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"name": "root", "message": "[ab5d56e5] Received chat request: What is ROS 2?...", "module": "agent", "lineno": 331, "pathname": "/mnt/e/7. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/agent.py", "thread": 133059488706688, "threadName": "MainThread", "processName": "MainProcess", "process": 10618, "taskName": "Task-4"} {"timestamp": "2026-02-16T23:10:21.243183Z", "level": "INFO", "name": "root", "message": "[ab5d56e5] Starting agent execution for question: What is ROS 2?...", "module": "agent", "lineno": 204, "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": "2026-02-16T23:10:21.243819Z", "level": "INFO", "name": "root", "message": "Retrieving chunks for query: What is ROS 2?...", "module": "agent", "lineno": 138, "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": "2026-02-16T23:10:21.244247Z", "level": "ERROR", "name": "root", "message": "Retrieval failed: attempted relative import with no known parent package", "module": "agent", "lineno": 177, "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 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": "2026-02-16T23:10:21.247103Z", "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. Low Code Agentic AI/Hackathon I/humanoid-ai-robotics-book-chatbot/backend/agent.py", "thread": 133059488706688, "threadName": "MainThread", "processName": "MainProcess", "process": 10618, "taskName": "Task-4"} {"timestamp": "2026-02-16T23:12:31.768295Z", "level": "DEBUG", "name": "asyncio", "message": "Using proactor: IocpProactor", "module": "proactor_events", "lineno": 633, "pathname": "C:\\Users\\Muhammad Ahmed\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\asyncio\\proactor_events.py", "thread": 15700, "threadName": "MainThread", "processName": "MainProcess", "process": 2728, "taskName": null} {"timestamp": "2026-02-16T23:12:31.819830Z", "level": "INFO", "name": "root", "message": "============================================================", "module": "agent", "lineno": 415, "pathname": "E:\\7. Low Code Agentic AI\\Hackathon I\\humanoid-ai-robotics-book-chatbot\\backend\\agent.py", "thread": 15700, "threadName": "MainThread", "processName": "MainProcess", "process": 2728, "taskName": "Task-2"} {"timestamp": "2026-02-16T23:12:31.820104Z", "level": "INFO", "name": "root", "message": "RAG Agent FastAPI Server Starting", "module": "agent", "lineno": 416, "pathname": "E:\\7. Low Code Agentic AI\\Hackathon I\\humanoid-ai-robotics-book-chatbot\\backend\\agent.py", "thread": 15700, "threadName": "MainThread", "processName": "MainProcess", "process": 2728, "taskName": "Task-2"} {"timestamp": "2026-02-16T23:12:31.820310Z", "level": "INFO", "name": "root", "message": "============================================================", "module": "agent", "lineno": 417, "pathname": "E:\\7. Low Code Agentic AI\\Hackathon I\\humanoid-ai-robotics-book-chatbot\\backend\\agent.py", "thread": 15700, "threadName": "MainThread", "processName": "MainProcess", "process": 2728, "taskName": "Task-2"} {"timestamp": "2026-02-16T23:12:31.820613Z", "level": "ERROR", "name": "root", "message": "Retrieval test failed: attempted relative import with no known parent package", "module": "agent", "lineno": 443, "pathname": "E:\\7. Low Code Agentic AI\\Hackathon I\\humanoid-ai-robotics-book-chatbot\\backend\\agent.py", "thread": 15700, "threadName": "MainThread", "processName": "MainProcess", "process": 2728, "taskName": "Task-2"} {"timestamp": "2026-02-16T23:12:31.820817Z", "level": "INFO", "name": "root", "message": "Server startup complete", "module": "agent", "lineno": 445, "pathname": "E:\\7. 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"/home/m-ahmad-official/venv/lib/python3.12/site-packages/httpx/_client.py", "thread": 127881977561216, "threadName": "MainThread", "processName": "MainProcess", "process": 13073, "taskName": "Task-2"} {"timestamp": "2026-02-17T00:05:24.444105Z", "level": "DEBUG", "name": "httpcore.http11", "message": "receive_response_body.started request=", "module": "_trace", "lineno": 47, "pathname": "/home/m-ahmad-official/venv/lib/python3.12/site-packages/httpcore/_trace.py", "thread": 127881977561216, "threadName": "MainThread", "processName": "MainProcess", "process": 13073, "taskName": "Task-2"} {"timestamp": "2026-02-17T00:05:24.445143Z", "level": "DEBUG", "name": "httpcore.http11", "message": "receive_response_body.complete", "module": "_trace", "lineno": 47, "pathname": "/home/m-ahmad-official/venv/lib/python3.12/site-packages/httpcore/_trace.py", "thread": 127881977561216, "threadName": "MainThread", "processName": "MainProcess", "process": 13073, "taskName": "Task-2"} {"timestamp": 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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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"/home/m-ahmad-official/venv/lib/python3.12/site-packages/httpx/_client.py", "thread": 140183285104768, "threadName": "MainThread", "processName": "MainProcess", "process": 13889, "taskName": "Task-2"} {"timestamp": "2026-02-17T00:14:55.451267Z", "level": "DEBUG", "name": "httpcore.http11", "message": "receive_response_body.started request=", "module": "_trace", "lineno": 47, "pathname": "/home/m-ahmad-official/venv/lib/python3.12/site-packages/httpcore/_trace.py", "thread": 140183285104768, "threadName": "MainThread", "processName": "MainProcess", "process": 13889, "taskName": "Task-2"} {"timestamp": "2026-02-17T00:14:55.452099Z", "level": "DEBUG", "name": "httpcore.http11", "message": "receive_response_body.complete", "module": "_trace", "lineno": 47, "pathname": "/home/m-ahmad-official/venv/lib/python3.12/site-packages/httpcore/_trace.py", "thread": 140183285104768, "threadName": "MainThread", "processName": "MainProcess", "process": 13889, "taskName": "Task-2"} {"timestamp": 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"/home/m-ahmad-official/venv/lib/python3.12/site-packages/httpx/_client.py", "thread": 126422636544128, "threadName": "MainThread", "processName": "MainProcess", "process": 14172, "taskName": "Task-2"} {"timestamp": "2026-02-17T00:17:05.423847Z", "level": "DEBUG", "name": "httpcore.http11", "message": "receive_response_body.started request=", "module": "_trace", "lineno": 47, "pathname": "/home/m-ahmad-official/venv/lib/python3.12/site-packages/httpcore/_trace.py", "thread": 126422636544128, "threadName": "MainThread", "processName": "MainProcess", "process": 14172, "taskName": "Task-2"} {"timestamp": "2026-02-17T00:17:05.424725Z", "level": "DEBUG", "name": "httpcore.http11", "message": "receive_response_body.complete", "module": "_trace", "lineno": 47, "pathname": "/home/m-ahmad-official/venv/lib/python3.12/site-packages/httpcore/_trace.py", "thread": 126422636544128, "threadName": "MainThread", "processName": "MainProcess", "process": 14172, "taskName": "Task-2"} {"timestamp": 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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. 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. 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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. 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request=", "module": "_trace", "lineno": 47, "pathname": "C:\\Users\\Muhammad Ahmed\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\httpcore\\_trace.py", "thread": 4648, "threadName": "MainThread", "processName": "MainProcess", "process": 12748, "taskName": "Task-2"} {"timestamp": "2026-02-17T00:26:47.894704Z", "level": "DEBUG", "name": "httpcore.http11", "message": "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'Tue, 17 Feb 2026 00:26:44 GMT'), (b'Vary', b'accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers'), (b'Transfer-Encoding', b'chunked')])", "module": "_trace", "lineno": 47, "pathname": "C:\\Users\\Muhammad Ahmed\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\httpcore\\_trace.py", "thread": 4648, "threadName": "MainThread", "processName": "MainProcess", "process": 12748, "taskName": "Task-2"} {"timestamp": "2026-02-17T00:26:47.895190Z", "level": "INFO", "name": "httpx", "message": "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\"", "module": "_client", "lineno": 1025, "pathname": "C:\\Users\\Muhammad Ahmed\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\httpx\\_client.py", "thread": 4648, "threadName": "MainThread", "processName": "MainProcess", "process": 12748, "taskName": "Task-2"} {"timestamp": "2026-02-17T00:26:47.895629Z", "level": "DEBUG", "name": "httpcore.http11", "message": "receive_response_body.started request=", "module": "_trace", "lineno": 47, "pathname": "C:\\Users\\Muhammad Ahmed\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\httpcore\\_trace.py", "thread": 4648, "threadName": "MainThread", "processName": "MainProcess", "process": 12748, "taskName": "Task-2"} {"timestamp": "2026-02-17T00:26:47.896298Z", "level": "DEBUG", "name": "httpcore.http11", "message": "receive_response_body.complete", "module": "_trace", "lineno": 47, "pathname": "C:\\Users\\Muhammad Ahmed\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\httpcore\\_trace.py", "thread": 4648, "threadName": "MainThread", "processName": "MainProcess", "process": 12748, "taskName": "Task-2"} {"timestamp": "2026-02-17T00:26:47.896535Z", "level": "DEBUG", "name": "httpcore.http11", "message": "response_closed.started", "module": "_trace", "lineno": 47, "pathname": "C:\\Users\\Muhammad Ahmed\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\httpcore\\_trace.py", "thread": 4648, "threadName": "MainThread", "processName": "MainProcess", "process": 12748, "taskName": "Task-2"} {"timestamp": "2026-02-17T00:26:47.896687Z", "level": "DEBUG", "name": "httpcore.http11", "message": "response_closed.complete", "module": "_trace", "lineno": 47, "pathname": "C:\\Users\\Muhammad 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{"timestamp": "2026-02-17T00:27:03.008521Z", "level": "INFO", "name": "backend.retrieve", "message": "Embedding query: 'test...' 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{"timestamp": "2026-02-17T00:27:03.874643Z", "level": "INFO", "name": "httpx", "message": "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\"", "module": "_client", "lineno": 1025, "pathname": "C:\\Users\\Muhammad Ahmed\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\httpx\\_client.py", "thread": 7072, "threadName": "MainThread", "processName": "MainProcess", "process": 2940, "taskName": "Task-2"} {"timestamp": "2026-02-17T00:27:03.875222Z", "level": "DEBUG", "name": "httpcore.http11", "message": "receive_response_body.started request=", "module": "_trace", "lineno": 47, "pathname": "C:\\Users\\Muhammad Ahmed\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\httpcore\\_trace.py", "thread": 7072, "threadName": "MainThread", "processName": "MainProcess", "process": 2940, "taskName": "Task-2"} {"timestamp": "2026-02-17T00:27:03.875866Z", 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Low Code Agentic AI\\Hackathon I\\humanoid-ai-robotics-book-chatbot\\backend\\retrieve.py", "thread": 7072, "threadName": "MainThread", "processName": "MainProcess", "process": 2940, "taskName": "Task-2"} {"timestamp": "2026-02-17T00:27:03.879053Z", "level": "INFO", "name": "backend.retrieve", "message": "Total query time: 0.87s", "module": "retrieve", "lineno": 249, "pathname": "E:\\7. Low Code Agentic AI\\Hackathon I\\humanoid-ai-robotics-book-chatbot\\backend\\retrieve.py", "thread": 7072, "threadName": "MainThread", "processName": "MainProcess", "process": 2940, "taskName": "Task-2"} {"timestamp": "2026-02-17T00:27:03.881163Z", "level": "INFO", "name": "root", "message": "Retrieval test OK: 1 results", "module": "agent", "lineno": 311, "pathname": "E:\\7. Low Code Agentic AI\\Hackathon I\\humanoid-ai-robotics-book-chatbot\\backend\\agent.py", "thread": 7072, "threadName": "MainThread", "processName": "MainProcess", "process": 2940, "taskName": "Task-2"} {"timestamp": "2026-02-17T00:27:03.882770Z", "level": "INFO", "name": "root", "message": "Server startup complete", "module": "agent", "lineno": 315, "pathname": "E:\\7. 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Low Code Agentic AI\\Hackathon I\\humanoid-ai-robotics-book-chatbot\\backend\\retrieve.py", "thread": 15704, "threadName": "MainThread", "processName": "MainProcess", "process": 10136, "taskName": "Task-2"} {"timestamp": "2026-02-17T19:58:50.057455Z", "level": "INFO", "name": "backend.retrieve", "message": "Total query time: 1.02s", "module": "retrieve", "lineno": 249, "pathname": "E:\\7. Low Code Agentic AI\\Hackathon I\\humanoid-ai-robotics-book-chatbot\\backend\\retrieve.py", "thread": 15704, "threadName": "MainThread", "processName": "MainProcess", "process": 10136, "taskName": "Task-2"} {"timestamp": "2026-02-17T19:58:50.058034Z", "level": "INFO", "name": "root", "message": "Retrieval test OK: 1 results", "module": "agent", "lineno": 351, "pathname": "E:\\7. 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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. 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. 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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. 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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': 'What is ROS 2?'}, {'role': 'assistant', 'content': None, 'tool_calls': [{'id': 'call_91745519c846455890eef062', 'type': 'function', 'function': {'name': 'retrieve_chunks', 'arguments': '{\"query\": \"ROS 2\", \"top_k\": 5}'}}]}, {'role': 'tool', 'tool_call_id': 'call_91745519c846455890eef062', 'content': '[{\\'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\\': 0.6184138, \\'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. 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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. 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 ROS 2?'}, {'role': 'assistant', 'content': None, 'tool_calls': [{'id': 'call_eecec1733e84419984bb99e1', 'type': 'function', 'function': {'name': 'retrieve_chunks', 'arguments': '{\"query\": \"ROS 2\", \"top_k\": 5}'}}]}, {'role': 'tool', 'tool_call_id': 'call_eecec1733e84419984bb99e1', 'content': '[{\\'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\\': 0.6181822, \\'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\\': 0.59883505, \\'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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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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NVIDIA Isaac provides essential tools and frameworks that enhance the ROS 2 ecosystem, enabling the development of more intelligent and capable humanoid robots. 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Middleware like ROS 2 provides the essential communication infrastructure that allows these different subsystems to work together seamlessly.\\\\nFor\\', \\'score\\': 0.61855507, \\'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. 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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. 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\\': 0.55835235, \\'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. 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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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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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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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Middleware like ROS 2 provides the essential communication infrastructure that allows these different subsystems to work together seamlessly.\\\\nFor\\', \\'score\\': 0.35488188, \\'source_number\\': 2}, {\\'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\\': 0.34552598, \\'source_number\\': 3}, {\\'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. 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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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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': '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. 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 system?'}, {'role': 'assistant', 'content': None, 'tool_calls': [{'id': 'call_8b24f349f45b4db0a9b007f7', 'type': 'function', 'function': {'name': 'retrieve_chunks', 'arguments': '{\"query\": \"what is ASR system\", \"top_k\": 5}'}}]}, {'role': 'tool', 'tool_call_id': 'call_8b24f349f45b4db0a9b007f7', '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\\': 0.55913305, \\'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 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\\': 0.39231187, \\'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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'files': None, 'idempotency_key': 'stainless-python-retry-f85c1b43-3445-4707-9ffe-64ddd09bad90', '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': '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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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': '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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Low Code Agentic AI\\Hackathon I\\humanoid-ai-robotics-book-chatbot\\backend\\retrieve.py", "thread": 15732, "threadName": "asyncio_0", "processName": "MainProcess", "process": 12668, "taskName": null} {"timestamp": "2026-02-18T21:35:54.356597Z", "level": "INFO", "name": "backend.retrieve", "message": "Total query time: 0.77s", "module": "retrieve", "lineno": 249, "pathname": "E:\\7. Low Code Agentic AI\\Hackathon I\\humanoid-ai-robotics-book-chatbot\\backend\\retrieve.py", "thread": 15732, "threadName": "asyncio_0", "processName": "MainProcess", "process": 12668, "taskName": null} {"timestamp": "2026-02-18T21:35:54.357128Z", "level": "INFO", "name": "root", "message": "[Tool] Retrieved 5 chunks", "module": "agent", "lineno": 160, "pathname": "E:\\7. Low Code Agentic AI\\Hackathon I\\humanoid-ai-robotics-book-chatbot\\backend\\agent.py", "thread": 15732, "threadName": "asyncio_0", "processName": "MainProcess", "process": 12668, "taskName": null} {"timestamp": "2026-02-18T21:35:54.357509Z", "level": "DEBUG", "name": "httpcore.connection", "message": "close.started", "module": "_trace", "lineno": 47, "pathname": "C:\\Users\\Muhammad Ahmed\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\httpcore\\_trace.py", "thread": 15732, "threadName": "asyncio_0", "processName": "MainProcess", "process": 12668, "taskName": null} {"timestamp": "2026-02-18T21:35:54.357783Z", "level": "DEBUG", "name": "httpcore.connection", "message": "close.complete", "module": "_trace", "lineno": 47, "pathname": "C:\\Users\\Muhammad Ahmed\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\httpcore\\_trace.py", "thread": 15732, "threadName": "asyncio_0", "processName": "MainProcess", "process": 12668, "taskName": null} {"timestamp": "2026-02-18T21:35:54.358489Z", "level": "DEBUG", "name": "openai.agents", "message": "Tool retrieve_chunks completed.", "module": "tool", "lineno": 1143, "pathname": "C:\\Users\\Muhammad Ahmed\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\agents\\tool.py", "thread": 11468, "threadName": "MainThread", "processName": "MainProcess", "process": 12668, "taskName": "Task-30"} {"timestamp": "2026-02-18T21:35:54.359201Z", "level": "DEBUG", "name": "openai.agents", "message": "Running agent RAG Book Assistant (turn 2)", "module": "run", "lineno": 930, "pathname": "C:\\Users\\Muhammad Ahmed\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\agents\\run.py", "thread": 11468, "threadName": "MainThread", "processName": "MainProcess", "process": 12668, "taskName": "Task-13"} {"timestamp": "2026-02-18T21:35:54.359653Z", "level": "DEBUG", "name": "openai.agents", "message": "No conversation_id available for request", "module": "run_loop", "lineno": 1462, "pathname": "C:\\Users\\Muhammad Ahmed\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\agents\\run_internal\\run_loop.py", "thread": 11468, "threadName": "MainThread", "processName": "MainProcess", "process": 12668, "taskName": "Task-13"} {"timestamp": "2026-02-18T21:35:54.359847Z", "level": "DEBUG", "name": "openai.agents", "message": "Creating span with id None", "module": "provider", "lineno": 356, "pathname": "C:\\Users\\Muhammad Ahmed\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\agents\\tracing\\provider.py", "thread": 11468, "threadName": "MainThread", "processName": "MainProcess", "process": 12668, "taskName": "Task-13"} {"timestamp": "2026-02-18T21:35:54.360458Z", "level": "DEBUG", "name": "openai.agents", "message": "Calling LLM", "module": "openai_chatcompletions", "lineno": 308, "pathname": "C:\\Users\\Muhammad Ahmed\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\agents\\models\\openai_chatcompletions.py", "thread": 11468, "threadName": "MainThread", "processName": "MainProcess", "process": 12668, "taskName": "Task-13"} {"timestamp": "2026-02-18T21:35:54.363111Z", "level": "DEBUG", "name": "openai._base_client", "message": "Request options: {'method': 'post', 'url': '/chat/completions', 'headers': {'User-Agent': 'Agents/Python 0.9.0'}, 'files': None, 'idempotency_key': 'stainless-python-retry-63f3e229-c633-4c2f-8bf1-7dbea2d47530', '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 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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