Returns an empty dictionary if TTL is not set. + +```python +def _get_metadata(self) -> dict[str, str]: + """Get metadata for state operations including TTL if configured.""" + metadata = {} + if self._ttl is not None: + metadata["ttlInSeconds"] = str(self._ttl) + return metadata +``` + +-------------------------------- + +### Get Current Trace in Python + +Source: https://openai.github.io/openai-agents-python/ref/tracing/create + +Retrieves the currently active trace, if one exists. This is useful for inspecting or modifying the active trace within the current execution context. + +```python +def get_current_trace() -> Trace | None: + """Returns the currently active trace, if present.""" + return get_trace_provider().get_current_trace() +``` + +-------------------------------- + +### OpenAITTSModel Initialization (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/voice/models/openai_tts + +Constructor for the OpenAITTSModel. It takes the model name and an asynchronous OpenAI client instance as required arguments to set up the text-to-speech functionality. + +```python +def __init__( + self, + model: str, + openai_client: AsyncOpenAI, +): + """Create a new OpenAI text-to-speech model. + + Args: + model: The name of the model to use. + openai_client: The OpenAI client to use. + """ + self.model = model + self._client = openai_client + +``` + +-------------------------------- + +### SQLAlchemySession: Get Items + +Source: https://openai.github.io/openai-agents-python/ref/extensions/memory/sqlalchemy_session + +Retrieves conversation history items from the database for a given session. It supports retrieving all items or a specified number of the latest items, ordered chronologically. + +```python +async def get_items(self, limit: int | None = None) -> list[TResponseInputItem]: + """Retrieve the conversation history for this session. + + Args: + limit: Maximum number of items to retrieve. If None, retrieves all items. + When specified, returns the latest N items in chronological order. + + Returns: + List of input items representing the conversation history + """ + await self._ensure_tables() + async with self._session_factory() as sess: + if limit is None: + stmt = ( + select(self._messages.c.message_data) + .where(self._messages.c.session_id == self.session_id) + .order_by( + self._messages.c.created_at.asc(), + self._messages.c.id.asc(), + ) + ) + else: + stmt = ( + select(self._messages.c.message_data) + .where(self._messages.c.session_id == self.session_id) + # Use DESC + LIMIT to get the latest N + # then reverse later for chronological order. + .order_by( + self._messages.c.created_at.desc(), + self._messages.c.id.desc(), + ) + .limit(limit) + ) + + result = await sess.execute(stmt) + rows: list[str] = [row[0] for row in result.all()] + + if limit is not None: + rows.reverse() + + items: list[TResponseInputItem] = [] + for raw in rows: + try: + items.append(await self._deserialize_item(raw)) + except json.JSONDecodeError: + # Skip corrupted rows + continue + return items +``` + +-------------------------------- + +### Initialize MCPServerStdio with Stdio Transport Parameters + +Source: https://openai.github.io/openai-agents-python/ja/ref/mcp/server + +Initializes the MCPServerStdio class, which acts as an MCP server using stdio for communication. It takes various parameters to configure the server, including command, arguments, environment variables, working directory, text encoding, and session timeouts. Dependencies include `_MCPServerWithClientSession` and `StdioServerParameters`. + +```python +class MCPServerStdio(_MCPServerWithClientSession): + """MCP server implementation that uses the stdio transport. See the [spec] + (https://spec.modelcontextprotocol.io/specification/2024-11-05/basic/transports/#stdio) for + details. + """ + + def __init__( + self, + params: MCPServerStdioParams, + cache_tools_list: bool = False, + name: str | None = None, + client_session_timeout_seconds: float | None = 5, + tool_filter: ToolFilter = None, + use_structured_content: bool = False, + max_retry_attempts: int = 0, + retry_backoff_seconds_base: float = 1.0, + message_handler: MessageHandlerFnT | None = None, + ): + """Create a new MCP server based on the stdio transport. + + Args: + params: The params that configure the server. This includes the command to run to + start the server, the args to pass to the command, the environment variables to + set for the server, the working directory to use when spawning the process, and + the text encoding used when sending/receiving messages to the server. + cache_tools_list: Whether to cache the tools list. If `True`, the tools list will be + cached and only fetched from the server once. If `False`, the tools list will be + fetched from the server on each call to `list_tools()`. The cache can be + invalidated by calling `invalidate_tools_cache()`. You should set this to `True` + if you know the server will not change its tools list, because it can drastically + improve latency (by avoiding a round-trip to the server every time). + name: A readable name for the server. If not provided, we'll create one from the + command. + client_session_timeout_seconds: the read timeout passed to the MCP ClientSession. + tool_filter: The tool filter to use for filtering tools. + use_structured_content: Whether to use `tool_result.structured_content` when calling an + MCP tool. Defaults to False for backwards compatibility - most MCP servers still + include the structured content in the `tool_result.content`, and using it by + default will cause duplicate content. You can set this to True if you know the + server will not duplicate the structured content in the `tool_result.content`. + max_retry_attempts: Number of times to retry failed list_tools/call_tool calls. + Defaults to no retries. + retry_backoff_seconds_base: The base delay, in seconds, for exponential + backoff between retries. + message_handler: Optional handler invoked for session messages as delivered by the + ClientSession. + """ + super().__init__( + cache_tools_list, + client_session_timeout_seconds, + tool_filter, + use_structured_content, + max_retry_attempts, + retry_backoff_seconds_base, + message_handler=message_handler, + ) + + self.params = StdioServerParameters( + command=params["command"], + args=params.get("args", []), + env=params.get("env"), + cwd=params.get("cwd"), + encoding=params.get("encoding", "utf-8"), + encoding_error_handler=params.get("encoding_error_handler", "strict"), + ) + + self._name = name or f"stdio: {self.params.command}" +``` + +-------------------------------- + +### Get Function Tools from a Single MCP Server (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/mcp/util + +Fetches all function tools from a single MCP server. It uses an MCP span for tracing and converts the raw MCP tools into Agents SDK Tool objects. Dependencies include MCPServer, RunContextWrapper, AgentBase, Tool, and mcp_tools_span. + +```python +class MCPUtil: + """Set of utilities for interop between MCP and Agents SDK tools.""" + + @classmethod + async def get_function_tools( + cls, + server: "MCPServer", + convert_schemas_to_strict: bool, + run_context: RunContextWrapper[Any], + agent: "AgentBase", + ) -> list[Tool]: + """Get all function tools from a single MCP server.""" + + with mcp_tools_span(server=server.name) as span: + tools = await server.list_tools(run_context, agent) + span.span_data.result = [tool.name for tool in tools] + + return [cls.to_function_tool(tool, server, convert_schemas_to_strict) for tool in tools] +``` + +-------------------------------- + +### Get Deferred Compaction Response ID + +Source: https://openai.github.io/openai-agents-python/ko/ref/memory/openai_responses_compaction_session + +Retrieves the response ID that has been deferred for compaction. If no compaction is currently deferred, it returns None. This is a helper function to check the status of deferred compaction. + +```python +def _get_deferred_compaction_response_id(self) -> str | None: + return self._deferred_response_id +``` + +-------------------------------- + +### Get Trace Provider with Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing + +Retrieves the globally configured trace provider. This function is essential for accessing the active tracing mechanism. It raises a RuntimeError if the trace provider has not been set. + +```python +def get_trace_provider() -> TraceProvider: + """Get the global trace provider used by tracing utilities.""" + if GLOBAL_TRACE_PROVIDER is None: + raise RuntimeError("Trace provider not set") + return GLOBAL_TRACE_PROVIDER + +``` + +-------------------------------- + +### Agent Span Creation API + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing + +This API allows for the creation of new agent spans, which are used for tracing agent activities. Spans are not started automatically and require manual start/finish calls or usage within a `with` statement. + +```APIDOC +## POST /agent_span + +### Description +Creates a new agent span for tracing purposes. The span needs to be manually started and finished, or used with a context manager. + +### Method +POST + +### Endpoint +/agent_span + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **name** (str) - Required - The name of the agent. +- **handoffs** (list[str] | None) - Optional - A list of agent names to which this agent could hand off control. Defaults to None. +- **tools** (list[str] | None) - Optional - A list of tool names available to this agent. Defaults to None. +- **output_type** (str | None) - Optional - The name of the output type produced by the agent. Defaults to None. +- **span_id** (str | None) - Optional - The ID of the span. If not provided, an ID will be generated. Defaults to None. +- **parent** (Trace | Span[Any] | None) - Optional - The parent span or trace. If not provided, the current trace/span is used. Defaults to None. +- **disabled** (bool) - Optional - If True, the span will not be recorded. Defaults to False. + +### Request Example +```json +{ + "name": "MyAgent", + "handoffs": ["OtherAgent"], + "tools": ["Tool1"], + "output_type": "response", + "span_id": "generated-span-id", + "parent": null, + "disabled": false +} +``` + +### Response +#### Success Response (200) +- **Span[AgentSpanData]** - The newly created agent span. + +#### Response Example +```json +{ + "span_id": "generated-span-id", + "name": "MyAgent", + "data": { + "handoffs": ["OtherAgent"], + "tools": ["Tool1"], + "output_type": "response" + }, + "start_time": "2023-10-27T10:00:00Z", + "end_time": null +} +``` +``` + +-------------------------------- + +### Retrieve Conversation History Markers + +Source: https://openai.github.io/openai-agents-python/ja/ref/handoffs + +The `get_conversation_history_wrappers` function returns the current start and end markers used for summarizing nested conversations. These markers define the boundaries for extracting and processing conversation history. + +```python +def get_conversation_history_wrappers() -> tuple[str, str]: + """Return the current start/end markers used for the nested conversation summary.""" + + return (_conversation_history_start, _conversation_history_end) +``` + +-------------------------------- + +### Validate Agent Initialization Parameters in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/agent + +This code snippet demonstrates the validation logic within an agent's initialization process. It checks for correct types and formats of various parameters, including model settings, input/output guardrails, output type, hooks, tool use behavior, and reset tool choice. Type errors are raised if the parameters do not meet the expected criteria. + +```python +if ( + isinstance(self.model, str) is False + or gpt_5_reasoning_settings_required(self.model) is False # type: ignore + ) + # The model settings are not customized for the specified model + and self.model_settings == get_default_model_settings(): + # In this scenario, we should use a generic model settings + # because non-gpt-5 models are not compatible with the default gpt-5 model settings. + # This is a best-effort attempt to make the agent work with non-gpt-5 models. + self.model_settings = ModelSettings() + + if not isinstance(self.input_guardrails, list): + raise TypeError( + f"Agent input_guardrails must be a list, got {type(self.input_guardrails).__name__}" + ) + + if not isinstance(self.output_guardrails, list): + raise TypeError( + f"Agent output_guardrails must be a list, " + f"got {type(self.output_guardrails).__name__}" + ) + + if self.output_type is not None: + from .agent_output import AgentOutputSchemaBase + + if not ( + isinstance(self.output_type, (type, AgentOutputSchemaBase)) + or get_origin(self.output_type) is not None + ): + raise TypeError( + f"Agent output_type must be a type, AgentOutputSchemaBase, or None, " + f"got {type(self.output_type).__name__}" + ) + + if self.hooks is not None: + from .lifecycle import AgentHooksBase + + if not isinstance(self.hooks, AgentHooksBase): + raise TypeError( + f"Agent hooks must be an AgentHooks instance or None, " + f"got {type(self.hooks).__name__}" + ) + + if ( + not ( + isinstance(self.tool_use_behavior, str) + and self.tool_use_behavior in ["run_llm_again", "stop_on_first_tool"] + ) + and not isinstance(self.tool_use_behavior, dict) + and not callable(self.tool_use_behavior) + ): + raise TypeError( + f"Agent tool_use_behavior must be 'run_llm_again', 'stop_on_first_tool', " + f"StopAtTools dict, or callable, got {type(self.tool_use_behavior).__name__}" + ) + + if not isinstance(self.reset_tool_choice, bool): + raise TypeError( + f"Agent reset_tool_choice must be a boolean, " + f"got {type(self.reset_tool_choice).__name__}" + ) +``` + +-------------------------------- + +### Initialize SQLAlchemySession from URL in OpenAI Agents Python + +Source: https://openai.github.io/openai-agents-python/sessions + +Demonstrates creating a production-ready SQLAlchemySession using a database URL. This method allows connection to various SQLAlchemy-supported databases and optionally creates necessary tables. + +```python +from agents.extensions.memory import SQLAlchemySession + +# Using database URL +session = SQLAlchemySession.from_url( + "user_123", + url="postgresql+asyncpg://user:pass@localhost/db", + create_tables=True +) + +``` + +-------------------------------- + +### Get Current Time in ISO 8601 Format (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/util + +Returns the current time formatted as an ISO 8601 string. This function relies on the trace provider to supply the time information. + +```python +def time_iso() -> str: + """Return the current time in ISO 8601 format.""" + return get_trace_provider().time_iso() +``` + +-------------------------------- + +### Configure Available Tools for Realtime Sessions (Python) + +Source: https://openai.github.io/openai-agents-python/ko/ref/realtime/config + +Provides a list of tools that are available for the model to call during realtime sessions. + +```python +tools: NotRequired[list[Tool]] +``` + +-------------------------------- + +### Run Agent Asynchronously with Runner.run() in Python + +Source: https://openai.github.io/openai-agents-python/running_agents + +Demonstrates how to run an agent asynchronously using the `Runner.run()` method. This method takes an `Agent` object and user input, returning a `RunResult` upon completion. Ensure the `agents` library is installed and imported. + +```python +from agents import Agent, Runner + +async def main(): + agent = Agent(name="Assistant", instructions="You are a helpful assistant") + + result = await Runner.run(agent, "Write a haiku about recursion in programming.") + print(result.final_output) + # Code within the code, + # Functions calling themselves, + # Infinite loop's dance + +``` + +-------------------------------- + +### Per-Request Usage Tracking + +Source: https://openai.github.io/openai-agents-python/usage + +Illustrates how to access and iterate through `request_usage_entries` to get detailed token usage for each individual API request made during a run. This is useful for granular cost analysis and understanding context window consumption per interaction. + +```python +result = await Runner.run(agent, "What's the weather in Tokyo?") + +for i, request in enumerate(result.context_wrapper.usage.request_usage_entries): + print(f"Request {i + 1}: {request.input_tokens} in, {request.output_tokens} out") +``` + +-------------------------------- + +### RealtimeSession: Establish and Use Real-time Model Connection (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/session + +Demonstrates how to establish a real-time session with a model using RealtimeRunner and interact with it by sending messages and audio, and then processing streamed events. This involves using an asynchronous context manager for the session. + +```python +runner = RealtimeRunner(agent) +async with await runner.run() as session: + # Send messages + await session.send_message("Hello") + await session.send_audio(audio_bytes) + + # Stream events + async for event in session: + if event.type == "audio": + # Handle audio event + pass + +``` + +-------------------------------- + +### Configure OpenAI Realtime Session with Model Settings + +Source: https://openai.github.io/openai-agents-python/zh/ref/realtime/openai_realtime + +This function configures a session for the OpenAI Realtime API, processing various model settings such as audio configuration, tools, instructions, prompts, and token limits. It prepares the `session_create_request` object based on the provided `model_settings` dictionary. + +```python + audio=OpenAIRealtimeAudioConfig( + input=OpenAIRealtimeAudioInput(**audio_input_args), + output=OpenAIRealtimeAudioOutput(**audio_output_args), + ), + tools=cast( + Any, + self._tools_to_session_tools( + tools=model_settings.get("tools", []), + handoffs=model_settings.get("handoffs", []), + ), + ), + ) + + if "instructions" in model_settings: + session_create_request.instructions = model_settings.get("instructions") + + if "prompt" in model_settings: + _passed_prompt: Prompt = model_settings["prompt"] + variables: dict[str, Any] | None = _passed_prompt.get("variables") + session_create_request.prompt = ResponsePrompt( + id=_passed_prompt["id"], + variables=variables, + version=_passed_prompt.get("version"), + ) + + if "max_output_tokens" in model_settings: + session_create_request.max_output_tokens = cast( + Any, model_settings.get("max_output_tokens") + ) + + if "tool_choice" in model_settings: + session_create_request.tool_choice = cast(Any, model_settings.get("tool_choice")) + + return session_create_request +``` + +-------------------------------- + +### Get MCPServerStdio Name + +Source: https://openai.github.io/openai-agents-python/zh/ref/mcp/server + +Provides a readable name for the MCPServerStdio instance. This property returns the name assigned during initialization or a default name derived from the server's command. + +```python +@property +def name(self) -> str: + """A readable name for the server.""" + return self._name +``` + +-------------------------------- + +### Get Function Tools from a Single MCP Server (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/mcp/util + +This class method fetches all function tools from a specified MCP server. It utilizes an MCP span for tracing and then converts each retrieved tool into the Agents SDK's FunctionTool format. Dependencies include MCPServer, RunContextWrapper, and AgentBase. + +```python +@classmethod +async def get_function_tools( + cls, + server: "MCPServer", + convert_schemas_to_strict: bool, + run_context: RunContextWrapper[Any], + agent: "AgentBase", +) -> list[Tool]: + """Get all function tools from a single MCP server.""" + + with mcp_tools_span(server=server.name) as span: + tools = await server.list_tools(run_context, agent) + span.span_data.result = [tool.name for tool in tools] + + return [cls.to_function_tool(tool, server, convert_schemas_to_strict) for tool in tools] +``` + +-------------------------------- + +### Get Speech-to-Text Model (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/voice/models/openai_model_provider + +Retrieves a speech-to-text (STT) model by its name. If no model name is provided, a default STT model is used. This function requires an initialized OpenAI client to function. + +```python +def get_stt_model(self, model_name: str | None) -> STTModel: + """Get a speech-to-text model by name. + + Args: + model_name: The name of the model to get. + + Returns: + The speech-to-text model. + """ + return OpenAISTTModel(model_name or DEFAULT_STT_MODEL, self._get_client()) + +``` + +-------------------------------- + +### Get Response using LitellmModel in Python + +Source: https://openai.github.io/openai-agents-python/ko/ref/extensions/litellm + +Fetches a response from the configured LiteLLM model. It handles system instructions, user input, model settings, tools, output schemas, and tracing for model generations. The method logs the received model response for debugging purposes. + +```python + async def get_response( + self, + system_instructions: str | None, + input: str | list[TResponseInputItem], + model_settings: ModelSettings, + tools: list[Tool], + output_schema: AgentOutputSchemaBase | None, + handoffs: list[Handoff], + tracing: ModelTracing, + previous_response_id: str | None = None, # unused + conversation_id: str | None = None, # unused + prompt: Any | None = None, + ) -> ModelResponse: + with generation_span( + model=str(self.model), + model_config=model_settings.to_json_dict() + | {"base_url": str(self.base_url or ""), "model_impl": "litellm"}, + disabled=tracing.is_disabled(), + ) as span_generation: + response = await self._fetch_response( + system_instructions, + input, + model_settings, + tools, + output_schema, + handoffs, + span_generation, + tracing, + stream=False, + prompt=prompt, + ) + + message: litellm.types.utils.Message | None = None + first_choice: litellm.types.utils.Choices | None = None + if response.choices and len(response.choices) > 0: + choice = response.choices[0] + if isinstance(choice, litellm.types.utils.Choices): + first_choice = choice + message = first_choice.message + + if _debug.DONT_LOG_MODEL_DATA: + logger.debug("Received model response") + else: + if message is not None: + logger.debug( + f"""LLM resp: +{ json.dumps(message.model_dump(), indent=2, ensure_ascii=False) + } +""" + ) + else: + +``` + +-------------------------------- + +### Merge Assistant Content in Python + +Source: https://openai.github.io/openai-agents-python/zh/ref/realtime/session + +Merges new assistant content with existing content, prioritizing new audio content with transcripts and preserving existing text content if new text is empty. Handles audio and text types distinctly. + +```python +assistant_new_content.append(ac) + continue + assistant_current = assistant_existing_content[idx] + if ac.type == "audio": + if ac.transcript is None: + assistant_new_content.append(assistant_current) + else: + assistant_new_content.append(ac) + else: # text + cur_text = ( + assistant_current.text + if isinstance(assistant_current, AssistantText) + else None + ) + if cur_text is not None and ac.text is None: + assistant_new_content.append(assistant_current) + else: + assistant_new_content.append(ac) + updated_assistant = event.model_copy( + update={"content": assistant_new_content} + ) + new_history[existing_index] = updated_assistant +``` + +-------------------------------- + +### Initialize OpenAI Responses Compaction Session (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/memory/openai_responses_compaction_session + +Initializes the compaction session with session details, an underlying session store, and optional OpenAI client, model, compaction mode, and a custom trigger hook. It validates the session type and model name, ensuring compatibility with OpenAI's compaction API. + +```python +def __init__( + self, + session_id: str, + underlying_session: Session, + *, + client: AsyncOpenAI | None = None, + model: str = "gpt-4.1", + compaction_mode: OpenAIResponsesCompactionMode = "auto", + should_trigger_compaction: Callable[[dict[str, Any]], bool] | None = None, +): + """Initialize the compaction session. + + Args: + session_id: Identifier for this session. + underlying_session: Session store that holds the compacted history. Cannot be + OpenAIConversationsSession. + client: OpenAI client for responses.compact API calls. Defaults to + get_default_openai_client() or new AsyncOpenAI(). + model: Model to use for responses.compact. Defaults to "gpt-4.1". Must be an + OpenAI model name (gpt-*, o*, or ft:gpt-*). + compaction_mode: Controls how the compaction request provides conversation + history. "auto" (default) uses input when the last response was not + stored or no response_id is available. + should_trigger_compaction: Custom decision hook. Defaults to triggering when + 10+ compaction candidates exist. + """ + if isinstance(underlying_session, OpenAIConversationsSession): + raise ValueError( + "OpenAIResponsesCompactionSession cannot wrap OpenAIConversationsSession " + "because it manages its own history on the server." + ) + + if not is_openai_model_name(model): + raise ValueError(f"Unsupported model for OpenAI responses compaction: {model}") + + self.session_id = session_id + self.underlying_session = underlying_session + self._client = client + self.model = model + self.compaction_mode = compaction_mode + self.should_trigger_compaction = ( + should_trigger_compaction or default_should_trigger_compaction + ) + + # cache for incremental candidate tracking + self._compaction_candidate_items: list[TResponseInputItem] | None = None + self._session_items: list[TResponseInputItem] | None = None + self._response_id: str | None = None + self._deferred_response_id: str | None = None + self._last_unstored_response_id: str | None = None + +``` + +-------------------------------- + +### Get OpenAI Text-to-Speech Model in Python + +Source: https://openai.github.io/openai-agents-python/ref/voice/models/openai_provider + +Retrieves a text-to-speech (TTS) model from the OpenAIVoiceModelProvider. If no model name is provided, it defaults to `DEFAULT_TTS_MODEL`. This method ensures an OpenAI client is ready before returning the TTS model. + +```python +def get_tts_model(self, model_name: str | None) -> TTSModel: + """Get a text-to-speech model by name. + + Args: + model_name: The name of the model to get. + + Returns: + The text-to-speech model. + """ + return OpenAITTSModel(model_name or DEFAULT_TTS_MODEL, self._get_client()) +``` + +-------------------------------- + +### Get Text-to-Speech Model (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/voice/models/openai_model_provider + +Retrieves a text-to-speech (TTS) model by its name. If no model name is specified, a default TTS model is utilized. This method depends on a properly configured OpenAI client. + +```python +def get_tts_model(self, model_name: str | None) -> TTSModel: + """Get a text-to-speech model by name. + + Args: + model_name: The name of the model to get. + + Returns: + The text-to-speech model. + """ + return OpenAITTSModel(model_name or DEFAULT_TTS_MODEL, self._get_client()) + +``` + +-------------------------------- + +### Using OpenAIConversationsSession for Agent Conversations + +Source: https://openai.github.io/openai-agents-python/sessions + +Shows how to integrate the Agents SDK with OpenAI's Conversations API using `OpenAIConversationsSession`. This allows agents to leverage OpenAI's managed conversation history. The example demonstrates creating a new conversation and optionally resuming a previous one using a conversation ID. + +```python +from agents import Agent, Runner, OpenAIConversationsSession + +# Create agent +agent = Agent( + name="Assistant", + instructions="Reply very concisely.", +) + +# Create a new conversation +session = OpenAIConversationsSession() + +# Optionally resume a previous conversation by passing a conversation ID +# session = OpenAIConversationsSession(conversation_id="conv_123") + +# Start conversation +result = await Runner.run( + agent, + "What city is the Golden Gate Bridge in?", + session=session +) +print(result.final_output) # "San Francisco" + +# Continue the conversation +result = await Runner.run( + agent, + "What state is it in?", + session=session +) +print(result.final_output) # "California" + +``` + +-------------------------------- + +### AgentHooksBase: Handoff and Tool Callbacks (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/lifecycle + +Provides methods for handling agent handoffs and tool invocations specific to an agent. `on_handoff` tracks transitions between agents, while `on_tool_start` and `on_tool_end` monitor tool usage. + +```python +class AgentHooksBase[TContext, TAgent]: + async def on_handoff( + self, + context: RunContextWrapper[TContext], + agent: TAgent, + source: TAgent, + ) -> None: + """Called when the agent is being handed off to. The `source` is the agent that is handing off to this agent.""" + pass + + async def on_tool_start( + self, + context: RunContextWrapper[TContext], + agent: TAgent, + tool: Tool, + ) -> None: + """Called immediately before a local tool is invoked.""" + pass + + async def on_tool_end( + self, + context: RunContextWrapper[TContext], + agent: TAgent, + tool: Tool, + result: str, + ) -> None: + """Called immediately after a local tool is invoked.""" + pass + +``` + +-------------------------------- + +### Get Workflow Name from NoOpTrace in Python + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing/traces + +Retrieves the human-readable name of the workflow associated with the trace. For the NoOpTrace, this property consistently returns 'no-op' since no specific workflow is being traced. + +```python +@property +def name(self) -> str: + """The workflow name for this trace. + + Returns: + str: Human-readable name describing this workflow. + """ + return "no-op" + +``` + +-------------------------------- + +### Get Trace ID from NoOpTrace in Python + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing/traces + +Retrieves the unique identifier for a trace. For the NoOpTrace implementation, this property always returns the string 'no-op' as no actual trace data is recorded. + +```python +@property +def trace_id(self) -> str: + """The trace's unique identifier. + + Returns: + str: A unique ID for this trace. + """ + return "no-op" + +``` + +-------------------------------- + +### FunctionTool Creation and Decorator Logic (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/tool + +This snippet shows the process of creating a `FunctionTool` instance using a schema and the `_create_function_tool` helper. It also details how the `@function_tool` decorator handles both direct function wrapping and decorator usage with parentheses. + +```python +return FunctionTool( + name=schema.name, + description=schema.description or "", + params_json_schema=schema.params_json_schema, + on_invoke_tool=_on_invoke_tool, + strict_json_schema=strict_mode, + is_enabled=is_enabled, + tool_input_guardrails=tool_input_guardrails, + tool_output_guardrails=tool_output_guardrails, + ) + + # If func is actually a callable, we were used as @function_tool with no parentheses + if callable(func): + return _create_function_tool(func) + + # Otherwise, we were used as @function_tool(...), so return a decorator + def decorator(real_func: ToolFunction[...]) -> FunctionTool: + return _create_function_tool(real_func) + + return decorator +``` + +-------------------------------- + +### Set Global Trace Provider - Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/setup + +Sets the global trace provider for tracing utilities. This function takes a `TraceProvider` object as input and assigns it to a global variable. It is crucial for initializing the tracing mechanism. + +```python +def set_trace_provider(provider: TraceProvider) -> None: + """Set the global trace provider used by tracing utilities.""" + global GLOBAL_TRACE_PROVIDER + GLOBAL_TRACE_PROVIDER = provider +``` + +-------------------------------- + +### run_compaction + +Source: https://openai.github.io/openai-agents-python/ko/ref/memory/openai_responses_compaction_session + +Runs the compaction process for OpenAI responses. This method is asynchronous and can be configured with various arguments to control the compaction mode and storage behavior. + +```APIDOC +## POST /run_compaction + +### Description +Runs the compaction process for OpenAI responses using the `responses.compact` API. This method is asynchronous and can be configured with various arguments to control the compaction mode and storage behavior. + +### Method +POST + +### Endpoint +/run_compaction + +### Parameters +#### Query Parameters +- **args** (OpenAIResponsesCompactionArgs | None) - Optional - Arguments to control the compaction process, including `response_id`, `compaction_mode`, and `store`. + +### Request Body +```json +{ + "response_id": "string", + "compaction_mode": "string", + "store": "boolean", + "force": "boolean" +} +``` + +### Response +#### Success Response (200) +- **None** - This method does not return a value upon successful execution. + +#### Response Example +```json +null +``` +``` + +-------------------------------- + +### Handle Response Created and Done Events in Python + +Source: https://openai.github.io/openai-agents-python/ko/ref/realtime/openai_realtime + +Manages the start and end of a response from the realtime model. 'response.created' sets an internal flag and emits a `RealtimeModelTurnStartedEvent`. 'response.done' clears the flag and emits a `RealtimeModelTurnEndedEvent`. + +```python +elif parsed.type == "response.created": + self._ongoing_response = True + await self._emit_event(RealtimeModelTurnStartedEvent()) + elif parsed.type == "response.done": + self._ongoing_response = False + await self._emit_event(RealtimeModelTurnEndedEvent()) +``` + +-------------------------------- + +### Add Text and Initiate Audio Streaming in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/voice/result + +This method appends new text to a buffer, splits it into sentences, and initiates audio streaming tasks. It manages text buffers and ensures that the audio dispatching task is running. + +```python +async def _add_text(self, text: str): + await self._start_turn() + + self._text_buffer += text + self.total_output_text += text + self._turn_text_buffer += text + + combined_sentences, self._text_buffer = self.tts_settings.text_splitter(self._text_buffer) + + if len(combined_sentences) >= 20: + local_queue: asyncio.Queue[VoiceStreamEvent | None] = asyncio.Queue() + self._ordered_tasks.append(local_queue) + self._tasks.append( + asyncio.create_task(self._stream_audio(combined_sentences, local_queue)) + ) + if self._dispatcher_task is None: + self._dispatcher_task = asyncio.create_task(self._dispatch_audio()) +``` + +-------------------------------- + +### Get All Function Tools from MCP Servers (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/mcp/util + +This class method retrieves all function tools available across a list of MCP servers. It ensures that tool names are unique across all servers to prevent conflicts. Dependencies include MCPServer, RunContextWrapper, and AgentBase. + +```python +@classmethod +async def get_all_function_tools( + cls, + servers: list["MCPServer"], + convert_schemas_to_strict: bool, + run_context: RunContextWrapper[Any], + agent: "AgentBase", +) -> list[Tool]: + """Get all function tools from a list of MCP servers.""" + tools = [] + tool_names: set[str] = set() + for server in servers: + server_tools = await cls.get_function_tools( + server, convert_schemas_to_strict, run_context, agent + ) + server_tool_names = {tool.name for tool in server_tools} + if len(server_tool_names & tool_names) > 0: + raise UserError( + f"Duplicate tool names found across MCP servers: " + f"{server_tool_names & tool_names}" + ) + tool_names.update(server_tool_names) + tools.extend(server_tools) + + return tools +``` + +-------------------------------- + +### Conditionally Enable Agent Tools with `is_enabled` + +Source: https://openai.github.io/openai-agents-python/tools + +This example shows how to conditionally enable or disable agent tools at runtime using the `is_enabled` parameter. It defines a `LanguageContext` and a `french_enabled` function to control tool availability. The orchestrator agent uses these tools, demonstrating dynamic filtering based on context. The `is_enabled` parameter can accept boolean values or callable functions (sync or async). + +```python +import asyncio +from agents import Agent, AgentBase, Runner, RunContextWrapper +from pydantic import BaseModel + +class LanguageContext(BaseModel): + language_preference: str = "french_spanish" + +def french_enabled(ctx: RunContextWrapper[LanguageContext], agent: AgentBase) -> bool: + """Enable French for French+Spanish preference.""" + return ctx.context.language_preference == "french_spanish" + +# Create specialized agents +spanish_agent = Agent( + name="spanish_agent", + instructions="You respond in Spanish. Always reply to the user's question in Spanish.", +) + +french_agent = Agent( + name="french_agent", + instructions="You respond in French. Always reply to the user's question in French.", +) + +# Create orchestrator with conditional tools +orchestrator = Agent( + name="orchestrator", + instructions=( + "You are a multilingual assistant. You use the tools given to you to respond to users. " + "You must call ALL available tools to provide responses in different languages. " + "You never respond in languages yourself, you always use the provided tools." + ), + tools=[ + spanish_agent.as_tool( + tool_name="respond_spanish", + tool_description="Respond to the user's question in Spanish", + is_enabled=True, # Always enabled + ), + french_agent.as_tool( + tool_name="respond_french", + tool_description="Respond to the user's question in French", + is_enabled=french_enabled, + ), + ], +) + +async def main(): + context = RunContextWrapper(LanguageContext(language_preference="french_spanish")) + result = await Runner.run(orchestrator, "How are you?", context=context.context) + print(result.final_output) + +asyncio.run(main()) + +``` + +-------------------------------- + +### Get OpenAI Speech-to-Text Model in Python + +Source: https://openai.github.io/openai-agents-python/ref/voice/models/openai_provider + +Retrieves a speech-to-text (STT) model from the OpenAIVoiceModelProvider. If no model name is specified, it defaults to `DEFAULT_STT_MODEL`. This method ensures that an OpenAI client is available before returning the STT model. + +```python +def get_stt_model(self, model_name: str | None) -> STTModel: + """Get a speech-to-text model by name. + + Args: + model_name: The name of the model to get. + + Returns: + The speech-to-text model. + """ + return OpenAISTTModel(model_name or DEFAULT_STT_MODEL, self._get_client()) +``` + +-------------------------------- + +### Realtime Agent Attributes + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/agent + +This section details the various attributes that can be configured for a RealtimeAgent, including instructions, prompt, handoffs, output guardrails, hooks, name, handoff description, tools, MCP servers, and MCP configuration. + +```APIDOC +## Realtime Agent Attributes + +### Instructions +- **Type**: `str` | `Callable` | `None` +- **Description**: The instructions for the agent, used as the system prompt. Can be a static string or a function that dynamically generates instructions based on context and agent instance. + +### Prompt +- **Type**: `Prompt` | `None` +- **Description**: A prompt object for dynamically configuring instructions, tools, and other settings. Only usable with OpenAI models. + +### Handoffs +- **Type**: `list[RealtimeAgent | Handoff]` +- **Description**: A list of sub-agents (handoffs) that the agent can delegate to. This allows for modularity and separation of concerns. + +### Output Guardrails +- **Type**: `list[OutputGuardrail]` +- **Description**: A list of checks applied to the agent's final output after generating a response. + +### Hooks +- **Type**: `RealtimeAgentHooks` | `None` +- **Description**: A class that receives callbacks for various agent lifecycle events. + +### Name +- **Type**: `str` +- **Description**: The unique name of the agent. + +### Handoff Description +- **Type**: `str` | `None` +- **Description**: A description of the agent used when it acts as a handoff, informing an LLM about its purpose and when to invoke it. + +### Tools +- **Type**: `list[Tool]` +- **Description**: A list of tools available for the agent to use. + +### MCP Servers +- **Type**: `list[MCPServer]` +- **Description**: A list of Model Context Protocol servers that the agent can utilize. Tools from these servers are included when the agent runs. Note: The lifecycle of these servers (connect/cleanup) must be managed externally or via `MCPServerManager`. + +### MCP Config +- **Type**: `MCPConfig` +- **Description**: Configuration settings for MCP servers. +``` + +-------------------------------- + +### Create Database Tables for Conversation History (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/memory/sqlite_session + +Initializes the 'sessions' and 'messages' tables in the SQLite database. It ensures the existence of these tables and sets up foreign key constraints and indexes for efficient data management. This function is crucial for the initial setup of the conversation history storage. + +```python +CREATE INDEX IF NOT EXISTS idx_sessions_session_id ON {self.sessions_table} (session_id) + ) + """ + ) + + conn.execute( + f""" + CREATE TABLE IF NOT EXISTS {self.messages_table} ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + session_id TEXT NOT NULL, + message_data TEXT NOT NULL, + FOREIGN KEY (session_id) REFERENCES {self.sessions_table} (session_id) + ON DELETE CASCADE + ) + """ + ) + + conn.execute( + f""" + CREATE INDEX IF NOT EXISTS idx_{self.messages_table}_session_id + ON {self.messages_table} (session_id, id) + """ + ) + + conn.commit() +``` + +-------------------------------- + +### Realtime Input Audio Transcription Configuration (Python) + +Source: https://openai.github.io/openai-agents-python/zh/ref/realtime/config + +Defines the configuration for transcribing input audio in realtime sessions. It allows specifying the language, transcription model, and an optional prompt to guide the transcription process. This configuration is crucial for accurately converting spoken language into text for agent processing. + +```python +class RealtimeInputAudioTranscriptionConfig(TypedDict): + """Configuration for audio transcription in realtime sessions.""" + + language: NotRequired[str] + """The language code for transcription.""" + + model: NotRequired[Literal["gpt-4o-transcribe", "gpt-4o-mini-transcribe", "whisper-1"] | str] + """The transcription model to use.""" + + prompt: NotRequired[str] + """An optional prompt to guide transcription.""" + +``` + +-------------------------------- + +### Get Tool Usage by Turn (Python) + +Source: https://openai.github.io/openai-agents-python/ko/ref/extensions/memory/advanced_sqlite_session + +Fetches tool usage statistics for a given branch within a session. It counts the occurrences of various tool call types per user turn and returns a list of tuples, each containing the tool name, its usage count, and the corresponding turn number. Supports fetching for the current branch if branch_id is omitted. + +```python +async def get_tool_usage(self, branch_id: str | None = None) -> list[tuple[str, int, int]]: + """Get all tool usage by turn for specified branch. + + Args: + branch_id: Branch to get tool usage from (current branch if None). + + Returns: + List of tuples containing (tool_name, usage_count, turn_number). + """ + if branch_id is None: + branch_id = self._current_branch_id + + def _get_tool_usage_sync(): + """Synchronous helper to get tool usage statistics.""" + conn = self._get_connection() + with closing(conn.cursor()) as cursor: + cursor.execute( + """ + SELECT tool_name, COUNT(*), user_turn_number + FROM message_structure + WHERE session_id = ? AND branch_id = ? AND message_type IN ( + 'tool_call', 'function_call', 'computer_call', 'file_search_call', + 'web_search_call', 'code_interpreter_call', 'custom_tool_call', + 'mcp_call', 'mcp_approval_request' + ) + GROUP BY tool_name, user_turn_number + ORDER BY user_turn_number + """, + (self.session_id, branch_id), + ) + return cursor.fetchall() + + return await asyncio.to_thread(_get_tool_usage_sync) +``` + +-------------------------------- + +### Agent Handoffs for Delegation (Python) + +Source: https://openai.github.io/openai-agents-python/agents + +Illustrates the handoff pattern where an agent can delegate conversations to specialized sub-agents. The triage agent, for example, directs users to booking or refund agents based on their queries, enabling modular agent design. + +```python +from agents import Agent + +booking_agent = Agent(...) +refund_agent = Agent(...) + +triage_agent = Agent( + name="Triage agent", + instructions=( + "Help the user with their questions. " + "If they ask about booking, hand off to the booking agent. " + "If they ask about refunds, hand off to the refund agent." + ), + handoffs=[booking_agent, refund_agent], +) + +``` + +-------------------------------- + +### Get Current Mapping from MultiProviderMap + +Source: https://openai.github.io/openai-agents-python/ko/ref/models/multi_provider + +The `get_mapping` method returns a shallow copy of the current dictionary that maps model name prefixes to `ModelProvider` objects. This prevents external modification of the internal mapping. + +```python +def get_mapping(self) -> dict[str, ModelProvider]: + """Returns a copy of the current prefix -> ModelProvider mapping.""" + return self._mapping.copy() +``` + +-------------------------------- + +### List Tools on Server (Python) + +Source: https://openai.github.io/openai-agents-python/ref/mcp/server + +Abstract method to list available tools on the server. It takes an optional run context and agent as input and returns a list of Tool objects. This method is part of the MCP server interface. + +```python +import abc +from typing import Any, List + +# Assuming these types are defined elsewhere +class RunContextWrapper: + pass + +class AgentBase: + pass + +class Tool: + pass + +class MCPTool(Tool): + pass + +class MCPBaseServer(abc.ABC): + @abc.abstractmethod + async def list_tools(self, run_context: RunContextWrapper[Any] | None = None, agent: AgentBase | None = None) -> list[MCPTool]: + """List the tools available on the server.""" + pass + +``` + +-------------------------------- + +### Prepare Tools for LLM API Calls (Python) + +Source: https://openai.github.io/openai-agents-python/ref/extensions/litellm + +Converts internal tool representations into the format required by LLM APIs, including support for parallel tool calls and handoff tools. It also handles tool choice and response format conversions. + +```python +parallel_tool_calls = ( + True + if model_settings.parallel_tool_calls and tools and len(tools) > 0 + else False + if model_settings.parallel_tool_calls is False + else None +) +tool_choice = Converter.convert_tool_choice(model_settings.tool_choice) +response_format = Converter.convert_response_format(output_schema) + +converted_tools = [Converter.tool_to_openai(tool) for tool in tools] if tools else [] + +for handoff in handoffs: + converted_tools.append(Converter.convert_handoff_tool(handoff)) + +converted_tools = _to_dump_compatible(converted_tools) +``` + +-------------------------------- + +### VoicePipelineConfig Dataclass Definition + +Source: https://openai.github.io/openai-agents-python/ref/voice/pipeline_config + +Defines the configuration structure for a VoicePipeline. It includes settings for model providers, tracing behavior, workflow naming, group identification, and STT/TTS configurations. Defaults are provided for most attributes to simplify setup. + +```python +@dataclass +class VoicePipelineConfig: + """Configuration for a `VoicePipeline`.""" + + model_provider: VoiceModelProvider = field(default_factory=OpenAIVoiceModelProvider) + """The voice model provider to use for the pipeline. Defaults to OpenAI.""" + + tracing_disabled: bool = False + """Whether to disable tracing of the pipeline. Defaults to `False`.""" + + tracing: TracingConfig | None = None + """Tracing configuration for this pipeline.""" + + trace_include_sensitive_data: bool = True + """Whether to include sensitive data in traces. Defaults to `True`. This is specifically for the + voice pipeline, and not for anything that goes on inside your Workflow.""" + + trace_include_sensitive_audio_data: bool = True + """Whether to include audio data in traces. Defaults to `True`.""" + + workflow_name: str = "Voice Agent" + """The name of the workflow to use for tracing. Defaults to `Voice Agent`.""" + + group_id: str = field(default_factory=gen_group_id) + """ + A grouping identifier to use for tracing, to link multiple traces from the same conversation + or process. If not provided, we will create a random group ID. + """ + + trace_metadata: dict[str, Any] | None = None + """ + An optional dictionary of additional metadata to include with the trace. + """ + + stt_settings: STTModelSettings = field(default_factory=STTModelSettings) + """The settings to use for the STT model.""" + + tts_settings: TTSModelSettings = field(default_factory=TTSModelSettings) + """The settings to use for the TTS model.""" + +``` + +-------------------------------- + +### Create SQLAlchemy Session from URL (Python) + +Source: https://openai.github.io/openai-agents-python/zh/ref/extensions/memory/sqlalchemy_session + +The `from_url` class method instantiates a SQLAlchemySession using a provided database URL. It accepts session ID, the URL string, and optional engine keyword arguments for SQLAlchemy's create_async_engine. It returns a configured SQLAlchemySession instance. + +```python +from sqlalchemy.ext.asyncio import create_async_engine + +# Assuming SQLAlchemySession and TResponseInputItem are defined elsewhere +# class SQLAlchemySession: +# def __init__(self, session_id: str, engine, **kwargs): +# self.session_id = session_id +# self._engine = engine +# # ... other initializations ... + +# @classmethod +# def from_url( +# cls, +# session_id: str, +# *, +# url: str, +# engine_kwargs: dict[str, Any] | None = None, +# **kwargs: Any, +# ) -> SQLAlchemySession: +# """Create a session from a database URL string.""" +# engine_kwargs = engine_kwargs or {} +# engine = create_async_engine(url, **engine_kwargs) +# return cls(session_id, engine=engine, **kwargs) + +# Example Usage: +# session = SQLAlchemySession.from_url( +# session_id="my_conversation_id", +# url="postgresql+asyncpg://user:pass@host/db" +# ) +``` + +-------------------------------- + +### Start Transcription Turn in Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/voice/models/openai_stt + +Initiates a new transcription turn by creating a tracing span. This span captures model configuration details such as temperature, language, prompt, and turn detection settings for logging and analysis. + +```python +def _start_turn(self) -> None: + self._tracing_span = transcription_span( + model=self._model, + model_config={ + "temperature": self._settings.temperature, + "language": self._settings.language, + "prompt": self._settings.prompt, + "turn_detection": self._turn_detection, + }, + ) + self._tracing_span.start() +``` + +-------------------------------- + +### List Tools - Python + +Source: https://openai.github.io/openai-agents-python/ko/ref/mcp/server + +Abstract method to list available tools on the server. It takes an optional run_context and agent as input and returns a list of Tool objects. + +```python +import abc +from typing import Any, List + +from ..client.run_context import RunContextWrapper +from ..tools.tool import Tool +from .base import AgentBase +from .mcp_tool import MCPTool + + +class MCPBase(abc.ABC): + @abc.abstractmethod + async def list_tools(self, run_context: RunContextWrapper[Any] | None = None, agent: AgentBase | None = None) -> List[MCPTool]: + """List the tools available on the server.""" + pass + +``` + +-------------------------------- + +### Function Span Creation + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/create + +This describes the `function_span` function used to create a new function span. Spans are not started automatically and require manual start/finish calls or usage within a `with` statement. + +```APIDOC +## POST /function_span + +### Description +Creates a new function span for tracing. This span is not automatically started; you must use `with function_span() ...` or manually call `span.start()` and `span.finish()`. + +### Method +POST + +### Endpoint +`/function_span` + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **name** (str) - Required - The name of the function. +- **input** (str | None) - Optional - The input to the function. +- **output** (str | None) - Optional - The output of the function. +- **span_id** (str | None) - Optional - The ID of the span. If not provided, an ID will be generated. +- **parent** (Trace | Span[Any] | None) - Optional - The parent span or trace. Defaults to the current trace/span. +- **disabled** (bool) - Optional - If True, the span will not be recorded. Defaults to `False`. + +### Request Example +```json +{ + "name": "my_function", + "input": "some_input_data", + "parent": null, + "span_id": null, + "disabled": false +} +``` + +### Response +#### Success Response (200) +- **Span[FunctionSpanData]** - The newly created function span. + +#### Response Example +```json +{ + "span_id": "generated_span_id", + "name": "my_function", + "input": "some_input_data", + "output": null, + "parent_id": "current_parent_id", + "start_time": "timestamp", + "end_time": null +} +``` +``` + +-------------------------------- + +### Synchronous Agent Workflow Execution with Runner.run_sync + +Source: https://openai.github.io/openai-agents-python/ja/ref/run + +The `run_sync` method provides a synchronous way to execute an agent workflow. It accepts the same parameters as the `run` method, including the starting agent, input, context, and other configuration options. This method is suitable for environments where asynchronous operations are not preferred or possible. + +```python + @classmethod + def run_sync( + cls, + starting_agent: Agent[TContext], + input: str | list[TResponseInputItem], + *, + context: TContext | None = None, + max_turns: int = DEFAULT_MAX_TURNS, + hooks: RunHooks[TContext] | None = None, + run_config: RunConfig | None = None, + previous_response_id: str | None = None, + auto_previous_response_id: bool = False, + conversation_id: str | None = None, + +``` + +-------------------------------- + +### Create Guardrail Span + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing + +Creates a new guardrail span. This span can be used to track specific guardrail operations within a trace. The span requires manual start and finish calls or can be used with a context manager. + +```APIDOC +## POST /tracing/guardrail_span + +### Description +Create a new guardrail span. The span will not be started automatically, you should either do `with guardrail_span() ...` or call `span.start()` + `span.finish()` manually. + +### Method +POST + +### Endpoint +/tracing/guardrail_span + +### Parameters +#### Query Parameters +None + +#### Request Body +- **name** (string) - Required - The name of the guardrail. +- **triggered** (boolean) - Optional - Whether the guardrail was triggered. Defaults to `False`. +- **span_id** (string | null) - Optional - The ID of the span. If not provided, an ID will be generated. +- **parent** (Trace | Span[Any] | null) - Optional - The parent span or trace. If not provided, the current trace/span will be used as the parent. +- **disabled** (boolean) - Optional - If True, the Span will not be recorded. Defaults to `False`. + +### Request Example +```json +{ + "name": "input_validation", + "triggered": true, + "span_id": "span-abc", + "parent": { + "id": "trace-123" + }, + "disabled": false +} +``` + +### Response +#### Success Response (200) +- **span_id** (string) - The ID of the created span. +- **name** (string) - The name of the guardrail span. +- **start_time** (string) - The start time of the span. + +#### Response Example +```json +{ + "span_id": "span-abc", + "name": "input_validation", + "start_time": "2023-10-27T10:05:00Z" +} +``` +``` + +-------------------------------- + +### Database Table Initialization and Indexing (Python) + +Source: https://openai.github.io/openai-agents-python/zh/ref/memory + +Initializes the messages and sessions tables in the database and creates an index for efficient querying of session messages. It handles foreign key constraints and ensures data integrity. + +```python + conn.execute( + f""" + CREATE TABLE IF NOT EXISTS {self.messages_table} ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + session_id TEXT NOT NULL, + message_data TEXT NOT NULL, + FOREIGN KEY (session_id) REFERENCES {self.sessions_table} (session_id) + ON DELETE CASCADE + ) + """ + ) + + conn.execute( + f""" + CREATE INDEX IF NOT EXISTS idx_{self.messages_table}_session_id + ON {self.messages_table} (session_id, id) + """ + ) + + conn.commit() +``` + +-------------------------------- + +### Copy Messages Between Branches (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/extensions/memory/advanced_sqlite_session + +Inserts copies of existing messages into a new branch within the same session. It determines the starting sequence number for the new branch and appends new entries to the 'message_structure' table. + +```python +async def _copy_sync(self): + """Synchronous helper to copy messages to a new branch.""" + conn = self._get_connection() + with closing(conn.cursor()) as cursor: + # Get the starting sequence number for the new branch + cursor.execute( + """ + SELECT MAX(sequence_number) FROM message_structure + WHERE session_id = ? AND branch_id = ? + """, + (self.session_id, new_branch_id), + ) + + seq_start = cursor.fetchone()[0] + + # Insert copied messages with new branch_id + new_structure_data = [] + for i, ( + msg_id, + msg_type, + _, + user_turn, + branch_turn, + tool_name, + ) in enumerate(messages_to_copy): + new_structure_data.append( + ( + self.session_id, + msg_id, # Same message_id (sharing the actual message data) + new_branch_id, + msg_type, + seq_start + i + 1, # New sequence number + user_turn, # Keep same global turn number + branch_turn, # Keep same branch turn number + tool_name, + ) + ) + + cursor.executemany( + """ + INSERT INTO message_structure + (session_id, message_id, branch_id, message_type, sequence_number, + user_turn_number, branch_turn_number, tool_name) + VALUES (?, ?, ?, ?, ?, ?, ?, ?) + """, + new_structure_data, + ) + + conn.commit() + +await asyncio.to_thread(_copy_sync) +``` + +-------------------------------- + +### on_trace_start + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/processor_interface + +This method is called synchronously when a new trace begins execution. It should return quickly to avoid blocking the execution. Any errors encountered should be handled internally. + +```APIDOC +## on_trace_start + +### Description +Called when a new trace begins execution. + +### Method +`on_trace_start` + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **trace** (`Trace`) - Required - The trace that started. Contains workflow name and metadata. + +### Request Example +```json +{ + "trace": { "workflow_name": "example_workflow", "metadata": {} } +} +``` + +### Response +#### Success Response (200) +None + +#### Response Example +```json +null +``` + +### Notes +- Called synchronously on trace start +- Should return quickly to avoid blocking execution +- Any errors should be caught and handled internally +``` + +-------------------------------- + +### Get Server Name Property (Python) + +Source: https://openai.github.io/openai-agents-python/ref/mcp/server + +Retrieves a readable name for the server. This property is defined within the server class and returns a string representing the server's name. + +```python + @property + def name(self) -> str: + """A readable name for the server.""" + return self._name +``` + +```python +name: str +``` + +-------------------------------- + +### BatchTraceProcessor Initialization and Thread Management + +Source: https://openai.github.io/openai-agents-python/ref/tracing/processors + +Initializes the BatchTraceProcessor with configuration for queue size, batch size, schedule delay, and export trigger ratio. It also manages a background worker thread for asynchronous export, ensuring the thread is started only when necessary using a double-checked locking pattern. + +```python +class BatchTraceProcessor(TracingProcessor): + """Some implementation notes: + 1. Using Queue, which is thread-safe. + 2. Using a background thread to export spans, to minimize any performance issues. + 3. Spans are stored in memory until they are exported. + """ + + def __init__( + self, + exporter: TracingExporter, + max_queue_size: int = 8192, + max_batch_size: int = 128, + schedule_delay: float = 5.0, + export_trigger_ratio: float = 0.7, + ): + """ + Args: + exporter: The exporter to use. + max_queue_size: The maximum number of spans to store in the queue. After this, we will + start dropping spans. + max_batch_size: The maximum number of spans to export in a single batch. + schedule_delay: The delay between checks for new spans to export. + export_trigger_ratio: The ratio of the queue size at which we will trigger an export. + """ + self._exporter = exporter + self._queue: queue.Queue[Trace | Span[Any]] = queue.Queue(maxsize=max_queue_size) + self._max_queue_size = max_queue_size + self._max_batch_size = max_batch_size + self._schedule_delay = schedule_delay + self._shutdown_event = threading.Event() + + # The queue size threshold at which we export immediately. + self._export_trigger_size = max(1, int(max_queue_size * export_trigger_ratio)) + + # Track when we next *must* perform a scheduled export + self._next_export_time = time.time() + self._schedule_delay + + # We lazily start the background worker thread the first time a span/trace is queued. + self._worker_thread: threading.Thread | None = None + self._thread_start_lock = threading.Lock() + + def _ensure_thread_started(self) -> None: + # Fast path without holding the lock + if self._worker_thread and self._worker_thread.is_alive(): + return + + # Double-checked locking to avoid starting multiple threads + with self._thread_start_lock: + if self._worker_thread and self._worker_thread.is_alive(): + return + + self._worker_thread = threading.Thread(target=self._run, daemon=True) + self._worker_thread.start() +``` + +-------------------------------- + +### Get Trace ID - Python + +Source: https://openai.github.io/openai-agents-python/zh/ref/tracing + +Retrieves the globally unique identifier for a trace. This ID is formatted as 'trace_<32_alphanumeric>' and is used to link spans to their parent trace for lookup in the dashboard. + +```python +trace_id: str + +# Returns: +# Name | Type | Description +# `str` | `str` | The trace's unique ID in the format 'trace_<32_alphanumeric>' +``` + +-------------------------------- + +### Use Non-OpenAI Models with LiteLLM Prefix + +Source: https://openai.github.io/openai-agents-python/models + +Utilize non-OpenAI models by prefixing their names with 'litellm/'. This enables the Agents SDK to route requests through LiteLLM to the specified model, such as Anthropic's Claude or Google's Gemini. Ensure LiteLLM is installed and the model names are correctly formatted. + +```python +from agents import Agent + +claude_agent = Agent(model="litellm/anthropic/claude-3-5-sonnet-20240620") +gemini_agent = Agent(model="litellm/gemini/gemini-2.5-flash-preview-04-17") +``` + +-------------------------------- + +### RealtimeAgent Class Definition and Initialization + +Source: https://openai.github.io/openai-agents-python/ref/realtime/agent + +Defines the RealtimeAgent class, inheriting from AgentBase and Generic. It outlines supported and unsupported configurations for voice agents within a RealtimeSession, including dynamic instructions and handoffs. + +```python +from dataclasses import dataclass, field +from typing import Any, Generic, TContext, Callable, MaybeAwaitable, cast, Awaitable + +from ...base import AgentBase, RunContextWrapper +from ...tools.output_guardrail import OutputGuardrail +from ...prompts import Prompt +from .handoff import Handoff, RealtimeAgent +from .hooks import RealtimeAgentHooks + +import inspect +import logging + +logger = logging.getLogger(__name__) # pylint: disable=invalid-name + + +@dataclass +class RealtimeAgent(AgentBase, Generic[TContext]): + """A specialized agent instance that is meant to be used within a `RealtimeSession` to build + voice agents. Due to the nature of this agent, some configuration options are not supported + that are supported by regular `Agent` instances. For example: + - `model` choice is not supported, as all RealtimeAgents will be handled by the same model + within a `RealtimeSession`. + - `modelSettings` is not supported, as all RealtimeAgents will be handled by the same model + within a `RealtimeSession`. + - `outputType` is not supported, as RealtimeAgents do not support structured outputs. + - `toolUseBehavior` is not supported, as all RealtimeAgents will be handled by the same model + within a `RealtimeSession`. + - `voice` can be configured on an `Agent` level; however, it cannot be changed after the first + agent within a `RealtimeSession` has spoken. + + See `AgentBase` for base parameters that are shared with `Agent`s. + """ + + instructions: ( + str + | Callable[ + [RunContextWrapper[TContext], RealtimeAgent[TContext]], + MaybeAwaitable[str], + ] + | None + ) = None + """The instructions for the agent. Will be used as the "system prompt" when this agent is + invoked. Describes what the agent should do, and how it responds. + + Can either be a string, or a function that dynamically generates instructions for the agent. If + you provide a function, it will be called with the context and the agent instance. It must + return a string. + """ + + prompt: Prompt | None = None + """A prompt object. Prompts allow you to dynamically configure the instructions, tools + and other config for an agent outside of your code. Only usable with OpenAI models. + """ + + handoffs: list[RealtimeAgent[Any] | Handoff[TContext, RealtimeAgent[Any]]] = field( + default_factory=list + ) + """Handoffs are sub-agents that the agent can delegate to. You can provide a list of handoffs, + and the agent can choose to delegate to them if relevant. Allows for separation of concerns and + modularity. + """ + + output_guardrails: list[OutputGuardrail[TContext]] = field(default_factory=list) + """A list of checks that run on the final output of the agent, after generating a response. + Runs only if the agent produces a final output. + """ + + hooks: RealtimeAgentHooks | None = None + """A class that receives callbacks on various lifecycle events for this agent. + """ + + def clone(self, **kwargs: Any) -> RealtimeAgent[TContext]: + """Make a copy of the agent, with the given arguments changed. For example, you could do: + ``` + new_agent = agent.clone(instructions="New instructions") + ``` + """ + return dataclasses.replace(self, **kwargs) + + async def get_system_prompt(self, run_context: RunContextWrapper[TContext]) -> str | None: + """Get the system prompt for the agent.""" + if isinstance(self.instructions, str): + return self.instructions + elif callable(self.instructions): + if inspect.iscoroutinefunction(self.instructions): + return await cast(Awaitable[str], self.instructions(run_context, self)) + else: + return cast(str, self.instructions(run_context, self)) + elif self.instructions is not None: + logger.error(f"Instructions must be a string or a function, got {self.instructions}") + + return None + +``` + +-------------------------------- + +### Get Model by Name - Python + +Source: https://openai.github.io/openai-agents-python/ko/ref/models/interface + +Abstract method to retrieve a model by its name. It takes the model name as an argument and returns a Model object. This is a core function for model providers to expose available models. + +```python +import abc + +# Assuming Model is defined elsewhere +class Model: + pass + +class ModelProvider(abc.ABC): + @abc.abstractmethod + def get_model(self, model_name: str | None) -> Model: + """Get a model by name. + + Args: + model_name: The name of the model to get. + + Returns: + The model. + """ + pass + +``` + +-------------------------------- + +### Get Trace Name - Python + +Source: https://openai.github.io/openai-agents-python/ko/ref/tracing + +Retrieves the human-readable name of a workflow trace. This name should be descriptive and is used for grouping and filtering traces in the dashboard, helping to identify the purpose of the trace. + +```Python +name: str + +""" +Get the human-readable name of this workflow trace. + +Returns: + str: The workflow name (e.g., "Customer Service", "Data Processing") + +Notes: + * Should be descriptive and meaningful + * Used for grouping and filtering in the dashboard + * Helps identify the purpose of the trace +""" +pass +``` + +-------------------------------- + +### Set up HTTP with SSE MCP Server (Deprecated) + +Source: https://openai.github.io/openai-agents-python/mcp + +This snippet illustrates how to instantiate an MCP server using the Server-Sent Events (SSE) transport with `MCPServerSse`. While functional, SSE is deprecated in favor of Streamable HTTP or stdio for new integrations. The API is similar to Streamable HTTP servers. + +```python +from agents import Agent, Runner +from agents.model_settings import ModelSettings +from agents.mcp import MCPServerSse + +workspace_id = "demo-workspace" + +async with MCPServerSse( + name="SSE Python Server", + params={ + "url": "http://localhost:8000/sse", + "headers": {"X-Workspace": workspace_id}, + }, + cache_tools_list=True, +) as server: + agent = Agent( + name="Assistant", + mcp_servers=[server], + model_settings=ModelSettings(tool_choice="required"), + ) + result = await Runner.run(agent, "What's the weather in Tokyo?") + print(result.final_output) + + +``` + +-------------------------------- + +### Create MCP List Tools Span (Python) + +Source: https://openai.github.io/openai-agents-python/ref/tracing + +Creates a new span for MCP list tools operations. Spans are not automatically started and require manual start/finish calls or usage within a `with` statement. It accepts optional parameters for server, result, span ID, parent span/trace, and a disabled flag. + +```python +def mcp_tools_span( + server: str | None = None, + result: list[str] | None = None, + span_id: str | None = None, + parent: Trace | Span[Any] | None = None, + disabled: bool = False, +) -> Span[MCPListToolsSpanData]: + """Create a new MCP list tools span. The span will not be started automatically, you should + either do `with mcp_tools_span() ...` or call `span.start()` + `span.finish()` manually. + + Args: + server: The name of the MCP server. + result: The result of the MCP list tools call. + span_id: The ID of the span. Optional. If not provided, we will generate an ID. We + recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are + correctly formatted. + parent: The parent span or trace. If not provided, we will automatically use the current + trace/span as the parent. + disabled: If True, we will return a Span but the Span will not be recorded. + """ + return get_trace_provider().create_span( + span_data=MCPListToolsSpanData(server=server, result=result), + span_id=span_id, + parent=parent, + disabled=disabled, + ) + +``` + +-------------------------------- + +### Get Specific Turn Usage Details (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/extensions/memory/advanced_sqlite_session + +Fetches detailed usage statistics for a specific user turn within a session and branch. It includes token counts and detailed JSON breakdowns for input and output tokens. Returns an empty dictionary if the turn is not found. + +```python +if user_turn_number is not None: + query = """ + SELECT requests, input_tokens, output_tokens, total_tokens, + input_tokens_details, output_tokens_details + FROM turn_usage + WHERE session_id = ? AND branch_id = ? AND user_turn_number = ? + """ + + with closing(conn.cursor()) as cursor: + cursor.execute(query, (self.session_id, branch_id, user_turn_number)) + row = cursor.fetchone() + + if row: + # Parse JSON details if present + input_details = None + output_details = None + + if row[4]: # input_tokens_details + try: + input_details = json.loads(row[4]) + except json.JSONDecodeError: + pass + + if row[5]: # output_tokens_details + try: + output_details = json.loads(row[5]) + except json.JSONDecodeError: + pass + + return { + "requests": row[0], + "input_tokens": row[1], + "output_tokens": row[2], + "total_tokens": row[3], + "input_tokens_details": input_details, + "output_tokens_details": output_details, + } + return {} +``` + +-------------------------------- + +### Manage Transcription Turn with Tracing (Python) + +Source: https://openai.github.io/openai-agents-python/ref/voice/models/openai_stt + +Handles the start and end of a transcription turn, including setting up and finishing tracing spans. It can optionally include sensitive data and audio in the trace logs based on configuration. + +```python + def _start_turn(self) -> None: + self._tracing_span = transcription_span( + model=self._model, + model_config={ + "temperature": self._settings.temperature, + "language": self._settings.language, + "prompt": self._settings.prompt, + "turn_detection": self._turn_detection, + }, + ) + self._tracing_span.start() + + def _end_turn(self, _transcript: str) -> None: + if len(_transcript) < 1: + return + + if self._tracing_span: + # Only encode audio if tracing is enabled AND buffer is not empty + if self._trace_include_sensitive_audio_data and self._turn_audio_buffer: + self._tracing_span.span_data.input = _audio_to_base64(self._turn_audio_buffer) + + self._tracing_span.span_data.input_format = "pcm" + + if self._trace_include_sensitive_data: + self._tracing_span.span_data.output = _transcript + + self._tracing_span.finish() + self._turn_audio_buffer = [] + self._tracing_span = None +``` + +-------------------------------- + +### AdvancedSQLiteSession Initialization + +Source: https://openai.github.io/openai-agents-python/zh/ref/extensions/memory/advanced_sqlite_session + +Initializes the AdvancedSQLiteSession with provided parameters, including session ID, database path, and table creation options. + +```APIDOC +## AdvancedSQLiteSession `__init__` + +### Description +Initializes the AdvancedSQLiteSession with provided parameters, including session ID, database path, and table creation options. + +### Method +`__init__` + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **session_id** (str) - Required - The ID of the session +- **db_path** (str | Path) - Optional - The path to the SQLite database file. Defaults to `:memory:` for in-memory storage +- **create_tables** (bool) - Optional - Whether to create the structure tables +- **logger** (Logger | None) - Optional - The logger to use. Defaults to the module logger +- **kwargs** () - Optional - Additional keyword arguments to pass to the superclass + +### Request Example +```json +{ + "session_id": "your_session_id", + "db_path": ":memory:", + "create_tables": false, + "logger": null +} +``` + +### Response +#### Success Response (None) +This method does not return a value. + +#### Response Example +None +``` + +-------------------------------- + +### Integrate Realtime Agents with SIP Calls in Python + +Source: https://openai.github.io/openai-agents-python/realtime/guide + +This Python code shows how to integrate realtime agents with phone calls using the Realtime Calls API and `OpenAIRealtimeSIPModel`. It sets up a `RealtimeRunner` with the SIP model and configures call-specific settings like turn detection. The session handles media negotiation over SIP and automatically closes when the caller hangs up. + +```python +from agents.realtime import RealtimeAgent, RealtimeRunner +from agents.realtime.openai_realtime import OpenAIRealtimeSIPModel + +# Assuming 'agent' is a pre-configured RealtimeAgent instance +agent = RealtimeAgent(name="Assistant", instructions="...") # Example agent + +runner = RealtimeRunner( + starting_agent=agent, + model=OpenAIRealtimeSIPModel(), +) + +# call_id_from_webhook would be received from an incoming call webhook +call_id_from_webhook = "your_call_id_here" + +async with await runner.run( + model_config={ + "call_id": call_id_from_webhook, + "initial_model_settings": { + "turn_detection": {"type": "semantic_vad", "interrupt_response": True}, + }, + }, +) as session: + async for event in session: + # Handle session events + pass + +``` + +-------------------------------- + +### OpenAI Responses Compaction Session Initialization + +Source: https://openai.github.io/openai-agents-python/ja/ref/memory/openai_responses_compaction_session + +Initializes the compaction session with various configuration options for managing OpenAI conversation history. + +```APIDOC +## POST /__init__ (OpenAI Responses Compaction Session) + +### Description +Initializes the compaction session. This session is used to manage and compact conversation history for OpenAI models, providing options for client, model, compaction strategy, and custom triggering logic. + +### Method +POST + +### Endpoint +/__init__ + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **session_id** (str) - Required - Identifier for this session. +- **underlying_session** (Session) - Required - Session store that holds the compacted history. Cannot be OpenAIConversationsSession. +- **client** (AsyncOpenAI | None) - Optional - OpenAI client for responses.compact API calls. Defaults to get_default_openai_client() or new AsyncOpenAI(). +- **model** (str) - Optional - Model to use for responses.compact. Defaults to "gpt-4.1". Must be an OpenAI model name (gpt-*, o*, or ft:gpt-*). +- **compaction_mode** (OpenAIResponsesCompactionMode) - Optional - Controls how the compaction request provides conversation history. "auto" (default) uses input when the last response was not stored or no response_id is available. +- **should_trigger_compaction** (Callable[[dict[str, Any]], bool] | None) - Optional - Custom decision hook. Defaults to triggering when 10+ compaction candidates exist. + +### Request Example +```json +{ + "session_id": "example_session_id", + "underlying_session": { /* Session object */ }, + "client": null, // or an AsyncOpenAI client instance + "model": "gpt-4.1", + "compaction_mode": "auto", + "should_trigger_compaction": null // or a callable function +} +``` + +### Response +#### Success Response (200) +This method initializes the session and does not return a specific value, but rather sets up the internal state of the `OpenAIResponsesCompactionSession` object. + +#### Response Example +(No explicit response body for initialization, object state is modified.) +``` + +-------------------------------- + +### Get Output Type Name - Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/agent_output + +Retrieves the string representation of the output type. This function is useful for debugging or logging purposes, providing a human-readable name for the expected output format. + +```python +def name(self) -> str: + """The name of the output type.""" + return _type_to_str(self.output_type) +``` + +-------------------------------- + +### Check if GPT-5 Reasoning Settings are Required (Python) + +Source: https://openai.github.io/openai-agents-python/ko/ref/models/default_models + +Determines if a given model name corresponds to a GPT-5 model that requires specific reasoning settings. It checks if the model name starts with 'gpt-5' but excludes 'gpt-5-chat-latest'. + +```python +def gpt_5_reasoning_settings_required(model_name: str) -> bool: + """ + Returns True if the model name is a GPT-5 model and reasoning settings are required. + """ + if model_name.startswith("gpt-5-chat"): + # gpt-5-chat-latest does not require reasoning settings + return False + # matches any of gpt-5 models + return model_name.startswith("gpt-5") +``` + +-------------------------------- + +### RealtimeSession Initialization API + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/session + +Initializes a new RealtimeSession with the specified model, agent, and context. Optional configurations for the model and runtime can also be provided. + +```APIDOC +## __init__ RealtimeSession + +### Description +Initializes the session with a model, agent, and context. Optional model and run configurations can be provided. + +### Method +__init__ + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +- **model** (`RealtimeModel`) - Required - The model to use. +- **agent** (`RealtimeAgent`) - Required - The current agent. +- **context** (`TContext | None`) - Required - The context object. +- **model_config** (`RealtimeModelConfig | None`) - Optional - Model configuration. Defaults to None. +- **run_config** (`RealtimeRunConfig | None`) - Optional - Runtime configuration including guardrails. Defaults to None. + +### Request Example +```python +# This is a constructor, not a typical request example. +# Initialization requires object instances: +# session = RealtimeSession(model=my_model, agent=my_agent, context=my_context) +``` + +### Response +#### Success Response (None) +This is a constructor, no return value. + +#### Response Example +None +``` + +-------------------------------- + +### POST /connect + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/model + +Establishes a connection to the realtime model. This method should be called before sending any events or adding listeners. + +```APIDOC +## POST /connect + +### Description +Establish a connection to the model and keep it alive. + +### Method +POST + +### Endpoint +/connect + +#### Parameters + +##### Request Body +- **options** (RealtimeModelConfig) - Required - Configuration options for the realtime model connection. +``` + +-------------------------------- + +### RealtimeSession enter Method + +Source: https://openai.github.io/openai-agents-python/zh/ref/realtime/session + +Provides a non-context manager way to enter the session, requiring manual closing. + +```APIDOC +## enter RealtimeSession + +### Description +Manually enters the session's asynchronous context. It's recommended to use the `async with` statement instead. If this method is used, `close()` must be called manually to ensure proper session termination. + +### Method +enter (async) + +### Endpoint +N/A (Instance method) + +### Parameters +None + +### Request Example +```python +session = RealtimeSession(model=my_model, agent=my_agent, context=my_context) +await session.enter() +try: + # Use the session here + await session.send_message("Hello") +finally: + await session.close() # Manual closing is required +``` + +### Response +#### Success Response (200) +- **enter**: Returns the `RealtimeSession` instance. + +#### Response Example +```json +{ + "message": "Session entered successfully." +} +``` +``` + +-------------------------------- + +### Create Custom Function Tools with OpenAI Agents SDK + +Source: https://openai.github.io/openai-agents-python/tools + +Illustrates how to manually create a `FunctionTool` instance when direct Python function integration is not desired. This method requires explicit definition of the tool's name, description, parameter schema using Pydantic, and an asynchronous invocation function (`on_invoke_tool`). + +```python +from typing import Any + +from pydantic import BaseModel + +from agents import RunContextWrapper, FunctionTool + + +def do_some_work(data: str) -> str: + return "done" + + +class FunctionArgs(BaseModel): + username: str + age: int + + +async def run_function(ctx: RunContextWrapper[Any], args: str) -> str: + parsed = FunctionArgs.model_validate_json(args) + return do_some_work(data=f"{parsed.username} is {parsed.age} years old") + + +tool = FunctionTool( + name="process_user", + description="Processes extracted user data", + params_json_schema=FunctionArgs.model_json_schema(), + on_invoke_tool=run_function, +) + +``` + +-------------------------------- + +### Run Response Compaction - Python + +Source: https://openai.github.io/openai-agents-python/ja/ref/memory/openai_responses_compaction_session + +Executes the response compaction process. It determines the compaction mode, checks if compaction should be triggered based on force or decision hooks, and then calls the OpenAI client's `responses.compact` API. The output is processed and added back to the session. + +```python +async def run_compaction(self, args: OpenAIResponsesCompactionArgs | None = None) -> None: + """Run compaction using responses.compact API.""" + if args and args.get("response_id"): + self._response_id = args["response_id"] + requested_mode = args.get("compaction_mode") if args else None + if args and "store" in args: + store = args["store"] + if store is False and self._response_id: + self._last_unstored_response_id = self._response_id + elif store is True and self._response_id == self._last_unstored_response_id: + self._last_unstored_response_id = None + else: + store = None + resolved_mode = self._resolve_compaction_mode_for_response( + response_id=self._response_id, + store=store, + requested_mode=requested_mode, + ) + + if resolved_mode == "previous_response_id" and not self._response_id: + raise ValueError( + "OpenAIResponsesCompactionSession.run_compaction requires a response_id " + "when using previous_response_id compaction." + ) + + compaction_candidate_items, session_items = await self._ensure_compaction_candidates() + + force = args.get("force", False) if args else False + should_compact = force or self.should_trigger_compaction( + { + "response_id": self._response_id, + "compaction_mode": resolved_mode, + "compaction_candidate_items": compaction_candidate_items, + "session_items": session_items, + } + ) + + if not should_compact: + logger.debug( + f"skip: decision hook declined compaction for {self._response_id} " + f"(mode={resolved_mode})" + ) + return + + self._deferred_response_id = None + logger.debug( + f"compact: start for {self._response_id} using {self.model} (mode={resolved_mode})" + ) + + compact_kwargs: dict[str, Any] = {"model": self.model} + if resolved_mode == "previous_response_id": + compact_kwargs["previous_response_id"] = self._response_id + else: + compact_kwargs["input"] = session_items + + compacted = await self.client.responses.compact(**compact_kwargs) + + await self.underlying_session.clear_session() + output_items: list[TResponseInputItem] = [] + if compacted.output: + for item in compacted.output: + if isinstance(item, dict): + output_items.append(item) + else: + # Suppress Pydantic literal warnings: responses.compact can return + # user-style input_text content inside ResponseOutputMessage. + output_items.append( + item.model_dump(exclude_unset=True, warnings=False) # type: ignore + ) + + if output_items: + await self.underlying_session.add_items(output_items) + + self._compaction_candidate_items = select_compaction_candidate_items(output_items) + self._session_items = output_items + + logger.debug( + f"compact: done for {self._response_id} " + f"(mode={resolved_mode}, output={len(output_items)}, " + f"candidates={len(self._compaction_candidate_items)})" + ) + +``` + +-------------------------------- + +### Get Trace Name - Python + +Source: https://openai.github.io/openai-agents-python/zh/ref/tracing + +Retrieves the human-readable name of a workflow trace. This name should be descriptive and is used for grouping and filtering traces in the dashboard, helping to identify the trace's purpose. + +```python +name: str + +# Returns: +# Name | Type | Description +# `str` | `str` | The workflow name (e.g., "Customer Service", "Data Processing") +``` + +-------------------------------- + +### Get Provider from MultiProviderMap by Prefix (Python) + +Source: https://openai.github.io/openai-agents-python/zh/ref/models/multi_provider + +Implements the `get_provider` method for the MultiProviderMap class. This method retrieves the ModelProvider associated with a given prefix. It takes a string prefix as input and returns either a ModelProvider object or None if the prefix is not found. + +```python +def get_provider(self, prefix: str) -> ModelProvider | None: + """Returns the ModelProvider for the given prefix. + + Args: + prefix: The prefix of the model name e.g. "openai" or "my_prefix". + """ + return self._mapping.get(prefix) + +``` + +-------------------------------- + +### Update Model Settings from Agent Configuration (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/realtime/session + +This asynchronous function retrieves and merges model settings from both base configurations and specific agent settings. It fetches system prompts, tools, and handoffs from the agent, applies them to the updated settings, and incorporates any provided starting settings. It also handles the disabling of tracing based on configuration. + +```python + async def _get_updated_model_settings_from_agent( + self, + starting_settings: RealtimeSessionModelSettings | None, + agent: RealtimeAgent, + ) -> RealtimeSessionModelSettings: + # Start with the merged base settings from run and model configuration. + updated_settings = self._base_model_settings.copy() + + if agent.prompt is not None: + updated_settings["prompt"] = agent.prompt + + instructions, tools, handoffs = await asyncio.gather( + agent.get_system_prompt(self._context_wrapper), + agent.get_all_tools(self._context_wrapper), + self._get_handoffs(agent, self._context_wrapper), + ) + updated_settings["instructions"] = instructions or "" + updated_settings["tools"] = tools or [] + updated_settings["handoffs"] = handoffs or [] + + # Apply starting settings (from model config) next + if starting_settings: + updated_settings.update(starting_settings) + + disable_tracing = self._run_config.get("tracing_disabled", False) + if disable_tracing: + updated_settings["tracing"] = None + + return updated_settings + +``` + +-------------------------------- + +### Get Trace Name in Python + +Source: https://openai.github.io/openai-agents-python/zh/ref/tracing/traces + +Retrieves the human-readable name for a trace, typically representing the workflow it belongs to. This property is part of the tracing mechanism. For no-operation traces, it returns 'no-op'. + +```python +name: str + +"""The workflow name for this trace. + +Returns: + str: Human-readable name describing this workflow. +""" +return "no-op" +``` + +-------------------------------- + +### Realtime Model Configuration Definition (Python) + +Source: https://openai.github.io/openai-agents-python/ko/ref/realtime/config + +Defines options for connecting to a realtime model. Includes parameters for API keys, URLs, headers, initial model settings, playback tracking, and call IDs. + +```python +class RealtimeModelConfig(TypedDict): + """Options for connecting to a realtime model.""" + + api_key: NotRequired[str | Callable[[], MaybeAwaitable[str]]] + """The API key (or function that returns a key) to use when connecting. If unset, the model will +try to use a sane default. For example, the OpenAI Realtime model will try to use the +`OPENAI_API_KEY` environment variable. +""" + + url: NotRequired[str] + """The URL to use when connecting. If unset, the model will use a sane default. For example, +the OpenAI Realtime model will use the default OpenAI WebSocket URL. +""" + + headers: NotRequired[dict[str, str]] + """The headers to use when connecting. If unset, the model will use a sane default. + Note that, when you set this, authorization header won't be set under the hood. + e.g., {"api-key": "your api key here"} for Azure OpenAI Realtime WebSocket connections. + """ + + initial_model_settings: NotRequired[RealtimeSessionModelSettings] + """The initial model settings to use when connecting.""" + + playback_tracker: NotRequired[RealtimePlaybackTracker] + """The playback tracker to use when tracking audio playback progress. If not set, the model will + use a default implementation that assumes audio is played immediately, at realtime speed. + + A playback tracker is useful for interruptions. The model generates audio much faster than + realtime playback speed. So if there's an interruption, its useful for the model to know how + much of the audio has been played by the user. In low-latency scenarios, it's fine to assume + that audio is played back immediately at realtime speed. But in scenarios like phone calls or + other remote interactions, you can set a playback tracker that lets the model know when audio + is played to the user. + """ + + call_id: NotRequired[str] + """Attach to an existing realtime call instead of creating a new session. + + When provided, the transport connects using the `call_id` query string parameter rather than a + model name. This is used for SIP-originated calls that are accepted via the Realtime Calls API. + """ + +``` + +-------------------------------- + +### Convert Standard Tools to JSON (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/models/openai_responses + +Converts various tool types including FileSearchTool, ComputerTool, and others into a dictionary representation suitable for serialization. It handles specific attributes for each tool type, such as search parameters or computer dimensions. Raises a UserError for unknown tool types. + +```python +def _convert_tool(self, tool): + """ + Convert tool to a JSON serializable format. + """ + if isinstance(tool, SearchTool): + converted_tool = { + "type": "search", + "user_location": tool.user_location, + "search_context_size": tool.search_context_size, + } + includes = None + elif isinstance(tool, FileSearchTool): + converted_tool = { + "type": "file_search", + "vector_store_ids": tool.vector_store_ids, + } + if tool.max_num_results: + converted_tool["max_num_results"] = tool.max_num_results + if tool.ranking_options: + converted_tool["ranking_options"] = tool.ranking_options + if tool.filters: + converted_tool["filters"] = tool.filters + + includes = "file_search_call.results" if tool.include_search_results else None + elif isinstance(tool, ComputerTool): + computer = tool.computer + if not isinstance(computer, (Computer, AsyncComputer)): + raise UserError( + "Computer tool is not initialized for serialization. Call " + "resolve_computer({ tool, run_context }) with a run context first " + "when building payloads manually." + ) + converted_tool = { + "type": "computer_use_preview", + "environment": computer.environment, + "display_width": computer.dimensions[0], + "display_height": computer.dimensions[1], + } + includes = None + elif isinstance(tool, HostedMCPTool): + converted_tool = tool.tool_config + includes = None + elif isinstance(tool, ApplyPatchTool): + converted_tool = cast(ToolParam, {"type": "apply_patch"}) + includes = None + elif isinstance(tool, ShellTool): + converted_tool = cast(ToolParam, {"type": "shell"}) + includes = None + elif isinstance(tool, ImageGenerationTool): + converted_tool = tool.tool_config + includes = None + elif isinstance(tool, CodeInterpreterTool): + converted_tool = tool.tool_config + includes = None + elif isinstance(tool, LocalShellTool): + converted_tool = { + "type": "local_shell", + } + includes = None + else: + raise UserError(f"Unknown tool type: {type(tool)}, tool") + + return converted_tool, includes +``` + +-------------------------------- + +### Configure Playback Tracker for Realtime Audio - Python + +Source: https://openai.github.io/openai-agents-python/ko/ref/realtime/config + +Defines the 'playback_tracker' attribute for realtime model sessions. This allows custom audio playback tracking, essential for managing interruptions and synchronizing with user playback speed. If not provided, a default assumes immediate playback. + +```python +playback_tracker: NotRequired[RealtimePlaybackTracker] + +""" +The playback tracker to use when tracking audio playback progress. If not set, the model will use a default implementation that assumes audio is played immediately, at realtime speed. +A playback tracker is useful for interruptions. The model generates audio much faster than realtime playback speed. So if there's an interruption, its useful for the model to know how much of the audio has been played by the user. In low-latency scenarios, it's fine to assume that audio is played back immediately at realtime speed. But in scenarios like phone calls or other remote interactions, you can set a playback tracker that lets the model know when audio is played to the user. +""" +``` + +-------------------------------- + +### Get Speech-to-Text Model (Python) + +Source: https://openai.github.io/openai-agents-python/ja/ref/voice/model + +The `get_stt_model` method retrieves a speech-to-text model based on its name. This is part of the `VoiceModelProvider` interface, allowing implementations to provide specific STT models. + +```python +import abc + +# Assume STTModel and VoiceModelProvider are defined +class STTModel: + pass + +class VoiceModelProvider(abc.ABC): + @abc.abstractmethod + def get_stt_model(self, model_name: str | None) -> STTModel: + """Get a speech-to-text model by name. + + Args: + model_name: The name of the model to get. + + Returns: + The speech-to-text model. + """ + pass +``` + +-------------------------------- + +### Get Turn Usage Statistics with Token Details - Python + +Source: https://openai.github.io/openai-agents-python/ko/ref/extensions/memory/advanced_sqlite_session + +Fetches detailed usage statistics for specific turns within a session and branch. It parses JSON data for token details and handles cases where a specific turn number is requested or all turns are returned. Includes error handling for JSON parsing. + +```python + if branch_id is None: + branch_id = self._current_branch_id + + def _get_turn_usage_sync(): + """Synchronous helper to get turn usage statistics.""" + conn = self._get_connection() + + if user_turn_number is not None: + query = """ + SELECT requests, input_tokens, output_tokens, total_tokens, + input_tokens_details, output_tokens_details + FROM turn_usage + WHERE session_id = ? AND branch_id = ? AND user_turn_number = ? + """ + + with closing(conn.cursor()) as cursor: + cursor.execute(query, (self.session_id, branch_id, user_turn_number)) + row = cursor.fetchone() + + if row: + # Parse JSON details if present + input_details = None + output_details = None + + if row[4]: # input_tokens_details + try: + input_details = json.loads(row[4]) + except json.JSONDecodeError: + pass + + if row[5]: # output_tokens_details + try: + output_details = json.loads(row[5]) + except json.JSONDecodeError: + pass + + return { + "requests": row[0], + "input_tokens": row[1], + "output_tokens": row[2], + "total_tokens": row[3], + "input_tokens_details": input_details, + "output_tokens_details": output_details, + } + return {} + else: + query = """ + SELECT user_turn_number, requests, input_tokens, output_tokens, + total_tokens, input_tokens_details, output_tokens_details + FROM turn_usage + WHERE session_id = ? AND branch_id = ? + ORDER BY user_turn_number + """ + + with closing(conn.cursor()) as cursor: + cursor.execute(query, (self.session_id, branch_id)) +``` + +-------------------------------- + +### Tracer Initialization (`__init__`) + +Source: https://openai.github.io/openai-agents-python/ja/ref/tracing/processors + +Initializes the tracer with API credentials, endpoint, and retry configurations. It sets up an httpx client for making requests to the trace ingestion endpoint. + +```APIDOC +## POST /v1/traces/ingest (Implicit) + +### Description +Initializes the tracer with API credentials, endpoint, and retry configurations. It sets up an httpx client for making requests to the trace ingestion endpoint. + +### Method +POST (Implicit, used by the internal client) + +### Endpoint +`https://api.openai.com/v1/traces/ingest` (default) + +### Parameters +#### Path Parameters +None + +#### Query Parameters +None + +#### Request Body +None (This is the constructor, not an endpoint call) + +### Initialization Parameters +- **api_key** (`str | None`) - Required/Optional - The API key for the "Authorization" header. Defaults to `os.environ["OPENAI_API_KEY"]` if not provided. +- **organization** (`str | None`) - Required/Optional - The OpenAI organization to use. Defaults to `os.environ["OPENAI_ORG_ID"]` if not provided. +- **project** (`str | None`) - Required/Optional - The OpenAI project to use. Defaults to `os.environ["OPENAI_PROJECT_ID"]` if not provided. +- **endpoint** (`str`) - Required - The HTTP endpoint to which traces/spans are posted. Defaults to `'https://api.openai.com/v1/traces/ingest'`. +- **max_retries** (`int`) - Required - Maximum number of retries upon failures. Defaults to `3`. +- **base_delay** (`float`) - Required - Base delay (in seconds) for the first backoff. Defaults to `1.0`. +- **max_delay** (`float`) - Required - Maximum delay (in seconds) for backoff growth. Defaults to `30.0`. + +### Request Example +```python +from agents.tracing.processors import Tracer + +tracer = Tracer(api_key="YOUR_API_KEY", organization="YOUR_ORG_ID") +``` + +### Response +This is a constructor, no direct response. +``` + +-------------------------------- + +### Get Text-to-Speech Model (Python) + +Source: https://openai.github.io/openai-agents-python/zh/ref/voice/model + +Retrieves a text-to-speech (TTS) model by its name. This is an abstract method that concrete implementations of VoiceModelProvider must define. It takes an optional model name and returns a TTSModel object. + +```python +import abc + +# Assuming TTSModel is defined elsewhere + +class VoiceModelProvider(abc.ABC): + @abc.abstractmethod + def get_tts_model(self, model_name: str | None) -> TTSModel: + """Get a text-to-speech model by name.""" + pass +``` \ No newline at end of file diff --git a/agent_test.log b/agent_test.log new file mode 100644 index 0000000000000000000000000000000000000000..825a57506dd7b178b1bd461e0ee96ef9a9c89a29 --- /dev/null +++ b/agent_test.log @@ -0,0 +1,4 @@ +Traceback (most recent call last): + File "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/agent.py", line 22, in + from .config import get_config +ImportError: attempted relative import with no known parent package diff --git a/agent_uvicorn.log b/agent_uvicorn.log new file mode 100644 index 0000000000000000000000000000000000000000..296803d1720d4d170967bac14f1c59e9d7f1e252 --- /dev/null +++ b/agent_uvicorn.log @@ -0,0 +1,96 @@ +INFO: Started server process [9071] +INFO: Waiting for application startup. +INFO: ============================================================ +INFO: RAG Agent FastAPI Server Starting +INFO: ============================================================ +INFO: HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +INFO: Embedding query: 'test...' (top_k=1) +INFO: HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +INFO: HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +INFO: Search completed in 0.61s, returned 1 results +INFO: Total query time: 1.14s +INFO: Retrieval test successful: 1 results +INFO: Server startup complete +INFO: Application startup complete. +INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit) +INFO: HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +INFO: HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +INFO: HTTP Request: GET https://api.openai.com/v1/models "HTTP/1.1 200 OK" +INFO: 127.0.0.1:46502 - "GET /health HTTP/1.1" 200 OK +INFO: 127.0.0.1:58298 - "GET /docs HTTP/1.1" 200 OK +INFO: 127.0.0.1:58298 - "GET /openapi.json HTTP/1.1" 200 OK +INFO: [e87ec0e7] Received chat request: What is ROS 2?... +INFO: [e87ec0e7] Starting agent execution for question: What is ROS 2?... +INFO: Retrieving chunks for query: What is ROS 2?... +INFO: HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +INFO: Embedding query: 'What is ROS 2?...' (top_k=5) +INFO: HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +INFO: HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +INFO: Search completed in 0.63s, returned 5 results +INFO: Total query time: 1.04s +INFO: Retrieved 5 chunks +INFO: HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 429 Too Many Requests" +INFO: Retrying request to /chat/completions in 0.439727 seconds +INFO: HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 429 Too Many Requests" +INFO: Retrying request to /chat/completions in 0.899512 seconds +INFO: HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 429 Too Many Requests" +INFO: HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 429 Too Many Requests" +INFO: Retrying request to /chat/completions in 0.428043 seconds +INFO: HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 429 Too Many Requests" +INFO: Retrying request to /chat/completions in 0.881976 seconds +ERROR: [e87ec0e7] Request timeout after 10s +INFO: 127.0.0.1:34080 - "POST /chat HTTP/1.1" 504 Gateway Timeout +INFO: HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 429 Too Many Requests" +INFO: HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 429 Too Many Requests" +INFO: Retrying request to /chat/completions in 0.403914 seconds +INFO: HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 429 Too Many Requests" +INFO: Retrying request to /chat/completions in 0.922430 seconds +INFO: HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 429 Too Many Requests" +ERROR: [e87ec0e7] Agent failed after 12.08s: Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}} +Traceback (most recent call last): + File "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/agent.py", line 224, in run + response = retry_with_backoff(call_openai, max_retries=3) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/utils.py", line 49, in retry_with_backoff + return func() + ^^^^^^ + File "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/agent.py", line 216, in call_openai + response = self.client.chat.completions.create( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/m-ahmad-official/venv/lib/python3.12/site-packages/openai/_utils/_utils.py", line 286, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "/home/m-ahmad-official/venv/lib/python3.12/site-packages/openai/resources/chat/completions/completions.py", line 1192, in create + return self._post( + ^^^^^^^^^^^ + File "/home/m-ahmad-official/venv/lib/python3.12/site-packages/openai/_base_client.py", line 1297, in post + return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/m-ahmad-official/venv/lib/python3.12/site-packages/openai/_base_client.py", line 1070, in request + raise self._make_status_error_from_response(err.response) from None +openai.RateLimitError: Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}} +INFO: 127.0.0.1:48658 - "GET /docs HTTP/1.1" 200 OK +INFO: 127.0.0.1:48658 - "GET /openapi.json HTTP/1.1" 200 OK +INFO: HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +INFO: HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +INFO: HTTP Request: GET https://api.openai.com/v1/models "HTTP/1.1 200 OK" +INFO: 127.0.0.1:42400 - "GET /health HTTP/1.1" 200 OK +INFO: [19abeba2] Received chat request: What is ROS 2?... +INFO: [19abeba2] Starting agent execution for question: What is ROS 2?... +INFO: Retrieving chunks for query: What is ROS 2?... +INFO: HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +INFO: Embedding query: 'What is ROS 2?...' (top_k=5) +INFO: HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +INFO: HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +INFO: Search completed in 0.60s, returned 5 results +INFO: Total query time: 1.07s +INFO: Retrieved 5 chunks +INFO: HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK" +INFO: [19abeba2] Agent completed in 4.17s, tokens=1360, sources=5 +INFO: 127.0.0.1:36912 - "POST /chat HTTP/1.1" 200 OK +INFO: 127.0.0.1:56750 - "GET /docs HTTP/1.1" 200 OK +INFO: 127.0.0.1:56750 - "GET /openapi.json HTTP/1.1" 200 OK +INFO: Shutting down +INFO: Waiting for application shutdown. +INFO: Application shutdown complete. +INFO: Finished server process [9071] diff --git a/agent_uvicorn_new.log b/agent_uvicorn_new.log new file mode 100644 index 0000000000000000000000000000000000000000..c546ab90a8aefcdd235f1f0db0ab3bd26938ba6e --- /dev/null +++ b/agent_uvicorn_new.log @@ -0,0 +1,87 @@ +INFO: Started server process [13073] +INFO: Waiting for application startup. +05:05:21 - INFO - root - ============================================================ +05:05:21 - INFO - root - RAG Agent FastAPI Server Starting +05:05:21 - INFO - root - ============================================================ +05:05:23 - INFO - httpx - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +05:05:23 - INFO - backend.retrieve - Embedding query: 'test...' (top_k=1) +05:05:23 - INFO - httpx - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +05:05:24 - INFO - httpx - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +05:05:24 - INFO - backend.retrieve - Search completed in 0.61s, returned 1 results +05:05:24 - INFO - backend.retrieve - Total query time: 1.14s +05:05:24 - INFO - root - Retrieval test OK: 1 results +05:05:24 - INFO - root - Server startup complete +INFO: Application startup complete. +INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit) +05:05:53 - INFO - httpx - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +05:05:54 - INFO - httpx - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +05:05:56 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/responses "HTTP/1.1 200 OK" +INFO: 127.0.0.1:56784 - "GET /health HTTP/1.1" 200 OK +05:06:01 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/traces/ingest "HTTP/1.1 204 No Content" +05:06:46 - INFO - httpx - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +05:06:46 - INFO - httpx - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +05:06:47 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/traces/ingest "HTTP/1.1 204 No Content" +05:06:48 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/responses "HTTP/1.1 200 OK" +INFO: 127.0.0.1:60030 - "GET /health HTTP/1.1" 200 OK +05:06:54 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/traces/ingest "HTTP/1.1 204 No Content" +05:07:01 - INFO - root - [c8a20a2e] Received chat: What is ROS 2?... +05:07:02 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/responses "HTTP/1.1 200 OK" +05:07:02 - INFO - root - [Tool] retrieve_chunks called: query='What is ROS 2?...', top_k=5 +05:07:03 - INFO - httpx - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +05:07:03 - INFO - backend.retrieve - Embedding query: 'What is ROS 2?...' (top_k=5) +05:07:03 - INFO - httpx - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +05:07:04 - INFO - httpx - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +05:07:04 - INFO - backend.retrieve - Search completed in 0.65s, returned 5 results +05:07:04 - INFO - backend.retrieve - Total query time: 1.06s +05:07:04 - INFO - root - [Tool] Retrieved 5 chunks +05:07:05 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/traces/ingest "HTTP/1.1 204 No Content" +05:07:05 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/traces/ingest "HTTP/1.1 204 No Content" +05:07:09 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/responses "HTTP/1.1 200 OK" +05:07:09 - INFO - root - [c8a20a2e] Completed: tokens=2006, sources=5 +INFO: 127.0.0.1:33240 - "POST /chat HTTP/1.1" 200 OK +05:07:11 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/traces/ingest "HTTP/1.1 204 No Content" +05:07:44 - INFO - httpx - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +05:07:45 - INFO - httpx - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +05:07:46 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/responses "HTTP/1.1 200 OK" +INFO: 127.0.0.1:54614 - "GET /health HTTP/1.1" 200 OK +05:07:47 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/traces/ingest "HTTP/1.1 204 No Content" +05:07:58 - INFO - root - [6ad72fe1] Received chat: What is ROS 2?... +05:07:59 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/responses "HTTP/1.1 200 OK" +05:07:59 - INFO - root - [Tool] retrieve_chunks called: query='What is ROS 2?...', top_k=5 +05:08:00 - INFO - httpx - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +05:08:00 - INFO - backend.retrieve - Embedding query: 'What is ROS 2?...' (top_k=5) +05:08:01 - INFO - httpx - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +05:08:01 - INFO - httpx - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +05:08:01 - INFO - backend.retrieve - Search completed in 0.65s, returned 5 results +05:08:01 - INFO - backend.retrieve - Total query time: 1.09s +05:08:01 - INFO - root - [Tool] Retrieved 5 chunks +05:08:03 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/traces/ingest "HTTP/1.1 204 No Content" +05:08:06 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/responses "HTTP/1.1 200 OK" +05:08:06 - INFO - root - [6ad72fe1] Completed: tokens=1957, sources=5 +INFO: 127.0.0.1:51824 - "POST /chat HTTP/1.1" 200 OK +05:08:08 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/traces/ingest "HTTP/1.1 204 No Content" +05:11:23 - INFO - httpx - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +05:11:23 - INFO - httpx - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +05:11:24 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/responses "HTTP/1.1 200 OK" +INFO: 127.0.0.1:56880 - "GET /health HTTP/1.1" 200 OK +05:11:27 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/traces/ingest "HTTP/1.1 204 No Content" +05:11:36 - INFO - root - [5177fa40] Received chat: What is ROS 2?... +05:11:37 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/traces/ingest "HTTP/1.1 204 No Content" +05:11:39 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/responses "HTTP/1.1 200 OK" +05:11:39 - INFO - root - [Tool] retrieve_chunks called: query='What is ROS 2?...', top_k=5 +05:11:40 - INFO - httpx - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +05:11:40 - INFO - backend.retrieve - Embedding query: 'What is ROS 2?...' (top_k=5) +05:11:40 - INFO - httpx - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +05:11:38 - INFO - httpx - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +05:11:38 - INFO - backend.retrieve - Search completed in -1.87s, returned 5 results +05:11:38 - INFO - backend.retrieve - Total query time: -1.18s +05:11:38 - INFO - root - [Tool] Retrieved 5 chunks +05:11:43 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/traces/ingest "HTTP/1.1 204 No Content" +05:11:43 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/responses "HTTP/1.1 200 OK" +05:11:43 - INFO - root - [5177fa40] Completed: tokens=1979, sources=5 +INFO: 127.0.0.1:54308 - "POST /chat HTTP/1.1" 200 OK +05:11:49 - INFO - httpx - HTTP Request: POST https://api.openai.com/v1/traces/ingest "HTTP/1.1 204 No Content" +INFO: Shutting down +INFO: Waiting for application shutdown. +INFO: Application shutdown complete. +INFO: Finished server process [13073] diff --git a/api.py b/api.py new file mode 100644 index 0000000000000000000000000000000000000000..e18d645dcab7c72cdacb4289b152c2a5c33e0aa2 --- /dev/null +++ b/api.py @@ -0,0 +1,194 @@ +""" +FastAPI wrapper for the RAG Book Assistant agent. + +This module provides a standalone FastAPI application that exposes the +/chat endpoint using the agent defined in agent.py. It is separate from +agent.py to allow independent deployment and testing. +""" + +import os +import sys +import uuid +import asyncio +from datetime import datetime +from typing import List, Dict, Any, Optional + +from fastapi import FastAPI, HTTPException +from fastapi.responses import JSONResponse +from fastapi.middleware.cors import CORSMiddleware +from pydantic import BaseModel, Field, validator +from dotenv import load_dotenv + +# Make backend package importable +current_dir = os.path.dirname(os.path.abspath(__file__)) +if current_dir not in sys.path: + sys.path.insert(0, current_dir) + +# Load environment +load_dotenv() + +# Import agent components +try: + from agent import get_agent, Runner + from agent import ToolCallOutputItem, Source as AgentSource +except ImportError as e: + raise ImportError(f"Failed to import agent module: {e}") + +# Initialize FastAPI app +app = FastAPI( + title="RAG Chatbot API", + version="1.0.0", + description="FastAPI wrapper for RAG Book Assistant" +) + +# ============ CORS Configuration ============ + +app.add_middleware( + CORSMiddleware, + allow_origins=[ + "http://localhost:3000", + "http://127.0.0.1:3000", + "https://hackathon-1-humanoid-ai-robotics.vercel.app", + "https://*.vercel.app", + ], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# ============ Pydantic Models ============ + + +class ChatRequest(BaseModel): + question: str = Field(..., min_length=1, max_length=1000) + + @validator('question') + def validate_question(cls, v): + if not v or not v.strip(): + raise ValueError("Question cannot be empty") + return v.strip() + + +class Source(BaseModel): + url: str + chunk_index: int + text_snippet: str + + +class ChatResponse(BaseModel): + answer: str + sources: List[Source] + tokens_used: int + agent_trace: Optional[str] = None + + +class HealthStatus(BaseModel): + status: str + qdrant: str + openai: str + timestamp: str + + +# ============ Health Check ============ + +def check_qdrant_health() -> str: + try: + from backend.config import get_config + from qdrant_client import QdrantClient + cfg = get_config() + client = QdrantClient(url=cfg["qdrant_url"], api_key=cfg["qdrant_api_key"]) + client.get_collection(cfg["qdrant_collection"]) + return "connected" + except Exception as e: + return "disconnected" + + +def check_openai_health() -> str: + try: + api_key = os.getenv("OPENAI_API_KEY") + if not api_key: + return "disconnected" + import openai + client = openai.OpenAI(api_key=api_key) + client.models.list() + return "connected" + except Exception: + return "disconnected" + + +@app.get("/health", response_model=HealthStatus) +async def health_check(): + qdrant = check_qdrant_health() + openai = check_openai_health() + status = "healthy" if qdrant == "connected" and openai == "connected" else "degraded" + return HealthStatus( + status=status, + qdrant=qdrant, + openai=openai, + timestamp=datetime.utcnow().isoformat() + "Z" + ) + + +# ============ Chat Endpoint ============ + +@app.post("/chat") +async def chat_endpoint(request: ChatRequest): + request_id = str(uuid.uuid4())[:8] + question = request.question.strip() + + try: + agent = get_agent() + + # Run agent with timeout (20s to accommodate full workflow) + result = await asyncio.wait_for( + Runner.run(agent, question), + timeout=20.0 + ) + + # Extract sources from tool call outputs + sources = [] + if result.new_items: + for item in result.new_items: + if isinstance(item, ToolCallOutputItem): + output = item.output + if isinstance(output, list): + for chunk in output: + sources.append(Source( + url=chunk.get("url", ""), + chunk_index=chunk.get("chunk_index", 0), + text_snippet=chunk.get("text", "")[:200] + )) + + # Get token usage + tokens_used = 0 + if result.context_wrapper and hasattr(result.context_wrapper, 'usage'): + tokens_used = result.context_wrapper.usage.total_tokens + + return ChatResponse( + answer=result.final_output, + sources=sources, + tokens_used=tokens_used, + agent_trace=f"{request_id}: completed" + ) + + except asyncio.TimeoutError: + return JSONResponse( + status_code=504, + content={"error": "timeout", "message": "The chatbot is taking too long to respond. Please try a shorter question."} + ) + + except Exception as e: + if "openai" in str(e).lower() or "rate limit" in str(e).lower(): + return JSONResponse( + status_code=503, + content={"error": "openai_failed", "message": "The AI service is currently unavailable. Please try again in a few minutes."} + ) + return JSONResponse( + status_code=500, + content={"error": "internal_error", "message": "An unexpected error occurred. Please refresh the page and try again."} + ) + + +if __name__ == "__main__": + import uvicorn + uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/check_collection.py b/check_collection.py new file mode 100644 index 0000000000000000000000000000000000000000..0753ac99ca7e87ba5209a9e06bdc851851b30c08 --- /dev/null +++ b/check_collection.py @@ -0,0 +1,61 @@ +""" +Check contents of the Qdrant collection. +""" +import sys +from pathlib import Path +sys.path.insert(0, str(Path(__file__).parent)) + +import config +from qdrant_client import QdrantClient + +cfg = config.get_config() +client = QdrantClient(url=cfg['qdrant_url'], api_key=cfg['qdrant_api_key']) + +# Get collection info +info = client.get_collection(cfg['qdrant_collection']) +print(f"Collection: {cfg['qdrant_collection']}") +print(f"Total points: {info.points_count}") +print(f"Vector size: {info.config.params.vectors.size}") +print() + +# Scroll through all points +if info.points_count > 0: + records = client.scroll( + collection_name=cfg['qdrant_collection'], + limit=min(10, info.points_count), + with_payload=True, + with_vectors=False + )[0] + + print(f"Showing {len(records)} sample points:") + print("=" * 80) + + for i, record in enumerate(records, 1): + print(f"\nPoint {i}:") + print(f" ID: {record.id}") + payload = record.payload or {} + print(f" URL: {payload.get('url', 'N/A')}") + print(f" Title: {payload.get('title', 'N/A')}") + print(f" Chunk index: {payload.get('chunk_index', 'N/A')}") + text = payload.get('text', '') + print(f" Text length: {len(text)} chars") + print(f" Text preview: {text[:200]}...") + + print("\n" + "=" * 80) + + # Check for unique URLs + all_records = client.scroll( + collection_name=cfg['qdrant_collection'], + limit=1000, + with_payload=True, + with_vectors=False + )[0] + + unique_urls = set() + for rec in all_records: + if rec.payload and 'url' in rec.payload: + unique_urls.add(rec.payload['url']) + + print(f"Unique URLs in collection: {len(unique_urls)}") + for url in unique_urls: + print(f" - {url}") diff --git a/check_detailed.py b/check_detailed.py new file mode 100644 index 0000000000000000000000000000000000000000..2cd0bdc0147cb306522ea302fd7a43e5b8b70168 --- /dev/null +++ b/check_detailed.py @@ -0,0 +1,36 @@ +""" +Detailed check of all points in the collection. +""" +import sys +from pathlib import Path +sys.path.insert(0, str(Path(__file__).parent)) + +import config +from qdrant_client import QdrantClient + +cfg = config.get_config() +client = QdrantClient(url=cfg['qdrant_url'], api_key=cfg['qdrant_api_key']) + +# Scroll through all points with full details +all_points = client.scroll( + collection_name=cfg['qdrant_collection'], + limit=1000, + with_payload=True, + with_vectors=False +)[0] + +print(f"Total points in collection: {len(all_points)}") +print("=" * 100) + +for i, point in enumerate(all_points, 1): + print(f"\nPoint {i}:") + print(f" ID: {point.id}") + payload = point.payload or {} + print(f" URL: {payload.get('url', 'N/A')}") + print(f" Title: {payload.get('title', 'N/A')}") + print(f" Section: {payload.get('section', 'N/A')}") + print(f" Chunk index: {payload.get('chunk_index', 'N/A')}") + text = payload.get('text', '') + print(f" Text length: {len(text)} chars") + print(f" Text (full):\n{text}") + print("-" * 100) diff --git a/config.py b/config.py new file mode 100644 index 0000000000000000000000000000000000000000..c61cf3c55c5aefacbf7f46713f2f2c7d5ca9c1b1 --- /dev/null +++ b/config.py @@ -0,0 +1,27 @@ +""" +Configuration loader for the ingestion pipeline. +Reads environment variables with .env support. +""" +import os +from dotenv import load_dotenv + +# Load .env file if present +load_dotenv() + +def get_config(): + """Get configuration from environment variables.""" + return { + "cohere_api_key": os.getenv("COHERE_API_KEY"), + "qdrant_url": os.getenv("QDRANT_URL"), + "qdrant_api_key": os.getenv("QDRANT_API_KEY"), + "qdrant_collection": os.getenv("QDRANT_COLLECTION", "book_embeddings"), + "openai_api_key": os.getenv("OPENAI_API_KEY"), + } + +def validate_config(config): + """Validate that all required config values are set.""" + required = ["cohere_api_key", "qdrant_url", "qdrant_api_key"] + missing = [key for key in required if not config.get(key)] + if missing: + raise ValueError(f"Missing required environment variables: {', '.join(missing)}") + return True diff --git a/exceptions.py b/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..aa298190f46eca09f362581337dadb5d0ace0ed0 --- /dev/null +++ b/exceptions.py @@ -0,0 +1,26 @@ +""" +Custom exceptions for the RAG retrieval pipeline. +""" +from typing import Optional + + +class ConfigurationError(Exception): + """Raised when required configuration is missing or invalid.""" + pass + + +class CollectionNotFoundError(Exception): + """Raised when the specified Qdrant collection does not exist.""" + pass + + +class DimensionMismatchError(Exception): + """Raised when embedding dimension doesn't match collection vector size.""" + pass + + +class APIError(Exception): + """Raised when an external API (Cohere or Qdrant) call fails after retries.""" + def __init__(self, message: str, original_exception: Optional[Exception] = None): + super().__init__(message) + self.original_exception = original_exception diff --git a/extract_sitemap.py b/extract_sitemap.py new file mode 100644 index 0000000000000000000000000000000000000000..0d6d65910f91d6a411420296f5bdf810db66908d --- /dev/null +++ b/extract_sitemap.py @@ -0,0 +1,32 @@ +""" +Fetch sitemap.xml and extract all page URLs. +""" +import sys +from pathlib import Path +import httpx +import re + +sys.path.insert(0, str(Path(__file__).parent)) + +sitemap_url = "https://humanoid-ai-robotics-book-1.vercel.app/sitemap.xml" + +print(f"Fetching sitemap from: {sitemap_url}") +response = httpx.get(sitemap_url, timeout=30.0) +response.raise_for_status() + +# Use regex to extract URLs from XML +content = response.text +urls = re.findall(r']*>(.*?)', content, re.DOTALL) +urls = [url.strip() for url in urls if url.strip()] + +print(f"Found {len(urls)} URLs in sitemap:") +for url in urls: + print(f" - {url}") + +# Save to file +output_file = Path(__file__).parent / 'sitemap_urls.txt' +with open(output_file, 'w') as f: + for url in urls: + f.write(url + '\n') + +print(f"\nSaved to: {output_file}") diff --git a/history/prompts/frontend-chat-integration/4-implementation.tasks.prompt.md b/history/prompts/frontend-chat-integration/4-implementation.tasks.prompt.md new file mode 100644 index 0000000000000000000000000000000000000000..5005ab61ab2e5c6275ab5885d170946bb44df50b --- /dev/null +++ b/history/prompts/frontend-chat-integration/4-implementation.tasks.prompt.md @@ -0,0 +1,56 @@ +--- +id: 4 +title: Implement frontend-backend chat integration +stage: tasks +date: 2025-02-18 +surface: agent +model: claude-sonnet-4-5-20250929 +feature: frontend-chat-integration +branch: 8-frontend-chat-integration +user: m-ahmad-official +command: /sp.implement +labels: [implementation, backend, frontend, fastapi, react, docusaurus] +links: + spec: specs/8-frontend-chat-integration/spec.md + plan: specs/8-frontend-chat-integration/plan.md + tasks: specs/8-frontend-chat-integration/tasks.md + adr: null + pr: null +files: + - backend/api.py + - frontend_book/src/components/ChatBot.tsx + - frontend_book/src/components/ChatBot.module.css + - frontend_book/src/theme/Root.tsx + - frontend_book/.env +tests: [] +--- + +## Prompt + +/sp.implement (execute all tasks) + +## Response snapshot + +Executed all 28 tasks from tasks.md across 6 phases: + +**Phase 1 (Setup)**: Verified backend agent.py, frontend_book structure, created .env with VITE_API_BASE_URL, installed npm dependencies +**Phase 2 (Foundational)**: Created backend/api.py with FastAPI app, CORS middleware, health check +**Phase 3 (US1-P1)**: Implemented POST /chat with agent invocation, 20s timeout, source extraction, token tracking, error handling; tested with curl (success) +**Phase 4 (US2-P2)**: Created ChatBot.module.css with responsive styles: desktop (380px fixed panel) and mobile (full-screen overlay) +**Phase 5 (US3-P3)**: Implemented localStorage persistence in ChatBot.tsx with versioned schema +**Phase 6 (Polish)**: Integrated globally via src/theme/Root.tsx, added character count (FR-015) and Clear Chat button + +All files created and validated. + +## Outcome + +- ✅ Impact: Complete implementation; backend/frontend integrated and ready for testing +- 🧪 Tests: End-to-end manual testing recommended; /chat endpoint verified +- 📁 Files: api.py, ChatBot.tsx, ChatBot.module.css, Root.tsx, .env created +- 🔁 Next prompts: Manual integration testing; consider /sp.test if applicable +- 🧠 Reflection: Minimal, isolated changes respect implementation boundaries. Backend wrapper cleanly reuses agent.py; frontend component self-contained. + +## Evaluation notes (flywheel) + +- Failure modes: None +- Next experiment: N/A diff --git a/ingestion.log b/ingestion.log new file mode 100644 index 0000000000000000000000000000000000000000..53ba85733f3d1e4668bcdc7bf3736e0cc659d7a8 --- /dev/null +++ b/ingestion.log @@ -0,0 +1,5356 @@ +2026-02-17 01:36:52 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:36:52 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:36:52 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:36:52 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:36:52 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:36:52 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:36:52 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:36:52 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:36:52 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:36:49 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:36:53 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:36:50 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:36:53 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:36:53 - INFO - main.py:143 - Creating collection 'book_embeddings' +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:36:53 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:36:54 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:36:50 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:36:54 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 01:36:54 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:36:54 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:36:54 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:36:54 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:36:54 - INFO - main.py:148 - Collection 'book_embeddings' created with vector size 1024 +2026-02-17 01:36:56 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:36:56 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:37:51 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:37:51 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:37:51 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:37:51 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:37:52 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:37:52 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:37:52 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:37:52 - INFO - main.py:150 - Collection 'book_embeddings' already exists +2026-02-17 01:37:52 - INFO - main.py:245 - Starting ingestion: 3 URLs +2026-02-17 01:37:52 - INFO - main.py:246 - Chunk size: 500, overlap: 50 +2026-02-17 01:37:52 - INFO - main.py:34 - Fetching https://bookshelf.math.buffalo.edu/bookshelf-free-books-direction.html... +2026-02-17 01:37:52 - DEBUG - _trace.py:47 - connect_tcp.started host='bookshelf.math.buffalo.edu' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:37:53 - DEBUG - _trace.py:47 - connect_tcp.failed exception=ConnectError(gaierror(-2, 'Name or service not known')) +2026-02-17 01:37:54 - DEBUG - _trace.py:47 - connect_tcp.started host='bookshelf.math.buffalo.edu' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:37:54 - DEBUG - _trace.py:47 - connect_tcp.failed exception=ConnectError(gaierror(-2, 'Name or service not known')) +2026-02-17 01:37:56 - DEBUG - _trace.py:47 - connect_tcp.started host='bookshelf.math.buffalo.edu' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:37:56 - DEBUG - _trace.py:47 - connect_tcp.failed exception=ConnectError(gaierror(-2, 'Name or service not known')) +2026-02-17 01:38:00 - DEBUG - _trace.py:47 - connect_tcp.started host='bookshelf.math.buffalo.edu' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:38:00 - DEBUG - _trace.py:47 - connect_tcp.failed exception=ConnectError(gaierror(-2, 'Name or service not known')) +2026-02-17 01:38:08 - DEBUG - _trace.py:47 - connect_tcp.started host='bookshelf.math.buffalo.edu' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:38:08 - DEBUG - _trace.py:47 - connect_tcp.failed exception=ConnectError(gaierror(-2, 'Name or service not known')) +2026-02-17 01:38:08 - ERROR - main.py:43 - Failed to fetch https://bookshelf.math.buffalo.edu/bookshelf-free-books-direction.html after 5 attempts: [Errno -2] Name or service not known +2026-02-17 01:38:08 - ERROR - main.py:294 - Failed to process https://bookshelf.math.buffalo.edu/bookshelf-free-books-direction.html: [Errno -2] Name or service not known +2026-02-17 01:38:08 - INFO - main.py:34 - Fetching https://en.wikipedia.org/wiki/Book... +2026-02-17 01:38:08 - DEBUG - _trace.py:47 - connect_tcp.started host='en.wikipedia.org' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:38:08 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:38:08 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='en.wikipedia.org' timeout=30.0 +2026-02-17 01:38:09 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:38:09 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:38:09 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:38:09 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:38:09 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:38:09 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:38:09 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 403, b'Forbidden', [(b'content-length', b'126'), (b'content-type', b'text/plain'), (b'x-request-id', b'c91d2c87-0f7a-4105-bdb9-0c31e2de9f97'), (b'server', b'HAProxy'), (b'x-cache', b'cp5021 int'), (b'x-cache-status', b'int-tls'), (b'x-analytics', b'')]) +2026-02-17 01:38:09 - INFO - _client.py:1025 - HTTP Request: GET https://en.wikipedia.org/wiki/Book "HTTP/1.1 403 Forbidden" +2026-02-17 01:38:09 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:38:09 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:38:09 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:38:09 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:38:10 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:38:10 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:38:10 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:38:10 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:38:10 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:38:10 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 403, b'Forbidden', [(b'content-length', b'126'), (b'content-type', b'text/plain'), (b'x-request-id', b'fd91f6b5-47f4-4487-8cf7-88813c8e10b6'), (b'server', b'HAProxy'), (b'x-cache', b'cp5021 int'), (b'x-cache-status', b'int-tls'), (b'x-analytics', b'')]) +2026-02-17 01:38:10 - INFO - _client.py:1025 - HTTP Request: GET https://en.wikipedia.org/wiki/Book "HTTP/1.1 403 Forbidden" +2026-02-17 01:38:10 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:38:10 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:38:10 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:38:10 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:38:12 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:38:12 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:38:12 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:38:12 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:38:12 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:38:12 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 403, b'Forbidden', [(b'content-length', b'126'), (b'content-type', b'text/plain'), (b'x-request-id', b'4764c04b-199a-4301-b700-aaae46baa74a'), (b'server', b'HAProxy'), (b'x-cache', b'cp5021 int'), (b'x-cache-status', b'int-tls'), (b'x-analytics', b'')]) +2026-02-17 01:38:12 - INFO - _client.py:1025 - HTTP Request: GET https://en.wikipedia.org/wiki/Book "HTTP/1.1 403 Forbidden" +2026-02-17 01:38:12 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:38:12 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:38:12 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:38:12 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:38:16 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:38:16 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:38:16 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:38:16 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:38:16 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:38:16 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 403, b'Forbidden', [(b'content-length', b'126'), (b'content-type', b'text/plain'), (b'x-request-id', b'0ffb7de0-1716-4fd0-8958-3bae31fef2ad'), (b'server', b'HAProxy'), (b'x-cache', b'cp5021 int'), (b'x-cache-status', b'int-tls'), (b'x-analytics', b'')]) +2026-02-17 01:38:16 - INFO - _client.py:1025 - HTTP Request: GET https://en.wikipedia.org/wiki/Book "HTTP/1.1 403 Forbidden" +2026-02-17 01:38:16 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:38:16 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:38:16 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:38:16 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:38:22 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:38:22 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:38:22 - DEBUG - _trace.py:47 - connect_tcp.started host='en.wikipedia.org' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:38:22 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:38:22 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='en.wikipedia.org' timeout=30.0 +2026-02-17 01:38:22 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:38:22 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:38:22 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:38:22 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:38:22 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:38:22 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:38:22 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 403, b'Forbidden', [(b'content-length', b'126'), (b'content-type', b'text/plain'), (b'x-request-id', b'789c4377-d7ad-4a52-aa80-0fe37866bfc3'), (b'server', b'HAProxy'), (b'x-cache', b'cp5021 int'), (b'x-cache-status', b'int-tls'), (b'x-analytics', b'')]) +2026-02-17 01:38:22 - INFO - _client.py:1025 - HTTP Request: GET https://en.wikipedia.org/wiki/Book "HTTP/1.1 403 Forbidden" +2026-02-17 01:38:22 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:38:22 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:38:22 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:38:22 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:38:22 - ERROR - main.py:43 - Failed to fetch https://en.wikipedia.org/wiki/Book after 5 attempts: Client error '403 Forbidden' for url 'https://en.wikipedia.org/wiki/Book' +For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403 +2026-02-17 01:38:22 - ERROR - main.py:294 - Failed to process https://en.wikipedia.org/wiki/Book: Client error '403 Forbidden' for url 'https://en.wikipedia.org/wiki/Book' +For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403 +2026-02-17 01:38:22 - INFO - main.py:34 - Fetching https://www.gutenberg.org/files/1342/1342-h/1342-h.htm... +2026-02-17 01:38:22 - DEBUG - _trace.py:47 - connect_tcp.started host='www.gutenberg.org' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:38:23 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:38:23 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='www.gutenberg.org' timeout=30.0 +2026-02-17 01:38:23 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:38:23 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:38:23 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:38:23 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:38:23 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:38:23 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:38:24 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'date', b'Mon, 16 Feb 2026 20:38:23 GMT'), (b'server', b'Apache'), (b'last-modified', b'Tue, 10 Feb 2026 17:53:56 GMT'), (b'accept-ranges', b'bytes'), (b'content-length', b'806295'), (b'x-backend', b'gutenweb7'), (b'content-type', b'text/html')]) +2026-02-17 01:38:24 - INFO - _client.py:1025 - HTTP Request: GET https://www.gutenberg.org/files/1342/1342-h/1342-h.htm "HTTP/1.1 200 OK" +2026-02-17 01:38:24 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:38:34 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:38:34 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:38:34 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:38:34 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:38:34 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:38:34 - DEBUG - main.py:39 - Successfully fetched https://www.gutenberg.org/files/1342/1342-h/1342-h.htm (status: 200) +2026-02-17 01:38:34 - INFO - main.py:264 - Extracted 717156 characters from https://www.gutenberg.org/files/1342/1342-h/1342-h.htm, chunked into 1435 segments +2026-02-17 01:38:34 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 01:38:34 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:38:34 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 01:38:34 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:38:34 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:38:34 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:38:34 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:38:34 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:38:34 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 400, b'Bad Request', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'1da9e24a767597a154eaa71df36dcb52'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 20:38:33 GMT'), (b'x-envoy-upstream-service-time', b'5'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:38:35 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 400 Bad Request" +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:38:35 - ERROR - main.py:129 - Failed to generate embeddings for batch starting at index 0: headers: {'access-control-expose-headers': 'X-Debug-Trace-ID', 'cache-control': 'no-cache, no-store, no-transform, must-revalidate, private, max-age=0', 'content-encoding': 'gzip', 'content-type': 'application/json', 'expires': 'Thu, 01 Jan 1970 00:00:00 GMT', 'pragma': 'no-cache', 'vary': 'Origin,Accept-Encoding', 'x-accel-expires': '0', 'x-debug-trace-id': '1da9e24a767597a154eaa71df36dcb52', 'x-endpoint-monthly-call-limit': '1000', 'x-trial-endpoint-call-limit': '100', 'x-trial-endpoint-call-remaining': '99', 'date': 'Mon, 16 Feb 2026 20:38:33 GMT', 'x-envoy-upstream-service-time': '5', 'server': 'envoy', 'via': '1.1 google', 'alt-svc': 'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000', 'transfer-encoding': 'chunked'}, status_code: 400, body: {'id': 'cd20a5e4-1efc-44b5-a9f6-43d7a24004ce', 'message': 'invalid request: total number of texts must be at most 96 - received 100'} +2026-02-17 01:38:35 - ERROR - main.py:294 - Failed to process https://www.gutenberg.org/files/1342/1342-h/1342-h.htm: headers: {'access-control-expose-headers': 'X-Debug-Trace-ID', 'cache-control': 'no-cache, no-store, no-transform, must-revalidate, private, max-age=0', 'content-encoding': 'gzip', 'content-type': 'application/json', 'expires': 'Thu, 01 Jan 1970 00:00:00 GMT', 'pragma': 'no-cache', 'vary': 'Origin,Accept-Encoding', 'x-accel-expires': '0', 'x-debug-trace-id': '1da9e24a767597a154eaa71df36dcb52', 'x-endpoint-monthly-call-limit': '1000', 'x-trial-endpoint-call-limit': '100', 'x-trial-endpoint-call-remaining': '99', 'date': 'Mon, 16 Feb 2026 20:38:33 GMT', 'x-envoy-upstream-service-time': '5', 'server': 'envoy', 'via': '1.1 google', 'alt-svc': 'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000', 'transfer-encoding': 'chunked'}, status_code: 400, body: {'id': 'cd20a5e4-1efc-44b5-a9f6-43d7a24004ce', 'message': 'invalid request: total number of texts must be at most 96 - received 100'} +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:38:35 - INFO - main.py:299 - ================================================== +2026-02-17 01:38:35 - INFO - main.py:300 - Ingestion complete! +2026-02-17 01:38:35 - INFO - main.py:301 - Total pages processed: 0 +2026-02-17 01:38:35 - INFO - main.py:302 - Total chunks stored: 0 +2026-02-17 01:38:35 - WARNING - main.py:304 - Failed URLs (3): https://bookshelf.math.buffalo.edu/bookshelf-free-books-direction.html, https://en.wikipedia.org/wiki/Book, https://www.gutenberg.org/files/1342/1342-h/1342-h.htm +2026-02-17 01:38:35 - INFO - main.py:305 - ================================================== +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:38:33 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:38:35 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:38:35 - INFO - main.py:310 - Qdrant collection 'book_embeddings' now has 0 points +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:38:35 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:41:13 - INFO - test_local.py:39 - Extracted 781 characters from fixture +2026-02-17 01:41:13 - INFO - test_local.py:44 - Chunked into 2 segments +2026-02-17 01:41:13 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 01:41:13 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:41:13 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 01:41:13 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:41:13 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:41:13 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:41:13 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:41:13 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:41:13 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'831'), (b'num_tokens', b'153'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'001f609bf187ba21f10baef24b8ec92e'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 20:41:11 GMT'), (b'x-envoy-upstream-service-time', b'35'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:41:14 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:41:14 - DEBUG - main.py:127 - Generated embeddings for batch 1/1 +2026-02-17 01:41:14 - INFO - test_local.py:51 - Generated 6 embeddings +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:41:12 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:41:14 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:41:14 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:41:15 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:41:12 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:41:15 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 01:41:15 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:41:15 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:41:15 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:41:15 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:41:15 - INFO - main.py:150 - Collection 'book_embeddings' already exists +2026-02-17 01:41:15 - ERROR - test_local.py:95 - Test failed: 'PosixPath' object has no attribute 'path' +Traceback (most recent call last): + File "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/test_local.py", line 93, in + test_with_fixture() + File "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/test_local.py", line 67, in test_with_fixture + 'title': Path(url).path, + ^^^^^^^^^^^^^^ +AttributeError: 'PosixPath' object has no attribute 'path' +2026-02-17 01:41:15 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:41:15 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:43:20 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:43:20 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:43:21 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:43:20 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:43:21 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:43:21 - INFO - main.py:150 - Collection 'book_embeddings' already exists +2026-02-17 01:43:21 - INFO - main.py:245 - Starting ingestion: 1 URLs +2026-02-17 01:43:21 - INFO - main.py:246 - Chunk size: 200, overlap: 20 +2026-02-17 01:43:21 - INFO - main.py:34 - Fetching http://example.com... +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - connect_tcp.started host='example.com' port=80 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Mon, 16 Feb 2026 20:43:21 GMT'), (b'Content-Type', b'text/html'), (b'Transfer-Encoding', b'chunked'), (b'Connection', b'keep-alive'), (b'Content-Encoding', b'gzip'), (b'Last-Modified', b'Thu, 12 Feb 2026 13:58:32 GMT'), (b'Allow', b'GET, HEAD'), (b'Age', b'13360'), (b'cf-cache-status', b'HIT'), (b'Vary', b'Accept-Encoding'), (b'Server', b'cloudflare'), (b'CF-RAY', b'9cefe0727a219089-KHI')]) +2026-02-17 01:43:21 - INFO - _client.py:1025 - HTTP Request: GET http://example.com "HTTP/1.1 200 OK" +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:43:21 - DEBUG - main.py:39 - Successfully fetched http://example.com (status: 200) +2026-02-17 01:43:21 - INFO - main.py:264 - Extracted 142 characters from http://example.com, chunked into 1 segments +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:43:21 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'142'), (b'num_tokens', b'23'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'88bd9845b878d98675f4112e74abd15c'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 20:43:21 GMT'), (b'x-envoy-upstream-service-time', b'45'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:43:22 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:43:22 - DEBUG - main.py:127 - Generated embeddings for batch 1/1 +2026-02-17 01:43:22 - ERROR - main.py:294 - Failed to process http://example.com: Unexpected embedding dimension: 2 +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:43:22 - INFO - main.py:299 - ================================================== +2026-02-17 01:43:22 - INFO - main.py:300 - Ingestion complete! +2026-02-17 01:43:22 - INFO - main.py:301 - Total pages processed: 0 +2026-02-17 01:43:22 - INFO - main.py:302 - Total chunks stored: 0 +2026-02-17 01:43:22 - WARNING - main.py:304 - Failed URLs (1): http://example.com +2026-02-17 01:43:22 - INFO - main.py:305 - ================================================== +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:43:21 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:43:22 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:43:22 - INFO - main.py:310 - Qdrant collection 'book_embeddings' now has 0 points +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:43:22 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:43:45 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:43:45 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:43:45 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:43:45 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:43:45 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:43:45 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:43:45 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:43:45 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:43:45 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:43:45 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:43:46 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:43:46 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:43:46 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:43:46 - INFO - main.py:162 - Collection 'book_embeddings' already exists +2026-02-17 01:43:46 - INFO - main.py:257 - Starting ingestion: 1 URLs +2026-02-17 01:43:46 - INFO - main.py:258 - Chunk size: 200, overlap: 20 +2026-02-17 01:43:46 - INFO - main.py:34 - Fetching http://example.com... +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - connect_tcp.started host='example.com' port=80 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Mon, 16 Feb 2026 20:43:46 GMT'), (b'Content-Type', b'text/html'), (b'Transfer-Encoding', b'chunked'), (b'Connection', b'keep-alive'), (b'Content-Encoding', b'gzip'), (b'Last-Modified', b'Thu, 12 Feb 2026 13:58:32 GMT'), (b'Allow', b'GET, HEAD'), (b'Age', b'13385'), (b'cf-cache-status', b'HIT'), (b'Vary', b'Accept-Encoding'), (b'Server', b'cloudflare'), (b'CF-RAY', b'9cefe10fdf6a908c-KHI')]) +2026-02-17 01:43:46 - INFO - _client.py:1025 - HTTP Request: GET http://example.com "HTTP/1.1 200 OK" +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:43:46 - DEBUG - main.py:39 - Successfully fetched http://example.com (status: 200) +2026-02-17 01:43:46 - INFO - main.py:276 - Extracted 142 characters from http://example.com, chunked into 1 segments +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:43:46 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'142'), (b'num_tokens', b'23'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'549cb0ee7940cb48dc80ade73b3e77f7'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'98'), (b'date', b'Mon, 16 Feb 2026 20:43:46 GMT'), (b'x-envoy-upstream-service-time', b'57'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:43:47 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:43:47 - ERROR - main.py:132 - Failed to generate embeddings for batch starting at index 0: 'EmbedByTypeResponseEmbeddings' object is not subscriptable +2026-02-17 01:43:47 - ERROR - main.py:306 - Failed to process http://example.com: 'EmbedByTypeResponseEmbeddings' object is not subscriptable +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:43:47 - INFO - main.py:311 - ================================================== +2026-02-17 01:43:47 - INFO - main.py:312 - Ingestion complete! +2026-02-17 01:43:47 - INFO - main.py:313 - Total pages processed: 0 +2026-02-17 01:43:47 - INFO - main.py:314 - Total chunks stored: 0 +2026-02-17 01:43:47 - WARNING - main.py:316 - Failed URLs (1): http://example.com +2026-02-17 01:43:47 - INFO - main.py:317 - ================================================== +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:43:46 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:43:47 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:43:47 - INFO - main.py:322 - Qdrant collection 'book_embeddings' now has 0 points +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:43:47 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:44:12 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:44:12 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:44:12 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:44:12 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:44:12 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:44:12 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:44:12 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:44:12 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:44:12 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:44:10 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:44:13 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:44:10 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:44:13 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:44:13 - INFO - main.py:180 - Collection 'book_embeddings' already exists +2026-02-17 01:44:13 - INFO - main.py:275 - Starting ingestion: 1 URLs +2026-02-17 01:44:13 - INFO - main.py:276 - Chunk size: 200, overlap: 20 +2026-02-17 01:44:13 - INFO - main.py:34 - Fetching http://example.com... +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - connect_tcp.started host='example.com' port=80 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Mon, 16 Feb 2026 20:44:11 GMT'), (b'Content-Type', b'text/html'), (b'Transfer-Encoding', b'chunked'), (b'Connection', b'keep-alive'), (b'Content-Encoding', b'gzip'), (b'Last-Modified', b'Thu, 12 Feb 2026 13:58:32 GMT'), (b'Allow', b'GET, HEAD'), (b'Age', b'13410'), (b'cf-cache-status', b'HIT'), (b'Vary', b'Accept-Encoding'), (b'Server', b'cloudflare'), (b'CF-RAY', b'9cefe1ab5de1909f-KHI')]) +2026-02-17 01:44:13 - INFO - _client.py:1025 - HTTP Request: GET http://example.com "HTTP/1.1 200 OK" +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:44:13 - DEBUG - main.py:39 - Successfully fetched http://example.com (status: 200) +2026-02-17 01:44:13 - INFO - main.py:294 - Extracted 142 characters from http://example.com, chunked into 1 segments +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:44:13 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'142'), (b'num_tokens', b'23'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'25a304f886eef2ccac7282a772538a1c'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'97'), (b'date', b'Mon, 16 Feb 2026 20:44:11 GMT'), (b'x-envoy-upstream-service-time', b'36'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:44:14 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:44:14 - INFO - main.py:138 - Cohere returned embeddings with dimension: 2 +2026-02-17 01:44:14 - DEBUG - main.py:149 - Generated embeddings for batch 1/1 +2026-02-17 01:44:14 - WARNING - main.py:160 - Unexpected embedding dimension: 2 (expected 1024). Proceeding anyway. +2026-02-17 01:44:14 - ERROR - main.py:324 - Failed to process http://example.com: Unexpected embedding dimension: 2 +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:44:14 - INFO - main.py:329 - ================================================== +2026-02-17 01:44:14 - INFO - main.py:330 - Ingestion complete! +2026-02-17 01:44:14 - INFO - main.py:331 - Total pages processed: 0 +2026-02-17 01:44:14 - INFO - main.py:332 - Total chunks stored: 0 +2026-02-17 01:44:14 - WARNING - main.py:334 - Failed URLs (1): http://example.com +2026-02-17 01:44:14 - INFO - main.py:335 - ================================================== +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:44:11 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:44:14 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:44:14 - INFO - main.py:340 - Qdrant collection 'book_embeddings' now has 0 points +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:44:14 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:44:33 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:44:33 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:44:33 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:44:33 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:44:33 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:44:33 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:44:33 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:44:33 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:44:33 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:44:33 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:44:32 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:44:33 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 01:44:33 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:44:33 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:44:33 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:44:33 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:44:33 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:44:33 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:44:33 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:44:32 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:44:34 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:44:34 - INFO - main.py:180 - Collection 'book_embeddings' already exists +2026-02-17 01:44:34 - INFO - main.py:275 - Starting ingestion: 1 URLs +2026-02-17 01:44:34 - INFO - main.py:276 - Chunk size: 200, overlap: 20 +2026-02-17 01:44:34 - INFO - main.py:34 - Fetching http://example.com... +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - connect_tcp.started host='example.com' port=80 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Mon, 16 Feb 2026 20:44:32 GMT'), (b'Content-Type', b'text/html'), (b'Transfer-Encoding', b'chunked'), (b'Connection', b'keep-alive'), (b'Content-Encoding', b'gzip'), (b'Last-Modified', b'Thu, 12 Feb 2026 13:58:32 GMT'), (b'Allow', b'GET, HEAD'), (b'Age', b'13431'), (b'cf-cache-status', b'HIT'), (b'Vary', b'Accept-Encoding'), (b'Server', b'cloudflare'), (b'CF-RAY', b'9cefe231be4f909f-KHI')]) +2026-02-17 01:44:34 - INFO - _client.py:1025 - HTTP Request: GET http://example.com "HTTP/1.1 200 OK" +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:44:34 - DEBUG - main.py:39 - Successfully fetched http://example.com (status: 200) +2026-02-17 01:44:34 - INFO - main.py:294 - Extracted 142 characters from http://example.com, chunked into 1 segments +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'142'), (b'num_tokens', b'23'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'd6bfcd0ef8411f32cb7917ae5ce54593'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'97'), (b'date', b'Mon, 16 Feb 2026 20:44:33 GMT'), (b'x-envoy-upstream-service-time', b'46'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:44:34 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:44:34 - INFO - main.py:138 - Cohere returned embeddings with dimension: 2 +2026-02-17 01:44:34 - DEBUG - main.py:149 - Generated embeddings for batch 1/1 +2026-02-17 01:44:34 - WARNING - main.py:160 - Unexpected embedding dimension: 2 (expected 1024). Proceeding anyway. +2026-02-17 01:44:34 - WARNING - main.py:304 - Embedding dimension 2 != 1024. Check Cohere model. Proceeding anyway. +2026-02-17 01:44:34 - ERROR - main.py:326 - Failed to process http://example.com: 8 validation errors for PointStruct +vector.list[float].0 + Input should be a valid number [type=float_type, input_value='float_', input_type=str] + For further information visit https://errors.pydantic.dev/2.12/v/float_type +vector.list[float].1 + Input should be a valid number [type=float_type, input_value=[[0.011100769, -0.0129318...019546509, 0.012245178]], input_type=list] + For further information visit https://errors.pydantic.dev/2.12/v/float_type +vector.list[list[float]].0 + Input should be a valid list [type=list_type, input_value='float_', input_type=str] + For further information visit https://errors.pydantic.dev/2.12/v/list_type +vector.list[list[float]].1.0 + Input should be a valid number [type=float_type, input_value=[0.011100769, -0.01293182....019546509, 0.012245178], input_type=list] + For further information visit https://errors.pydantic.dev/2.12/v/float_type +vector.dict[str,union[list[float],SparseVector,list[list[float]],Document,Image,InferenceObject]] + Input should be a valid dictionary [type=dict_type, input_value=['float_', [[0.011100769,...19546509, 0.012245178]]], input_type=list] + For further information visit https://errors.pydantic.dev/2.12/v/dict_type +vector.Document + Input should be a valid dictionary or instance of Document [type=model_type, input_value=['float_', [[0.011100769,...19546509, 0.012245178]]], input_type=list] + For further information visit https://errors.pydantic.dev/2.12/v/model_type +vector.Image + Input should be a valid dictionary or instance of Image [type=model_type, input_value=['float_', [[0.011100769,...19546509, 0.012245178]]], input_type=list] + For further information visit https://errors.pydantic.dev/2.12/v/model_type +vector.InferenceObject + Input should be a valid dictionary or instance of InferenceObject [type=model_type, input_value=['float_', [[0.011100769,...19546509, 0.012245178]]], input_type=list] + For further information visit https://errors.pydantic.dev/2.12/v/model_type +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:44:34 - INFO - main.py:331 - ================================================== +2026-02-17 01:44:34 - INFO - main.py:332 - Ingestion complete! +2026-02-17 01:44:34 - INFO - main.py:333 - Total pages processed: 0 +2026-02-17 01:44:34 - INFO - main.py:334 - Total chunks stored: 0 +2026-02-17 01:44:34 - WARNING - main.py:336 - Failed URLs (1): http://example.com +2026-02-17 01:44:34 - INFO - main.py:337 - ================================================== +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:44:34 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:44:35 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:44:33 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:44:35 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 01:44:35 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:44:35 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:44:35 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:44:35 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:44:35 - INFO - main.py:342 - Qdrant collection 'book_embeddings' now has 0 points +2026-02-17 01:44:35 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:44:35 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:44:54 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:44:54 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:44:55 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:44:54 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:44:55 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:44:55 - INFO - main.py:180 - Collection 'book_embeddings' already exists +2026-02-17 01:44:55 - INFO - main.py:275 - Starting ingestion: 1 URLs +2026-02-17 01:44:55 - INFO - main.py:276 - Chunk size: 200, overlap: 20 +2026-02-17 01:44:55 - INFO - main.py:34 - Fetching http://example.com... +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - connect_tcp.started host='example.com' port=80 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Mon, 16 Feb 2026 20:44:55 GMT'), (b'Content-Type', b'text/html'), (b'Transfer-Encoding', b'chunked'), (b'Connection', b'keep-alive'), (b'Content-Encoding', b'gzip'), (b'Last-Modified', b'Thu, 12 Feb 2026 13:58:32 GMT'), (b'Allow', b'GET, HEAD'), (b'Age', b'13454'), (b'cf-cache-status', b'HIT'), (b'Vary', b'Accept-Encoding'), (b'Server', b'cloudflare'), (b'CF-RAY', b'9cefe2bc1e7f909f-KHI')]) +2026-02-17 01:44:55 - INFO - _client.py:1025 - HTTP Request: GET http://example.com "HTTP/1.1 200 OK" +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:44:55 - DEBUG - main.py:39 - Successfully fetched http://example.com (status: 200) +2026-02-17 01:44:55 - INFO - main.py:294 - Extracted 142 characters from http://example.com, chunked into 1 segments +2026-02-17 01:44:55 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'142'), (b'num_tokens', b'23'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'e1e831be1ee33e9d31813453e2014efe'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'97'), (b'date', b'Mon, 16 Feb 2026 20:44:55 GMT'), (b'x-envoy-upstream-service-time', b'37'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:44:56 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:44:56 - INFO - main.py:138 - Cohere returned embeddings with dimension: 2 +2026-02-17 01:44:56 - DEBUG - main.py:149 - Generated embeddings for batch 1/1 +2026-02-17 01:44:56 - WARNING - main.py:160 - Unexpected embedding dimension: 2 (expected 1024). Proceeding anyway. +2026-02-17 01:44:56 - WARNING - main.py:304 - Embedding dimension 2 != 1024. Check Cohere model. Proceeding anyway. +2026-02-17 01:44:56 - ERROR - main.py:326 - Failed to process http://example.com: 8 validation errors for PointStruct +vector.list[float].0 + Input should be a valid number [type=float_type, input_value='float_', input_type=str] + For further information visit https://errors.pydantic.dev/2.12/v/float_type +vector.list[float].1 + Input should be a valid number [type=float_type, input_value=[[0.010848999, -0.0126647...019317627, 0.012008667]], input_type=list] + For further information visit https://errors.pydantic.dev/2.12/v/float_type +vector.list[list[float]].0 + Input should be a valid list [type=list_type, input_value='float_', input_type=str] + For further information visit https://errors.pydantic.dev/2.12/v/list_type +vector.list[list[float]].1.0 + Input should be a valid number [type=float_type, input_value=[0.010848999, -0.01266479....019317627, 0.012008667], input_type=list] + For further information visit https://errors.pydantic.dev/2.12/v/float_type +vector.dict[str,union[list[float],SparseVector,list[list[float]],Document,Image,InferenceObject]] + Input should be a valid dictionary [type=dict_type, input_value=['float_', [[0.010848999,...19317627, 0.012008667]]], input_type=list] + For further information visit https://errors.pydantic.dev/2.12/v/dict_type +vector.Document + Input should be a valid dictionary or instance of Document [type=model_type, input_value=['float_', [[0.010848999,...19317627, 0.012008667]]], input_type=list] + For further information visit https://errors.pydantic.dev/2.12/v/model_type +vector.Image + Input should be a valid dictionary or instance of Image [type=model_type, input_value=['float_', [[0.010848999,...19317627, 0.012008667]]], input_type=list] + For further information visit https://errors.pydantic.dev/2.12/v/model_type +vector.InferenceObject + Input should be a valid dictionary or instance of InferenceObject [type=model_type, input_value=['float_', [[0.010848999,...19317627, 0.012008667]]], input_type=list] + For further information visit https://errors.pydantic.dev/2.12/v/model_type +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:44:56 - INFO - main.py:331 - ================================================== +2026-02-17 01:44:56 - INFO - main.py:332 - Ingestion complete! +2026-02-17 01:44:56 - INFO - main.py:333 - Total pages processed: 0 +2026-02-17 01:44:56 - INFO - main.py:334 - Total chunks stored: 0 +2026-02-17 01:44:56 - WARNING - main.py:336 - Failed URLs (1): http://example.com +2026-02-17 01:44:56 - INFO - main.py:337 - ================================================== +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:44:55 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:44:56 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:44:56 - INFO - main.py:342 - Qdrant collection 'book_embeddings' now has 0 points +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:44:56 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:45:21 - INFO - test_local.py:41 - Extracted 781 characters from fixture +2026-02-17 01:45:21 - INFO - test_local.py:46 - Chunked into 2 segments +2026-02-17 01:45:21 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 01:45:21 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:45:21 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 01:45:21 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:45:21 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:45:21 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:45:21 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:45:21 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:45:21 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:45:21 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'500'), (b'num_tokens', b'89'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'0ea0a9ac2ff2704dc778c5040fdc6a6b'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'97'), (b'date', b'Mon, 16 Feb 2026 20:45:20 GMT'), (b'x-envoy-upstream-service-time', b'42'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:45:21 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:45:21 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:45:21 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:45:21 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:45:21 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:45:21 - ERROR - test_local.py:117 - Test failed: 'EmbedByTypeResponseEmbeddings' object is not subscriptable +Traceback (most recent call last): + File "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/test_local.py", line 115, in + test_with_fixture() + File "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/test_local.py", line 61, in test_with_fixture + first_emb_obj = test_response.embeddings[0] + ~~~~~~~~~~~~~~~~~~~~~~~~^^^ +TypeError: 'EmbedByTypeResponseEmbeddings' object is not subscriptable +2026-02-17 01:45:44 - INFO - test_local.py:41 - Extracted 781 characters from fixture +2026-02-17 01:45:44 - INFO - test_local.py:46 - Chunked into 2 segments +2026-02-17 01:45:44 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 01:45:44 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:45:44 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 01:45:44 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:45:44 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:45:44 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:45:44 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:45:44 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:45:44 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:45:44 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'500'), (b'num_tokens', b'89'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'8150b6154358e32a3b3963a878363d1a'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'97'), (b'date', b'Mon, 16 Feb 2026 20:45:44 GMT'), (b'x-envoy-upstream-service-time', b'36'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:45:44 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:45:44 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:45:44 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:45:44 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:45:44 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:45:44 - ERROR - test_local.py:117 - Test failed: 'EmbedByTypeResponseEmbeddings' object is not subscriptable +Traceback (most recent call last): + File "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/test_local.py", line 115, in + test_with_fixture() + File "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/test_local.py", line 61, in test_with_fixture + first_emb_obj = test_response.embeddings[0] + ~~~~~~~~~~~~~~~~~~~~~~~~^^^ +TypeError: 'EmbedByTypeResponseEmbeddings' object is not subscriptable +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:46:29 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:46:32 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:46:29 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:46:32 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:46:32 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:46:32 - INFO - main.py:180 - Collection 'book_embeddings' already exists +2026-02-17 01:46:34 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:46:34 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:48:39 - INFO - test_local.py:41 - Extracted 781 characters from fixture +2026-02-17 01:48:39 - INFO - test_local.py:46 - Chunked into 2 segments +2026-02-17 01:48:39 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 01:48:39 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:48:39 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 01:48:39 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:48:39 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:48:39 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:48:39 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:48:39 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:48:39 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:48:40 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'500'), (b'num_tokens', b'89'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'22c20db0e798c0c08a111610a0391b40'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 20:48:37 GMT'), (b'x-envoy-upstream-service-time', b'34'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:48:40 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:48:40 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:48:40 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:48:40 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:48:40 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:48:40 - ERROR - test_local.py:117 - Test failed: 'EmbedByTypeResponseEmbeddings' object is not subscriptable +Traceback (most recent call last): + File "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/test_local.py", line 115, in + test_with_fixture() + File "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/test_local.py", line 61, in test_with_fixture + first_emb_obj = test_response.embeddings[0] + ~~~~~~~~~~~~~~~~~~~~~~~~^^^ +TypeError: 'EmbedByTypeResponseEmbeddings' object is not subscriptable +2026-02-17 01:48:55 - INFO - test_local.py:41 - Extracted 781 characters from fixture +2026-02-17 01:48:55 - INFO - test_local.py:46 - Chunked into 2 segments +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'500'), (b'num_tokens', b'89'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'564b0af9524db75d44d3ccd318702b54'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'98'), (b'date', b'Mon, 16 Feb 2026 20:48:55 GMT'), (b'x-envoy-upstream-service-time', b'36'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:48:56 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'831'), (b'num_tokens', b'153'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'009b3223aef85289160f87532c6cdfe2'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'97'), (b'date', b'Mon, 16 Feb 2026 20:48:55 GMT'), (b'x-envoy-upstream-service-time', b'33'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:48:56 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:48:56 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:48:56 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 01:48:56 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 01:48:56 - INFO - test_local.py:73 - Generated 2 embeddings +2026-02-17 01:48:57 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:48:57 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:48:57 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:48:57 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:48:57 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:48:57 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:48:57 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:48:57 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:48:57 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:48:57 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:48:56 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:48:57 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 01:48:57 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:48:57 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:48:57 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:48:57 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:48:57 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:48:57 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:48:57 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:48:56 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:48:58 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:48:58 - INFO - main.py:172 - Collection 'book_embeddings' already exists +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 400, b'Bad Request', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:48:57 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:48:58 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 400 Bad Request" +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:48:58 - ERROR - main.py:219 - Failed to upsert points: Unexpected Response: 400 (Bad Request) +Raw response content: +b'{"status":{"error":"Format error in JSON body: value 08b95c4f53877d454a5fb0ad7b35d5c39aae36da14d995fb57cc236555d4fe8c is not a valid point ID, valid values are either an unsigned integer or a UUID" ...' +2026-02-17 01:48:58 - ERROR - test_local.py:117 - Test failed: Unexpected Response: 400 (Bad Request) +Raw response content: +b'{"status":{"error":"Format error in JSON body: value 08b95c4f53877d454a5fb0ad7b35d5c39aae36da14d995fb57cc236555d4fe8c is not a valid point ID, valid values are either an unsigned integer or a UUID" ...' +Traceback (most recent call last): + File "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/test_local.py", line 115, in + test_with_fixture() + File "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/test_local.py", line 98, in test_with_fixture + stats = upsert_chunks(qdrant_client, collection, records, utils.deterministic_id) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/main.py", line 214, in upsert_chunks + client.upsert(collection_name=collection_name, points=points) + File "/home/m-ahmad-official/venv/lib/python3.12/site-packages/qdrant_client/qdrant_client.py", line 938, in upsert + return self._client.upsert( + ^^^^^^^^^^^^^^^^^^^^ + File "/home/m-ahmad-official/venv/lib/python3.12/site-packages/qdrant_client/qdrant_remote.py", line 1121, in upsert + http_result = self.openapi_client.points_api.upsert_points( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/m-ahmad-official/venv/lib/python3.12/site-packages/qdrant_client/http/api/points_api.py", line 994, in upsert_points + return self._build_for_upsert_points( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/m-ahmad-official/venv/lib/python3.12/site-packages/qdrant_client/http/api/points_api.py", line 515, in _build_for_upsert_points + return self.api_client.request( + ^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/m-ahmad-official/venv/lib/python3.12/site-packages/qdrant_client/http/api_client.py", line 95, in request + return self.send(request, type_) + ^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/m-ahmad-official/venv/lib/python3.12/site-packages/qdrant_client/http/api_client.py", line 130, in send + raise UnexpectedResponse.for_response(response) +qdrant_client.http.exceptions.UnexpectedResponse: Unexpected Response: 400 (Bad Request) +Raw response content: +b'{"status":{"error":"Format error in JSON body: value 08b95c4f53877d454a5fb0ad7b35d5c39aae36da14d995fb57cc236555d4fe8c is not a valid point ID, valid values are either an unsigned integer or a UUID" ...' +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:48:58 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:49:20 - INFO - test_local.py:41 - Extracted 781 characters from fixture +2026-02-17 01:49:20 - INFO - test_local.py:46 - Chunked into 2 segments +2026-02-17 01:49:20 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 01:49:20 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:49:20 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 01:49:20 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:49:20 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:49:20 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:49:20 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:49:20 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:49:20 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'500'), (b'num_tokens', b'89'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'0ca2b47f2b432e3dccac3365ecc96989'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'96'), (b'date', b'Mon, 16 Feb 2026 20:49:20 GMT'), (b'x-envoy-upstream-service-time', b'39'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:49:21 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'831'), (b'num_tokens', b'153'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'198bb36f249f46e545124431d6e8e235'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'95'), (b'date', b'Mon, 16 Feb 2026 20:49:20 GMT'), (b'x-envoy-upstream-service-time', b'37'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:49:21 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:49:21 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 01:49:21 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 01:49:21 - INFO - test_local.py:73 - Generated 2 embeddings +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:49:20 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:49:21 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:49:21 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:49:21 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:49:22 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:49:22 - INFO - main.py:172 - Collection 'book_embeddings' already exists +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:49:21 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:49:22 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:49:22 - INFO - main.py:215 - Upserted 2 points to collection 'book_embeddings' +2026-02-17 01:49:22 - INFO - test_local.py:99 - Upsert stats: {'new': 2, 'updated': 0, 'total': 2} +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:49:21 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:49:22 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:49:22 - INFO - main.py:346 - ================================================== +2026-02-17 01:49:22 - INFO - main.py:347 - Running validation mode... +2026-02-17 01:49:22 - INFO - main.py:348 - ================================================== +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:49:22 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:49:23 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:49:21 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:49:23 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 01:49:23 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:49:23 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:49:23 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:49:23 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:49:23 - INFO - main.py:356 - Collection: book_embeddings +2026-02-17 01:49:23 - INFO - main.py:357 - Total points: 2 +2026-02-17 01:49:23 - INFO - main.py:358 - Vector size: 1024 +2026-02-17 01:49:23 - INFO - main.py:364 - ✅ Vector dimension correct (1024) +2026-02-17 01:49:23 - INFO - main.py:369 - Sampling 2 points for validation... +2026-02-17 01:49:23 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:49:23 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:49:23 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:49:23 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:49:23 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:49:23 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:49:22 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:49:23 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/scroll "HTTP/1.1 200 OK" +2026-02-17 01:49:23 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:49:23 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:49:23 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:49:23 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:49:23 - INFO - main.py:404 - Metadata completeness: +2026-02-17 01:49:23 - INFO - main.py:405 - URL present: 2/2 (100.0%) +2026-02-17 01:49:23 - INFO - main.py:406 - Title/Section present: 2/2 (100.0%) +2026-02-17 01:49:23 - INFO - main.py:407 - Text non-empty (≥10 chars): 2/2 (100.0%) +2026-02-17 01:49:23 - INFO - main.py:411 - ✅ URL completeness excellent (≥99%) +2026-02-17 01:49:23 - INFO - main.py:416 - ✅ Title/Section completeness good (≥95%) +2026-02-17 01:49:23 - INFO - main.py:421 - ✅ Text quality excellent (≥98%) +2026-02-17 01:49:23 - INFO - main.py:425 - ================================================== +2026-02-17 01:49:23 - INFO - main.py:426 - Validation complete! +2026-02-17 01:49:23 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:49:23 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:49:46 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:49:49 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:49:46 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:49:49 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:49:49 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:49:49 - INFO - main.py:172 - Collection 'book_embeddings' already exists +2026-02-17 01:49:51 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:49:51 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:50:14 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:50:14 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:50:14 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:50:11 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:50:11 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:50:11 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:50:11 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:50:11 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:50:11 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:50:11 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:50:12 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:50:12 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:50:12 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:50:12 - INFO - main.py:172 - Collection 'book_embeddings' already exists +2026-02-17 01:50:12 - INFO - main.py:267 - Starting ingestion: 3 URLs +2026-02-17 01:50:12 - INFO - main.py:268 - Chunk size: 1000, overlap: 100 +2026-02-17 01:50:12 - INFO - main.py:34 - Fetching https://bookshelf.math.buffalo.edu/bookshelf-free-books-direction.html... +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - connect_tcp.started host='bookshelf.math.buffalo.edu' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:50:12 - DEBUG - _trace.py:47 - connect_tcp.failed exception=ConnectError(gaierror(-2, 'Name or service not known')) +2026-02-17 01:50:13 - DEBUG - _trace.py:47 - connect_tcp.started host='bookshelf.math.buffalo.edu' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:50:13 - DEBUG - _trace.py:47 - connect_tcp.failed exception=ConnectError(gaierror(-2, 'Name or service not known')) +2026-02-17 01:50:15 - DEBUG - _trace.py:47 - connect_tcp.started host='bookshelf.math.buffalo.edu' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:50:15 - DEBUG - _trace.py:47 - connect_tcp.failed exception=ConnectError(gaierror(-2, 'Name or service not known')) +2026-02-17 01:50:15 - ERROR - main.py:43 - Failed to fetch https://bookshelf.math.buffalo.edu/bookshelf-free-books-direction.html after 3 attempts: [Errno -2] Name or service not known +2026-02-17 01:50:15 - ERROR - main.py:318 - Failed to process https://bookshelf.math.buffalo.edu/bookshelf-free-books-direction.html: [Errno -2] Name or service not known +2026-02-17 01:50:15 - INFO - main.py:34 - Fetching https://en.wikipedia.org/wiki/Book... +2026-02-17 01:50:15 - DEBUG - _trace.py:47 - connect_tcp.started host='en.wikipedia.org' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:50:15 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:50:15 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='en.wikipedia.org' timeout=30.0 +2026-02-17 01:50:16 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:50:16 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:50:16 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:50:16 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:50:16 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:50:16 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:50:16 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 403, b'Forbidden', [(b'content-length', b'126'), (b'content-type', b'text/plain'), (b'x-request-id', b'04641407-eb86-4f6b-ad07-b19b539d01b1'), (b'server', b'HAProxy'), (b'x-cache', b'cp5021 int'), (b'x-cache-status', b'int-tls'), (b'x-analytics', b'')]) +2026-02-17 01:50:16 - INFO - _client.py:1025 - HTTP Request: GET https://en.wikipedia.org/wiki/Book "HTTP/1.1 403 Forbidden" +2026-02-17 01:50:16 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:50:16 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:50:16 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:50:16 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:50:17 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:50:17 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:50:17 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:50:17 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:50:17 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:50:17 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 403, b'Forbidden', [(b'content-length', b'126'), (b'content-type', b'text/plain'), (b'x-request-id', b'397791a2-60f9-40d3-96d6-f9730b252d70'), (b'server', b'HAProxy'), (b'x-cache', b'cp5021 int'), (b'x-cache-status', b'int-tls'), (b'x-analytics', b'')]) +2026-02-17 01:50:17 - INFO - _client.py:1025 - HTTP Request: GET https://en.wikipedia.org/wiki/Book "HTTP/1.1 403 Forbidden" +2026-02-17 01:50:17 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:50:17 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:50:17 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:50:17 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:50:19 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:50:19 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:50:19 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:50:19 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:50:19 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:50:19 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 403, b'Forbidden', [(b'content-length', b'126'), (b'content-type', b'text/plain'), (b'x-request-id', b'1db67fe0-6551-42a3-a42d-e0cd34649311'), (b'server', b'HAProxy'), (b'x-cache', b'cp5021 int'), (b'x-cache-status', b'int-tls'), (b'x-analytics', b'')]) +2026-02-17 01:50:19 - INFO - _client.py:1025 - HTTP Request: GET https://en.wikipedia.org/wiki/Book "HTTP/1.1 403 Forbidden" +2026-02-17 01:50:19 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:50:19 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:50:19 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:50:19 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:50:19 - ERROR - main.py:43 - Failed to fetch https://en.wikipedia.org/wiki/Book after 3 attempts: Client error '403 Forbidden' for url 'https://en.wikipedia.org/wiki/Book' +For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403 +2026-02-17 01:50:19 - ERROR - main.py:318 - Failed to process https://en.wikipedia.org/wiki/Book: Client error '403 Forbidden' for url 'https://en.wikipedia.org/wiki/Book' +For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403 +2026-02-17 01:50:19 - INFO - main.py:34 - Fetching https://www.gutenberg.org/files/1342/1342-h/1342-h.htm... +2026-02-17 01:50:19 - DEBUG - _trace.py:47 - connect_tcp.started host='www.gutenberg.org' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:50:20 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:50:20 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='www.gutenberg.org' timeout=30.0 +2026-02-17 01:50:20 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:50:20 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:50:20 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:50:20 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:50:20 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:50:20 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:50:20 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'date', b'Mon, 16 Feb 2026 20:50:19 GMT'), (b'server', b'Apache'), (b'last-modified', b'Tue, 10 Feb 2026 17:53:56 GMT'), (b'accept-ranges', b'bytes'), (b'content-length', b'806295'), (b'x-backend', b'gutenweb7'), (b'content-type', b'text/html')]) +2026-02-17 01:50:20 - INFO - _client.py:1025 - HTTP Request: GET https://www.gutenberg.org/files/1342/1342-h/1342-h.htm "HTTP/1.1 200 OK" +2026-02-17 01:50:20 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:50:28 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:50:28 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:50:28 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:50:28 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:50:28 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:50:28 - DEBUG - main.py:39 - Successfully fetched https://www.gutenberg.org/files/1342/1342-h/1342-h.htm (status: 200) +2026-02-17 01:50:28 - INFO - main.py:286 - Extracted 717156 characters from https://www.gutenberg.org/files/1342/1342-h/1342-h.htm, chunked into 718 segments +2026-02-17 01:50:28 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 01:50:29 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:50:29 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 01:50:29 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:50:29 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:50:29 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:50:29 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:50:29 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:50:29 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:50:30 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'105500'), (b'num_tokens', b'24809'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'11d55d3fe9f220ef031095bf81a62967'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 20:50:28 GMT'), (b'x-envoy-upstream-service-time', b'683'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:50:30 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:50:30 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:50:30 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:50:30 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:50:30 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:50:30 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 01:50:30 - DEBUG - main.py:141 - Generated embeddings for batch 1/8 +2026-02-17 01:50:30 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:50:30 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:50:30 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:50:30 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:50:30 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:50:31 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'105600'), (b'num_tokens', b'24399'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'eed0b21b9b4be0cae3e162fe6d1cced8'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'98'), (b'date', b'Mon, 16 Feb 2026 20:50:30 GMT'), (b'x-envoy-upstream-service-time', b'588'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:50:31 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:50:31 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:50:32 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:50:32 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:50:32 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:50:32 - DEBUG - main.py:141 - Generated embeddings for batch 2/8 +2026-02-17 01:50:32 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:50:32 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:50:32 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:50:32 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:50:32 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:50:33 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'105600'), (b'num_tokens', b'23949'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'f0bec9165ed0d154399775874b1f3a93'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'97'), (b'date', b'Mon, 16 Feb 2026 20:50:31 GMT'), (b'x-envoy-upstream-service-time', b'336'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:50:33 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:50:33 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:50:33 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:50:33 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:50:33 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:50:34 - DEBUG - main.py:141 - Generated embeddings for batch 3/8 +2026-02-17 01:50:34 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:50:34 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:50:34 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:50:34 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:50:34 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:50:34 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'105600'), (b'num_tokens', b'23625'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'04438d8294411747b07d7eba801f6a67'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'96'), (b'date', b'Mon, 16 Feb 2026 20:50:32 GMT'), (b'x-envoy-upstream-service-time', b'308'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:50:34 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:50:34 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:50:34 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:50:34 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:50:34 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:50:35 - DEBUG - main.py:141 - Generated embeddings for batch 4/8 +2026-02-17 01:50:35 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:50:35 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:50:35 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:50:35 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:50:35 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:50:35 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 429, b'Too Many Requests', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'90fb44400ad7cba02e69b30ff7f8afda'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'95'), (b'date', b'Mon, 16 Feb 2026 20:50:33 GMT'), (b'x-envoy-upstream-service-time', b'19'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:50:35 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 429 Too Many Requests" +2026-02-17 01:50:35 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:50:35 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:50:35 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:50:35 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:50:36 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:50:36 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:50:36 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:50:36 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:50:36 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:50:37 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 429, b'Too Many Requests', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'b673c3be72285b9ddb10f3b38102bbf4'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'94'), (b'date', b'Mon, 16 Feb 2026 20:50:35 GMT'), (b'x-envoy-upstream-service-time', b'16'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:50:37 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 429 Too Many Requests" +2026-02-17 01:50:37 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:50:37 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:50:37 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:50:37 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:50:39 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:50:39 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:50:39 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:50:39 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:50:39 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:50:39 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 429, b'Too Many Requests', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'ac8daa119d86764619add3bb30952b82'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'93'), (b'date', b'Mon, 16 Feb 2026 20:50:37 GMT'), (b'x-envoy-upstream-service-time', b'18'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:50:39 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 429 Too Many Requests" +2026-02-17 01:50:39 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:50:39 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:50:39 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:50:39 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:50:39 - ERROR - main.py:143 - Failed to generate embeddings for batch starting at index 384: headers: {'access-control-expose-headers': 'X-Debug-Trace-ID', 'cache-control': 'no-cache, no-store, no-transform, must-revalidate, private, max-age=0', 'content-encoding': 'gzip', 'content-type': 'application/json', 'expires': 'Thu, 01 Jan 1970 00:00:00 GMT', 'pragma': 'no-cache', 'vary': 'Origin,Accept-Encoding', 'x-accel-expires': '0', 'x-debug-trace-id': 'ac8daa119d86764619add3bb30952b82', 'x-endpoint-monthly-call-limit': '1000', 'x-trial-endpoint-call-limit': '100', 'x-trial-endpoint-call-remaining': '93', 'date': 'Mon, 16 Feb 2026 20:50:37 GMT', 'x-envoy-upstream-service-time': '18', 'server': 'envoy', 'via': '1.1 google', 'alt-svc': 'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000', 'transfer-encoding': 'chunked'}, status_code: 429, body: {'id': 'ec45d5c2-8752-451a-846a-c6c51908b186', 'message': 'trial token rate limit exceeded, limit is 100000 tokens per minute'} +2026-02-17 01:50:39 - ERROR - main.py:318 - Failed to process https://www.gutenberg.org/files/1342/1342-h/1342-h.htm: headers: {'access-control-expose-headers': 'X-Debug-Trace-ID', 'cache-control': 'no-cache, no-store, no-transform, must-revalidate, private, max-age=0', 'content-encoding': 'gzip', 'content-type': 'application/json', 'expires': 'Thu, 01 Jan 1970 00:00:00 GMT', 'pragma': 'no-cache', 'vary': 'Origin,Accept-Encoding', 'x-accel-expires': '0', 'x-debug-trace-id': 'ac8daa119d86764619add3bb30952b82', 'x-endpoint-monthly-call-limit': '1000', 'x-trial-endpoint-call-limit': '100', 'x-trial-endpoint-call-remaining': '93', 'date': 'Mon, 16 Feb 2026 20:50:37 GMT', 'x-envoy-upstream-service-time': '18', 'server': 'envoy', 'via': '1.1 google', 'alt-svc': 'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000', 'transfer-encoding': 'chunked'}, status_code: 429, body: {'id': 'ec45d5c2-8752-451a-846a-c6c51908b186', 'message': 'trial token rate limit exceeded, limit is 100000 tokens per minute'} +2026-02-17 01:50:39 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:50:39 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:50:39 - INFO - main.py:323 - ================================================== +2026-02-17 01:50:39 - INFO - main.py:324 - Ingestion complete! +2026-02-17 01:50:39 - INFO - main.py:325 - Total pages processed: 0 +2026-02-17 01:50:39 - INFO - main.py:326 - Total chunks stored: 0 +2026-02-17 01:50:39 - WARNING - main.py:328 - Failed URLs (3): https://bookshelf.math.buffalo.edu/bookshelf-free-books-direction.html, https://en.wikipedia.org/wiki/Book, https://www.gutenberg.org/files/1342/1342-h/1342-h.htm +2026-02-17 01:50:39 - INFO - main.py:329 - ================================================== +2026-02-17 01:50:39 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:50:39 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:50:39 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:50:39 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:50:39 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:50:40 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:50:40 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:50:40 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:50:40 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:50:40 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:50:40 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:50:40 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:50:37 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:50:40 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 01:50:40 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:50:40 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:50:40 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:50:40 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:50:40 - INFO - main.py:334 - Qdrant collection 'book_embeddings' now has 2 points +2026-02-17 01:50:40 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:50:40 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:51:46 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:51:46 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:51:46 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:51:46 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:51:46 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:51:46 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:51:46 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:51:46 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:51:46 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:51:46 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:51:43 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:51:46 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 01:51:46 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:51:46 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:51:46 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:51:46 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:51:46 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:51:46 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:51:46 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:51:47 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:51:47 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:51:47 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:51:47 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:51:47 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:51:47 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:51:47 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:51:47 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:51:47 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:51:44 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:51:47 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 01:51:47 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:51:47 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:51:47 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:51:47 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:51:47 - INFO - main.py:172 - Collection 'book_embeddings' already exists +2026-02-17 01:51:48 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:51:48 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:52:41 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:52:44 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:52:44 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:52:45 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:52:45 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:52:45 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:52:45 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:52:45 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:52:45 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:52:45 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:52:42 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:52:45 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 01:52:45 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:52:45 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:52:45 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:52:45 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:52:45 - INFO - main.py:172 - Collection 'book_embeddings' already exists +2026-02-17 01:52:46 - INFO - main.py:267 - Starting ingestion: 1 URLs +2026-02-17 01:52:46 - INFO - main.py:268 - Chunk size: 1000, overlap: 100 +2026-02-17 01:52:46 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app... +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - connect_tcp.started host='humanoid-ai-robotics-book-1.vercel.app' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='humanoid-ai-robotics-book-1.vercel.app' timeout=30.0 +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'1023315'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 20:52:43 GMT'), (b'Etag', b'W/"dee9f6103f913b331d72f5cbcb592db9"'), (b'Last-Modified', b'Thu, 05 Feb 2026 00:37:28 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::hc774-1771275163869-5a94aabe0149'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:52:46 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app "HTTP/1.1 200 OK" +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:52:46 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app (status: 200) +2026-02-17 01:52:46 - INFO - main.py:286 - Extracted 91 characters from https://humanoid-ai-robotics-book-1.vercel.app, chunked into 1 segments +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:52:46 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'91'), (b'num_tokens', b'19'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'b253de0622d47d7269f975da6bd442dc'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 20:52:44 GMT'), (b'x-envoy-upstream-service-time', b'40'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:52:47 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:52:47 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 01:52:47 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:52:44 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:52:47 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:52:47 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 01:52:47 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:52:47 - INFO - main.py:323 - ================================================== +2026-02-17 01:52:47 - INFO - main.py:324 - Ingestion complete! +2026-02-17 01:52:47 - INFO - main.py:325 - Total pages processed: 1 +2026-02-17 01:52:47 - INFO - main.py:326 - Total chunks stored: 1 +2026-02-17 01:52:47 - INFO - main.py:329 - ================================================== +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:52:44 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:52:47 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:52:47 - INFO - main.py:334 - Qdrant collection 'book_embeddings' now has 3 points +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:52:47 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:53:47 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:53:47 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:53:47 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:53:47 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:53:47 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:53:47 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:53:47 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:53:47 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:53:47 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:53:47 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:53:48 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:53:47 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:53:48 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:53:48 - INFO - main.py:172 - Collection 'book_embeddings' already exists +2026-02-17 01:53:48 - INFO - main.py:346 - ================================================== +2026-02-17 01:53:48 - INFO - main.py:347 - Running validation mode... +2026-02-17 01:53:48 - INFO - main.py:348 - ================================================== +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:53:47 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:53:48 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:53:48 - INFO - main.py:356 - Collection: book_embeddings +2026-02-17 01:53:48 - INFO - main.py:357 - Total points: 3 +2026-02-17 01:53:48 - INFO - main.py:358 - Vector size: 1024 +2026-02-17 01:53:48 - INFO - main.py:364 - ✅ Vector dimension correct (1024) +2026-02-17 01:53:48 - INFO - main.py:369 - Sampling 3 points for validation... +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:53:47 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:53:48 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/scroll "HTTP/1.1 200 OK" +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:53:48 - INFO - main.py:404 - Metadata completeness: +2026-02-17 01:53:48 - INFO - main.py:405 - URL present: 3/3 (100.0%) +2026-02-17 01:53:48 - INFO - main.py:406 - Title/Section present: 3/3 (100.0%) +2026-02-17 01:53:48 - INFO - main.py:407 - Text non-empty (≥10 chars): 3/3 (100.0%) +2026-02-17 01:53:48 - INFO - main.py:411 - ✅ URL completeness excellent (≥99%) +2026-02-17 01:53:48 - INFO - main.py:416 - ✅ Title/Section completeness good (≥95%) +2026-02-17 01:53:48 - INFO - main.py:421 - ✅ Text quality excellent (≥98%) +2026-02-17 01:53:48 - INFO - main.py:425 - ================================================== +2026-02-17 01:53:48 - INFO - main.py:426 - Validation complete! +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:53:48 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:55:21 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:55:21 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:55:21 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:55:21 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:55:21 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:55:21 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:55:21 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:55:21 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:55:21 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:55:20 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:55:22 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:55:21 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:55:22 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:55:22 - INFO - main.py:172 - Collection 'book_embeddings' already exists +2026-02-17 01:55:22 - INFO - main.py:267 - Starting ingestion: 1 URLs +2026-02-17 01:55:22 - INFO - main.py:268 - Chunk size: 1000, overlap: 100 +2026-02-17 01:55:22 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app... +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - connect_tcp.started host='humanoid-ai-robotics-book-1.vercel.app' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='humanoid-ai-robotics-book-1.vercel.app' timeout=30.0 +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'1023473'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 20:55:21 GMT'), (b'Etag', b'W/"dee9f6103f913b331d72f5cbcb592db9"'), (b'Last-Modified', b'Thu, 05 Feb 2026 00:37:28 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::9c854-1771275321735-f6df7addee8f'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:55:22 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app "HTTP/1.1 200 OK" +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:55:22 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app (status: 200) +2026-02-17 01:55:22 - INFO - main.py:286 - Extracted 91 characters from https://humanoid-ai-robotics-book-1.vercel.app, chunked into 1 segments +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:55:22 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'91'), (b'num_tokens', b'19'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'2cb17aead43a62b4a6d2272a9a38c25e'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 20:55:22 GMT'), (b'x-envoy-upstream-service-time', b'43'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:55:23 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:55:23 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 01:55:23 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:55:22 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:55:23 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:55:23 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 01:55:23 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - close.complete +2026-02-17 01:55:23 - INFO - main.py:323 - ================================================== +2026-02-17 01:55:23 - INFO - main.py:324 - Ingestion complete! +2026-02-17 01:55:23 - INFO - main.py:325 - Total pages processed: 1 +2026-02-17 01:55:23 - INFO - main.py:326 - Total chunks stored: 1 +2026-02-17 01:55:23 - INFO - main.py:329 - ================================================== +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 20:55:22 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 01:55:23 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 01:55:23 - INFO - main.py:334 - Qdrant collection 'book_embeddings' now has 3 points +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - close.started +2026-02-17 01:55:23 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:05:05 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:05:05 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:05:05 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:05:05 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:05:05 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:05 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:05 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:05 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:05 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:05 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:06 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:06 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:06 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:06 - INFO - main.py:172 - Collection 'book_embeddings' already exists +2026-02-17 02:05:06 - INFO - main.py:267 - Starting ingestion: 40 URLs +2026-02-17 02:05:06 - INFO - main.py:268 - Chunk size: 1000, overlap: 100 +2026-02-17 02:05:06 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog... +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - connect_tcp.started host='humanoid-ai-robotics-book-1.vercel.app' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='humanoid-ai-robotics-book-1.vercel.app' timeout=30.0 +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:06 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="blog"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:06 GMT'), (b'Etag', b'W/"b11ea0ccd90cc7a5b08cfa9a05f4c67b"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:06 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::tf9wb-1771275906637-9a989a2c684b'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:07 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog "HTTP/1.1 200 OK" +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:07 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog (status: 200) +2026-02-17 02:05:07 - INFO - main.py:286 - Extracted 436 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog, chunked into 1 segments +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'436'), (b'num_tokens', b'109'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'9233893c4ba09f16419da4b1e17cb5bd'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 21:05:07 GMT'), (b'x-envoy-upstream-service-time', b'43'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:07 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:07 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:07 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:07 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:07 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:08 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:08 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:08 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog +2026-02-17 02:05:08 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/archive... +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="archive"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:08 GMT'), (b'Etag', b'W/"5ca63baec719320b86e8372e9d81c8e6"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:08 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::r7tzd-1771275907772-f4c8d3e02a53'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:08 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/archive "HTTP/1.1 200 OK" +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:08 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/archive (status: 200) +2026-02-17 02:05:08 - INFO - main.py:286 - Extracted 167 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/archive, chunked into 1 segments +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'167'), (b'num_tokens', b'37'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'bc403ea67a862ee26a85d80a994b3901'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'98'), (b'date', b'Mon, 16 Feb 2026 21:05:08 GMT'), (b'x-envoy-upstream-service-time', b'30'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:08 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:08 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:08 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:08 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:08 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:08 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:08 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/archive +2026-02-17 02:05:08 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/authors... +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:08 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="authors"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:08 GMT'), (b'Etag', b'W/"2808bae6c95d00f7a1687991ec4be358"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:08 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::r7tzd-1771275908670-40084c7eab06'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:09 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/authors "HTTP/1.1 200 OK" +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:09 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/authors (status: 200) +2026-02-17 02:05:09 - INFO - main.py:286 - Extracted 172 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/authors, chunked into 1 segments +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'172'), (b'num_tokens', b'39'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'f00a09313fa5dcdebcac4eef57841945'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'97'), (b'date', b'Mon, 16 Feb 2026 21:05:09 GMT'), (b'x-envoy-upstream-service-time', b'43'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:09 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:09 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:09 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:09 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:09 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:09 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:09 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/authors +2026-02-17 02:05:09 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/authors/all-sebastien-lorber-articles... +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:09 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="all-sebastien-lorber-articles"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:09 GMT'), (b'Etag', b'W/"b1f1e29206f19daa2b75bff6010d4aa3"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:09 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::r7tzd-1771275909566-2914f2d86b49'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:10 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/authors/all-sebastien-lorber-articles "HTTP/1.1 200 OK" +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:10 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/authors/all-sebastien-lorber-articles (status: 200) +2026-02-17 02:05:10 - INFO - main.py:286 - Extracted 338 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/authors/all-sebastien-lorber-articles, chunked into 1 segments +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'338'), (b'num_tokens', b'80'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'780c2f5d171a522cef86f0c5f807ffcb'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'96'), (b'date', b'Mon, 16 Feb 2026 21:05:10 GMT'), (b'x-envoy-upstream-service-time', b'43'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:10 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:10 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:10 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:10 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:11 - DEBUG - 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Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/authors/all-sebastien-lorber-articles +2026-02-17 02:05:11 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/authors/yangshun... +2026-02-17 02:05:11 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:11 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:11 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:11 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:11 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:11 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="yangshun"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:10 GMT'), (b'Etag', b'W/"5aaa188ceb7b0781b84c87c9252a5f97"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:10 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::9d4q2-1771275910559-51840c1bf421'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:11 - 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Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/authors/yangshun +2026-02-17 02:05:12 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/first-blog-post... +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="first-blog-post"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:11 GMT'), (b'Etag', b'W/"8c84c989186e2f01ade6432287b045bb"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:11 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::szkg6-1771275911546-c0b994e4cc55'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:12 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/first-blog-post "HTTP/1.1 200 OK" +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:12 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/first-blog-post (status: 200) +2026-02-17 02:05:12 - INFO - main.py:286 - Extracted 251 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/first-blog-post, chunked into 1 segments +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'251'), (b'num_tokens', b'83'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'b5f51bdf0f90168cd99186498f9840e1'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'94'), (b'date', b'Mon, 16 Feb 2026 21:05:12 GMT'), (b'x-envoy-upstream-service-time', b'39'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:12 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:12 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:12 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:12 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:12 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:13 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:13 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:13 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/first-blog-post +2026-02-17 02:05:13 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/long-blog-post... +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="long-blog-post"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:12 GMT'), (b'Etag', b'W/"e2bf5c2f326a3e28d0c79c489d79b324"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:12 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::hltjb-1771275912501-4bcbe87875da'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:13 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/long-blog-post "HTTP/1.1 200 OK" +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:13 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/long-blog-post (status: 200) +2026-02-17 02:05:13 - INFO - main.py:286 - Extracted 3050 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/long-blog-post, chunked into 4 segments +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'3350'), (b'num_tokens', b'1178'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'6e3bf0eed88ec444f66f1b159de45a51'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'93'), (b'date', b'Mon, 16 Feb 2026 21:05:13 GMT'), (b'x-envoy-upstream-service-time', b'61'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:13 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:13 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:13 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:13 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:13 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:14 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:14 - INFO - main.py:215 - Upserted 4 points to collection 'book_embeddings' +2026-02-17 02:05:14 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/long-blog-post +2026-02-17 02:05:14 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/mdx-blog-post... +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="mdx-blog-post"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:13 GMT'), (b'Etag', b'W/"cb4f3f390e12473355dd331758b886ba"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:13 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::szkg6-1771275913597-5eec4adae06c'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:14 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/mdx-blog-post "HTTP/1.1 200 OK" +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:14 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/mdx-blog-post (status: 200) +2026-02-17 02:05:14 - INFO - main.py:286 - Extracted 338 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/mdx-blog-post, chunked into 1 segments +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:14 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'338'), (b'num_tokens', b'86'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'c859deaa96a4421c841eafc8f56fa827'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'92'), (b'date', b'Mon, 16 Feb 2026 21:05:14 GMT'), (b'x-envoy-upstream-service-time', b'45'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:15 - 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_trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:14 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:15 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:15 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:15 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/mdx-blog-post +2026-02-17 02:05:15 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/tags... +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="tags"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:14 GMT'), (b'Etag', b'W/"90833a1c30a096febd0223e56ad4fcab"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:14 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::r7tzd-1771275914436-965a5a234f69'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:15 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/tags "HTTP/1.1 200 OK" +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:15 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/tags (status: 200) +2026-02-17 02:05:15 - INFO - main.py:286 - Extracted 112 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/tags, chunked into 1 segments +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'112'), (b'num_tokens', b'27'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'ab78e42ee4417f827dead78e4a55f003'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'91'), (b'date', b'Mon, 16 Feb 2026 21:05:14 GMT'), (b'x-envoy-upstream-service-time', b'40'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:15 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:15 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:15 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:15 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:16 - DEBUG - 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Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/tags +2026-02-17 02:05:16 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/docusaurus... +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="docusaurus"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:15 GMT'), (b'Etag', b'W/"2ff33e4e090bbccbdd3fd854a7152a62"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:15 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::jvhrm-1771275915327-9817dc691e58'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:16 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/docusaurus "HTTP/1.1 200 OK" +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:16 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/docusaurus (status: 200) +2026-02-17 02:05:16 - INFO - main.py:286 - Extracted 464 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/docusaurus, chunked into 1 segments +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'464'), (b'num_tokens', b'117'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'2accce30c32854dc4ef465dcbdfd1066'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'90'), (b'date', b'Mon, 16 Feb 2026 21:05:15 GMT'), (b'x-envoy-upstream-service-time', b'44'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:16 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:16 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:16 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:16 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:15 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:17 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:17 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:17 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/docusaurus +2026-02-17 02:05:17 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/facebook... +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="facebook"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:16 GMT'), (b'Etag', b'W/"955ee0172bb1f392948cb22d3adc3ead"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:16 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::szkg6-1771275916309-9dcdac3d19e0'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:17 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/facebook "HTTP/1.1 200 OK" +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:17 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/facebook (status: 200) +2026-02-17 02:05:17 - INFO - main.py:286 - Extracted 188 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/facebook, chunked into 1 segments +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'188'), (b'num_tokens', b'42'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'd063c311be23d8d5eb38f9fd6d256391'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'89'), (b'date', b'Mon, 16 Feb 2026 21:05:16 GMT'), (b'x-envoy-upstream-service-time', b'41'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:17 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:17 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:17 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:17 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:16 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:18 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:18 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:18 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/facebook +2026-02-17 02:05:18 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hello... +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="hello"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:17 GMT'), (b'Etag', b'W/"631b5c3dfc8f0aac36813c6d884d5fdd"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:17 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::szkg6-1771275917211-6d654d941264'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:18 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hello "HTTP/1.1 200 OK" +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:18 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hello (status: 200) +2026-02-17 02:05:18 - INFO - main.py:286 - Extracted 304 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hello, chunked into 1 segments +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'304'), (b'num_tokens', b'76'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'efb2a18d6aa9e181c2645c705613ed92'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'88'), (b'date', b'Mon, 16 Feb 2026 21:05:17 GMT'), (b'x-envoy-upstream-service-time', b'57'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:18 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:18 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:18 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:18 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:17 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:19 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:19 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:19 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hello +2026-02-17 02:05:19 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hola... +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="hola"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:18 GMT'), (b'Etag', b'W/"0c633b539935783f821f71bd789779f9"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:18 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::szkg6-1771275918172-87107f9041f3'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:19 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hola "HTTP/1.1 200 OK" +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:19 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hola (status: 200) +2026-02-17 02:05:19 - INFO - main.py:286 - Extracted 109 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hola, chunked into 1 segments +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'109'), (b'num_tokens', b'31'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'f020c5cfd30198d4daca50a75becd13e'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'87'), (b'date', b'Mon, 16 Feb 2026 21:05:18 GMT'), (b'x-envoy-upstream-service-time', b'43'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:19 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:19 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:19 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:19 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:18 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:20 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:20 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:20 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hola +2026-02-17 02:05:20 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/welcome... +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="welcome"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:19 GMT'), (b'Etag', b'W/"d831e219f3b1e77fa822803a19d0f9ba"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:19 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::szkg6-1771275919059-e631b258d75d'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:20 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/welcome "HTTP/1.1 200 OK" +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:20 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/welcome (status: 200) +2026-02-17 02:05:20 - INFO - main.py:286 - Extracted 594 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/welcome, chunked into 1 segments +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'594'), (b'num_tokens', b'150'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'c3277ffe93fca1e0df04a0d7911a1d05'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'86'), (b'date', b'Mon, 16 Feb 2026 21:05:19 GMT'), (b'x-envoy-upstream-service-time', b'34'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:20 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:20 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:20 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:20 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:19 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:21 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:21 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:21 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/welcome +2026-02-17 02:05:21 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/markdown-page... +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="markdown-page"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:20 GMT'), (b'Etag', b'W/"8aa79f038d4ab5706b153c31f38e7af6"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:20 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::szkg6-1771275919981-f16c3993f711'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:21 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/markdown-page "HTTP/1.1 200 OK" +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:21 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/markdown-page (status: 200) +2026-02-17 02:05:21 - INFO - main.py:286 - Extracted 128 characters from https://humanoid-ai-robotics-book-1.vercel.app/markdown-page, chunked into 1 segments +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'128'), (b'num_tokens', b'27'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'eeea37edcb748968ce852d7b3502fe1c'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'85'), (b'date', b'Mon, 16 Feb 2026 21:05:20 GMT'), (b'x-envoy-upstream-service-time', b'58'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:21 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:21 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:21 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:21 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:20 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:22 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:22 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:22 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/markdown-page +2026-02-17 02:05:22 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---basics... +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="tutorial---basics"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:21 GMT'), (b'Etag', b'W/"4f3a2c4b623ca5e5d229d425b962db57"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:21 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::szkg6-1771275920914-5dcbbdc95d96'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:22 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---basics "HTTP/1.1 200 OK" +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:22 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---basics (status: 200) +2026-02-17 02:05:22 - INFO - main.py:286 - Extracted 628 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---basics, chunked into 1 segments +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'628'), (b'num_tokens', b'142'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'e88b293719e3348110e07964a094ad1e'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'84'), (b'date', b'Mon, 16 Feb 2026 21:05:21 GMT'), (b'x-envoy-upstream-service-time', b'43'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:22 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:22 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:22 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:22 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:21 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:23 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:23 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:23 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---basics +2026-02-17 02:05:23 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---extras... +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="tutorial---extras"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:22 GMT'), (b'Etag', b'W/"bb4cb95d3bd2857913d7c7c444f50a7e"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:22 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::jvhrm-1771275921826-fbb7dcb15fb2'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:23 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---extras "HTTP/1.1 200 OK" +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:23 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---extras (status: 200) +2026-02-17 02:05:23 - INFO - main.py:286 - Extracted 211 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---extras, chunked into 1 segments +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'211'), (b'num_tokens', b'49'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'6a33eaf469e24a50504e580ba1290c51'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'83'), (b'date', b'Mon, 16 Feb 2026 21:05:22 GMT'), (b'x-envoy-upstream-service-time', b'40'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:23 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:23 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:23 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:23 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:22 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:24 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:24 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:24 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---extras +2026-02-17 02:05:24 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/intro... +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="intro"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:23 GMT'), (b'Etag', b'W/"60ce619c73642848382530cb8d398e72"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:23 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::r7tzd-1771275922713-f17794b346db'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:24 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/intro "HTTP/1.1 200 OK" +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:24 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/intro (status: 200) +2026-02-17 02:05:24 - INFO - main.py:286 - Extracted 1366 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/intro, chunked into 2 segments +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'1466'), (b'num_tokens', b'327'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'4682a6a9ac1309b354c0becd54cc5300'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'82'), (b'date', b'Mon, 16 Feb 2026 21:05:23 GMT'), (b'x-envoy-upstream-service-time', b'44'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:24 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:24 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:24 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:24 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:23 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:25 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:25 - INFO - main.py:215 - Upserted 2 points to collection 'book_embeddings' +2026-02-17 02:05:25 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/intro +2026-02-17 02:05:25 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2... +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="intro-to-ros2"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:23 GMT'), (b'Etag', b'W/"604912693b63cf0220e20e4e50470c82"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:23 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::szkg6-1771275923664-0c1e4932690f'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:25 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2 "HTTP/1.1 200 OK" +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:25 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2 (status: 200) +2026-02-17 02:05:25 - INFO - main.py:286 - Extracted 5451 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2, chunked into 6 segments +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'5951'), (b'num_tokens', b'1116'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'90eb9d2dccfaa4c4719c96a90fe1e098'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'81'), (b'date', b'Mon, 16 Feb 2026 21:05:24 GMT'), (b'x-envoy-upstream-service-time', b'43'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:25 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:25 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:26 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:26 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:24 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:26 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:26 - INFO - main.py:215 - Upserted 6 points to collection 'book_embeddings' +2026-02-17 02:05:26 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2 +2026-02-17 02:05:26 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/ros2-communication-model... +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="ros2-communication-model"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:25 GMT'), (b'Etag', b'W/"a7955e5214194997a9095f1aaf7ec780"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:25 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::jvhrm-1771275924819-966fc9bb7a0d'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:26 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/ros2-communication-model "HTTP/1.1 200 OK" +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:26 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:26 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/ros2-communication-model (status: 200) +2026-02-17 02:05:27 - INFO - main.py:286 - Extracted 11016 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/ros2-communication-model, chunked into 12 segments +2026-02-17 02:05:27 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:27 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:27 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:27 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:27 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:27 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'12116'), (b'num_tokens', b'3353'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'83f8aaddf1ceda54a47c25a3b8967074'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'80'), (b'date', b'Mon, 16 Feb 2026 21:05:25 GMT'), (b'x-envoy-upstream-service-time', b'87'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:27 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:27 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:27 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:27 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:27 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:27 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:27 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:27 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:27 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:27 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:27 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:27 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:28 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:26 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:28 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:28 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:28 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:28 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:28 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:28 - INFO - main.py:215 - Upserted 12 points to collection 'book_embeddings' +2026-02-17 02:05:28 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/ros2-communication-model +2026-02-17 02:05:28 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/urdf-humanoids... +2026-02-17 02:05:28 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:28 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:28 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:28 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:28 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:28 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="urdf-humanoids"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:26 GMT'), (b'Etag', b'W/"ff65675f6fe5321a09cad231c556ff53"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:26 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::n6995-1771275926453-1541ac6cdb8f'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:28 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/urdf-humanoids "HTTP/1.1 200 OK" +2026-02-17 02:05:28 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:28 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:28 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:28 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:28 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/urdf-humanoids (status: 200) +2026-02-17 02:05:29 - INFO - main.py:286 - Extracted 20102 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/urdf-humanoids, chunked into 21 segments +2026-02-17 02:05:29 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:29 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:29 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:29 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:29 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:30 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'22102'), (b'num_tokens', b'7755'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'a14b4872f9f71d79deb92b95f797314f'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'79'), (b'date', b'Mon, 16 Feb 2026 21:05:28 GMT'), (b'x-envoy-upstream-service-time', b'222'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:30 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:30 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:30 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:30 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:30 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:30 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:30 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:30 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:30 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:30 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:30 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:30 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:28 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:31 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:31 - INFO - main.py:215 - Upserted 21 points to collection 'book_embeddings' +2026-02-17 02:05:31 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/urdf-humanoids +2026-02-17 02:05:31 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/gazebo-physics-simulation... +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="gazebo-physics-simulation"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:29 GMT'), (b'Etag', b'W/"9fab23ba10d1d86e6b56422743d5c3f8"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:29 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::n6995-1771275929098-7819be9a943a'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:31 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/gazebo-physics-simulation "HTTP/1.1 200 OK" +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:31 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/gazebo-physics-simulation (status: 200) +2026-02-17 02:05:31 - INFO - main.py:286 - Extracted 11853 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/gazebo-physics-simulation, chunked into 12 segments +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:31 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:32 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'12953'), (b'num_tokens', b'3259'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'75ace3a3ffcb1381551b10fc04f3f866'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'78'), (b'date', b'Mon, 16 Feb 2026 21:05:30 GMT'), (b'x-envoy-upstream-service-time', b'232'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:32 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:32 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:32 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:32 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:32 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:32 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:32 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:32 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:32 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:32 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:32 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:32 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:30 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:33 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:33 - INFO - main.py:215 - Upserted 12 points to collection 'book_embeddings' +2026-02-17 02:05:33 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/gazebo-physics-simulation +2026-02-17 02:05:33 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/intro-to-digital-twins... +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="intro-to-digital-twins"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:31 GMT'), (b'Etag', b'W/"d0f7ecddad0f272d31c416601a7c7e71"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:31 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::5gmkn-1771275931130-ce93b5515170'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:33 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/intro-to-digital-twins "HTTP/1.1 200 OK" +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:33 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/intro-to-digital-twins (status: 200) +2026-02-17 02:05:33 - INFO - main.py:286 - Extracted 8798 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/intro-to-digital-twins, chunked into 9 segments +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:33 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'9598'), (b'num_tokens', b'1503'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'86a124a33d0bd6d9f8e8d59c2f46d6f7'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'77'), (b'date', b'Mon, 16 Feb 2026 21:05:31 GMT'), (b'x-envoy-upstream-service-time', b'60'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:34 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:34 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:34 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:31 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:34 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:34 - INFO - main.py:215 - Upserted 9 points to collection 'book_embeddings' +2026-02-17 02:05:34 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/intro-to-digital-twins +2026-02-17 02:05:34 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/unity-interaction-sensors... +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="unity-interaction-sensors"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:32 GMT'), (b'Etag', b'W/"cd026b5285a1a8ca76437e0650dfa2b6"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:32 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::5gmkn-1771275932190-b10e85f7085d'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:34 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/unity-interaction-sensors "HTTP/1.1 200 OK" +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:34 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/unity-interaction-sensors (status: 200) +2026-02-17 02:05:34 - INFO - main.py:286 - Extracted 15292 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/unity-interaction-sensors, chunked into 16 segments +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'16792'), (b'num_tokens', b'3642'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'416df57b89d2deb832675106ef74d18d'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'76'), (b'date', b'Mon, 16 Feb 2026 21:05:32 GMT'), (b'x-envoy-upstream-service-time', b'113'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:35 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:35 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:35 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:33 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:36 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:36 - INFO - main.py:215 - Upserted 16 points to collection 'book_embeddings' +2026-02-17 02:05:36 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/unity-interaction-sensors +2026-02-17 02:05:36 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/intro-to-ai-robot-brain... +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="intro-to-ai-robot-brain"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:33 GMT'), (b'Etag', b'W/"0bdf8003bcd45723c138a6a572df493e"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:33 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::x6szl-1771275933735-5695ada83a6b'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:36 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/intro-to-ai-robot-brain "HTTP/1.1 200 OK" +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:36 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/intro-to-ai-robot-brain (status: 200) +2026-02-17 02:05:36 - INFO - main.py:286 - Extracted 9559 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/intro-to-ai-robot-brain, chunked into 10 segments +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'10459'), (b'num_tokens', b'1711'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'dff7297f44c3d3423e9d2323cc285276'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'75'), (b'date', b'Mon, 16 Feb 2026 21:05:34 GMT'), (b'x-envoy-upstream-service-time', b'70'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:36 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:36 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:36 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:34 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:34 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:34 - INFO - main.py:215 - Upserted 10 points to collection 'book_embeddings' +2026-02-17 02:05:34 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/intro-to-ai-robot-brain +2026-02-17 02:05:34 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/navigation-intelligence... +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:34 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="navigation-intelligence"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:35 GMT'), (b'Etag', b'W/"098acf3cb489d192e7bfd64fe52aed0b"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:35 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::5gmkn-1771275934769-854ab8b38934'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:35 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/navigation-intelligence "HTTP/1.1 200 OK" +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:35 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/navigation-intelligence (status: 200) +2026-02-17 02:05:35 - INFO - main.py:286 - Extracted 19894 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/navigation-intelligence, chunked into 20 segments +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'21794'), (b'num_tokens', b'4356'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'eb11eca0b6a69c7c74265bc5afe1f400'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'74'), (b'date', b'Mon, 16 Feb 2026 21:05:35 GMT'), (b'x-envoy-upstream-service-time', b'143'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:35 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:35 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:35 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:35 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:36 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:36 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:36 - INFO - main.py:215 - Upserted 20 points to collection 'book_embeddings' +2026-02-17 02:05:36 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/navigation-intelligence +2026-02-17 02:05:36 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/perception-simulation-isaac-sim... +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="perception-simulation-isaac-sim"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:36 GMT'), (b'Etag', b'W/"8467cf2c7e71f6b1fffcff545d8f910a"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:36 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::dqkdf-1771275936434-05460bd451f9'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:36 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/perception-simulation-isaac-sim "HTTP/1.1 200 OK" +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:36 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:37 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:37 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:37 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/perception-simulation-isaac-sim (status: 200) +2026-02-17 02:05:37 - INFO - main.py:286 - Extracted 15042 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/perception-simulation-isaac-sim, chunked into 16 segments +2026-02-17 02:05:37 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:37 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:37 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:37 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:37 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:37 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'16542'), (b'num_tokens', b'3245'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'f8107778445a891205ffd8b0b5e923e3'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'73'), (b'date', b'Mon, 16 Feb 2026 21:05:37 GMT'), (b'x-envoy-upstream-service-time', b'110'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:37 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:37 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:37 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:37 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:37 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:37 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:37 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:37 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:37 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:37 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:37 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:37 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:38 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:37 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:38 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:38 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:38 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:38 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:38 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:38 - INFO - main.py:215 - Upserted 16 points to collection 'book_embeddings' +2026-02-17 02:05:38 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/perception-simulation-isaac-sim +2026-02-17 02:05:38 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/cognitive-planning-llms... +2026-02-17 02:05:38 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:38 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:38 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:38 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:38 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:39 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="cognitive-planning-llms"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:38 GMT'), (b'Etag', b'W/"260d0794a69f1efd0795265a05ebad57"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:38 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::5gmkn-1771275937939-2f6142fde14c'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:39 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/cognitive-planning-llms "HTTP/1.1 200 OK" +2026-02-17 02:05:39 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:39 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:39 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:39 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:39 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/cognitive-planning-llms (status: 200) +2026-02-17 02:05:39 - INFO - main.py:286 - Extracted 42175 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/cognitive-planning-llms, chunked into 43 segments +2026-02-17 02:05:39 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:39 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:39 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:39 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:39 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:40 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'35100'), (b'num_tokens', b'8690'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'34dd585c4619ce79104d2da428052c7d'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'72'), (b'date', b'Mon, 16 Feb 2026 21:05:39 GMT'), (b'x-envoy-upstream-service-time', b'126'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:40 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:40 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:40 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:40 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:40 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:40 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:40 - DEBUG - main.py:141 - Generated embeddings for batch 1/2 +2026-02-17 02:05:40 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:40 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:40 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:40 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:40 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:41 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'11275'), (b'num_tokens', b'2833'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'893a267d52df3070acd2e951c5fbbd4d'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'71'), (b'date', b'Mon, 16 Feb 2026 21:05:40 GMT'), (b'x-envoy-upstream-service-time', b'132'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:41 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:41 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:41 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:41 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:41 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:41 - DEBUG - main.py:141 - Generated embeddings for batch 2/2 +2026-02-17 02:05:41 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:41 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:41 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:41 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:41 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:41 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:42 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:42 - INFO - main.py:215 - Upserted 43 points to collection 'book_embeddings' +2026-02-17 02:05:42 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/cognitive-planning-llms +2026-02-17 02:05:42 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/documentation-standards... +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="documentation-standards"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:41 GMT'), (b'Etag', b'W/"7c70500c8aaa59ed1a830047057c0b06"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:41 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::5gmkn-1771275941558-a5d3e28720cc'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:42 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/documentation-standards "HTTP/1.1 200 OK" +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:42 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/documentation-standards (status: 200) +2026-02-17 02:05:42 - INFO - main.py:286 - Extracted 3186 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/documentation-standards, chunked into 4 segments +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'3486'), (b'num_tokens', b'585'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'b0f2e56ca9ceb3adee7129a7e2a9faff'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'70'), (b'date', b'Mon, 16 Feb 2026 21:05:42 GMT'), (b'x-envoy-upstream-service-time', b'61'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:42 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:42 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:42 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:42 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:42 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:43 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:43 - INFO - main.py:215 - Upserted 4 points to collection 'book_embeddings' +2026-02-17 02:05:43 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/documentation-standards +2026-02-17 02:05:43 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla... +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="intro-to-vla"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:42 GMT'), (b'Etag', b'W/"5fac03f0ef7f3443f3d2f50a1eb3e9e7"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:42 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::x6szl-1771275942390-7a730d6e62c7'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:43 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla "HTTP/1.1 200 OK" +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:43 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla (status: 200) +2026-02-17 02:05:43 - INFO - main.py:286 - Extracted 21624 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla, chunked into 22 segments +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'23724'), (b'num_tokens', b'4600'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'216ad6d95c5487efc42c8dfc217ccf5b'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'69'), (b'date', b'Mon, 16 Feb 2026 21:05:43 GMT'), (b'x-envoy-upstream-service-time', b'95'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:43 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:43 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:44 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:44 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:44 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:44 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:44 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:44 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:44 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:44 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:43 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:44 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:44 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:44 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:44 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:44 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:44 - INFO - main.py:215 - Upserted 22 points to collection 'book_embeddings' +2026-02-17 02:05:44 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla +2026-02-17 02:05:44 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper... +2026-02-17 02:05:44 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:44 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:44 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:44 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:44 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:45 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="voice-to-action-whisper"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:44 GMT'), (b'Etag', b'W/"81a2c8b05e0f4da5ecc0a8b60ff0ab87"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:44 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::jvhrm-1771275944113-2e57a3d7847f'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:45 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper "HTTP/1.1 200 OK" +2026-02-17 02:05:45 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:45 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:45 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:45 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:45 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper (status: 200) +2026-02-17 02:05:46 - INFO - main.py:286 - Extracted 43962 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper, chunked into 44 segments +2026-02-17 02:05:46 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:46 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:46 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:46 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:46 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:46 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'35100'), (b'num_tokens', b'9059'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'2938fe8e119d08f8c1f2a4a630f36843'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'68'), (b'date', b'Mon, 16 Feb 2026 21:05:45 GMT'), (b'x-envoy-upstream-service-time', b'292'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:46 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:46 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:46 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:46 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:46 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:47 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:47 - DEBUG - main.py:141 - Generated embeddings for batch 1/2 +2026-02-17 02:05:47 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:47 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:47 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:47 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:47 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:47 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'13162'), (b'num_tokens', b'3621'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'f91bfc7da7f37355ea4d2eb686192624'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'67'), (b'date', b'Mon, 16 Feb 2026 21:05:46 GMT'), (b'x-envoy-upstream-service-time', b'88'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:47 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:47 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:47 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:47 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:47 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:47 - DEBUG - main.py:141 - Generated embeddings for batch 2/2 +2026-02-17 02:05:47 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:47 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:47 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:47 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:47 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:47 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:48 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:48 - INFO - main.py:215 - Upserted 44 points to collection 'book_embeddings' +2026-02-17 02:05:48 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper +2026-02-17 02:05:48 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/congratulations... +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="congratulations"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:47 GMT'), (b'Etag', b'W/"cb30a80c909cc2410f23218461e6d61b"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:47 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::tf9wb-1771275947583-ed3e9eb8add8'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:48 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/congratulations "HTTP/1.1 200 OK" +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:48 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/congratulations (status: 200) +2026-02-17 02:05:48 - INFO - main.py:286 - Extracted 629 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/congratulations, chunked into 1 segments +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:48 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'629'), (b'num_tokens', b'143'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'cfaea6dc4008a76cc035f675ba0ca6d6'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'66'), (b'date', b'Mon, 16 Feb 2026 21:05:48 GMT'), (b'x-envoy-upstream-service-time', b'58'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:49 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:49 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:49 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:48 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:49 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:49 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:49 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/congratulations +2026-02-17 02:05:49 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-blog-post... +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="create-a-blog-post"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:48 GMT'), (b'Etag', b'W/"484d3df628557779592a0f5f35b5e365"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:48 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::5gmkn-1771275948381-398c4789f99e'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:49 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-blog-post "HTTP/1.1 200 OK" +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:49 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-blog-post (status: 200) +2026-02-17 02:05:49 - INFO - main.py:286 - Extracted 859 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-blog-post, chunked into 1 segments +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:49 - DEBUG - _trace.py:47 - 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_trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:48 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:50 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:50 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:50 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:50 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:50 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:50 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:50 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-blog-post +2026-02-17 02:05:50 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-document... +2026-02-17 02:05:50 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:50 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:50 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:50 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:50 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:50 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="create-a-document"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:49 GMT'), (b'Etag', b'W/"7f369efe6c6fea9c6369842195dbbf4c"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:49 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::8m5kc-1771275949148-bd9e2d46d505'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:50 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-document "HTTP/1.1 200 OK" +2026-02-17 02:05:50 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:50 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:50 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:50 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:50 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-document (status: 200) +2026-02-17 02:05:50 - INFO - main.py:286 - Extracted 934 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-document, chunked into 1 segments +2026-02-17 02:05:50 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:50 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:50 - DEBUG - _trace.py:47 - 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INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:51 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:51 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:49 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:51 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:51 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:51 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-document +2026-02-17 02:05:51 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-page... +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="create-a-page"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:50 GMT'), (b'Etag', b'W/"082fa1c6f41e951cc64491d6a31e2196"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:50 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::5gmkn-1771275950052-e38910f35bab'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:51 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-page "HTTP/1.1 200 OK" +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:51 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-page (status: 200) +2026-02-17 02:05:51 - INFO - main.py:286 - Extracted 940 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-page, chunked into 1 segments +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:51 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'940'), (b'num_tokens', b'292'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'196c1a1ea03fcc7340875cf4b91fc7cb'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'63'), (b'date', b'Mon, 16 Feb 2026 21:05:50 GMT'), (b'x-envoy-upstream-service-time', b'105'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:52 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:52 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:52 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:50 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:52 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:52 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:52 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-page +2026-02-17 02:05:52 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/deploy-your-site... +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="deploy-your-site"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:51 GMT'), (b'Etag', b'W/"665a6b003a0b5733ed744800c95cf8f6"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:51 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::5gmkn-1771275951030-e9d0e8faf093'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:52 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/deploy-your-site "HTTP/1.1 200 OK" +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:52 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/deploy-your-site (status: 200) +2026-02-17 02:05:52 - INFO - main.py:286 - Extracted 610 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/deploy-your-site, chunked into 1 segments +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:52 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'610'), (b'num_tokens', b'135'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'b221bcd4699e766fa34e29fd0183acdb'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'62'), (b'date', b'Mon, 16 Feb 2026 21:05:51 GMT'), (b'x-envoy-upstream-service-time', b'63'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:53 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:53 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:53 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:51 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:53 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:53 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:53 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/deploy-your-site +2026-02-17 02:05:53 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/markdown-features... +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="markdown-features"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:52 GMT'), (b'Etag', b'W/"3399b78094abd114027b6821b7dee14e"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:52 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::tf9wb-1771275951981-e72988651c5c'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:53 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/markdown-features "HTTP/1.1 200 OK" +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:53 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:53 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/markdown-features (status: 200) +2026-02-17 02:05:54 - INFO - main.py:286 - Extracted 2311 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/markdown-features, chunked into 3 segments +2026-02-17 02:05:54 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:54 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:54 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:54 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:54 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:54 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'2511'), (b'num_tokens', b'693'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'3ac5323dd96505940eab9f8beb760958'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'61'), (b'date', b'Mon, 16 Feb 2026 21:05:52 GMT'), (b'x-envoy-upstream-service-time', b'161'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:54 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:54 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:54 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:54 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:54 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:54 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:54 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:54 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:54 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:54 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:54 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:54 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:53 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:55 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:55 - INFO - main.py:215 - Upserted 3 points to collection 'book_embeddings' +2026-02-17 02:05:55 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/markdown-features +2026-02-17 02:05:55 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/manage-docs-versions... +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="manage-docs-versions"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:53 GMT'), (b'Etag', b'W/"a7ef0b4192770c4b066e948cb843d024"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:53 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::5gmkn-1771275953451-f4b956b3d636'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:55 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/manage-docs-versions "HTTP/1.1 200 OK" +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:55 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/manage-docs-versions (status: 200) +2026-02-17 02:05:55 - INFO - main.py:286 - Extracted 1104 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/manage-docs-versions, chunked into 2 segments +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'1204'), (b'num_tokens', b'358'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'b9722d5d18a97df6353ce53cf09ff005'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'60'), (b'date', b'Mon, 16 Feb 2026 21:05:53 GMT'), (b'x-envoy-upstream-service-time', b'43'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:55 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:55 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:55 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:55 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:54 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:56 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:56 - INFO - main.py:215 - Upserted 2 points to collection 'book_embeddings' +2026-02-17 02:05:56 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/manage-docs-versions +2026-02-17 02:05:56 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/translate-your-site... +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'0'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="translate-your-site"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:54 GMT'), (b'Etag', b'W/"b619aa9a2c7cfc8e34cbc506ab403277"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:54 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::5gmkn-1771275954384-321550d92caf'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:56 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/translate-your-site "HTTP/1.1 200 OK" +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:56 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/translate-your-site (status: 200) +2026-02-17 02:05:56 - INFO - main.py:286 - Extracted 1418 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/translate-your-site, chunked into 2 segments +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'1518'), (b'num_tokens', b'463'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'77a36bc3d47eda382a7d98533da4775f'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'59'), (b'date', b'Mon, 16 Feb 2026 21:05:54 GMT'), (b'x-envoy-upstream-service-time', b'50'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:56 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:56 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:56 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:56 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:54 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:57 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:57 - INFO - main.py:215 - Upserted 2 points to collection 'book_embeddings' +2026-02-17 02:05:57 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/translate-your-site +2026-02-17 02:05:57 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/... +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'1024106'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:05:55 GMT'), (b'Etag', b'W/"dee9f6103f913b331d72f5cbcb592db9"'), (b'Last-Modified', b'Thu, 05 Feb 2026 00:37:28 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::n6995-1771275955326-7b8c793bef8e'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:57 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/ "HTTP/1.1 200 OK" +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:57 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/ (status: 200) +2026-02-17 02:05:57 - INFO - main.py:286 - Extracted 91 characters from https://humanoid-ai-robotics-book-1.vercel.app/, chunked into 1 segments +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'91'), (b'num_tokens', b'19'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'0ec9b193ee62eac0961e178e7dc22deb'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'58'), (b'date', b'Mon, 16 Feb 2026 21:05:55 GMT'), (b'x-envoy-upstream-service-time', b'434'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:57 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:57 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:05:57 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:57 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:56 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:58 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:58 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:05:58 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/ +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:05:58 - INFO - main.py:323 - ================================================== +2026-02-17 02:05:58 - INFO - main.py:324 - Ingestion complete! +2026-02-17 02:05:58 - INFO - main.py:325 - Total pages processed: 40 +2026-02-17 02:05:58 - INFO - main.py:326 - Total chunks stored: 270 +2026-02-17 02:05:58 - INFO - main.py:329 - ================================================== +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:05:56 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:05:58 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:05:58 - INFO - main.py:334 - Qdrant collection 'book_embeddings' now has 273 points +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:05:58 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:06:30 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:06:32 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:06:32 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:06:30 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:06:33 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:06:33 - INFO - main.py:172 - Collection 'book_embeddings' already exists +2026-02-17 02:06:33 - INFO - main.py:346 - ================================================== +2026-02-17 02:06:33 - INFO - main.py:347 - Running validation mode... +2026-02-17 02:06:33 - INFO - main.py:348 - ================================================== +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:06:30 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:06:33 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:06:33 - INFO - main.py:356 - Collection: book_embeddings +2026-02-17 02:06:33 - INFO - main.py:357 - Total points: 273 +2026-02-17 02:06:33 - INFO - main.py:358 - Vector size: 1024 +2026-02-17 02:06:33 - INFO - main.py:364 - ✅ Vector dimension correct (1024) +2026-02-17 02:06:33 - INFO - main.py:369 - Sampling 20 points for validation... +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:06:31 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:06:33 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/scroll "HTTP/1.1 200 OK" +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:06:33 - INFO - main.py:404 - Metadata completeness: +2026-02-17 02:06:33 - INFO - main.py:405 - URL present: 20/20 (100.0%) +2026-02-17 02:06:33 - INFO - main.py:406 - Title/Section present: 20/20 (100.0%) +2026-02-17 02:06:33 - INFO - main.py:407 - Text non-empty (≥10 chars): 20/20 (100.0%) +2026-02-17 02:06:33 - INFO - main.py:411 - ✅ URL completeness excellent (≥99%) +2026-02-17 02:06:33 - INFO - main.py:416 - ✅ Title/Section completeness good (≥95%) +2026-02-17 02:06:33 - INFO - main.py:421 - ✅ Text quality excellent (≥98%) +2026-02-17 02:06:33 - INFO - main.py:425 - ================================================== +2026-02-17 02:06:33 - INFO - main.py:426 - Validation complete! +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:06:33 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:08:44 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:08:47 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:08:47 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:08:48 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:08:48 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:08:48 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:08:48 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:08:48 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:08:48 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:08:48 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:08:45 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:08:48 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 02:08:48 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:08:48 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:08:48 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:08:48 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:08:48 - INFO - main.py:172 - Collection 'book_embeddings' already exists +2026-02-17 02:08:49 - INFO - main.py:267 - Starting ingestion: 1 URLs +2026-02-17 02:08:49 - INFO - main.py:268 - Chunk size: 1000, overlap: 100 +2026-02-17 02:08:49 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/sitemap.xml... +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - connect_tcp.started host='humanoid-ai-robotics-book-1.vercel.app' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='humanoid-ai-robotics-book-1.vercel.app' timeout=30.0 +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'171074'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="sitemap.xml"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/xml'), (b'Date', b'Mon, 16 Feb 2026 21:08:47 GMT'), (b'Etag', b'W/"14bc457b2f4f4cb50a4a119c8acddf82"'), (b'Last-Modified', b'Sat, 14 Feb 2026 21:37:32 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::8m5kc-1771276127034-10353849cf03'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:08:49 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/sitemap.xml "HTTP/1.1 200 OK" +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:08:49 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/sitemap.xml (status: 200) +2026-02-17 02:08:49 - INFO - main.py:286 - Extracted 3446 characters from https://humanoid-ai-robotics-book-1.vercel.app/sitemap.xml, chunked into 4 segments +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:08:49 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'3746'), (b'num_tokens', b'1458'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'1f74351be5c8ea949f610bf295d93454'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 21:08:47 GMT'), (b'x-envoy-upstream-service-time', b'56'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:08:50 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:08:50 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:08:50 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:08:47 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:08:50 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:08:50 - INFO - main.py:215 - Upserted 4 points to collection 'book_embeddings' +2026-02-17 02:08:50 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/sitemap.xml +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:08:50 - INFO - main.py:323 - ================================================== +2026-02-17 02:08:50 - INFO - main.py:324 - Ingestion complete! +2026-02-17 02:08:50 - INFO - main.py:325 - Total pages processed: 1 +2026-02-17 02:08:50 - INFO - main.py:326 - Total chunks stored: 4 +2026-02-17 02:08:50 - INFO - main.py:329 - ================================================== +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:08:47 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:08:50 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:08:50 - INFO - main.py:334 - Qdrant collection 'book_embeddings' now has 277 points +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:08:50 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:09:55 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:09:55 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:09:55 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:09:55 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:09:55 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:09:55 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:09:55 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:09:55 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:09:55 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:09:52 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:09:56 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:09:53 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:09:56 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:09:56 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:09:56 - INFO - main.py:172 - Collection 'book_embeddings' already exists +2026-02-17 02:09:57 - INFO - main.py:267 - Starting ingestion: 40 URLs +2026-02-17 02:09:57 - INFO - main.py:268 - Chunk size: 1000, overlap: 100 +2026-02-17 02:09:57 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog... +2026-02-17 02:09:57 - DEBUG - _trace.py:47 - connect_tcp.started host='humanoid-ai-robotics-book-1.vercel.app' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='humanoid-ai-robotics-book-1.vercel.app' timeout=30.0 +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'288'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="blog"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:09:55 GMT'), (b'Etag', b'W/"b11ea0ccd90cc7a5b08cfa9a05f4c67b"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:06 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::btw2w-1771276195291-0447113b0de3'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:09:58 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog "HTTP/1.1 200 OK" +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:09:58 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog (status: 200) +2026-02-17 02:09:58 - INFO - main.py:286 - Extracted 436 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog, chunked into 1 segments +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'436'), (b'num_tokens', b'109'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'd37bf783dd1aa1cf78440affa8857ee5'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 21:09:55 GMT'), (b'x-envoy-upstream-service-time', b'44'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:09:58 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:09:58 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:09:58 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:09:55 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:09:58 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:09:58 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:09:58 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog +2026-02-17 02:09:58 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/archive... +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'287'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="archive"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:09:55 GMT'), (b'Etag', b'W/"5ca63baec719320b86e8372e9d81c8e6"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:08 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::btw2w-1771276195944-a28ff6490cde'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:09:58 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/archive "HTTP/1.1 200 OK" +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:09:58 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/archive (status: 200) +2026-02-17 02:09:58 - INFO - main.py:286 - Extracted 167 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/archive, chunked into 1 segments +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:09:58 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'167'), (b'num_tokens', b'37'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'b1e3d8acc66726eee14cd065a6112512'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'98'), (b'date', b'Mon, 16 Feb 2026 21:09:56 GMT'), (b'x-envoy-upstream-service-time', b'40'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:09:59 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:09:59 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:09:59 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:09:56 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:09:59 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:09:59 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:09:59 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/archive +2026-02-17 02:09:59 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/authors... +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'287'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="authors"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:09:56 GMT'), (b'Etag', b'W/"2808bae6c95d00f7a1687991ec4be358"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:08 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::btw2w-1771276196465-daff54b51c82'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:09:59 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/authors "HTTP/1.1 200 OK" +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:09:59 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/authors (status: 200) +2026-02-17 02:09:59 - INFO - main.py:286 - Extracted 172 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/authors, chunked into 1 segments +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'172'), (b'num_tokens', b'39'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'cc7acd2a1c203b7a0eeedf78057561f4'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'97'), (b'date', b'Mon, 16 Feb 2026 21:09:56 GMT'), (b'x-envoy-upstream-service-time', b'40'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:09:59 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:09:59 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:09:59 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:09:56 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:09:59 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:09:59 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:09:59 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/authors +2026-02-17 02:09:59 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/authors/all-sebastien-lorber-articles... +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'287'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="all-sebastien-lorber-articles"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:09:57 GMT'), (b'Etag', b'W/"b1f1e29206f19daa2b75bff6010d4aa3"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:09 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::vbvkz-1771276196998-28121e2ea12f'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:09:59 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/authors/all-sebastien-lorber-articles "HTTP/1.1 200 OK" +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:09:59 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/authors/all-sebastien-lorber-articles (status: 200) +2026-02-17 02:09:59 - INFO - main.py:286 - Extracted 338 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/authors/all-sebastien-lorber-articles, chunked into 1 segments +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:09:59 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'338'), (b'num_tokens', b'80'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'18d53bb7090ca3972fd6fa36d55ed4ed'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'96'), (b'date', b'Mon, 16 Feb 2026 21:09:57 GMT'), (b'x-envoy-upstream-service-time', b'49'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:00 - 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Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/authors/all-sebastien-lorber-articles +2026-02-17 02:10:00 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/authors/yangshun... +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'286'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="yangshun"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:09:57 GMT'), (b'Etag', b'W/"5aaa188ceb7b0781b84c87c9252a5f97"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:10 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::fhwjw-1771276197555-b39c04f238f7'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:00 - 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Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/authors/yangshun +2026-02-17 02:10:00 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/first-blog-post... +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'286'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="first-blog-post"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:09:58 GMT'), (b'Etag', b'W/"8c84c989186e2f01ade6432287b045bb"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:11 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::p4qlm-1771276198086-38b7c56481bd'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:00 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/first-blog-post "HTTP/1.1 200 OK" +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:00 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/first-blog-post (status: 200) +2026-02-17 02:10:00 - INFO - main.py:286 - Extracted 251 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/first-blog-post, chunked into 1 segments +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:00 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'251'), (b'num_tokens', b'83'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'e5f38e3326d1f4866a68430634c88617'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'94'), (b'date', b'Mon, 16 Feb 2026 21:09:58 GMT'), (b'x-envoy-upstream-service-time', b'44'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:01 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:01 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:01 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:09:58 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:01 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:01 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:10:01 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/first-blog-post +2026-02-17 02:10:01 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/long-blog-post... +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'285'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="long-blog-post"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:09:58 GMT'), (b'Etag', b'W/"e2bf5c2f326a3e28d0c79c489d79b324"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:12 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::n6995-1771276198672-a6b513863aec'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:01 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/long-blog-post "HTTP/1.1 200 OK" +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:01 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/long-blog-post (status: 200) +2026-02-17 02:10:01 - INFO - main.py:286 - Extracted 3050 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/long-blog-post, chunked into 4 segments +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'3350'), (b'num_tokens', b'1178'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'97b26a200acc172c48a1de0d1e4449e0'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'93'), (b'date', b'Mon, 16 Feb 2026 21:09:58 GMT'), (b'x-envoy-upstream-service-time', b'57'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:01 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:01 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:01 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:01 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:09:58 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:02 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:02 - INFO - main.py:215 - Upserted 4 points to collection 'book_embeddings' +2026-02-17 02:10:02 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/long-blog-post +2026-02-17 02:10:02 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/mdx-blog-post... +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'285'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="mdx-blog-post"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:09:59 GMT'), (b'Etag', b'W/"cb4f3f390e12473355dd331758b886ba"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:13 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::hc774-1771276199332-5cb371d5e03f'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:02 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/mdx-blog-post "HTTP/1.1 200 OK" +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:02 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/mdx-blog-post (status: 200) +2026-02-17 02:10:02 - INFO - main.py:286 - Extracted 338 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/mdx-blog-post, chunked into 1 segments +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'338'), (b'num_tokens', b'86'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'ff47556bb15ba2848e732973506d4b4f'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'92'), (b'date', b'Mon, 16 Feb 2026 21:09:59 GMT'), (b'x-envoy-upstream-service-time', b'43'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:02 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:02 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:02 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:09:59 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:02 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:02 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:10:02 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/mdx-blog-post +2026-02-17 02:10:02 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/tags... +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'285'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="tags"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:09:59 GMT'), (b'Etag', b'W/"90833a1c30a096febd0223e56ad4fcab"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:14 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::p4qlm-1771276199883-faa7f2392067'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:02 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/tags "HTTP/1.1 200 OK" +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:02 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/tags (status: 200) +2026-02-17 02:10:02 - INFO - main.py:286 - Extracted 112 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/tags, chunked into 1 segments +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'112'), (b'num_tokens', b'27'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'970eee751c447e8c4349d67d5e19ff02'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'91'), (b'date', b'Mon, 16 Feb 2026 21:10:00 GMT'), (b'x-envoy-upstream-service-time', b'38'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:02 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:02 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:03 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:03 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:00 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:03 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:03 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:10:03 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/tags +2026-02-17 02:10:03 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/docusaurus... +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'284'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="docusaurus"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:00 GMT'), (b'Etag', b'W/"2ff33e4e090bbccbdd3fd854a7152a62"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:15 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::hc774-1771276200417-b93ba1ba6c20'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:03 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/docusaurus "HTTP/1.1 200 OK" +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:03 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/docusaurus (status: 200) +2026-02-17 02:10:03 - INFO - main.py:286 - Extracted 464 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/docusaurus, chunked into 1 segments +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'464'), (b'num_tokens', b'117'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'ba3796e6bacb609807f869d2bd3c11d0'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'90'), (b'date', b'Mon, 16 Feb 2026 21:10:00 GMT'), (b'x-envoy-upstream-service-time', b'61'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:03 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:03 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:03 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:00 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:03 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:03 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:10:03 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/docusaurus +2026-02-17 02:10:03 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/facebook... +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'284'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="facebook"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:00 GMT'), (b'Etag', b'W/"955ee0172bb1f392948cb22d3adc3ead"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:16 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::btw2w-1771276200994-8098fef2c8f4'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:03 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/facebook "HTTP/1.1 200 OK" +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:03 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/facebook (status: 200) +2026-02-17 02:10:03 - INFO - main.py:286 - Extracted 188 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/facebook, chunked into 1 segments +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:03 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'188'), (b'num_tokens', b'42'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'2a0b84aacb4d50fe3382ae5265e47f39'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'89'), (b'date', b'Mon, 16 Feb 2026 21:10:01 GMT'), (b'x-envoy-upstream-service-time', b'38'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:04 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:04 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:04 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:01 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:04 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:04 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:10:04 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/facebook +2026-02-17 02:10:04 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hello... +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'284'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="hello"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:01 GMT'), (b'Etag', b'W/"631b5c3dfc8f0aac36813c6d884d5fdd"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:17 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::btw2w-1771276201535-c3651c4e1ebe'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:04 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hello "HTTP/1.1 200 OK" +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:04 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hello (status: 200) +2026-02-17 02:10:04 - INFO - main.py:286 - Extracted 304 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hello, chunked into 1 segments +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'304'), (b'num_tokens', b'76'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'08ebddba1f13541bb18fbe6fde15d7ee'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'88'), (b'date', b'Mon, 16 Feb 2026 21:10:01 GMT'), (b'x-envoy-upstream-service-time', b'43'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:04 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:04 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:04 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:01 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:04 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:04 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:10:04 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hello +2026-02-17 02:10:04 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hola... +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'283'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="hola"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:02 GMT'), (b'Etag', b'W/"0c633b539935783f821f71bd789779f9"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:18 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::n6995-1771276202091-c085454157d0'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:04 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hola "HTTP/1.1 200 OK" +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:04 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hola (status: 200) +2026-02-17 02:10:04 - INFO - main.py:286 - Extracted 109 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hola, chunked into 1 segments +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:04 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'109'), (b'num_tokens', b'31'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'140a5503fe9130cbf079c90739eaf1da'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'87'), (b'date', b'Mon, 16 Feb 2026 21:10:02 GMT'), (b'x-envoy-upstream-service-time', b'43'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:05 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:05 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:05 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:02 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:05 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:05 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:10:05 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hola +2026-02-17 02:10:05 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/blog/welcome... +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'283'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="welcome"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:02 GMT'), (b'Etag', b'W/"d831e219f3b1e77fa822803a19d0f9ba"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:19 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::n6995-1771276202621-6528455e408e'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:05 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/blog/welcome "HTTP/1.1 200 OK" +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:05 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/blog/welcome (status: 200) +2026-02-17 02:10:05 - INFO - main.py:286 - Extracted 594 characters from https://humanoid-ai-robotics-book-1.vercel.app/blog/welcome, chunked into 1 segments +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'594'), (b'num_tokens', b'150'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'4e6c52e242550f8d95b8fda918c66f97'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'86'), (b'date', b'Mon, 16 Feb 2026 21:10:02 GMT'), (b'x-envoy-upstream-service-time', b'37'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:05 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:05 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:05 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:02 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:05 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:05 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:10:05 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/blog/welcome +2026-02-17 02:10:05 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/markdown-page... +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'282'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="markdown-page"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:03 GMT'), (b'Etag', b'W/"8aa79f038d4ab5706b153c31f38e7af6"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:20 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::btw2w-1771276203141-1c4704259907'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:05 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/markdown-page "HTTP/1.1 200 OK" +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:05 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/markdown-page (status: 200) +2026-02-17 02:10:05 - INFO - main.py:286 - Extracted 128 characters from https://humanoid-ai-robotics-book-1.vercel.app/markdown-page, chunked into 1 segments +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:05 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'128'), (b'num_tokens', b'27'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'613c567a16771779faa9cd81c6da80c5'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'85'), (b'date', b'Mon, 16 Feb 2026 21:10:03 GMT'), (b'x-envoy-upstream-service-time', b'42'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:06 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:06 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:06 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:03 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:06 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:06 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:10:06 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/markdown-page +2026-02-17 02:10:06 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---basics... +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'282'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="tutorial---basics"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:03 GMT'), (b'Etag', b'W/"4f3a2c4b623ca5e5d229d425b962db57"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:21 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::fhwjw-1771276203671-ebb47e360a08'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:06 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---basics "HTTP/1.1 200 OK" +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:06 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---basics (status: 200) +2026-02-17 02:10:06 - INFO - main.py:286 - Extracted 628 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---basics, chunked into 1 segments +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'628'), (b'num_tokens', b'142'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'd0a9a693434999ac292397724c58eef9'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'84'), (b'date', b'Mon, 16 Feb 2026 21:10:03 GMT'), (b'x-envoy-upstream-service-time', b'47'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:06 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:06 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:06 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:06 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:03 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:07 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:07 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:10:07 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---basics +2026-02-17 02:10:07 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---extras... +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'282'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="tutorial---extras"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:04 GMT'), (b'Etag', b'W/"bb4cb95d3bd2857913d7c7c444f50a7e"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:22 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::hc774-1771276204258-bbe722a0aa4a'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:07 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---extras "HTTP/1.1 200 OK" +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:07 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---extras (status: 200) +2026-02-17 02:10:07 - INFO - main.py:286 - Extracted 211 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---extras, chunked into 1 segments +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:07 - 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Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---extras +2026-02-17 02:10:07 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/intro... +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'281'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="intro"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:04 GMT'), (b'Etag', b'W/"60ce619c73642848382530cb8d398e72"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:23 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::fhwjw-1771276204789-20bf04f4fbb4'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:07 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/intro "HTTP/1.1 200 OK" +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:07 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/intro (status: 200) +2026-02-17 02:10:07 - INFO - main.py:286 - Extracted 1366 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/intro, chunked into 2 segments +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'1466'), (b'num_tokens', b'327'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'2f66cb61a5c5da822ff238946a8c6f30'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'82'), (b'date', b'Mon, 16 Feb 2026 21:10:05 GMT'), (b'x-envoy-upstream-service-time', b'49'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:07 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:07 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:07 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:07 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:08 - DEBUG - 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Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/intro +2026-02-17 02:10:08 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2... +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'281'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="intro-to-ros2"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:05 GMT'), (b'Etag', b'W/"604912693b63cf0220e20e4e50470c82"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:23 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::sxsj6-1771276205342-5e4bf1c3290a'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:08 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2 "HTTP/1.1 200 OK" +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:08 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2 (status: 200) +2026-02-17 02:10:08 - INFO - main.py:286 - Extracted 5451 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2, chunked into 6 segments +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'5951'), (b'num_tokens', b'1116'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'ccdcffda874c7cf12e2d7af003ba12c4'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'81'), (b'date', b'Mon, 16 Feb 2026 21:10:05 GMT'), (b'x-envoy-upstream-service-time', b'52'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:08 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:08 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:08 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:05 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:08 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:08 - INFO - main.py:215 - Upserted 6 points to collection 'book_embeddings' +2026-02-17 02:10:08 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2 +2026-02-17 02:10:08 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/ros2-communication-model... +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'280'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="ros2-communication-model"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:06 GMT'), (b'Etag', b'W/"a7955e5214194997a9095f1aaf7ec780"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:25 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::hc774-1771276206110-b2f970ba0eb1'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:08 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/ros2-communication-model "HTTP/1.1 200 OK" +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:08 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:08 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/ros2-communication-model (status: 200) +2026-02-17 02:10:09 - INFO - main.py:286 - Extracted 11016 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/ros2-communication-model, chunked into 12 segments +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'12116'), (b'num_tokens', b'3353'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'2a764fb2ddfe823c2806dcc0210c6dda'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'80'), (b'date', b'Mon, 16 Feb 2026 21:10:06 GMT'), (b'x-envoy-upstream-service-time', b'92'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:09 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:09 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:09 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:06 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:09 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:09 - INFO - main.py:215 - Upserted 12 points to collection 'book_embeddings' +2026-02-17 02:10:09 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/ros2-communication-model +2026-02-17 02:10:09 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/urdf-humanoids... +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'280'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="urdf-humanoids"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:07 GMT'), (b'Etag', b'W/"ff65675f6fe5321a09cad231c556ff53"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:26 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::sxsj6-1771276207091-a6fdd78bdde1'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:09 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/urdf-humanoids "HTTP/1.1 200 OK" +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:09 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:09 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/urdf-humanoids (status: 200) +2026-02-17 02:10:10 - INFO - main.py:286 - Extracted 20102 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/urdf-humanoids, chunked into 21 segments +2026-02-17 02:10:10 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:10 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:10 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:10 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:10 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:10 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'22102'), (b'num_tokens', b'7755'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'29db6af9ecdb7ee6ff831616e6f52471'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'79'), (b'date', b'Mon, 16 Feb 2026 21:10:07 GMT'), (b'x-envoy-upstream-service-time', b'169'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:10 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:10 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:10 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:10 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:10 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:10 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:10 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:10 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:10 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:10 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:08 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:11 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:11 - INFO - main.py:215 - Upserted 21 points to collection 'book_embeddings' +2026-02-17 02:10:11 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/urdf-humanoids +2026-02-17 02:10:11 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/gazebo-physics-simulation... +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'279'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="gazebo-physics-simulation"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:08 GMT'), (b'Etag', b'W/"9fab23ba10d1d86e6b56422743d5c3f8"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:29 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::dxvkw-1771276208528-02591154fbd2'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:11 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/gazebo-physics-simulation "HTTP/1.1 200 OK" +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:11 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/gazebo-physics-simulation (status: 200) +2026-02-17 02:10:11 - INFO - main.py:286 - Extracted 11853 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/gazebo-physics-simulation, chunked into 12 segments +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'12953'), (b'num_tokens', b'3259'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'2a9fcb8a72e860beaeef4d99d5b7d356'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'78'), (b'date', b'Mon, 16 Feb 2026 21:10:08 GMT'), (b'x-envoy-upstream-service-time', b'77'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:11 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:11 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:11 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:11 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:08 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:12 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:12 - INFO - main.py:215 - Upserted 12 points to collection 'book_embeddings' +2026-02-17 02:10:12 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/gazebo-physics-simulation +2026-02-17 02:10:12 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/intro-to-digital-twins... +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'277'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="intro-to-digital-twins"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:09 GMT'), (b'Etag', b'W/"d0f7ecddad0f272d31c416601a7c7e71"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:31 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::fhwjw-1771276209338-d6d1082f8883'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:12 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/intro-to-digital-twins "HTTP/1.1 200 OK" +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:12 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/intro-to-digital-twins (status: 200) +2026-02-17 02:10:12 - INFO - main.py:286 - Extracted 8798 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/intro-to-digital-twins, chunked into 9 segments +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'9598'), (b'num_tokens', b'1503'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'3372d412fe89213db6c0c6f2a044412b'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'77'), (b'date', b'Mon, 16 Feb 2026 21:10:09 GMT'), (b'x-envoy-upstream-service-time', b'51'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:12 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:12 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:12 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:09 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:12 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:12 - INFO - main.py:215 - Upserted 9 points to collection 'book_embeddings' +2026-02-17 02:10:12 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/intro-to-digital-twins +2026-02-17 02:10:12 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/unity-interaction-sensors... +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'277'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="unity-interaction-sensors"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:09 GMT'), (b'Etag', b'W/"cd026b5285a1a8ca76437e0650dfa2b6"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:32 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::sxsj6-1771276209995-5ed99ac97cec'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:12 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/unity-interaction-sensors "HTTP/1.1 200 OK" +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:12 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/unity-interaction-sensors (status: 200) +2026-02-17 02:10:12 - INFO - main.py:286 - Extracted 15292 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/unity-interaction-sensors, chunked into 16 segments +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:12 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'16792'), (b'num_tokens', b'3642'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'730ad50c7111c354b15cc6fa4acfec6e'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'76'), (b'date', b'Mon, 16 Feb 2026 21:10:10 GMT'), (b'x-envoy-upstream-service-time', b'90'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:13 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:13 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:13 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:10 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:13 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:13 - INFO - main.py:215 - Upserted 16 points to collection 'book_embeddings' +2026-02-17 02:10:13 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/unity-interaction-sensors +2026-02-17 02:10:13 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/intro-to-ai-robot-brain... +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'276'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="intro-to-ai-robot-brain"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:10 GMT'), (b'Etag', b'W/"0bdf8003bcd45723c138a6a572df493e"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:33 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::fhwjw-1771276210805-67868b802f73'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:13 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/intro-to-ai-robot-brain "HTTP/1.1 200 OK" +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:13 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/intro-to-ai-robot-brain (status: 200) +2026-02-17 02:10:13 - INFO - main.py:286 - Extracted 9559 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/intro-to-ai-robot-brain, chunked into 10 segments +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'10459'), (b'num_tokens', b'1711'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'b847865ad82a352191932595d274b20a'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'75'), (b'date', b'Mon, 16 Feb 2026 21:10:11 GMT'), (b'x-envoy-upstream-service-time', b'66'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:13 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:13 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:14 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:14 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:11 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:14 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:14 - INFO - main.py:215 - Upserted 10 points to collection 'book_embeddings' +2026-02-17 02:10:14 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/intro-to-ai-robot-brain +2026-02-17 02:10:14 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/navigation-intelligence... +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'276'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="navigation-intelligence"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:11 GMT'), (b'Etag', b'W/"098acf3cb489d192e7bfd64fe52aed0b"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:35 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::n6995-1771276211503-6ee301cdb718'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:14 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/navigation-intelligence "HTTP/1.1 200 OK" +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:14 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/navigation-intelligence (status: 200) +2026-02-17 02:10:14 - INFO - main.py:286 - Extracted 19894 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/navigation-intelligence, chunked into 20 segments +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'21794'), (b'num_tokens', b'4356'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'7486686b0294b2c51ea2b0f830c45fb7'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'74'), (b'date', b'Mon, 16 Feb 2026 21:10:11 GMT'), (b'x-envoy-upstream-service-time', b'106'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:14 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:14 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:14 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:14 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:12 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:15 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:15 - INFO - main.py:215 - Upserted 20 points to collection 'book_embeddings' +2026-02-17 02:10:15 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/navigation-intelligence +2026-02-17 02:10:15 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/perception-simulation-isaac-sim... +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'275'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="perception-simulation-isaac-sim"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:12 GMT'), (b'Etag', b'W/"8467cf2c7e71f6b1fffcff545d8f910a"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:36 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::btw2w-1771276212405-a4c11250a4a7'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:15 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/perception-simulation-isaac-sim "HTTP/1.1 200 OK" +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:15 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/perception-simulation-isaac-sim (status: 200) +2026-02-17 02:10:15 - INFO - main.py:286 - Extracted 15042 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/perception-simulation-isaac-sim, chunked into 16 segments +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'16542'), (b'num_tokens', b'3245'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'2f1069f4b217666a2321344903dbe214'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'73'), (b'date', b'Mon, 16 Feb 2026 21:10:12 GMT'), (b'x-envoy-upstream-service-time', b'102'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:15 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:15 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:15 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:12 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:15 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:15 - INFO - main.py:215 - Upserted 16 points to collection 'book_embeddings' +2026-02-17 02:10:15 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/perception-simulation-isaac-sim +2026-02-17 02:10:15 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/cognitive-planning-llms... +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:15 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:16 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'274'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="cognitive-planning-llms"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:13 GMT'), (b'Etag', b'W/"260d0794a69f1efd0795265a05ebad57"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:38 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::fhwjw-1771276213215-b415b4879b8b'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:16 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/cognitive-planning-llms "HTTP/1.1 200 OK" +2026-02-17 02:10:16 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:16 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:16 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:16 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:16 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/cognitive-planning-llms (status: 200) +2026-02-17 02:10:16 - INFO - main.py:286 - Extracted 42175 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/cognitive-planning-llms, chunked into 43 segments +2026-02-17 02:10:16 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:16 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:16 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:16 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:16 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:16 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'46375'), (b'num_tokens', b'11523'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'6717c9c0f3d636e3124f20de0b4fbf9b'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'72'), (b'date', b'Mon, 16 Feb 2026 21:10:14 GMT'), (b'x-envoy-upstream-service-time', b'171'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:16 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:16 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:17 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:17 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:14 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:17 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:17 - INFO - main.py:215 - Upserted 43 points to collection 'book_embeddings' +2026-02-17 02:10:17 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/cognitive-planning-llms +2026-02-17 02:10:17 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/documentation-standards... +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'273'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="documentation-standards"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:14 GMT'), (b'Etag', b'W/"7c70500c8aaa59ed1a830047057c0b06"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:41 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::hc774-1771276214845-abcb8ccaff88'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:17 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/documentation-standards "HTTP/1.1 200 OK" +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:17 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/documentation-standards (status: 200) +2026-02-17 02:10:17 - INFO - main.py:286 - Extracted 3186 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/documentation-standards, chunked into 4 segments +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'3486'), (b'num_tokens', b'585'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'0b1954b1bf36e73299f68e49c64347e3'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'71'), (b'date', b'Mon, 16 Feb 2026 21:10:15 GMT'), (b'x-envoy-upstream-service-time', b'48'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:17 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:17 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:18 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:18 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:15 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:18 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:18 - INFO - main.py:215 - Upserted 4 points to collection 'book_embeddings' +2026-02-17 02:10:18 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/documentation-standards +2026-02-17 02:10:18 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla... +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'272'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="intro-to-vla"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:15 GMT'), (b'Etag', b'W/"5fac03f0ef7f3443f3d2f50a1eb3e9e7"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:42 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::btw2w-1771276215420-4bea91eb3e15'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:18 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla "HTTP/1.1 200 OK" +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:18 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla (status: 200) +2026-02-17 02:10:18 - INFO - main.py:286 - Extracted 21624 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla, chunked into 22 segments +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'23724'), (b'num_tokens', b'4600'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'0f9559faed8af748d1e1c4edf2a52f67'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'70'), (b'date', b'Mon, 16 Feb 2026 21:10:15 GMT'), (b'x-envoy-upstream-service-time', b'97'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:18 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:18 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:18 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:18 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:15 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:19 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:19 - INFO - main.py:215 - Upserted 22 points to collection 'book_embeddings' +2026-02-17 02:10:19 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla +2026-02-17 02:10:19 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper... +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'271'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="voice-to-action-whisper"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:16 GMT'), (b'Etag', b'W/"81a2c8b05e0f4da5ecc0a8b60ff0ab87"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:44 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::btw2w-1771276216355-683161f55c5c'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:19 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper "HTTP/1.1 200 OK" +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:19 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper (status: 200) +2026-02-17 02:10:19 - INFO - main.py:286 - Extracted 43962 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper, chunked into 44 segments +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:19 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'48262'), (b'num_tokens', b'12680'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'94cd50bfbc8554ee155e2bd16619258b'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'69'), (b'date', b'Mon, 16 Feb 2026 21:10:17 GMT'), (b'x-envoy-upstream-service-time', b'194'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:20 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:20 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:20 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:17 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:20 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:20 - INFO - main.py:215 - Upserted 44 points to collection 'book_embeddings' +2026-02-17 02:10:20 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper +2026-02-17 02:10:20 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/congratulations... +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'270'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="congratulations"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:17 GMT'), (b'Etag', b'W/"cb30a80c909cc2410f23218461e6d61b"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:47 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::p4qlm-1771276217977-d4cb98509528'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:20 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/congratulations "HTTP/1.1 200 OK" +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:20 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/congratulations (status: 200) +2026-02-17 02:10:20 - INFO - main.py:286 - Extracted 629 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/congratulations, chunked into 1 segments +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:20 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'629'), (b'num_tokens', b'143'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'2d05f424c401f07a347afcefa08fe0f8'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'68'), (b'date', b'Mon, 16 Feb 2026 21:10:18 GMT'), (b'x-envoy-upstream-service-time', b'42'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:21 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:21 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:21 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:18 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:21 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:21 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:10:21 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/congratulations +2026-02-17 02:10:21 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-blog-post... +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'269'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="create-a-blog-post"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:18 GMT'), (b'Etag', b'W/"484d3df628557779592a0f5f35b5e365"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:48 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::fhwjw-1771276218515-9daaad9c5fe1'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:21 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-blog-post "HTTP/1.1 200 OK" +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:21 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-blog-post (status: 200) +2026-02-17 02:10:21 - INFO - main.py:286 - Extracted 859 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-blog-post, chunked into 1 segments +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - 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Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-blog-post +2026-02-17 02:10:21 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-document... +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'269'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="create-a-document"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:19 GMT'), (b'Etag', b'W/"7f369efe6c6fea9c6369842195dbbf4c"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:49 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::szkg6-1771276219081-60bb1ddf8c5a'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:21 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-document "HTTP/1.1 200 OK" +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:21 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-document (status: 200) +2026-02-17 02:10:21 - INFO - main.py:286 - Extracted 934 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-document, chunked into 1 segments +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:21 - DEBUG - _trace.py:47 - 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INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:22 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:22 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:22 - DEBUG - 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Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-document +2026-02-17 02:10:22 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-page... +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'269'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="create-a-page"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:19 GMT'), (b'Etag', b'W/"082fa1c6f41e951cc64491d6a31e2196"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:50 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::p4qlm-1771276219650-cdded9c97a55'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:22 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-page "HTTP/1.1 200 OK" +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:22 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-page (status: 200) +2026-02-17 02:10:22 - INFO - main.py:286 - Extracted 940 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-page, chunked into 1 segments +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'940'), (b'num_tokens', b'292'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'09b90bf3e644369c635e8893a6e01899'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'65'), (b'date', b'Mon, 16 Feb 2026 21:10:19 GMT'), (b'x-envoy-upstream-service-time', b'38'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:22 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:22 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:22 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:19 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:22 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:22 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:10:22 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-page +2026-02-17 02:10:22 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/deploy-your-site... +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:22 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'268'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="deploy-your-site"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:20 GMT'), (b'Etag', b'W/"665a6b003a0b5733ed744800c95cf8f6"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:51 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::n6995-1771276220219-3ff1f4e00e34'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:23 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/deploy-your-site "HTTP/1.1 200 OK" +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:23 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/deploy-your-site (status: 200) +2026-02-17 02:10:23 - INFO - main.py:286 - Extracted 610 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/deploy-your-site, chunked into 1 segments +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'610'), (b'num_tokens', b'135'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'33465600fa58c4d771088272996ab753'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'64'), (b'date', b'Mon, 16 Feb 2026 21:10:20 GMT'), (b'x-envoy-upstream-service-time', b'43'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:23 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:23 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:23 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:20 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:23 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:23 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:10:23 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/deploy-your-site +2026-02-17 02:10:23 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/markdown-features... +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'268'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="markdown-features"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:20 GMT'), (b'Etag', b'W/"3399b78094abd114027b6821b7dee14e"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:52 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::fhwjw-1771276220759-f693cfab2796'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:23 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/markdown-features "HTTP/1.1 200 OK" +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:23 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/markdown-features (status: 200) +2026-02-17 02:10:23 - INFO - main.py:286 - Extracted 2311 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/markdown-features, chunked into 3 segments +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'2511'), (b'num_tokens', b'693'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'119a7cc6dbeb305cea07e16df7f16ea2'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'63'), (b'date', b'Mon, 16 Feb 2026 21:10:21 GMT'), (b'x-envoy-upstream-service-time', b'47'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:23 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:23 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:23 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:23 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:20 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:24 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:24 - INFO - main.py:215 - Upserted 3 points to collection 'book_embeddings' +2026-02-17 02:10:24 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/markdown-features +2026-02-17 02:10:24 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/manage-docs-versions... +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'267'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="manage-docs-versions"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:21 GMT'), (b'Etag', b'W/"a7ef0b4192770c4b066e948cb843d024"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:53 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::fhwjw-1771276221347-edc54eebe9f3'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:24 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/manage-docs-versions "HTTP/1.1 200 OK" +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:24 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/manage-docs-versions (status: 200) +2026-02-17 02:10:24 - INFO - main.py:286 - Extracted 1104 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/manage-docs-versions, chunked into 2 segments +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'1204'), (b'num_tokens', b'358'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'b2f5cd433b3ea18c99b168fea867f71e'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'62'), (b'date', b'Mon, 16 Feb 2026 21:10:21 GMT'), (b'x-envoy-upstream-service-time', b'43'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:24 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:24 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:24 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:21 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:24 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:24 - INFO - main.py:215 - Upserted 2 points to collection 'book_embeddings' +2026-02-17 02:10:24 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/manage-docs-versions +2026-02-17 02:10:24 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/translate-your-site... +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'267'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline; filename="translate-your-site"'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:21 GMT'), (b'Etag', b'W/"b619aa9a2c7cfc8e34cbc506ab403277"'), (b'Last-Modified', b'Mon, 16 Feb 2026 21:05:54 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::p4qlm-1771276221913-5148e55fab8e'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:24 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/translate-your-site "HTTP/1.1 200 OK" +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:24 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/translate-your-site (status: 200) +2026-02-17 02:10:24 - INFO - main.py:286 - Extracted 1418 characters from https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/translate-your-site, chunked into 2 segments +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:24 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'1518'), (b'num_tokens', b'463'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'7a3c93d7d86b8807c4512c30b762aef6'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'61'), (b'date', b'Mon, 16 Feb 2026 21:10:22 GMT'), (b'x-envoy-upstream-service-time', b'396'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:25 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:25 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:25 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:22 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:25 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:25 - INFO - main.py:215 - Upserted 2 points to collection 'book_embeddings' +2026-02-17 02:10:25 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/translate-your-site +2026-02-17 02:10:25 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app/... +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'1024374'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:10:22 GMT'), (b'Etag', b'W/"dee9f6103f913b331d72f5cbcb592db9"'), (b'Last-Modified', b'Thu, 05 Feb 2026 00:37:28 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::z7kzm-1771276222822-c34622d90766'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:25 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app/ "HTTP/1.1 200 OK" +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:25 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app/ (status: 200) +2026-02-17 02:10:25 - INFO - main.py:286 - Extracted 91 characters from https://humanoid-ai-robotics-book-1.vercel.app/, chunked into 1 segments +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'91'), (b'num_tokens', b'19'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'7720ec13c25a5ba7acb7418f9e004bd1'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'60'), (b'date', b'Mon, 16 Feb 2026 21:10:23 GMT'), (b'x-envoy-upstream-service-time', b'37'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:25 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:25 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:10:25 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:25 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:22 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:26 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:26 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:10:26 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app/ +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:10:26 - INFO - main.py:323 - ================================================== +2026-02-17 02:10:26 - INFO - main.py:324 - Ingestion complete! +2026-02-17 02:10:26 - INFO - main.py:325 - Total pages processed: 40 +2026-02-17 02:10:26 - INFO - main.py:326 - Total chunks stored: 270 +2026-02-17 02:10:26 - INFO - main.py:329 - ================================================== +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:10:23 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:10:26 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:10:26 - INFO - main.py:334 - Qdrant collection 'book_embeddings' now has 277 points +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:10:26 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:11:17 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:11:15 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:11:18 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:11:15 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:11:18 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:11:18 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:11:18 - INFO - main.py:172 - Collection 'book_embeddings' already exists +2026-02-17 02:11:19 - INFO - main.py:267 - Starting ingestion: 1 URLs +2026-02-17 02:11:19 - INFO - main.py:268 - Chunk size: 1000, overlap: 100 +2026-02-17 02:11:20 - INFO - main.py:34 - Fetching https://humanoid-ai-robotics-book-1.vercel.app... +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - connect_tcp.started host='humanoid-ai-robotics-book-1.vercel.app' port=443 local_address=None timeout=30.0 socket_options=None +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='humanoid-ai-robotics-book-1.vercel.app' timeout=30.0 +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Access-Control-Allow-Origin', b'*'), (b'Age', b'1024428'), (b'Cache-Control', b'public, max-age=0, must-revalidate'), (b'Content-Disposition', b'inline'), (b'Content-Encoding', b'gzip'), (b'Content-Type', b'text/html; charset=utf-8'), (b'Date', b'Mon, 16 Feb 2026 21:11:17 GMT'), (b'Etag', b'W/"dee9f6103f913b331d72f5cbcb592db9"'), (b'Last-Modified', b'Thu, 05 Feb 2026 00:37:28 GMT'), (b'Server', b'Vercel'), (b'Strict-Transport-Security', b'max-age=63072000; includeSubDomains; preload'), (b'X-Vercel-Cache', b'HIT'), (b'X-Vercel-Id', b'dxb1::5gmkn-1771276277525-2fdcf52a1fb2'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:11:20 - INFO - _client.py:1025 - HTTP Request: GET https://humanoid-ai-robotics-book-1.vercel.app "HTTP/1.1 200 OK" +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:11:20 - DEBUG - main.py:39 - Successfully fetched https://humanoid-ai-robotics-book-1.vercel.app (status: 200) +2026-02-17 02:11:20 - INFO - main.py:286 - Extracted 91 characters from https://humanoid-ai-robotics-book-1.vercel.app, chunked into 1 segments +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'91'), (b'num_tokens', b'19'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'c81737bbb9a91ccea0da116699fa9db8'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'90'), (b'date', b'Mon, 16 Feb 2026 21:11:17 GMT'), (b'x-envoy-upstream-service-time', b'33'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:11:20 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:11:20 - INFO - main.py:134 - Cohere returned embeddings with dimension: 1024 +2026-02-17 02:11:20 - DEBUG - main.py:141 - Generated embeddings for batch 1/1 +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:11:17 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:11:20 - INFO - _client.py:1025 - HTTP Request: PUT https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points?wait=true "HTTP/1.1 200 OK" +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:11:20 - INFO - main.py:215 - Upserted 1 points to collection 'book_embeddings' +2026-02-17 02:11:20 - INFO - main.py:315 - Successfully processed https://humanoid-ai-robotics-book-1.vercel.app +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:11:20 - INFO - main.py:323 - ================================================== +2026-02-17 02:11:20 - INFO - main.py:324 - Ingestion complete! +2026-02-17 02:11:20 - INFO - main.py:325 - Total pages processed: 1 +2026-02-17 02:11:20 - INFO - main.py:326 - Total chunks stored: 1 +2026-02-17 02:11:20 - INFO - main.py:329 - ================================================== +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:11:20 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:11:21 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:11:17 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:11:21 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 02:11:21 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:11:21 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:11:21 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:11:21 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:11:21 - INFO - main.py:334 - Qdrant collection 'book_embeddings' now has 277 points +2026-02-17 02:11:21 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:11:21 - DEBUG - _trace.py:47 - close.complete diff --git a/local_test.log b/local_test.log new file mode 100644 index 0000000000000000000000000000000000000000..bd69ce154649f1443cc1fc8949aebc0637d52322 --- /dev/null +++ b/local_test.log @@ -0,0 +1,23 @@ +INFO: Extracted 781 characters from fixture +Text preview: Sample Book Page - Introduction +Chapter 1: Introduction +This is the first paragraph of the introduction. It provides an overview of the topic. +The second paragraph discusses the background and context... +INFO: Chunked into 2 segments +First chunk: Sample Book Page - Introduction +Chapter 1: Introduction +This is the first paragraph of the introduct... +INFO: HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +INFO: Generated 6 embeddings +Embedding dimension: 2 +INFO: HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +INFO: HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +INFO: Collection 'book_embeddings' already exists +ERROR: Test failed: 'PosixPath' object has no attribute 'path' +Traceback (most recent call last): + File "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/test_local.py", line 93, in + test_with_fixture() + File "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/test_local.py", line 67, in test_with_fixture + 'title': Path(url).path, + ^^^^^^^^^^^^^^ +AttributeError: 'PosixPath' object has no attribute 'path' diff --git a/logging_config.py b/logging_config.py new file mode 100644 index 0000000000000000000000000000000000000000..b301a9a722f3d945e8dfd8a1be7275e051c7fdfc --- /dev/null +++ b/logging_config.py @@ -0,0 +1,77 @@ +""" +Logging configuration for the ingestion pipeline. +""" +import logging +import sys +import json +from pathlib import Path +from datetime import datetime + +def setup_logging(log_file: str = "agent.log", console_level: str = None): + """ + Configure logging to both file (JSON structured) and console (human-readable). + + Args: + log_file: Path to log file (default: agent.log) + console_level: Log level for console output (defaults to LOG_LEVEL env var or INFO) + """ + import os + + logger = logging.getLogger() + logger.setLevel(logging.DEBUG) + + # Clear any existing handlers + logger.handlers = [] + + # Determine log level from environment or default + log_level_env = os.getenv("LOG_LEVEL", "INFO").upper() + console_level = console_level or log_level_env + + # Validate log level + valid_levels = ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] + if console_level not in valid_levels: + console_level = "INFO" + + # ============ File Handler - JSON Structured Format ============ + file_handler = logging.FileHandler(log_file, mode='a', encoding='utf-8') + file_handler.setLevel(logging.DEBUG) + + class JSONFormatter(logging.Formatter): + """Format log records as JSON for structured logging.""" + def format(self, record): + log_object = { + "timestamp": datetime.utcfromtimestamp(record.created).isoformat() + "Z", + "level": record.levelname, + "name": record.name, + "message": record.getMessage(), + "module": record.module, + "lineno": record.lineno, + } + # Include exception info if present + if record.exc_info: + log_object["exc_info"] = self.formatException(record.exc_info) + if record.stack_info: + log_object["stack_info"] = record.stack_info + # Include extra fields + for key, value in record.__dict__.items(): + if key not in ['name', 'msg', 'args', 'created', 'filename', 'module', + 'levelno', 'levelname', 'lineno', 'funcName', 'relativeCreated', + 'msecs', 'abs_path', 'exc_info', 'exc_text', 'stack_info', + 'message']: + log_object[key] = value + return json.dumps(log_object) + + file_formatter = JSONFormatter() + file_handler.setFormatter(file_formatter) + + # ============ Console Handler - Human-Readable ============ + console_handler = logging.StreamHandler(sys.stdout) + console_handler.setLevel(getattr(logging, console_level)) + console_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(name)s - %(message)s', + datefmt='%H:%M:%S') + console_handler.setFormatter(console_formatter) + + logger.addHandler(file_handler) + logger.addHandler(console_handler) + + return logger diff --git a/main.py b/main.py new file mode 100644 index 0000000000000000000000000000000000000000..9490d22fc35200d04459942f78c882e55b521ec0 --- /dev/null +++ b/main.py @@ -0,0 +1,434 @@ +""" +Vector Data Ingestion Pipeline + +Fetches book content from URLs, extracts text, chunks, generates embeddings with Cohere, +and stores in Qdrant Cloud. +""" +import argparse +import sys +import os +import time +from pathlib import Path +from typing import List, Dict, Any + +import httpx +from bs4 import BeautifulSoup +import cohere +from qdrant_client import QdrantClient +from qdrant_client.models import Distance, VectorParams, PointStruct +from dotenv import load_dotenv + +import config +import utils +from logging_config import setup_logging + +# Initialize logging +logger = setup_logging() + + +def fetch_url(url: str, client: httpx.Client, max_retries: int = 5) -> str: + """ + Fetch HTML content from a URL with retry logic. + Returns HTML string or raises after max retries. + """ + logger.info(f"Fetching {url}...") + for attempt in range(max_retries): + try: + response = client.get(url, timeout=30.0) + response.raise_for_status() + logger.debug(f"Successfully fetched {url} (status: {response.status_code})") + return response.text + except httpx.HTTPError as e: + if attempt == max_retries - 1: + logger.error(f"Failed to fetch {url} after {max_retries} attempts: {e}") + raise + delay = 2 ** attempt # Simple backoff + time.sleep(delay) + return "" + + +def extract_text(html: str, url: str) -> str: + """ + Extract clean text from HTML. + Removes scripts, styles, nav, footer, etc. + Preserves paragraph structure. + """ + soup = BeautifulSoup(html, 'html.parser') + + # Remove non-content elements + for selector in ['script', 'style', 'nav', 'footer', 'header', 'aside']: + for element in soup.find_all(selector): + element.decompose() + + # Get text with newlines between elements + text = soup.get_text(separator='\n', strip=True) + + # Normalize whitespace + lines = [line.strip() for line in text.split('\n') if line.strip()] + clean_text = '\n'.join(lines) + + return clean_text + + +def chunk_text(text: str, chunk_size: int = 1000, overlap: int = 100) -> List[Dict[str, Any]]: + """ + Split text into overlapping chunks. + Returns list of dicts with text and char_start/end. + """ + if len(text) <= chunk_size: + return [{'text': text, 'char_start': 0, 'char_end': len(text)}] + + chunks = [] + start = 0 + text_len = len(text) + + while start < text_len: + end = min(start + chunk_size, text_len) + + # If not first chunk, include overlap from previous chunk's tail + if start > 0: + start -= overlap + if start < 0: + start = 0 + + chunk_text = text[start:end] + chunks.append({ + 'text': chunk_text, + 'char_start': start, + 'char_end': end + }) + + # Move to next chunk (exclusive end becomes new start) + start = end + + # If we're at the end, break + if end >= text_len: + break + + return chunks + + +def generate_embeddings(texts: List[str], cohere_client: cohere.ClientV2, batch_size: int = 96) -> List[List[float]]: + """ + Generate embeddings for a list of texts using Cohere. + Batches requests to optimize throughput. + """ + all_embeddings = [] + + for i in range(0, len(texts), batch_size): + batch = texts[i:i+batch_size] + try: + response = cohere_client.embed( + texts=batch, + model="embed-english-v3.0", + input_type="search_document" + ) + # Cohere SDK v2 returns EmbedByTypeResponse with embeddings.float_ containing the vectors + embeddings_list = response.embeddings.float_ + + # Log the dimension from the first embedding + if embeddings_list and len(all_embeddings) == 0: + first_embed = embeddings_list[0] + if hasattr(first_embed, '__len__') and not isinstance(first_embed, str): + dim = len(first_embed) + logger.info(f"Cohere returned embeddings with dimension: {dim}") + else: + logger.info(f"Cohere returned embedding type: {type(first_embed)}") + + # embeddings_list is already a list of vectors (list of floats) + all_embeddings.extend(embeddings_list) + + logger.debug(f"Generated embeddings for batch {i//batch_size + 1}/{(len(texts)-1)//batch_size + 1}") + except Exception as e: + logger.error(f"Failed to generate embeddings for batch starting at index {i}: {e}") + raise + + if not all_embeddings: + raise ValueError("No embeddings generated") + + # Validate dimension - should be 1024 for embed-english-v3.0 + actual_dim = len(all_embeddings[0]) if hasattr(all_embeddings[0], '__len__') else len(all_embeddings[0]) + if actual_dim != 1024: + logger.warning(f"Unexpected embedding dimension: {actual_dim} (expected 1024). Proceeding anyway.") + + return all_embeddings + + +def ensure_collection(client: QdrantClient, collection_name: str, vector_size: int = 1024): + """ + Create collection if it doesn't exist. + """ + try: + collections = client.get_collections().collections + collection_names = [c.name for c in collections] + if collection_name not in collection_names: + logger.info(f"Creating collection '{collection_name}'") + client.create_collection( + collection_name=collection_name, + vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE) + ) + logger.info(f"Collection '{collection_name}' created with vector size {vector_size}") + else: + logger.info(f"Collection '{collection_name}' already exists") + except Exception as e: + logger.error(f"Failed to ensure collection: {e}") + raise + + +def upsert_chunks( + client: QdrantClient, + collection_name: str, + records: List[Dict[str, Any]], + deterministic_id_func, + track_changes: bool = False +) -> Dict[str, int]: + """ + Upsert chunk records to Qdrant. + Uses deterministic IDs for idempotency. + Returns statistics: {'new': X, 'updated': Y, 'total': Z} + """ + points = [] + for record in records: + point_id = deterministic_id_func(record['url'], record['chunk_index']) + # Verify ID is consistent (US2 - idempotency check) + verify_id = utils.verify_deterministic_id(record['url'], record['chunk_index'], point_id) + assert verify_id == point_id, "Deterministic ID verification failed" + + point = PointStruct( + id=point_id, + vector=record['embedding'], + payload={ + 'url': record['url'], + 'title': record.get('title', ''), + 'section': record.get('section', ''), + 'chunk_index': record['chunk_index'], + 'text': record['text'] + } + ) + points.append(point) + + # Batch upsert (Qdrant client handles batching internally if needed) + try: + # For now, we don't distinguish new vs updated without a pre-query + # But we can report total upserted + client.upsert(collection_name=collection_name, points=points) + logger.info(f"Upserted {len(points)} points to collection '{collection_name}'") + # In future, could check which points already existed by doing a scroll before upsert + return {'new': len(points), 'updated': 0, 'total': len(points)} + except Exception as e: + logger.error(f"Failed to upsert points: {e}") + raise + + +def main(): + """Main orchestration function.""" + parser = argparse.ArgumentParser(description="Ingest book content into Qdrant") + parser.add_argument('--urls', nargs='+', help='List of URLs to ingest') + parser.add_argument('--urls-file', help='File containing one URL per line') + parser.add_argument('--chunk-size', type=int, default=1000, help='Chunk size in characters') + parser.add_argument('--overlap', type=int, default=100, help='Overlap between chunks in characters') + parser.add_argument('--batch-size', type=int, default=96, help='Embedding API batch size (max 96 for Cohere trial)') + parser.add_argument('--max-retries', type=int, default=5, help='Max retries for API calls') + parser.add_argument('--validate', action='store_true', help='Run validation only (no ingestion)') + parser.add_argument('--validate-sample', type=int, default=100, help='Number of sample points to check during validation') + args = parser.parse_args() + + # Load and validate config + cfg = config.get_config() + config.validate_config(cfg) + + # Initialize Qdrant client + qdrant_client = QdrantClient( + url=cfg['qdrant_url'], + api_key=cfg['qdrant_api_key'] + ) + ensure_collection(qdrant_client, cfg['qdrant_collection']) + + # Validation-only mode + if args.validate: + run_validation(qdrant_client, cfg['qdrant_collection'], args.validate_sample) + sys.exit(0) + + # Ingestion mode + cohere_client = cohere.ClientV2(api_key=cfg['cohere_api_key']) + + # Get URLs + urls = [] + if args.urls: + urls.extend(args.urls) + if args.urls_file: + with open(args.urls_file, 'r') as f: + urls.extend([line.strip() for line in f if line.strip()]) + + if not urls: + logger.error("No URLs provided. Use --urls or --urls-file") + sys.exit(1) + + logger.info(f"Starting ingestion: {len(urls)} URLs") + logger.info(f"Chunk size: {args.chunk_size}, overlap: {args.overlap}") + + total_chunks = 0 + total_pages = 0 + errors = [] + + # Process each URL + with httpx.Client() as http_client: + for url in urls: + try: + html = fetch_url(url, http_client, max_retries=args.max_retries) + text = extract_text(html, url) + + if not text or len(text) < 10: + logger.warning(f"Page {url} has insufficient text content, skipping") + continue + + chunks_data = chunk_text(text, chunk_size=args.chunk_size, overlap=args.overlap) + logger.info(f"Extracted {len(text)} characters from {url}, chunked into {len(chunks_data)} segments") + + # Prepare texts for embedding + texts = [chunk['text'] for chunk in chunks_data] + embeddings = generate_embeddings(texts, cohere_client, batch_size=args.batch_size) + + # Validate dimensions + if embeddings: + actual_dim = len(embeddings[0]) if hasattr(embeddings[0], '__len__') else len(embeddings[0]) + if actual_dim != 1024: + logger.warning(f"Embedding dimension {actual_dim} != 1024. Check Cohere model. Proceeding anyway.") + + # Prepare records for Qdrant + records = [] + for i, (chunk, embedding) in enumerate(zip(chunks_data, embeddings)): + record = { + 'url': url, + 'title': url, # Use full URL as title + 'section': '', # TODO: extract from headings + 'chunk_index': i, + 'text': chunk['text'], + 'embedding': embedding + } + records.append(record) + + # Upsert to Qdrant + stats = upsert_chunks(qdrant_client, cfg['qdrant_collection'], records, utils.deterministic_id) + total_chunks += stats['total'] + total_pages += 1 + logger.info(f"Successfully processed {url}") + + except Exception as e: + logger.error(f"Failed to process {url}: {e}") + errors.append(url) + continue + + # Summary + logger.info("=" * 50) + logger.info(f"Ingestion complete!") + logger.info(f"Total pages processed: {total_pages}") + logger.info(f"Total chunks stored: {total_chunks}") + if errors: + logger.warning(f"Failed URLs ({len(errors)}): {', '.join(errors)}") + logger.info("=" * 50) + + # Verify collection + try: + info = qdrant_client.get_collection(cfg['qdrant_collection']) + logger.info(f"Qdrant collection '{cfg['qdrant_collection']}' now has {info.points_count} points") + except Exception as e: + logger.error(f"Could not verify collection: {e}") + + sys.exit(0 if not errors else 1) + + +def run_validation(client: QdrantClient, collection_name: str, sample_size: int = 100): + """ + Run validation checks on the Qdrant collection. + Checks: dimensions, metadata completeness, sampling. + """ + logger.info("=" * 50) + logger.info("Running validation mode...") + logger.info("=" * 50) + + try: + # Get collection info + info = client.get_collection(collection_name) + total_points = info.points_count + vector_size = info.config.params.vectors.size + + logger.info(f"Collection: {collection_name}") + logger.info(f"Total points: {total_points}") + logger.info(f"Vector size: {vector_size}") + + # Validate dimension + if vector_size != 1024: + logger.error(f"❌ Invalid vector size: {vector_size} (expected 1024)") + else: + logger.info("✅ Vector dimension correct (1024)") + + # Sample points to check metadata and text + if total_points > 0: + sample_count = min(sample_size, total_points) + logger.info(f"Sampling {sample_count} points for validation...") + + # Scroll through some points + records = client.scroll( + collection_name=collection_name, + limit=sample_count, + with_payload=True, + with_vectors=False + )[0] + + url_ok = 0 + title_section_ok = 0 + text_nonempty = 0 + dimension_checks = 0 + + for record in records: + payload = record.payload or {} + + # Check URL presence + if payload.get('url'): + url_ok += 1 + + # Check title or section presence + if payload.get('title') or payload.get('section'): + title_section_ok += 1 + + # Check text non-empty + if payload.get('text') and len(payload['text']) >= 10: + text_nonempty += 1 + + # Calculate percentages + url_pct = (url_ok / sample_count) * 100 + title_section_pct = (title_section_ok / sample_count) * 100 + text_pct = (text_nonempty / sample_count) * 100 + + logger.info(f"Metadata completeness:") + logger.info(f" URL present: {url_ok}/{sample_count} ({url_pct:.1f}%)") + logger.info(f" Title/Section present: {title_section_ok}/{sample_count} ({title_section_pct:.1f}%)") + logger.info(f" Text non-empty (≥10 chars): {text_nonempty}/{sample_count} ({text_pct:.1f}%)") + + # Validation thresholds + if url_pct >= 99: + logger.info("✅ URL completeness excellent (≥99%)") + else: + logger.warning(f"⚠️ URL completeness below 99%: {url_pct:.1f}%") + + if title_section_pct >= 95: + logger.info("✅ Title/Section completeness good (≥95%)") + else: + logger.warning(f"⚠️ Title/Section completeness below 95%: {title_section_pct:.1f}%") + + if text_pct >= 98: + logger.info("✅ Text quality excellent (≥98%)") + else: + logger.warning(f"⚠️ Text quality below 98%: {text_pct:.1f}%") + + logger.info("=" * 50) + logger.info("Validation complete!") + + except Exception as e: + logger.error(f"Validation failed: {e}") + raise + + +if __name__ == "__main__": + main() diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..0e262fe5c34079ee2fe670934256d2c0dfb55928 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,12 @@ +[project] +name = "vector-data-ingest" +version = "0.1.0" +description = "Vector data ingestion pipeline for book RAG system" +requires-python = ">=3.11" +dependencies = [ + "httpx>=0.27.0", + "beautifulsoup4>=4.12.0", + "cohere>=5.0.0", + "qdrant-client>=1.7.0", + "python-dotenv>=1.0.0", +] diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..b7bf4a0db29d2a01072b93ea5c9a2d0018afe44d --- /dev/null +++ b/requirements.txt @@ -0,0 +1,16 @@ +# Core dependencies +httpx>=0.27.0 +beautifulsoup4>=4.12.0 +cohere>=5.0.0 +qdrant-client>=1.7.0 +python-dotenv>=1.0.0 +fastapi>=0.104.0 +uvicorn[standard]>=0.24.0 +openai>=1.0.0 +pydantic>=2.0.0 +openai-agents>=0.8.0 + +# Additional dependencies for production +lxml>=4.9.0 +aiofiles>=23.0.0 +python-multipart>=0.0.6 diff --git a/retrieve.log b/retrieve.log new file mode 100644 index 0000000000000000000000000000000000000000..ddf4609699ddb17a5f9ea5d023be6b09619d92ae --- /dev/null +++ b/retrieve.log @@ -0,0 +1,577 @@ +2026-02-17 02:36:58 - INFO - retrieve.py:224 - === Retrieval Pipeline Started === +2026-02-17 02:36:58 - INFO - retrieve.py:228 - Loading config from .env +2026-02-17 02:36:58 - INFO - retrieve.py:233 - Initializing Cohere and Qdrant clients +2026-02-17 02:37:00 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:37:00 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:37:00 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:37:00 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:37:01 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:37:01 - INFO - retrieve.py:238 - Checking collection 'book_embeddings' +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:37:00 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:37:01 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:37:01 - INFO - retrieve.py:240 - Collection OK: vector_size=1024, points=277 +2026-02-17 02:37:01 - INFO - retrieve.py:121 - Embedding query: 'ROS 2...' (top_k=5) +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:37:01 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:37:02 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'5'), (b'num_tokens', b'3'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'3aff3ef949954c94990bb515bf13453d'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 21:37:01 GMT'), (b'x-envoy-upstream-service-time', b'44'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:37:02 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:37:02 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:37:02 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:37:02 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:37:02 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:37:02 - DEBUG - retrieve.py:133 - Generated embedding in 0.45s, dimension: 1024 +2026-02-17 02:37:05 - ERROR - retrieve.py:156 - Search failed: 'QdrantClient' object has no attribute 'search' +2026-02-17 02:37:05 - ERROR - retrieve.py:298 - API error: Qdrant search failed: 'QdrantClient' object has no attribute 'search' +2026-02-17 02:37:05 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:37:05 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:39:25 - INFO - retrieve.py:225 - === Retrieval Pipeline Started === +2026-02-17 02:39:25 - INFO - retrieve.py:229 - Loading config from .env +2026-02-17 02:39:25 - INFO - retrieve.py:234 - Initializing Cohere and Qdrant clients +2026-02-17 02:39:25 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:39:25 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:39:25 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:39:25 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:39:26 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:39:26 - INFO - retrieve.py:239 - Checking collection 'book_embeddings' +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:39:25 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:39:26 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:39:26 - INFO - retrieve.py:241 - Collection OK: vector_size=1024, points=277 +2026-02-17 02:39:26 - INFO - retrieve.py:121 - Embedding query: 'ROS 2...' (top_k=5) +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:39:26 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:39:27 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'5'), (b'num_tokens', b'3'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'9cfafd18a8c68f9fc1f1e0d937c5508a'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 21:39:26 GMT'), (b'x-envoy-upstream-service-time', b'48'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:39:27 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:39:27 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:39:27 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:39:27 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:39:27 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:39:27 - DEBUG - retrieve.py:133 - Generated embedding in 0.46s, dimension: 1024 +2026-02-17 02:39:27 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:39:27 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:39:27 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:39:27 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:39:27 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:39:27 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:39:26 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:39:27 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +2026-02-17 02:39:27 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:39:27 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:39:27 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:39:27 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:39:27 - INFO - retrieve.py:155 - Search completed in 0.31s, returned 5 results +2026-02-17 02:39:27 - INFO - retrieve.py:170 - Total query time: 0.78s +2026-02-17 02:39:27 - INFO - retrieve.py:279 - === Retrieval Pipeline Completed Successfully === +2026-02-17 02:39:27 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:39:27 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:41:01 - INFO - retrieve.py:299 - === Retrieval Pipeline Started === +2026-02-17 02:41:01 - INFO - retrieve.py:303 - Loading config from .env +2026-02-17 02:41:01 - INFO - retrieve.py:308 - Initializing Cohere and Qdrant clients +2026-02-17 02:41:02 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:41:03 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:41:03 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:41:03 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:41:03 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:41:03 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:41:02 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:41:04 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:41:04 - INFO - retrieve.py:313 - Checking collection 'book_embeddings' +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:41:04 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:41:03 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:41:05 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:41:05 - INFO - retrieve.py:315 - Collection OK: vector_size=1024, points=277 +2026-02-17 02:41:05 - INFO - retrieve.py:190 - Embedding query: 'ROS 2...' (top_k=5) +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'5'), (b'num_tokens', b'3'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'7eab84de888d9426719d5f02fbc98608'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 21:41:04 GMT'), (b'x-envoy-upstream-service-time', b'49'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:41:05 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:41:05 - DEBUG - retrieve.py:202 - Generated embedding in 0.44s, dimension: 1024 +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:41:04 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:41:05 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:41:05 - INFO - retrieve.py:224 - Search completed in 0.30s, returned 5 results +2026-02-17 02:41:05 - INFO - retrieve.py:239 - Total query time: 0.75s +2026-02-17 02:41:05 - DEBUG - retrieve.py:127 - Metadata completeness: 5/5 = 100.0% +2026-02-17 02:41:05 - DEBUG - retrieve.py:157 - Chunk sequencing invalid for https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2: expected [0, 1, 2, 3], got [0, 2, 4, 5] +2026-02-17 02:41:05 - INFO - retrieve.py:340 - Metadata completeness: 100.0% +2026-02-17 02:41:05 - INFO - retrieve.py:341 - Chunk sequencing: INVALID +2026-02-17 02:41:05 - INFO - retrieve.py:342 - Validation result: FAIL +2026-02-17 02:41:05 - INFO - retrieve.py:369 - === Retrieval Pipeline Completed Successfully === +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:41:05 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:42:10 - INFO - retrieve.py:309 - === Retrieval Pipeline Started === +2026-02-17 02:42:10 - INFO - retrieve.py:313 - Loading config from .env +2026-02-17 02:42:10 - INFO - retrieve.py:318 - Initializing Cohere and Qdrant clients +2026-02-17 02:42:10 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:42:09 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:42:11 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:42:11 - INFO - retrieve.py:323 - Checking collection 'book_embeddings' +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:42:11 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:42:09 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:42:12 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:42:12 - INFO - retrieve.py:325 - Collection OK: vector_size=1024, points=277 +2026-02-17 02:42:12 - INFO - retrieve.py:200 - Embedding query: 'ROS 2...' (top_k=5) +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'5'), (b'num_tokens', b'3'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'd004675c4a251ca57146bf895e986d52'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 21:42:10 GMT'), (b'x-envoy-upstream-service-time', b'41'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:42:12 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:42:12 - DEBUG - retrieve.py:212 - Generated embedding in 0.42s, dimension: 1024 +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:42:10 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:42:12 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:42:12 - INFO - retrieve.py:234 - Search completed in 0.31s, returned 5 results +2026-02-17 02:42:12 - INFO - retrieve.py:249 - Total query time: 0.74s +2026-02-17 02:42:12 - DEBUG - retrieve.py:127 - Metadata completeness: 5/5 = 100.0% +2026-02-17 02:42:12 - DEBUG - retrieve.py:170 - Chunk indexing valid for 2 URLs +2026-02-17 02:42:12 - INFO - retrieve.py:350 - Metadata completeness: 100.0% +2026-02-17 02:42:12 - INFO - retrieve.py:351 - Chunk sequencing: VALID +2026-02-17 02:42:12 - INFO - retrieve.py:352 - Validation result: PASS +2026-02-17 02:42:12 - INFO - retrieve.py:379 - === Retrieval Pipeline Completed Successfully === +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:42:12 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:45:35 - INFO - retrieve.py:309 - === Retrieval Pipeline Started === +2026-02-17 02:45:35 - INFO - retrieve.py:313 - Loading config from .env +2026-02-17 02:45:35 - INFO - retrieve.py:318 - Initializing Cohere and Qdrant clients +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:45:34 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:45:36 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:45:36 - INFO - retrieve.py:323 - Checking collection 'book_embeddings' +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:45:36 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:45:37 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:45:35 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:45:37 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 02:45:37 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:45:37 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:45:37 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:45:37 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:45:37 - INFO - retrieve.py:325 - Collection OK: vector_size=1024, points=277 +2026-02-17 02:45:37 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:45:37 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:45:53 - INFO - retrieve.py:309 - === Retrieval Pipeline Started === +2026-02-17 02:45:53 - INFO - retrieve.py:313 - Loading config from .env +2026-02-17 02:45:53 - INFO - retrieve.py:318 - Initializing Cohere and Qdrant clients +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:45:53 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:45:54 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:45:54 - INFO - retrieve.py:323 - Checking collection 'book_embeddings' +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:45:54 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:45:55 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:45:55 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:45:55 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:45:55 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:45:55 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:45:55 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:45:55 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:45:54 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:45:55 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 02:45:55 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:45:55 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:45:55 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:45:55 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:45:55 - INFO - retrieve.py:325 - Collection OK: vector_size=1024, points=277 +2026-02-17 02:45:55 - ERROR - retrieve.py:383 - Validation error: Query text must be non-empty +2026-02-17 02:45:55 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:45:55 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:46:17 - INFO - retrieve.py:309 - === Retrieval Pipeline Started === +2026-02-17 02:46:17 - INFO - retrieve.py:313 - Loading config from .env +2026-02-17 02:46:17 - INFO - retrieve.py:318 - Initializing Cohere and Qdrant clients +2026-02-17 02:46:17 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:46:17 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:46:17 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:46:17 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:46:17 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:46:17 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:46:17 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:46:17 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:46:17 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:46:15 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:46:18 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:46:18 - INFO - retrieve.py:323 - Checking collection 'book_embeddings' +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:46:15 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:46:18 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:46:18 - INFO - retrieve.py:325 - Collection OK: vector_size=1024, points=277 +2026-02-17 02:46:18 - ERROR - retrieve.py:383 - Validation error: top_k must be between 1 and 100 +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:46:18 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:46:34 - INFO - retrieve.py:309 - === Retrieval Pipeline Started === +2026-02-17 02:46:34 - INFO - retrieve.py:313 - Loading config from .env +2026-02-17 02:46:34 - INFO - retrieve.py:318 - Initializing Cohere and Qdrant clients +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:46:33 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:46:35 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:46:35 - INFO - retrieve.py:323 - Checking collection 'book_embeddings' +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:46:35 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:46:36 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:46:34 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:46:36 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 02:46:36 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:46:36 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:46:36 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:46:36 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:46:36 - INFO - retrieve.py:325 - Collection OK: vector_size=1024, points=277 +2026-02-17 02:46:36 - ERROR - retrieve.py:383 - Validation error: top_k must be between 1 and 100 +2026-02-17 02:46:36 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:46:36 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:46:56 - INFO - retrieve.py:309 - === Retrieval Pipeline Started === +2026-02-17 02:46:56 - INFO - retrieve.py:313 - Loading config from .env +2026-02-17 02:46:56 - INFO - retrieve.py:318 - Initializing Cohere and Qdrant clients +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:46:56 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:46:57 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:46:57 - INFO - retrieve.py:323 - Checking collection 'book_embeddings' +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:46:57 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:46:57 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:46:58 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:46:58 - INFO - retrieve.py:325 - Collection OK: vector_size=1024, points=277 +2026-02-17 02:46:58 - INFO - retrieve.py:200 - Embedding query: 'asdfghjkl1234567890xyz_nonexistent_query_should_return_zero_results...' (top_k=5) +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'67'), (b'num_tokens', b'28'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'fd1019b199609124264ce2207ddc1acf'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 21:46:57 GMT'), (b'x-envoy-upstream-service-time', b'56'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:46:58 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:46:58 - DEBUG - retrieve.py:212 - Generated embedding in 0.46s, dimension: 1024 +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:46:58 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:46:59 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:46:57 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:46:59 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +2026-02-17 02:46:59 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:46:59 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:46:59 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:46:59 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:46:59 - INFO - retrieve.py:234 - Search completed in 0.30s, returned 5 results +2026-02-17 02:46:59 - INFO - retrieve.py:249 - Total query time: 0.76s +2026-02-17 02:46:59 - INFO - retrieve.py:379 - === Retrieval Pipeline Completed Successfully === +2026-02-17 02:46:59 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:46:59 - DEBUG - _trace.py:47 - close.complete +2026-02-17 03:15:14 - INFO - retrieve.py:309 - === Retrieval Pipeline Started === +2026-02-17 03:15:14 - INFO - retrieve.py:313 - Loading config from .env +2026-02-17 03:15:14 - INFO - retrieve.py:318 - Initializing Cohere and Qdrant clients +2026-02-17 03:15:15 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 03:15:15 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 03:15:15 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 03:15:15 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 03:15:15 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 03:15:15 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 03:15:15 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 03:15:15 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 03:15:15 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 22:15:14 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 03:15:16 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - close.started +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - close.complete +2026-02-17 03:15:16 - INFO - retrieve.py:323 - Checking collection 'book_embeddings' +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 22:15:15 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 03:15:16 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 03:15:16 - INFO - retrieve.py:325 - Collection OK: vector_size=1024, points=277 +2026-02-17 03:15:16 - INFO - retrieve.py:200 - Embedding query: 'ROS 2...' (top_k=5) +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'5'), (b'num_tokens', b'3'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'4df4171dafe6d9e613e8c5e32b7ae73b'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 22:15:15 GMT'), (b'x-envoy-upstream-service-time', b'42'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 03:15:16 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 03:15:16 - DEBUG - retrieve.py:212 - Generated embedding in 0.49s, dimension: 1024 +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 03:15:16 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 03:15:17 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 22:15:15 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 03:15:17 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +2026-02-17 03:15:17 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 03:15:17 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 03:15:17 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 03:15:17 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 03:15:17 - INFO - retrieve.py:234 - Search completed in 0.31s, returned 5 results +2026-02-17 03:15:17 - INFO - retrieve.py:249 - Total query time: 0.80s +2026-02-17 03:15:17 - INFO - retrieve.py:379 - === Retrieval Pipeline Completed Successfully === +2026-02-17 03:15:17 - DEBUG - _trace.py:47 - close.started +2026-02-17 03:15:17 - DEBUG - _trace.py:47 - close.complete diff --git a/retrieve.py b/retrieve.py new file mode 100644 index 0000000000000000000000000000000000000000..fb5b171a40e02b1b3cb0b75a00352d68e008a86e --- /dev/null +++ b/retrieve.py @@ -0,0 +1,408 @@ +""" +Retrieval pipeline for RAG validation. + +This module provides functions to: +- Convert search queries to embeddings using Cohere +- Perform similarity search against Qdrant collection +- Format and return results with metadata +""" +import argparse +import json +import sys +import time +import logging +from pathlib import Path +from typing import List, Dict, Any + +# Add parent directory to path for imports +sys.path.insert(0, str(Path(__file__).parent)) + +import cohere +from qdrant_client import QdrantClient + +# Importfrom existing modules +import config +import utils +from logging_config import setup_logging + +# Initialize logger +logger = logging.getLogger(__name__) + +# Custom exceptions +class ConfigurationError(Exception): + """Raised when required configuration is missing.""" + pass + +class CollectionNotFoundError(Exception): + """Raised when Qdrant collection doesn't exist.""" + pass + +class DimensionMismatchError(Exception): + """Raised when embedding dimension doesn't match collection.""" + pass + +class APIError(Exception): + """Raised when Cohere or Qdrant API call fails after retries.""" + pass + +def validate_config(cfg: dict) -> None: + """Validate that all required config values are present.""" + required = ["cohere_api_key", "qdrant_url", "qdrant_api_key"] + missing = [key for key in required if not cfg.get(key)] + if missing: + raise ConfigurationError(f"Missing required environment variables: {', '.join(missing)}") + +def init_clients(cfg: dict): + """Initialize Cohere and Qdrant clients.""" + cohere_client = cohere.ClientV2(api_key=cfg["cohere_api_key"]) + qdrant_client = QdrantClient(url=cfg["qdrant_url"], api_key=cfg["qdrant_api_key"]) + return cohere_client, qdrant_client + +def check_collection(qdrant_client: QdrantClient, collection_name: str) -> Dict[str, Any]: + """Verify collection exists and has correct vector size.""" + try: + info = qdrant_client.get_collection(collection_name) + except Exception as e: + if "not found" in str(e).lower(): + raise CollectionNotFoundError(f"Collection '{collection_name}' does not exist") + raise + + vector_size = info.config.params.vectors.size + if vector_size != 1024: + raise DimensionMismatchError(f"Expected vector size 1024 but got {vector_size}") + + return { + "exists": True, + "vector_size": vector_size, + "points_count": info.points_count + } + +def embed_query(text: str, cohere_client: cohere.ClientV2) -> List[float]: + """Generate embedding for a search query using Cohere.""" + try: + response = cohere_client.embed( + texts=[text], + model="embed-english-v3.0", + input_type="search_query" + ) + # Extract embedding from response.embeddings.float_ + embedding = response.embeddings.float_[0] + return embedding + except Exception as e: + logger.error(f"Failed to generate embedding: {e}") + raise APIError(f"Cohere embedding failed: {e}") + +def validate_metadata_completeness(results: List[Dict[str, Any]]) -> float: + """ + Check metadata completeness in search results. + + Returns: + Percentage (0-100) of results with complete metadata: + - url present and non-empty + - text present with length ≥ 10 + - at least one of title or section non-empty + """ + if not results: + return 0.0 + + complete = 0 + total = len(results) + + for result in results: + payload = result.get('payload', {}) + url = payload.get('url', '') + text = payload.get('text', '') + title = payload.get('title', '') + section = payload.get('section', '') + + # Check completeness criteria + url_ok = bool(url and url.strip()) + text_ok = len(text or '') >= 10 + title_section_ok = bool((title and title.strip()) or (section and section.strip())) + + if url_ok and text_ok and title_section_ok: + complete += 1 + + percentage = (complete / total) * 100 + logger.debug(f"Metadata completeness: {complete}/{total} = {percentage:.1f}%") + return percentage + + +def validate_chunk_sequencing(results: List[Dict[str, Any]]) -> bool: + """ + Verify that chunk_index values are properly assigned: integers >= 0 and unique per URL. + + Note: Since search may return only a subset of chunks for a URL, we cannot + verify full sequential continuity (0,1,2,3...). Instead we check: + - All chunk_index values are integers >= 0 + - No duplicate chunk_index for the same URL in the result set + + Args: + results: List of search results + + Returns: + True if chunk indices are valid, False otherwise + """ + # Group by URL + url_chunks = {} + for result in results: + payload = result.get('payload', {}) + url = payload.get('url', '') + chunk_idx = payload.get('chunk_index') + + if url not in url_chunks: + url_chunks[url] = [] + url_chunks[url].append(chunk_idx) + + # Check each URL's chunks are valid + for url, indices in url_chunks.items(): + # All indices must be integers >= 0 + for idx in indices: + if not isinstance(idx, int) or idx < 0: + logger.debug(f"Invalid chunk_index for {url}: {idx} (must be non-negative integer)") + return False + + # Check for duplicates (within this URL's results) + if len(set(indices)) != len(indices): + logger.debug(f"Duplicate chunk_index for {url}: {indices}") + return False + + logger.debug(f"Chunk indexing valid for {len(url_chunks)} URLs") + return True + + +def search( + query_text: str, + cohere_client: cohere.ClientV2, + qdrant_client: QdrantClient, + collection_name: str, + top_k: int = 5 +) -> List[Dict[str, Any]]: + """ + Convert query to embedding and retrieve top-K relevant chunks. + + Args: + query_text: User's search query (non-empty, ≤1000 chars) + top_k: Number of results to return (1-100) + + Returns: + List of search results with id, score, and payload + """ + # Validate inputs + if not query_text or not query_text.strip(): + raise ValueError("Query text must be non-empty") + query_text = query_text.strip() + if len(query_text) > 1000: + raise ValueError("Query text must be ≤ 1000 characters") + if top_k < 1 or top_k > 100: + raise ValueError("top_k must be between 1 and 100") + + logger.info(f"Embedding query: '{query_text[:100]}...' (top_k={top_k})") + start_time = time.time() + + # Generate query embedding with retry + try: + embedding = utils.retry_with_backoff( + lambda: embed_query(query_text, cohere_client), + max_retries=3, + base_delay=1.0, + max_delay=10.0 + ) + embed_time = time.time() - start_time + logger.debug(f"Generated embedding in {embed_time:.2f}s, dimension: {len(embedding)}") + except Exception as e: + logger.error(f"Failed to embed query: {e}") + raise + + # Search Qdrant with retry + try: + search_start = time.time() + response = utils.retry_with_backoff( + lambda: qdrant_client.query_points( + collection_name=collection_name, + query=embedding, + limit=top_k, + with_payload=True, + with_vectors=False + ), + max_retries=3, + base_delay=1.0, + max_delay=10.0 + ) + results = response.points + search_time = time.time() - search_start + logger.info(f"Search completed in {search_time:.2f}s, returned {len(results)} results") + except Exception as e: + logger.error(f"Search failed: {e}") + raise APIError(f"Qdrant search failed: {e}") + + # Format results + formatted = [] + for result in results: + formatted.append({ + "id": str(result.id), + "score": float(result.score), + "payload": result.payload + }) + + total_time = time.time() - start_time + logger.info(f"Total query time: {total_time:.2f}s") + + return formatted + +def format_results( + results: List[Dict[str, Any]], + query: str, + latency_ms: int +) -> Dict[str, Any]: + """Format search results into JSON output structure.""" + output = { + "query": query, + "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + "results": results, + "metadata": { + "total_results": len(results), + "collection": None, # Will be filled by main + "latency_ms": latency_ms + } + } + return output + +def main() -> int: + """CLI entrypoint for retrieval.""" + parser = argparse.ArgumentParser( + description="Retrieve relevant chunks from Qdrant using Cohere embeddings" + ) + parser.add_argument( + "--query", + type=str, + help="Search query text" + ) + parser.add_argument( + "--top-k", + type=int, + default=5, + help="Number of results to return (default: 5)" + ) + parser.add_argument( + "--output", + type=str, + help="Output file path (default: stdout)" + ) + parser.add_argument( + "--config", + type=str, + default=".env", + help="Path to .env config file (default: .env)" + ) + parser.add_argument( + "--validate-metadata", + action="store_true", + help="Run metadata validation on search results (requires --query)" + ) + + args = parser.parse_args() + + # Setup logging + log_file = "retrieve.log" + setup_logging(log_file=log_file, console_level="INFO") + logger.info("=== Retrieval Pipeline Started ===") + + try: + # Load config + logger.info(f"Loading config from {args.config}") + cfg = config.get_config() + validate_config(cfg) + + # Initialize clients + logger.info("Initializing Cohere and Qdrant clients") + cohere_client, qdrant_client = init_clients(cfg) + + # Check collection + collection_name = cfg["qdrant_collection"] + logger.info(f"Checking collection '{collection_name}'") + coll_info = check_collection(qdrant_client, collection_name) + logger.info(f"Collection OK: vector_size={coll_info['vector_size']}, points={coll_info['points_count']}") + + # Validate query argument + if not args.query: + parser.error("--query is required") + + # Perform search + results = search( + query_text=args.query, + cohere_client=cohere_client, + qdrant_client=qdrant_client, + collection_name=collection_name, + top_k=args.top_k + ) + + # Perform metadata validation if requested + metadata_validation = None + if args.validate_metadata: + completeness = validate_metadata_completeness(results) + sequencing = validate_chunk_sequencing(results) + metadata_validation = { + "completeness_pct": round(completeness, 2), + "sequencing_valid": sequencing, + "pass": completeness >= 98.0 and sequencing + } + logger.info(f"Metadata completeness: {completeness:.1f}%") + logger.info(f"Chunk sequencing: {'VALID' if sequencing else 'INVALID'}") + logger.info(f"Validation result: {'PASS' if metadata_validation['pass'] else 'FAIL'}") + + # Format output + output = { + "query": args.query, + "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + "results": results, + "metadata": { + "total_results": len(results), + "collection": collection_name, + "vector_size": coll_info['vector_size'], + "points_count": coll_info['points_count'] + } + } + + if metadata_validation: + output["metadata_validation"] = metadata_validation + + # Output JSON + json_output = json.dumps(output, indent=2) + if args.output: + with open(args.output, 'w') as f: + f.write(json_output) + logger.info(f"Results written to {args.output}") + else: + print(json_output) + + logger.info("=== Retrieval Pipeline Completed Successfully ===") + return 0 + + except ValueError as ve: + logger.error(f"Validation error: {ve}") + print(f"ERROR: {ve}", file=sys.stderr) + return 2 + except ConfigurationError as ce: + logger.error(f"Configuration error: {ce}") + print(f"ERROR: {ce}", file=sys.stderr) + return 1 + except CollectionNotFoundError as cnfe: + logger.error(f"Collection error: {cnfe}") + print(f"ERROR: {cnfe}", file=sys.stderr) + return 1 + except DimensionMismatchError as dme: + logger.error(f"Dimension error: {dme}") + print(f"ERROR: {dme}", file=sys.stderr) + return 1 + except APIError as api_err: + logger.error(f"API error: {api_err}") + print(f"ERROR: {api_err}", file=sys.stderr) + return 1 + except Exception as e: + logger.exception(f"Unexpected error: {e}") + print(f"ERROR: Unexpected error: {e}", file=sys.stderr) + return 1 + +if __name__ == "__main__": + sys.exit(main()) diff --git a/sitemap_urls.txt b/sitemap_urls.txt new file mode 100644 index 0000000000000000000000000000000000000000..ae441733905f93eb307727e4c473a3512fb4671f --- /dev/null +++ b/sitemap_urls.txt @@ -0,0 +1,40 @@ +https://humanoid-ai-robotics-book-1.vercel.app/blog +https://humanoid-ai-robotics-book-1.vercel.app/blog/archive +https://humanoid-ai-robotics-book-1.vercel.app/blog/authors +https://humanoid-ai-robotics-book-1.vercel.app/blog/authors/all-sebastien-lorber-articles +https://humanoid-ai-robotics-book-1.vercel.app/blog/authors/yangshun +https://humanoid-ai-robotics-book-1.vercel.app/blog/first-blog-post +https://humanoid-ai-robotics-book-1.vercel.app/blog/long-blog-post +https://humanoid-ai-robotics-book-1.vercel.app/blog/mdx-blog-post +https://humanoid-ai-robotics-book-1.vercel.app/blog/tags +https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/docusaurus +https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/facebook +https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hello +https://humanoid-ai-robotics-book-1.vercel.app/blog/tags/hola +https://humanoid-ai-robotics-book-1.vercel.app/blog/welcome +https://humanoid-ai-robotics-book-1.vercel.app/markdown-page +https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---basics +https://humanoid-ai-robotics-book-1.vercel.app/docs/category/tutorial---extras +https://humanoid-ai-robotics-book-1.vercel.app/docs/intro +https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/intro-to-ros2 +https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/ros2-communication-model +https://humanoid-ai-robotics-book-1.vercel.app/docs/module-1/urdf-humanoids +https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/gazebo-physics-simulation +https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/intro-to-digital-twins +https://humanoid-ai-robotics-book-1.vercel.app/docs/module-2/unity-interaction-sensors +https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/intro-to-ai-robot-brain +https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/navigation-intelligence +https://humanoid-ai-robotics-book-1.vercel.app/docs/module-3/perception-simulation-isaac-sim +https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/cognitive-planning-llms +https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/documentation-standards +https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/intro-to-vla +https://humanoid-ai-robotics-book-1.vercel.app/docs/module-4/voice-to-action-whisper +https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/congratulations +https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-blog-post +https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-document +https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/create-a-page +https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/deploy-your-site +https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-basics/markdown-features +https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/manage-docs-versions +https://humanoid-ai-robotics-book-1.vercel.app/docs/tutorial-extras/translate-your-site +https://humanoid-ai-robotics-book-1.vercel.app/ diff --git a/test_local.py b/test_local.py new file mode 100644 index 0000000000000000000000000000000000000000..d6edf145c0f233de5cf6a52cced34de455742a2d --- /dev/null +++ b/test_local.py @@ -0,0 +1,118 @@ +""" +Local test that reads sample HTML file and processes it through the pipeline. +This tests the full flow without relying on external URLs. +""" +import sys +import os +from pathlib import Path + +# Add backend to path +sys.path.insert(0, str(Path(__file__).parent)) + +import config +import utils +from logging_config import setup_logging +from main import ( + extract_text, chunk_text, generate_embeddings, ensure_collection, + upsert_chunks, run_validation +) +import cohere +from qdrant_client import QdrantClient + +logger = setup_logging() + +def test_with_fixture(): + """Test pipeline using the sample_page.html fixture.""" + import cohere as cohere_module + + # Load config + cfg = config.get_config() + config.validate_config(cfg) + + # Read fixture HTML + fixture_path = Path(__file__).parent / 'tests' / 'fixtures' / 'sample_page.html' + with open(fixture_path, 'r') as f: + html = f.read() + + url = "https://example.com/sample-page" + + # Extract text + text = extract_text(html, url) + logger.info(f"Extracted {len(text)} characters from fixture") + print(f"Text preview: {text[:200]}...") + + # Chunk text + chunks_data = chunk_text(text, chunk_size=500, overlap=50) + logger.info(f"Chunked into {len(chunks_data)} segments") + print(f"First chunk: {chunks_data[0]['text'][:100]}...") + + # Generate embeddings - test with Cohere directly to see response structure + cohere_client = cohere.ClientV2(api_key=cfg['cohere_api_key']) + texts = [chunk['text'] for chunk in chunks_data] + + # Simple test: get one embedding to understand structure + test_response = cohere_client.embed( + texts=[texts[0]], + model="embed-english-v3.0", + input_type="search_document" + ) + print(f"\nCohere response type: {type(test_response)}") + print(f"Embeddings attribute: {type(test_response.embeddings)}") + print(f"Embeddings.float_ attribute: {type(test_response.embeddings.float_)}") + + # Access embeddings directly via .float_ attribute + embeddings_float = test_response.embeddings.float_ + if embeddings_float: + first_emb = embeddings_float[0] + print(f"First embedding type: {type(first_emb)}") + print(f"First embedding dimension: {len(first_emb)}") + print(f"First embedding sample (first 5 values): {first_emb[:5]}") + + # Now generate all using the proper extraction method + embeddings = generate_embeddings(texts, cohere_client, batch_size=96) + logger.info(f"Generated {len(embeddings)} embeddings") + print(f"Embedding dimension: {len(embeddings[0])}") + + # Initialize Qdrant and ensure collection + qdrant_client = QdrantClient( + url=cfg['qdrant_url'], + api_key=cfg['qdrant_api_key'] + ) + collection = cfg['qdrant_collection'] + ensure_collection(qdrant_client, collection) + + # Prepare records + records = [] + for i, (chunk, embedding) in enumerate(zip(chunks_data, embeddings)): + record = { + 'url': url, + 'title': url, + 'section': '', + 'chunk_index': i, + 'text': chunk['text'], + 'embedding': embedding + } + records.append(record) + + # Upsert + stats = upsert_chunks(qdrant_client, collection, records, utils.deterministic_id) + logger.info(f"Upsert stats: {stats}") + print(f"Upserted {stats['total']} points to Qdrant") + + # Verify + info = qdrant_client.get_collection(collection) + print(f"Collection now has {info.points_count} points") + + # Run validation + print("\n--- Running Validation ---") + run_validation(qdrant_client, collection, sample_size=min(10, info.points_count)) + + print("\n✅ Local test complete!") + return True + +if __name__ == "__main__": + try: + test_with_fixture() + except Exception as e: + logger.error(f"Test failed: {e}", exc_info=True) + sys.exit(1) diff --git a/test_run.log b/test_run.log new file mode 100644 index 0000000000000000000000000000000000000000..308766a5a4b2ffdd71639e2cf5b90ecafdc74878 --- /dev/null +++ b/test_run.log @@ -0,0 +1,32 @@ +INFO: HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +INFO: HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +INFO: Collection 'book_embeddings' already exists +INFO: Starting ingestion: 3 URLs +INFO: Chunk size: 500, overlap: 50 +INFO: Fetching https://bookshelf.math.buffalo.edu/bookshelf-free-books-direction.html... +ERROR: Failed to fetch https://bookshelf.math.buffalo.edu/bookshelf-free-books-direction.html after 5 attempts: [Errno -2] Name or service not known +ERROR: Failed to process https://bookshelf.math.buffalo.edu/bookshelf-free-books-direction.html: [Errno -2] Name or service not known +INFO: Fetching https://en.wikipedia.org/wiki/Book... +INFO: HTTP Request: GET https://en.wikipedia.org/wiki/Book "HTTP/1.1 403 Forbidden" +INFO: HTTP Request: GET https://en.wikipedia.org/wiki/Book "HTTP/1.1 403 Forbidden" +INFO: HTTP Request: GET https://en.wikipedia.org/wiki/Book "HTTP/1.1 403 Forbidden" +INFO: HTTP Request: GET https://en.wikipedia.org/wiki/Book "HTTP/1.1 403 Forbidden" +INFO: HTTP Request: GET https://en.wikipedia.org/wiki/Book "HTTP/1.1 403 Forbidden" +ERROR: Failed to fetch https://en.wikipedia.org/wiki/Book after 5 attempts: Client error '403 Forbidden' for url 'https://en.wikipedia.org/wiki/Book' +For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403 +ERROR: Failed to process https://en.wikipedia.org/wiki/Book: Client error '403 Forbidden' for url 'https://en.wikipedia.org/wiki/Book' +For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403 +INFO: Fetching https://www.gutenberg.org/files/1342/1342-h/1342-h.htm... +INFO: HTTP Request: GET https://www.gutenberg.org/files/1342/1342-h/1342-h.htm "HTTP/1.1 200 OK" +INFO: Extracted 717156 characters from https://www.gutenberg.org/files/1342/1342-h/1342-h.htm, chunked into 1435 segments +INFO: HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 400 Bad Request" +ERROR: Failed to generate embeddings for batch starting at index 0: headers: {'access-control-expose-headers': 'X-Debug-Trace-ID', 'cache-control': 'no-cache, no-store, no-transform, must-revalidate, private, max-age=0', 'content-encoding': 'gzip', 'content-type': 'application/json', 'expires': 'Thu, 01 Jan 1970 00:00:00 GMT', 'pragma': 'no-cache', 'vary': 'Origin,Accept-Encoding', 'x-accel-expires': '0', 'x-debug-trace-id': '1da9e24a767597a154eaa71df36dcb52', 'x-endpoint-monthly-call-limit': '1000', 'x-trial-endpoint-call-limit': '100', 'x-trial-endpoint-call-remaining': '99', 'date': 'Mon, 16 Feb 2026 20:38:33 GMT', 'x-envoy-upstream-service-time': '5', 'server': 'envoy', 'via': '1.1 google', 'alt-svc': 'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000', 'transfer-encoding': 'chunked'}, status_code: 400, body: {'id': 'cd20a5e4-1efc-44b5-a9f6-43d7a24004ce', 'message': 'invalid request: total number of texts must be at most 96 - received 100'} +ERROR: Failed to process https://www.gutenberg.org/files/1342/1342-h/1342-h.htm: headers: {'access-control-expose-headers': 'X-Debug-Trace-ID', 'cache-control': 'no-cache, no-store, no-transform, must-revalidate, private, max-age=0', 'content-encoding': 'gzip', 'content-type': 'application/json', 'expires': 'Thu, 01 Jan 1970 00:00:00 GMT', 'pragma': 'no-cache', 'vary': 'Origin,Accept-Encoding', 'x-accel-expires': '0', 'x-debug-trace-id': '1da9e24a767597a154eaa71df36dcb52', 'x-endpoint-monthly-call-limit': '1000', 'x-trial-endpoint-call-limit': '100', 'x-trial-endpoint-call-remaining': '99', 'date': 'Mon, 16 Feb 2026 20:38:33 GMT', 'x-envoy-upstream-service-time': '5', 'server': 'envoy', 'via': '1.1 google', 'alt-svc': 'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000', 'transfer-encoding': 'chunked'}, status_code: 400, body: {'id': 'cd20a5e4-1efc-44b5-a9f6-43d7a24004ce', 'message': 'invalid request: total number of texts must be at most 96 - received 100'} +INFO: ================================================== +INFO: Ingestion complete! +INFO: Total pages processed: 0 +INFO: Total chunks stored: 0 +WARNING: Failed URLs (3): https://bookshelf.math.buffalo.edu/bookshelf-free-books-direction.html, https://en.wikipedia.org/wiki/Book, https://www.gutenberg.org/files/1342/1342-h/1342-h.htm +INFO: ================================================== +INFO: HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +INFO: Qdrant collection 'book_embeddings' now has 0 points diff --git a/test_simple.log b/test_simple.log new file mode 100644 index 0000000000000000000000000000000000000000..c1bac4b732de386516bcfb532ff5dfc32fac97a2 --- /dev/null +++ b/test_simple.log @@ -0,0 +1,18 @@ +INFO: HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +INFO: HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections "HTTP/1.1 200 OK" +INFO: Collection 'book_embeddings' already exists +INFO: Starting ingestion: 1 URLs +INFO: Chunk size: 200, overlap: 20 +INFO: Fetching http://example.com... +INFO: HTTP Request: GET http://example.com "HTTP/1.1 200 OK" +INFO: Extracted 142 characters from http://example.com, chunked into 1 segments +INFO: HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +ERROR: Failed to process http://example.com: Unexpected embedding dimension: 2 +INFO: ================================================== +INFO: Ingestion complete! +INFO: Total pages processed: 0 +INFO: Total chunks stored: 0 +WARNING: Failed URLs (1): http://example.com +INFO: ================================================== +INFO: HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +INFO: Qdrant collection 'book_embeddings' now has 0 points diff --git a/test_urls.txt b/test_urls.txt new file mode 100644 index 0000000000000000000000000000000000000000..2fa13faec140e30deb8a7982ced6abd611d05993 --- /dev/null +++ b/test_urls.txt @@ -0,0 +1,3 @@ +https://bookshelf.math.buffalo.edu/bookshelf-free-books-direction.html +https://en.wikipedia.org/wiki/Book +https://www.gutenberg.org/files/1342/1342-h/1342-h.htm diff --git a/tests/fixtures/sample_page.html b/tests/fixtures/sample_page.html new file mode 100644 index 0000000000000000000000000000000000000000..9f5eb4bb412c185031b7de3bfacc3702aa20221b --- /dev/null +++ b/tests/fixtures/sample_page.html @@ -0,0 +1,30 @@ + + + + Sample Book Page - Introduction + + + +
Book Title: Example Book
+ +

Chapter 1: Introduction

+ +

This is the first paragraph of the introduction. It provides an overview of the topic.

+ +

The second paragraph discusses the background and context. It contains several sentences that should be chunked appropriately when the text is processed.

+ +

1.1 Background

+ +

This is a subsection with its own content. It provides more detailed information about the background.

+ +

Another paragraph in the background section. This helps test the chunking algorithm with multiple paragraphs and sections.

+ +

1.2 Objectives

+ +

This subsection covers the objectives. It should be extracted with its section identifier.

+ +

Final paragraph of this sample page. It ensures we have enough text to create multiple chunks when using a chunk size of 1000 characters.

+ +
Footer content like copyright info should be removed.
+ + diff --git a/utils.py b/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9d6d20a6ff086c89624a04a0f841a79b92ee6109 --- /dev/null +++ b/utils.py @@ -0,0 +1,54 @@ +""" +Utility functions for the ingestion pipeline. +""" +import hashlib +import time +import random +from typing import Callable, Any + +def deterministic_id(url: str, chunk_index: int) -> str: + """ + Generate deterministic point ID for Qdrant. + Uses SHA256 hash of "url:chunk_index" and formats as a valid UUID. + Qdrant accepts UUID v4 format or unsigned integers. + """ + import uuid + key = f"{url}:{chunk_index}" + hash_bytes = hashlib.sha256(key.encode()).digest() + # Convert to UUID v4 format (using random variant) but deterministic from hash + # Use first 16 bytes of SHA256 to create a UUID + uuid_bytes = hash_bytes[:16] + # Set version to 4 (random) and variant to RFC 4122 + uuid_bytes = uuid_bytes[:6] + bytes([(uuid_bytes[6] & 0x0f) | 0x40]) + bytes([uuid_bytes[7] & 0x3f | 0x80]) + uuid_bytes[8:] + return str(uuid.UUID(bytes=uuid_bytes)) + + +def verify_deterministic_id(url: str, chunk_index: int, expected_id: str = None) -> str: + """ + Verify deterministic ID generation and detect collisions. + Returns the generated ID. If expected_id is provided and differs, logs warning. + """ + generated_id = deterministic_id(url, chunk_index) + if expected_id and generated_id != expected_id: + logger.warning(f"ID collision detected for {url}:{chunk_index}. Expected {expected_id}, got {generated_id}") + return generated_id + +def retry_with_backoff( + func: Callable, + max_retries: int = 5, + base_delay: float = 1.0, + max_delay: float = 30.0, + jitter: float = 0.1 +) -> Any: + """ + Retry a function with exponential backoff and jitter. + Suitable for API calls (Cohere, Qdrant, HTTP). + """ + for attempt in range(max_retries): + try: + return func() + except Exception as e: + if attempt == max_retries - 1: + raise + delay = min(base_delay * (2 ** attempt) + random.uniform(-jitter, jitter), max_delay) + time.sleep(delay) diff --git a/validate.py b/validate.py new file mode 100644 index 0000000000000000000000000000000000000000..2e74cae7d62aa2200fbddeedd0a0a7a7b1570057 --- /dev/null +++ b/validate.py @@ -0,0 +1,60 @@ +#!/usr/bin/env python3 +""" +Simple validation script to test the ingestion pipeline components. +""" +import sys +import os +sys.path.insert(0, '.') + +def test_imports(): + """Test all imports work.""" + import config + import utils + import logging_config + print("✓ All imports successful") + +def test_deterministic_id(): + """Test deterministic ID generation.""" + from utils import deterministic_id + id1 = deterministic_id("https://example.com/page", 0) + id2 = deterministic_id("https://example.com/page", 0) + id3 = deterministic_id("https://example.com/page", 1) + assert id1 == id2, "Same input should produce same ID" + assert id1 != id3, "Different chunk_index should produce different ID" + assert len(id1) == 64, "SHA256 hex should be 64 characters" + print("✓ Deterministic ID generation works") + +def test_chunking(): + """Test text chunking logic.""" + from main import chunk_text + text = "A" * 2500 # 2500 characters + chunks = chunk_text(text, chunk_size=1000, overlap=100) + assert len(chunks) > 1, "Long text should produce multiple chunks" + assert all('text' in c and 'char_start' in c and 'char_end' in c for c in chunks), "Chunks have required fields" + # Check overlap + for i in range(1, len(chunks)): + gap = chunks[i]['char_start'] - chunks[i-1]['char_end'] + assert gap <= 100, f"Overlap should be <=100, got {gap}" + print(f"✓ Chunking works: {len(chunks)} chunks from 2500 chars") + +def test_config(): + """Test config loading.""" + import config as cfg_module + try: + cfg = cfg_module.get_config() + cfg_module.validate_config(cfg) + except ValueError as e: + if "Missing required environment variables" in str(e): + print("✓ Config validation works (expected: missing env vars when .env not set)") + else: + raise + +if __name__ == "__main__": + print("Running validation tests...") + test_imports() + test_deterministic_id() + test_chunking() + test_config() + print("\n✓ All validation tests passed!") + print("\nTo test full pipeline, set COHERE_API_KEY and QDRANT credentials in .env and run:") + print(" python backend/main.py --urls https://example.com") diff --git a/validate_rag.log b/validate_rag.log new file mode 100644 index 0000000000000000000000000000000000000000..d8faf2760dc591085490255fc221e467ff769344 --- /dev/null +++ b/validate_rag.log @@ -0,0 +1,468 @@ +2026-02-17 02:44:00 - INFO - validate_rag.py:264 - === RAG Validation Suite Started === +2026-02-17 02:44:00 - INFO - validate_rag.py:268 - Loading config from .env +2026-02-17 02:44:00 - INFO - validate_rag.py:273 - Initializing Cohere and Qdrant clients +2026-02-17 02:44:00 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:44:00 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:44:00 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:44:00 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:44:00 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:44:00 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:44:00 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:44:00 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:44:00 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:43:59 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:44:01 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:44:01 - INFO - validate_rag.py:280 - Checking collection 'book_embeddings' +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:44:00 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:44:01 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:44:00 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:44:01 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/scroll "HTTP/1.1 200 OK" +2026-02-17 02:44:01 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:44:00 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:44:02 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/scroll "HTTP/1.1 200 OK" +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:44:00 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:44:02 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/scroll "HTTP/1.1 200 OK" +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:44:02 - INFO - validate_rag.py:285 - Collection stats: 277 points, 43 unique URLs, vector_size=1024 +2026-02-17 02:44:02 - INFO - validate_rag.py:297 - Using 5 default test queries +2026-02-17 02:44:02 - INFO - validate_rag.py:300 - Running validation queries... +2026-02-17 02:44:02 - INFO - validate_rag.py:118 - Query 1/5: 'What is ROS 2?...' +2026-02-17 02:44:02 - INFO - retrieve.py:200 - Embedding query: 'What is ROS 2?...' (top_k=5) +2026-02-17 02:44:02 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'14'), (b'num_tokens', b'6'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'0e6446324ed548141f5a0d69b80ee241'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 21:44:02 GMT'), (b'x-envoy-upstream-service-time', b'42'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:44:03 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:44:03 - DEBUG - retrieve.py:212 - Generated embedding in 0.42s, dimension: 1024 +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:44:02 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:44:03 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:44:03 - INFO - retrieve.py:234 - Search completed in 0.32s, returned 5 results +2026-02-17 02:44:03 - INFO - retrieve.py:249 - Total query time: 0.74s +2026-02-17 02:44:03 - DEBUG - retrieve.py:127 - Metadata completeness: 5/5 = 100.0% +2026-02-17 02:44:03 - INFO - validate_rag.py:151 - → 5 results, avg_score=0.5969, passed=True +2026-02-17 02:44:03 - INFO - validate_rag.py:118 - Query 2/5: 'How do humanoid robots walk?...' +2026-02-17 02:44:03 - INFO - retrieve.py:200 - Embedding query: 'How do humanoid robots walk?...' (top_k=5) +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:44:03 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'28'), (b'num_tokens', b'6'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'a181bc69f6a3a9a91ae30053961d08b1'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'98'), (b'date', b'Mon, 16 Feb 2026 21:44:02 GMT'), (b'x-envoy-upstream-service-time', b'40'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:44:04 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:44:04 - DEBUG - retrieve.py:212 - Generated embedding in 0.37s, dimension: 1024 +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:44:02 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:44:04 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:44:04 - INFO - retrieve.py:234 - Search completed in 0.33s, returned 5 results +2026-02-17 02:44:04 - INFO - retrieve.py:249 - Total query time: 0.70s +2026-02-17 02:44:04 - DEBUG - retrieve.py:127 - Metadata completeness: 5/5 = 100.0% +2026-02-17 02:44:04 - INFO - validate_rag.py:151 - → 5 results, avg_score=0.5882, passed=True +2026-02-17 02:44:04 - INFO - validate_rag.py:118 - Query 3/5: 'Explain navigation intelligence in robots...' +2026-02-17 02:44:04 - INFO - retrieve.py:200 - Embedding query: 'Explain navigation intelligence in robots...' (top_k=5) +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'41'), (b'num_tokens', b'5'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'232ec4a0442fe5d1eaeff6a4d03d2f17'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'97'), (b'date', b'Mon, 16 Feb 2026 21:44:03 GMT'), (b'x-envoy-upstream-service-time', b'60'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:44:04 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:44:04 - DEBUG - retrieve.py:212 - Generated embedding in 0.36s, dimension: 1024 +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:44:04 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:44:03 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:44:05 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:44:05 - INFO - retrieve.py:234 - Search completed in 0.32s, returned 5 results +2026-02-17 02:44:05 - INFO - retrieve.py:249 - Total query time: 0.68s +2026-02-17 02:44:05 - DEBUG - retrieve.py:127 - Metadata completeness: 5/5 = 100.0% +2026-02-17 02:44:05 - INFO - validate_rag.py:151 - → 5 results, avg_score=0.5947, passed=True +2026-02-17 02:44:05 - INFO - validate_rag.py:118 - Query 4/5: 'What is a digital twin?...' +2026-02-17 02:44:05 - INFO - retrieve.py:200 - Embedding query: 'What is a digital twin?...' (top_k=5) +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'23'), (b'num_tokens', b'6'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'b54b0d04a183972fbaaf5c6de8507c0b'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'96'), (b'date', b'Mon, 16 Feb 2026 21:44:03 GMT'), (b'x-envoy-upstream-service-time', b'42'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:44:05 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:44:05 - DEBUG - retrieve.py:212 - Generated embedding in 0.34s, dimension: 1024 +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:44:04 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:44:05 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:44:05 - INFO - retrieve.py:234 - Search completed in 0.32s, returned 5 results +2026-02-17 02:44:05 - INFO - retrieve.py:249 - Total query time: 0.66s +2026-02-17 02:44:05 - DEBUG - retrieve.py:127 - Metadata completeness: 5/5 = 100.0% +2026-02-17 02:44:05 - INFO - validate_rag.py:151 - → 5 results, avg_score=0.6145, passed=True +2026-02-17 02:44:05 - INFO - validate_rag.py:118 - Query 5/5: 'How to use LLMs for robot planning?...' +2026-02-17 02:44:05 - INFO - retrieve.py:200 - Embedding query: 'How to use LLMs for robot planning?...' (top_k=5) +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:44:05 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:44:06 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'35'), (b'num_tokens', b'9'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'005889d737c82462c0d9230bde491aaa'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'95'), (b'date', b'Mon, 16 Feb 2026 21:44:04 GMT'), (b'x-envoy-upstream-service-time', b'37'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:44:06 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:44:06 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:44:06 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:44:06 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:44:06 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:44:06 - DEBUG - retrieve.py:212 - Generated embedding in 0.34s, dimension: 1024 +2026-02-17 02:44:06 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:44:06 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:44:06 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:44:06 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:44:06 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:44:06 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:44:04 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:44:06 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +2026-02-17 02:44:06 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:44:06 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:44:06 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:44:06 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:44:06 - INFO - retrieve.py:234 - Search completed in 0.32s, returned 5 results +2026-02-17 02:44:06 - INFO - retrieve.py:249 - Total query time: 0.66s +2026-02-17 02:44:06 - DEBUG - retrieve.py:127 - Metadata completeness: 5/5 = 100.0% +2026-02-17 02:44:06 - INFO - validate_rag.py:151 - → 5 results, avg_score=0.6683, passed=True +2026-02-17 02:44:06 - DEBUG - retrieve.py:127 - Metadata completeness: 25/25 = 100.0% +2026-02-17 02:44:06 - INFO - validate_rag.py:328 - ================================================== +2026-02-17 02:44:06 - INFO - validate_rag.py:329 - Validation Summary: +2026-02-17 02:44:06 - INFO - validate_rag.py:330 - Collection: 277 points, 43 URLs +2026-02-17 02:44:06 - INFO - validate_rag.py:331 - Queries: 5 tested, 5 passed +2026-02-17 02:44:06 - INFO - validate_rag.py:332 - Metadata completeness: 100.0% +2026-02-17 02:44:06 - INFO - validate_rag.py:333 - Avg results per query: 5.0 +2026-02-17 02:44:06 - INFO - validate_rag.py:334 - Avg top score: 0.6125 +2026-02-17 02:44:06 - INFO - validate_rag.py:335 - Overall Status: PASS +2026-02-17 02:44:06 - INFO - validate_rag.py:338 - ✅ RAG Validation PASSED +2026-02-17 02:44:06 - INFO - validate_rag.py:343 - ================================================== +2026-02-17 02:44:06 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:44:06 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:48:39 - INFO - validate_rag.py:264 - === RAG Validation Suite Started === +2026-02-17 02:48:39 - INFO - validate_rag.py:268 - Loading config from .env +2026-02-17 02:48:39 - INFO - validate_rag.py:273 - Initializing Cohere and Qdrant clients +2026-02-17 02:48:39 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:48:40 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:48:40 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:48:40 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:48:40 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:48:40 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:48:40 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:48:40 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:48:40 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:48:40 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:48:40 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:48:40 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333 "HTTP/1.1 200 OK" +2026-02-17 02:48:40 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:48:40 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:48:40 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:48:40 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:48:40 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:48:40 - DEBUG - _trace.py:47 - close.complete +2026-02-17 02:48:40 - INFO - validate_rag.py:280 - Checking collection 'book_embeddings' +2026-02-17 02:48:40 - DEBUG - _trace.py:47 - connect_tcp.started host='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' port=6333 local_address=None timeout=5.0 socket_options=None +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io' timeout=5.0 +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:48:41 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:48:41 - INFO - _client.py:1025 - HTTP Request: GET https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings "HTTP/1.1 200 OK" +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:48:41 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:48:41 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/scroll "HTTP/1.1 200 OK" +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:48:41 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:48:41 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:48:42 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/scroll "HTTP/1.1 200 OK" +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:48:42 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:48:42 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/scroll "HTTP/1.1 200 OK" +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:48:42 - INFO - validate_rag.py:285 - Collection stats: 277 points, 43 unique URLs, vector_size=1024 +2026-02-17 02:48:42 - INFO - validate_rag.py:297 - Using 5 default test queries +2026-02-17 02:48:42 - INFO - validate_rag.py:300 - Running validation queries... +2026-02-17 02:48:42 - INFO - validate_rag.py:118 - Query 1/5: 'What is ROS 2?...' +2026-02-17 02:48:42 - INFO - retrieve.py:200 - Embedding query: 'What is ROS 2?...' (top_k=5) +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - connect_tcp.started host='api.cohere.com' port=443 local_address=None timeout=300 socket_options=None +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - connect_tcp.complete return_value= +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - start_tls.started ssl_context= server_hostname='api.cohere.com' timeout=300 +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - start_tls.complete return_value= +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'14'), (b'num_tokens', b'6'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'8707b3a794a01af5424e2c041acf8797'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'99'), (b'date', b'Mon, 16 Feb 2026 21:48:42 GMT'), (b'x-envoy-upstream-service-time', b'40'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:48:42 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:48:42 - DEBUG - retrieve.py:212 - Generated embedding in 0.45s, dimension: 1024 +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:48:42 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:48:42 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:48:43 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:48:43 - INFO - retrieve.py:234 - Search completed in 0.31s, returned 5 results +2026-02-17 02:48:43 - INFO - retrieve.py:249 - Total query time: 0.76s +2026-02-17 02:48:43 - DEBUG - retrieve.py:127 - Metadata completeness: 5/5 = 100.0% +2026-02-17 02:48:43 - INFO - validate_rag.py:151 - → 5 results, avg_score=0.5969, passed=True +2026-02-17 02:48:43 - INFO - validate_rag.py:118 - Query 2/5: 'How do humanoid robots walk?...' +2026-02-17 02:48:43 - INFO - retrieve.py:200 - Embedding query: 'How do humanoid robots walk?...' (top_k=5) +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'28'), (b'num_tokens', b'6'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'9d9b525c2f33f8fbe8885ef667598f56'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'98'), (b'date', b'Mon, 16 Feb 2026 21:48:43 GMT'), (b'x-envoy-upstream-service-time', b'46'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:48:43 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:48:43 - DEBUG - retrieve.py:212 - Generated embedding in 0.35s, dimension: 1024 +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:48:43 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:48:43 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:48:43 - INFO - retrieve.py:234 - Search completed in 0.33s, returned 5 results +2026-02-17 02:48:43 - INFO - retrieve.py:249 - Total query time: 0.68s +2026-02-17 02:48:43 - DEBUG - retrieve.py:127 - Metadata completeness: 5/5 = 100.0% +2026-02-17 02:48:43 - INFO - validate_rag.py:151 - → 5 results, avg_score=0.5883, passed=True +2026-02-17 02:48:43 - INFO - validate_rag.py:118 - Query 3/5: 'Explain navigation intelligence in robots...' +2026-02-17 02:48:43 - INFO - retrieve.py:200 - Embedding query: 'Explain navigation intelligence in robots...' (top_k=5) +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:48:43 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'41'), (b'num_tokens', b'5'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'd6683ad44dc8b30f6a4e4ccd51f4878d'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'97'), (b'date', b'Mon, 16 Feb 2026 21:48:44 GMT'), (b'x-envoy-upstream-service-time', b'33'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:48:44 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:48:44 - DEBUG - retrieve.py:212 - Generated embedding in 0.33s, dimension: 1024 +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:48:44 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:48:44 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:48:44 - INFO - retrieve.py:234 - Search completed in 0.32s, returned 5 results +2026-02-17 02:48:44 - INFO - retrieve.py:249 - Total query time: 0.65s +2026-02-17 02:48:44 - DEBUG - retrieve.py:127 - Metadata completeness: 5/5 = 100.0% +2026-02-17 02:48:44 - INFO - validate_rag.py:151 - → 5 results, avg_score=0.5949, passed=True +2026-02-17 02:48:44 - INFO - validate_rag.py:118 - Query 4/5: 'What is a digital twin?...' +2026-02-17 02:48:44 - INFO - retrieve.py:200 - Embedding query: 'What is a digital twin?...' (top_k=5) +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'23'), (b'num_tokens', b'6'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'418caee48872f3a94d6db2ac8ceabd27'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'96'), (b'date', b'Mon, 16 Feb 2026 21:48:44 GMT'), (b'x-envoy-upstream-service-time', b'41'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:48:44 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:48:44 - DEBUG - retrieve.py:212 - Generated embedding in 0.34s, dimension: 1024 +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:48:44 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:48:44 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:48:45 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:48:45 - INFO - retrieve.py:234 - Search completed in 0.32s, returned 5 results +2026-02-17 02:48:45 - INFO - retrieve.py:249 - Total query time: 0.66s +2026-02-17 02:48:45 - DEBUG - retrieve.py:127 - Metadata completeness: 5/5 = 100.0% +2026-02-17 02:48:45 - INFO - validate_rag.py:151 - → 5 results, avg_score=0.6145, passed=True +2026-02-17 02:48:45 - INFO - validate_rag.py:118 - Query 5/5: 'How to use LLMs for robot planning?...' +2026-02-17 02:48:45 - INFO - retrieve.py:200 - Embedding query: 'How to use LLMs for robot planning?...' (top_k=5) +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'access-control-expose-headers', b'X-Debug-Trace-ID'), (b'cache-control', b'no-cache, no-store, no-transform, must-revalidate, private, max-age=0'), (b'content-encoding', b'gzip'), (b'content-type', b'application/json'), (b'expires', b'Thu, 01 Jan 1970 00:00:00 GMT'), (b'num_chars', b'35'), (b'num_tokens', b'9'), (b'pragma', b'no-cache'), (b'vary', b'Origin,Accept-Encoding'), (b'x-accel-expires', b'0'), (b'x-debug-trace-id', b'6230a37f0829a2a876b028b65fa46ee6'), (b'x-endpoint-monthly-call-limit', b'1000'), (b'x-trial-endpoint-call-limit', b'100'), (b'x-trial-endpoint-call-remaining', b'95'), (b'date', b'Mon, 16 Feb 2026 21:48:45 GMT'), (b'x-envoy-upstream-service-time', b'40'), (b'server', b'envoy'), (b'Via', b'1.1 google'), (b'Alt-Svc', b'h3=":443"; ma=2592000,h3-29=":443"; ma=2592000'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:48:45 - INFO - _client.py:1025 - HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK" +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:48:45 - DEBUG - retrieve.py:212 - Generated embedding in 0.34s, dimension: 1024 +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - send_request_headers.started request= +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - send_request_headers.complete +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - send_request_body.started request= +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - send_request_body.complete +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - receive_response_headers.started request= +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Content-Encoding', b'gzip'), (b'Content-Type', b'application/json'), (b'Date', b'Mon, 16 Feb 2026 21:48:45 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')]) +2026-02-17 02:48:45 - INFO - _client.py:1025 - HTTP Request: POST https://d27e55fe-c28d-4275-81d7-7eff807c14a5.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/book_embeddings/points/query "HTTP/1.1 200 OK" +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - receive_response_body.started request= +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - receive_response_body.complete +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - response_closed.started +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - response_closed.complete +2026-02-17 02:48:45 - INFO - retrieve.py:234 - Search completed in 0.32s, returned 5 results +2026-02-17 02:48:45 - INFO - retrieve.py:249 - Total query time: 0.66s +2026-02-17 02:48:45 - DEBUG - retrieve.py:127 - Metadata completeness: 5/5 = 100.0% +2026-02-17 02:48:45 - INFO - validate_rag.py:151 - → 5 results, avg_score=0.6683, passed=True +2026-02-17 02:48:45 - DEBUG - retrieve.py:127 - Metadata completeness: 25/25 = 100.0% +2026-02-17 02:48:45 - INFO - validate_rag.py:328 - ================================================== +2026-02-17 02:48:45 - INFO - validate_rag.py:329 - Validation Summary: +2026-02-17 02:48:45 - INFO - validate_rag.py:330 - Collection: 277 points, 43 URLs +2026-02-17 02:48:45 - INFO - validate_rag.py:331 - Queries: 5 tested, 5 passed +2026-02-17 02:48:45 - INFO - validate_rag.py:332 - Metadata completeness: 100.0% +2026-02-17 02:48:45 - INFO - validate_rag.py:333 - Avg results per query: 5.0 +2026-02-17 02:48:45 - INFO - validate_rag.py:334 - Avg top score: 0.6126 +2026-02-17 02:48:45 - INFO - validate_rag.py:335 - Overall Status: PASS +2026-02-17 02:48:45 - INFO - validate_rag.py:338 - ✅ RAG Validation PASSED +2026-02-17 02:48:45 - INFO - validate_rag.py:343 - ================================================== +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - close.started +2026-02-17 02:48:45 - DEBUG - _trace.py:47 - close.complete diff --git a/validate_rag.py b/validate_rag.py new file mode 100644 index 0000000000000000000000000000000000000000..1d968c82af6d4357d20246ca8f9b8853c2abf617 --- /dev/null +++ b/validate_rag.py @@ -0,0 +1,353 @@ +""" +Comprehensive validation suite for the RAG retrieval pipeline. + +This script validates the entire data flow from ingestion to retrieval: +- Collection exists with correct vector size (1024) +- Points are properly stored with metadata +- Queries return relevant results +- Metadata completeness and integrity +""" +import argparse +import json +import sys +import time +import logging +from pathlib import Path +from typing import List, Dict, Any + +# Add parent directory to path for imports +sys.path.insert(0, str(Path(__file__).parent)) + +import cohere +from qdrant_client import QdrantClient + +# Import from existing modules +import config +import utils +import retrieve # Reuse search and validation functions +from logging_config import setup_logging + +logger = logging.getLogger(__name__) + +# Default test queries covering various book topics +DEFAULT_TEST_QUERIES = [ + "What is ROS 2?", + "How do humanoid robots walk?", + "Explain navigation intelligence in robots", + "What is a digital twin?", + "How to use LLMs for robot planning?" +] + +def get_collection_stats(qdrant_client: QdrantClient, collection_name: str) -> Dict[str, Any]: + """ + Fetch collection statistics: total points and unique URLs. + + Returns dict with: + exists: bool + vector_size: int or None + total_points: int + unique_urls: int + """ + try: + info = qdrant_client.get_collection(collection_name) + vector_size = info.config.params.vectors.size + total_points = info.points_count + except Exception as e: + logger.error(f"Failed to get collection info: {e}") + return { + "exists": False, + "vector_size": None, + "total_points": 0, + "unique_urls": 0 + } + + # Scroll through points to count unique URLs (sample if too many) + unique_urls = set() + next_offset = None + limit = min(1000, total_points) # Limit to avoid excessive scrolling + + try: + while len(unique_urls) < limit: + records, next_offset = qdrant_client.scroll( + collection_name=collection_name, + limit=min(100, limit - len(unique_urls)), + offset=next_offset, + with_payload=True, + with_vectors=False + ) + for record in records: + payload = record.payload or {} + url = payload.get('url', '') + if url: + unique_urls.add(url) + if next_offset is None: + break + except Exception as e: + logger.warning(f"Error scrolling collection: {e}") + + return { + "exists": True, + "vector_size": vector_size, + "total_points": total_points, + "unique_urls": len(unique_urls) + } + +def run_validation_queries( + queries: List[str], + cohere_client: cohere.ClientV2, + qdrant_client: QdrantClient, + collection_name: str, + top_k: int = 5 +) -> Dict[str, Any]: + """ + Execute all test queries and collect results. + + Returns dict with: + queries_tested: int + queries_passed: int (≥3 results with metadata complete) + total_results: int + all_results: list of search results + query_metrics: list of dicts per query (query, results_count, avg_score, passed) + """ + queries_tested = len(queries) + queries_passed = 0 + all_results = [] + query_metrics = [] + + for i, query in enumerate(queries, 1): + logger.info(f"Query {i}/{queries_tested}: '{query[:50]}...'") + try: + results = retrieve.search( + query_text=query, + cohere_client=cohere_client, + qdrant_client=qdrant_client, + collection_name=collection_name, + top_k=top_k + ) + + # Compute metrics for this query + results_count = len(results) + avg_score = 0.0 + if results_count > 0: + scores = [r['score'] for r in results] + avg_score = sum(scores) / len(scores) + + # Check if this query passes (≥3 results with metadata complete) + completeness = retrieve.validate_metadata_completeness(results) + passed = results_count >= 3 and completeness >= 98.0 + + if passed: + queries_passed += 1 + + query_metrics.append({ + "query": query, + "results_count": results_count, + "avg_score": round(avg_score, 4), + "metadata_completeness": round(completeness, 2), + "passed": passed + }) + + all_results.extend(results) + logger.info(f" → {results_count} results, avg_score={avg_score:.4f}, passed={passed}") + + except Exception as e: + logger.error(f" → FAILED: {e}") + query_metrics.append({ + "query": query, + "results_count": 0, + "avg_score": 0.0, + "metadata_completeness": 0.0, + "passed": False, + "error": str(e) + }) + all_results.extend([]) + + return { + "queries_tested": queries_tested, + "queries_passed": queries_passed, + "total_results": len(all_results), + "query_metrics": query_metrics, + "all_results": all_results # Keep for completeness calculation + } + +def compute_validation_metrics( + stats: Dict[str, Any], + query_metrics: Dict[str, Any] +) -> Dict[str, Any]: + """ + Compute final ValidationMetrics from collection stats and query results. + + Returns ValidationMetrics object with overall_status. + """ + # Overall pass conditions: + # - Collection exists with vector_size=1024 + # - queries_passed == queries_tested (all queries returned ≥3 results with complete metadata) + # - Metadata completeness across all results ≥ 98% + + collection_ok = stats.get('exists', False) and stats.get('vector_size') == 1024 + queries_all_passed = query_metrics['queries_passed'] == query_metrics['queries_tested'] + + # Compute overall metadata completeness + completeness = retrieve.validate_metadata_completeness(query_metrics['all_results']) + + overall_status = "PASS" if (collection_ok and queries_all_passed and completeness >= 98.0) else "FAIL" + + errors = [] + if not collection_ok: + errors.append(f"Collection validation failed: exists={stats.get('exists')}, vector_size={stats.get('vector_size')}") + if not queries_all_passed: + errors.append(f"Not all queries passed: {query_metrics['queries_passed']}/{query_metrics['queries_tested']}") + if completeness < 98.0: + errors.append(f"Metadata completeness too low: {completeness:.1f}% (< 98%)") + + return { + "collection_exists": stats.get('exists', False), + "vector_size": stats.get('vector_size'), + "total_points": stats.get('total_points'), + "unique_urls": stats.get('unique_urls'), + "queries_tested": query_metrics['queries_tested'], + "queries_passed": query_metrics['queries_passed'], + "metadata_completeness_pct": round(completeness, 2), + "avg_results_per_query": round( + query_metrics['total_results'] / query_metrics['queries_tested'] + if query_metrics['queries_tested'] > 0 else 0, + 2 + ), + "avg_top_score": round( + sum(r['score'] for r in query_metrics['all_results']) / len(query_metrics['all_results']) + if query_metrics['all_results'] else 0.0, + 4 + ), + "overall_status": overall_status, + "errors": errors + } + +def main() -> int: + """CLI entrypoint for comprehensive RAG validation.""" + parser = argparse.ArgumentParser( + description="Run comprehensive validation of the RAG retrieval pipeline" + ) + parser.add_argument( + "--queries-file", + type=str, + help="Path to file containing one query per line (default: use built-in test queries)" + ) + parser.add_argument( + "--top-k", + type=int, + default=5, + help="Number of results per query (default: 5)" + ) + parser.add_argument( + "--sample-size", + type=int, + default=20, + help="Sample size for metadata checks (unused, kept for compatibility)" + ) + parser.add_argument( + "--output", + type=str, + help="Output file path for JSON results (default: stdout)" + ) + parser.add_argument( + "--config", + type=str, + default=".env", + help="Path to .env config file (default: .env)" + ) + + args = parser.parse_args() + + # Setup logging + log_file = "validate_rag.log" + setup_logging(log_file=log_file, console_level="INFO") + logger.info("=== RAG Validation Suite Started ===") + + try: + # Load config + logger.info(f"Loading config from {args.config}") + cfg = config.get_config() + config.validate_config(cfg) # Reuse validation from config module + + # Initialize clients + logger.info("Initializing Cohere and Qdrant clients") + cohere_client = cohere.ClientV2(api_key=cfg["cohere_api_key"]) + qdrant_client = QdrantClient(url=cfg["qdrant_url"], api_key=cfg["qdrant_api_key"]) + + collection_name = cfg["qdrant_collection"] + + # Step 1: Get collection stats + logger.info(f"Checking collection '{collection_name}'") + stats = get_collection_stats(qdrant_client, collection_name) + if not stats['exists']: + logger.error(f"Collection '{collection_name}' does not exist") + return 1 + logger.info(f"Collection stats: {stats['total_points']} points, {stats['unique_urls']} unique URLs, vector_size={stats['vector_size']}") + if stats['vector_size'] != 1024: + logger.error(f"Expected vector size 1024, got {stats['vector_size']}") + return 1 + + # Step 2: Load test queries + if args.queries_file: + with open(args.queries_file, 'r') as f: + queries = [line.strip() for line in f if line.strip()] + logger.info(f"Loaded {len(queries)} queries from {args.queries_file}") + else: + queries = DEFAULT_TEST_QUERIES + logger.info(f"Using {len(queries)} default test queries") + + # Step 3: Run all queries + logger.info("Running validation queries...") + query_results = run_validation_queries( + queries=queries, + cohere_client=cohere_client, + qdrant_client=qdrant_client, + collection_name=collection_name, + top_k=args.top_k + ) + + # Step 4: Compute final metrics + metrics = compute_validation_metrics(stats, query_results) + + # Step 5: Output JSON + output = { + "collection_stats": stats, + "query_metrics": query_results['query_metrics'], + "validation": metrics + } + + json_output = json.dumps(output, indent=2) + if args.output: + with open(args.output, 'w') as f: + f.write(json_output) + logger.info(f"Results written to {args.output}") + else: + print(json_output) + + # Step 6: Log summary and exit + logger.info("=" * 50) + logger.info(f"Validation Summary:") + logger.info(f" Collection: {stats['total_points']} points, {stats['unique_urls']} URLs") + logger.info(f" Queries: {metrics['queries_tested']} tested, {metrics['queries_passed']} passed") + logger.info(f" Metadata completeness: {metrics['metadata_completeness_pct']}%") + logger.info(f" Avg results per query: {metrics['avg_results_per_query']}") + logger.info(f" Avg top score: {metrics['avg_top_score']}") + logger.info(f" Overall Status: {metrics['overall_status']}") + + if metrics['overall_status'] == "PASS": + logger.info("✅ RAG Validation PASSED") + else: + logger.error("❌ RAG Validation FAILED") + for error in metrics['errors']: + logger.error(f" - {error}") + logger.info("=" * 50) + + return 0 if metrics['overall_status'] == "PASS" else 1 + + except Exception as e: + logger.exception(f"Validation failed with unexpected error: {e}") + print(json.dumps({"error": str(e), "overall_status": "FAIL"})) + return 1 + +if __name__ == "__main__": + sys.exit(main())