Automated sync to Hugging Face

.env.example

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+# Application
+APP_NAME="Graph RAG Service"
+DEBUG=false
+ENVIRONMENT=development
+
+# API Server
+API_HOST=0.0.0.0
+API_PORT=8000
+
+# Security
+# ⚠️  CRITICAL: Change SECRET_KEY before ANY deployment.
+# Generate one with: python -c "import secrets; print(secrets.token_hex(32))"
+SECRET_KEY=change-this-in-production-to-a-secure-random-key
+ACCESS_TOKEN_EXPIRE_MINUTES=30
+
+# CORS: comma-separated list of allowed origins.
+# Default allows only the local Vite dev server.
+# Example for production: CORS_ORIGINS=https://yourdomain.com
+CORS_ORIGINS=http://localhost:3000,http://localhost:5173
+
+# Neo4j
+NEO4J_URI=bolt://localhost:7687
+NEO4J_USER=neo4j
+NEO4J_PASSWORD=password
+NEO4J_DATABASE=neo4j
+
+# Redis
+REDIS_HOST=localhost
+REDIS_PORT=6379
+REDIS_DB=0
+
+# Celery
+CELERY_BROKER_URL=redis://localhost:6379/0
+CELERY_RESULT_BACKEND=redis://localhost:6379/0
+
+# LLMs
+DEFAULT_LLM_PROVIDER=ollama
+
+# OpenAI
+OPENAI_API_KEY=
+
+# Anthropic
+ANTHROPIC_API_KEY=
+
+# Google Gemini
+GOOGLE_API_KEY=
+
+# LlamaCloud (for LlamaParse)
+LLAMA_CLOUD_API_KEY=
+USE_LLAMA_PARSE=true
+
+# Ollama
+OLLAMA_BASE_URL=http://localhost:11434
+OLLAMA_MODEL=llama3.1:8b
+OLLAMA_EMBEDDING_MODEL=nomic-embed-text
+
+# Embedding
+EMBEDDING_PROVIDER=ollama
+EMBEDDING_DIMENSION=768
+
+# Agent Configuration
+MAX_AGENT_ITERATIONS=5
+AGENT_TIMEOUT_SECONDS=30
+
+# Observability
+ENABLE_TRACING=true
+ENABLE_METRICS=true
+LOG_LEVEL=INFO

.gitattributes

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+*.png filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.ico filter=lfs diff=lfs merge=lfs -text

.github/prompts/Solution_Architecture.prompt.md

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+# Solution Architecture: Agentic Graph RAG as a Service
+
+This document outlines a detailed technical approach to building the Agentic Graph RAG platform. It focuses on modularity, scalability, and the specific requirements of the Lyzr Hackathon, with a strong emphasis on production-grade robustness.
+
+## 1. High-Level Architecture
+
+The system is designed as a set of modular services centered around a shared Knowledge Graph and Vector Store, fortified with enterprise-grade security and observability layers.
+
+```mermaid
+graph TD
+    User[User / Client] --> Auth[Auth & Access Control]
+    Auth --> API[Unified API Gateway]
+    
+    subgraph "Observability Layer (OpenTelemetry)"
+        Logs[Structured Logging]
+        Traces[Agent Traces]
+        Metrics[Performance Metrics]
+    end
+    
+    API -.-> Logs & Traces & Metrics
+
+    subgraph "Ingestion Pipeline (Async Workers)"
+        API --> Queue[Task Queue (Redis/Celery)]
+        Queue --> Ingest[Ingestion Worker]
+        Ingest --> Chunking[Text Chunking]
+        Chunking --> OntologyGen[LLM Ontology Gen (Versioned)]
+        OntologyGen --> Extract[Entity & Relation Extraction]
+        Extract --> Resolution[Entity Resolution / Dedup]
+        Resolution --> GraphDB[(Neo4j / Neptune)]
+        Resolution --> VectorDB[(Vector Store)]
+    end
+    
+    subgraph "Retrieval Context"
+        API --> Agent[Agent Orchestrator]
+        Agent --> Decomp[Query Decomposer]
+        Decomp --> Router[Query Router / Planner]
+        
+        Router --> |Semantic Query| VectorSearch[Vector Search]
+        Router --> |Deep Relation| GraphSearch[Graph Traversal / Cypher]
+        Router --> |Structured| FilterSearch[Metadata Filter]
+        
+        VectorSearch & GraphSearch & FilterSearch --> Validator[Hallucination Guard / Schema Validator]
+        Validator --> Synthesizer[Response Synthesizer]
+        Synthesizer --> Agent
+    end
+```
+
+## 2. Technology Stack Selection
+
+*   **Language:** Python 3.12 (Standard for AI/ML engineering).
+*   **API Framework:** FastAPI (Async support, auto-documentation).
+*   **Orchestration:** LlamaIndex (Preferred for Graph RAG).
+*   **LLM:** multi-LLM support like ollama, open ai, gemini,claude (use lang-graph) (Reasoning & Extraction) & `BAAI bge-m3` this model is available on ollama so we will use from ollama (Embeddings).
+*   **Graph Database:** Neo4j (Primary) .
+*   **Vector Store:** Neo4j Vector Index (for unified storage) or Qdrant/Chroma.
+*   **Task Queue:** Celery with Redis (for async ingestion).
+*   **Monitoring:** OpenTelemetry + Prometheus/Grafana.
+*   **Frontend:**  React vite + tailwind css (for Visual Ontology Editor).
+
+## 3. Production-Grade Components
+
+### A. Document-to-Graph Pipeline (Ingestion)
+
+This pipeline converts unstructured text into a structured Knowledge Graph, robust to schema changes and duplicates.
+
+1.  **Ontology Generation & Evolution:**
+    *   *Initial:* Ask LLM to identify high-level concepts (nodes) and interactions (edges) from first $N$ chunks.
+    *   *Visual Editor:* Human approval step to refine the JSON schema.
+    *   **Drift Handling:** Incorporate an "Ontology Versioning" system. Every node/edge is tagged with `ontology_version: v1.0`. New documents causing schema changes trigger a "Migration Proposal" for approval.
+
+2.  **Extraction & Embedding:**
+    *   **Prompt Engineering:** "Given text + Ontology v1.0, extract entities/relationships."
+    *   **Hybrid Nodes:** Create `(:Chunk)` nodes linked to `(:Entity)` nodes (`(:Chunk)-[:MENTIONS]->(:Entity)`). This preserves ground truth source text alongside abstract graph relationships.
+
+3.  **Advanced Entity Resolution:**
+    *   *Naive:* Exact string match.
+    *   *Production:* Multi-stage blocking and merging.
+        1.  **Blocking:** Group entities by Label and similar name (e.g., phonetic match).
+        2.  **Semantic Check:** Compare embeddings of candidates.
+        3.  **Threshold:** If similarity > 0.95 -> Auto-merge. If 0.85-0.95 -> Flag for "Human Review Queue".
+
+### B. The Agentic Retrieval System (The Brain)
+
+A state machine loop designed for accuracy and fail-safe operation.
+
+**1. Query Decomposition & Routing**
+Instead of a single step, the Agent breaks down complexity:
+*   *User Query:* "How is the CEO of Lyzr related to OpenAI?"
+*   *Decomposition:*
+    1.  "Identify Lyzr CEO" (Vector/Graph lookup) -> *Result: user_X*
+    2.  "Find path between user_X and OpenAI" (Graph traversal).
+*   *Router:* Dynamically selects tools for each sub-step.
+
+**2. Tool Implementation with Guardrails:**
+*   **Vector Tool:** Top-k retrieval using embedding similarity.
+*   **Graph Tool (Text-to-Cypher):** Uses LLM to generate Cypher.
+    *   **Hallucination Guard:** The tool injects the *strict* allowed schema into the prompt. Generated Cypher is parsed and validated against a "Relationship Whitelist" before execution to prevent schema injection or invalid edge types.
+*   **Filter Tool:** Converts natural language to structured DB filters (WHERE clauses).
+
+**3. Latency & Performance Strategy:**
+*   **Timeouts:** Hard limit on agent reasoning steps (e.g., max 5 loops).
+*   **Fallback:** If Graph tool fails or times out, degrade gracefully to pure Vector Search for a "best effort" answer.
+
+### C. Parity & Extensibility Layer
+
+We define abstract base class interfaces to ensure no vendor lock-in.
+
+```python
+class GraphStore(ABC):
+    @abstractmethod
+    def execute_query(self, query: str, params: dict): pass
+
+class VectorStore(ABC):
+    @abstractmethod
+    def search(self, query_vector: List[float], k: int): pass
+
+class LLMProvider(ABC):
+    @abstractmethod
+    def complete(self, prompt: str): pass
+
+# Implementations: Neo4jStore, NeptuneStore, QdrantStore, OpenAIProvider, etc.
+```
+
+## 4. Scalability, Security & Observability
+
+To meet "Production-Grade" criteria, these non-functional requirements are critical:
+
+1.  **Access Control (RBAC):**
+    *   Pre-retrieval enforcement.
+    *   All queries filter by `user.tenant_id` or `user.permissions` to ensure users only retrieve data they are authorized to see.
+    
+2.  **Observability:**
+    *   **Tracing:** Log every step of the Agent's reasoning chain (Input -> Decomp -> Tool Call -> Result). This is vital for debugging "why did the bot say that?".
+    *   **Metrics:** Track Token Usage, Latency p95, and Cache Hit Rates.
+
+3.  **Async Ingestion:**
+    *   Ingestion is decoupled from the user request loop.
+    *   File Upload API -> Pushes ID to Redis Queue -> Background Worker picks up -> Runs Extraction -> Updates Graph.
+
+4.  **Caching Strategy:**
+    *   **Semantic Cache (Redis):** Before hitting the LLM, check if a semantically similar query has been answered recently. reduces cost and latency.
+    *   **Embedding Cache:** Store computed embeddings to avoid re-calculation for identical text chunks.
+
+## 5. Implementation Plan
+
+### Phase 1: Foundation (Hours 1-4)
+1.  Set up Repository, Python envf (Neo4j/Redis).
+2.  Implement `GraphStore` & `VectorStore` abstractions.
+3.  Create Basic Auth & Middleware logging.
+
+### Phase 2: Ingestion Engine (Hours 5-12)
+1.  Implement PDF extractor & Async Worker skeleton.
+2.  Build "Ontology Proposer" & "Graph Extractor" prompts.
+3.  Implement Entity Resolution logic.
+
+### Phase 3: The Retrieval Agent (Hours 13-20)
+1.  Set up Agent loop with Query Decomposition.
+2.  Implement `Text2Cypher` with schema validation.
+3.  Implement Latency Timeouts & Fallbacks.
+
+### Phase 4: Refinement & UI (Hours 21-24)
+1.  Build Visual Editor (Streamlit).
+2.  Add simple Evaluation Script (run known queries, check answers).
+3.  Write `README.md` highlighting the "Production Thinking" (RBAC, Async, Observability).
+
+## 6. Key Innovations
+1.  **Hybrid Chunk Nodes:** Storing source text explicitly in the graph for ground-truth verification.
+2.  **Self-Correcting Cypher:** If Cypher execution fails, feed the error back to the LLM to fix syntax automatically.
+3.  **Adaptive Retrieval:** The agent assigns a "confidence score" to each retrieval method. If Vector Search confidence is low (<0.7), it automatically triggers Graph Traversal to boost context.

.github/workflows/sync_to_hf.yml

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+name: Sync to Hugging Face
+on:
+  push:
+    branches: [master]
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: |
+          # 1. Turn on Large File Storage (LFS)
+          git lfs install
+          
+          # 2. Tell Hugging Face to properly handle images
+          echo "*.png filter=lfs diff=lfs merge=lfs -text" >> .gitattributes
+          echo "*.jpg filter=lfs diff=lfs merge=lfs -text" >> .gitattributes
+          echo "*.ico filter=lfs diff=lfs merge=lfs -text" >> .gitattributes
+          
+          # 3. Wipe the hidden git history in the runner to clear past image errors
+          rm -rf .git
+          git config --global user.email "action@github.com"
+          git config --global user.name "GitHub Action"
+          
+          # 4. Create a fresh, clean package and force push it
+          git init
+          git checkout -b main
+          git add .
+          git commit -m "Automated sync to Hugging Face"
+          git push --force https://anky2002:$HF_TOKEN@huggingface.co/spaces/anky2002/graph-rag main 

.gitignore

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+# ── Python ───────────────────────────────────────────────────────────────────
+__pycache__/
+*.py[oc]
+*.pyo
+*.pyd
+build/
+dist/
+wheels/
+*.egg-info/
+.eggs/
+*.egg
+
+# ── Virtual environments ──────────────────────────────────────────────────────
+.venv/
+venv/
+ENV/
+env/
+
+# ── Environment / Secrets (NEVER commit real keys) ────────────────────────────
+.env
+.env.local
+.env.*.local
+*.secret
+
+# ── Node.js / NPM ─────────────────────────────────────────────────────────────
+node_modules/
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+.npm/
+
+# ── Uploaded user data ────────────────────────────────────────────────────────
+data/uploads/*
+!data/uploads/.gitkeep
+
+# ── One-off debug / experiment test files ─────────────────────────────────────
+test_combinations.py
+test_document_embedding.py
+test_embedding.py
+test_exact_combination.py
+test_narrow_down.py
+test_nomic.py
+test_resume_text.py
+
+# ── IDE / OS ──────────────────────────────────────────────────────────────────
+.idea/
+.vscode/
+*.swp
+*.swo
+.DS_Store
+Thumbs.db
+
+# ── Logs & temp ───────────────────────────────────────────────────────────────
+*.log
+*.tmp
+*.temp
+htmlcov/
+.coverage
+.pytest_cache/
+.mypy_cache/
+.ruff_cache/
+
+# ── Docs that are project-internal notes (not needed in repo) ─────────────────
+BGE_M3_ISSUE_ANALYSIS.md
+CONFIGURATION_CHANGES.md
+PROJECT_COMPLETION_CHECKLIST.md
+LLAMAPARSE_SETUP.md
+
+# ── Lyzr Hackathon source material (not part of the submitted codebase) ────────
+Lyzr_Hackathon_Problem_Statement.md
+
+# ── UV lock / python version pins (repo-specific, not portable) ─────────────────
+# uv.lock          # keep: ensures reproducible installs for reviewers
+.python-version
+
+# ── pyvis / notebook temp files ──────────────────────────────────────────────
+*.html
+!frontend-react/index.html
+!frontend-react/*.html
+!frontend/*.html
+
+# ── Celery / task artefacts ───────────────────────────────────────────────────
+celerybeat-schedule
+celerybeat.pid
+
+# ── Neo4j local data (if ever mounted) ───────────────────────────────────────
+neo4j_data/
+
+# ── Windows shortcuts / artifacts ────────────────────────────────────────────
+*.lnk

ARCHITECTURE.md

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+# Graph RAG Service - Project Documentation
+
+## System Architecture
+
+### Overview
+The Graph RAG Service is built as a modular, production-grade platform with the following key components:
+
+1. **API Gateway (FastAPI)**: Handles all HTTP requests, authentication, and routing
+2. **Ingestion Pipeline**: Processes documents and constructs knowledge graphs
+3. **Retrieval Agent (LangGraph)**: Intelligent query routing and response synthesis
+4. **Storage Layer**: Neo4j for graph + vector storage
+5. **Task Queue**: Celery + Redis for async processing
+6. **Observability**: OpenTelemetry for tracing and metrics
+
+### Design Principles
+
+#### 1. No Vendor Lock-in
+All core components are abstracted behind interfaces:
+- `GraphStore`: Can swap Neo4j for AWS Neptune
+- `VectorStore`: Supports multiple vector databases
+- `LLMProvider`: Works with any LLM (OpenAI, Anthropic, Gemini, Ollama)
+
+#### 2. Production-Ready
+- **Async Processing**: Non-blocking I/O for all database operations
+- **Background Jobs**: Celery workers handle heavy ingestion tasks
+- **Authentication**: JWT-based with RBAC support
+- **Error Handling**: Graceful degradation and fallback mechanisms
+- **Observability**: Full tracing and metrics collection
+
+#### 3. Intelligent Retrieval
+The agentic system:
+- Decomposes complex queries into sub-queries
+- Dynamically selects retrieval methods (vector vs graph vs cypher)
+- Validates outputs against schema (hallucination guard)
+- Provides reasoning chains for transparency
+
+## Components Deep Dive
+
+### Core Abstractions (`src/graph_rag_service/core/`)
+
+#### GraphStore Interface
+```python
+class GraphStore(ABC):
+    @abstractmethod
+    async def create_node(entity: Entity) -> str
+    @abstractmethod
+    async def create_relationship(relationship: Relationship) -> str
+    @abstractmethod
+    async def execute_query(query: str, params: dict) -> List[dict]
+    @abstractmethod
+    async def find_path(source: str, target: str, max_depth: int) -> List[dict]
+```
+
+Implementation: `Neo4jStore` provides unified graph + vector storage using Neo4j 5.x vector capabilities.
+
+#### LLMProvider Interface
+```python
+class LLMProvider(ABC):
+    @abstractmethod
+    async def complete(prompt: str, **kwargs) -> str
+    @abstractmethod
+    async def embed(text: str) -> List[float]
+```
+
+Implementation: `UnifiedLLMProvider` wraps OpenAI, Anthropic, Gemini, and Ollama with a consistent interface.
+
+#### Entity Resolution
+Multi-stage resolution:
+1. **Blocking**: Group by entity type and name similarity (fast reject)
+2. **Semantic Check**: Compare embeddings for deep similarity
+3. **Threshold Matching**: Configurable thresholds (0.85 default)
+4. **Auto-merge**: High confidence merges (>0.95)
+5. **Human Review Queue**: Medium confidence flagged for review (0.85-0.95)
+
+### Ingestion Pipeline (`src/graph_rag_service/ingestion/`)
+
+#### Flow
+1. **Document Processing**: Extract text from PDF/TXT/MD/DOCX
+2. **Chunking**: Split into overlapping chunks (1024 tokens, 200 overlap)
+3. **Ontology Generation**: LLM analyzes samples to propose entity/relationship types
+4. **Entity Extraction**: Extract entities and relationships per chunk
+5. **Entity Resolution**: Deduplicate and merge entities
+6. **Embedding Generation**: Create vector embeddings (BGE-M3)
+7. **Graph Construction**: Store in Neo4j with hybrid nodes
+
+#### Hybrid Nodes
+Each chunk is stored as both:
+- A `(:Chunk)` node with text and embedding
+- Connected to `(:Entity)` nodes via `[:MENTIONS]` relationships
+
+This preserves source text for grounding while enabling abstract graph queries.
+
+### Retrieval System (`src/graph_rag_service/retrieval/`)
+
+#### Tools
+1. **VectorSearchTool**: Semantic similarity using embeddings
+2. **GraphTraversalTool**: Relationship exploration and path finding
+3. **CypherGenerationTool**: Text-to-Cypher with validation
+4. **MetadataFilterTool**: Structured queries on attributes
+
+#### Agent Workflow (LangGraph)
+```
+[Query] → [Decompose] → [Route] → [Vector/Graph/Cypher] → [Synthesize] → [Response]
+                           ↑                                      ↓
+                           └─────────────────────────────────────┘
+                                    (Iterative refinement)
+```
+
+#### Hallucination Guards
+- **Schema Injection**: Prompt includes allowed entity/relationship types
+- **Cypher Validation**: Parse and validate against whitelist
+- **Self-Correction**: Feed errors back to LLM to fix syntax
+- **Fallback**: If graph fails, degrade to vector search
+
+### API Layer (`src/graph_rag_service/api/`)
+
+#### Endpoints
+- `POST /api/auth/login`: Get JWT token
+- `POST /api/documents/upload`: Upload document (returns task ID)
+- `GET /api/documents/status/{task_id}`: Check ingestion progress
+- `POST /api/query`: Execute agentic query
+- `GET /api/ontology`: Get current ontology schema
+- `PUT /api/ontology`: Update ontology (admin only)
+- `GET /api/graph/visualization`: Get graph data for visualization
+- `GET /api/system/health`: System health check
+- `GET /api/system/stats`: System statistics
+
+#### Authentication
+- JWT tokens with configurable expiration (default: 30 min)
+- RBAC with scopes: `read`, `write`, `admin`
+- Dependency injection for protected endpoints
+
+### Workers (`src/graph_rag_service/workers/`)
+
+#### Celery Tasks
+- `ingest_document`: Process single document
+- `ingest_documents_batch`: Process multiple documents
+- `health_check`: Worker health verification
+
+#### Configuration
+- Broker: Redis
+- Result Backend: Redis
+- Serializer: JSON
+- Task timeout: 1 hour (configurable)
+
+### Observability (`src/graph_rag_service/observability/`)
+
+#### OpenTelemetry Integration
+- **Traces**: Agent reasoning steps, tool calls, database queries
+- **Metrics**: 
+  - `documents_ingested`: Counter
+  - `queries_executed`: Counter
+  - `query_duration_seconds`: Histogram
+  - `entities_extracted`: Counter
+
+#### Structured Logging
+- Log level: INFO (configurable)
+- Format: `%(asctime)s - %(name)s - %(levelname)s - %(message)s`
+- All async operations logged with context
+
+## Configuration
+
+### Environment Variables
+Key settings in `.env`:
+- **Neo4j**: `NEO4J_URI`, `NEO4J_USER`, `NEO4J_PASSWORD`
+- **Redis**: `REDIS_HOST`, `REDIS_PORT`
+- **LLM Provider**: `DEFAULT_LLM_PROVIDER` (openai/anthropic/gemini/ollama)
+- **API Keys**: `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `GOOGLE_API_KEY`
+- **Ollama**: `OLLAMA_BASE_URL`, `OLLAMA_MODEL`, `OLLAMA_EMBEDDING_MODEL`
+- **Security**: `SECRET_KEY`, `ACCESS_TOKEN_EXPIRE_MINUTES`
+
+### Tuning Parameters
+- `CHUNK_SIZE`: 1024 (text chunk size)
+- `CHUNK_OVERLAP`: 200 (overlap between chunks)
+- `MAX_AGENT_ITERATIONS`: 5 (max reasoning steps)
+- `AGENT_TIMEOUT_SECONDS`: 30 (query timeout)
+- `ENTITY_RESOLUTION_THRESHOLD`: 0.85 (similarity threshold)
+- `DEFAULT_TOP_K`: 5 (retrieval results)
+- `GRAPH_MAX_DEPTH`: 3 (graph traversal depth)
+
+## Deployment
+
+### Local Development
+```bash
+# 1. Ensure Neo4j and Redis are running
+# 2. Configure .env with connection details
+
+# 3. Start API server
+./start-server.sh  # or start-server.bat on Windows
+
+# 4. Start workers
+./start-worker.sh  # or start-worker.bat on Windows
+```
+
+### Production Considerations
+1. **Database**: Use managed Neo4j (Aura) or self-hosted cluster
+2. **Redis**: Use managed Redis (AWS ElastiCache, Redis Cloud)
+3. **Worker Scaling**: Add more Celery workers based on ingestion load
+4. **API Scaling**: Run multiple API instances behind load balancer
+5. **Monitoring**: Integrate with Prometheus/Grafana for metrics
+6. **Secrets**: Use secret management (AWS Secrets Manager, HashiCorp Vault)
+
+## Extensibility
+
+### Adding New LLM Provider
+1. Implement `LLMProvider` interface
+2. Add to `LLMFactory.create()` method
+3. Update config with new provider settings
+
+### Adding New Graph Database
+1. Implement `GraphStore` interface
+2. Update `IngestionPipeline` to use new store
+3. Test with existing workflows
+
+### Custom Retrieval Tools
+1. Create new tool class with `run()` method
+2. Add to `AgentRetrievalSystem.tools`
+3. Update routing logic in `_route_query()`
+
+## Testing Strategy
+
+### Unit Tests
+- Test each component independently
+- Mock external dependencies (Neo4j, Redis, LLMs)
+- Focus on business logic
+
+### Integration Tests
+- Test component interactions
+- Use test database instances
+- Verify end-to-end flows
+
+### Performance Tests
+- Benchmark ingestion throughput
+- Measure query latencies
+- Stress test with concurrent requests
+
+## Future Enhancements
+
+### Phase 1 (Current MVP)
+- ✅ Core ingestion pipeline
+- ✅ Agentic retrieval system
+- ✅ Multi-LLM support
+- ✅ Entity resolution
+- ✅ Async workers
+
+### Phase 2 (Next Steps)
+- [ ] React frontend with visual ontology editor
+- [ ] Graph visualization (D3.js/Cytoscape)
+- [ ] Advanced ontology evolution with migrations
+- [ ] Semantic cache with Redis
+- [ ] Batch ingestion optimization
+
+### Phase 3 (Advanced Features)
+- [ ] Multi-tenant support with data isolation
+- [ ] Fine-tuned entity extraction models
+- [ ] Graph neural network embeddings
+- [ ] Automated ontology quality metrics
+- [ ] Export/import ontology schemas
+
+## Troubleshooting
+
+### Common Issues
+
+#### Neo4j Connection Failed
+- Verify Neo4j is running and accessible
+- Verify credentials in `.env`
+- Try connecting with cypher-shell: `cypher-shell -u neo4j -p password`
+
+#### Celery Worker Not Processing
+- Check Redis is running: `redis-cli ping`
+- Verify broker URL in `.env`
+- Check worker logs
+
+#### Ollama Models Not Found
+- Pull models: `ollama pull llama3.2 && ollama pull bge-m3`
+- Verify Ollama is running: `curl http://localhost:11434/api/tags`
+
+#### Query Returns No Results
+- Verify documents are ingested: `GET /api/system/stats`
+- Check ontology exists: `GET /api/ontology`
+- Try simpler queries first
+
+## Support
+
+For issues or questions:
+1. Check documentation and troubleshooting guide
+2. Search existing GitHub issues
+3. Open new issue with:
+   - Clear description
+   - Steps to reproduce
+   - Environment details
+   - Relevant logs
+
+---
+
+**Last Updated**: February 2026
+**Version**: 0.1.0

Dockerfile

@@ -0,0 +1,155 @@
+FROM ubuntu:22.04
+
+# Avoid tzdata interactive prompts
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Install system dependencies and Python 3.12
+RUN apt-get update && apt-get install -y software-properties-common \
+    && add-apt-repository ppa:deadsnakes/ppa \
+    && apt-get update && apt-get install -y \
+    python3.12 \
+    python3.12-venv \
+    python3.12-dev \
+    python3-pip \
+    curl \
+    wget \
+    git \
+    redis-server \
+    openjdk-17-jdk \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Node.js for frontend build
+RUN curl -fsSL https://deb.nodesource.com/setup_20.x | bash - \
+    && apt-get install -y nodejs
+
+# Create app directory
+WORKDIR /app
+
+# Download and extract Neo4j and GDS plugin
+RUN wget -q https://neo4j.com/artifact.php?name=neo4j-community-5.18.0-unix.tar.gz -O neo4j.tar.gz \
+    && tar -xf neo4j.tar.gz \
+    && mv neo4j-community-5.18.0 neo4j \
+    && rm neo4j.tar.gz \
+    && wget -q https://github.com/neo4j/graph-data-science/releases/download/2.6.4/neo4j-graph-data-science-2.6.4.jar -O neo4j/plugins/neo4j-graph-data-science.jar
+
+# Configure Neo4j for demo mode (disable auth, limit memory, enable GDS)
+RUN echo "dbms.security.auth_enabled=false" >> neo4j/conf/neo4j.conf \
+    && echo "server.memory.heap.initial_size=512m" >> neo4j/conf/neo4j.conf \
+    && echo "server.memory.heap.max_size=1G" >> neo4j/conf/neo4j.conf \
+    && echo "server.memory.pagecache.size=1G" >> neo4j/conf/neo4j.conf \
+    && echo "dbms.security.procedures.unrestricted=gds.*" >> neo4j/conf/neo4j.conf
+
+# Copy project files
+COPY . .
+
+# Build frontend
+WORKDIR /app/frontend-react
+RUN npm install
+RUN npm run build
+
+# Setup Python backend
+WORKDIR /app
+RUN python3.12 -m venv .venv
+ENV PATH="/app/.venv/bin:$PATH"
+RUN pip install --upgrade pip
+RUN pip install -e .
+
+# Create start script
+RUN echo '#!/bin/bash\n\
+\n\
+# Start Redis\n\
+redis-server --daemonize yes\n\
+\n\
+# Start Neo4j in background\n\
+/app/neo4j/bin/neo4j start\n\
+\n\
+# Wait for Neo4j to be ready\n\
+echo "Waiting for Neo4j to start..."\n\
+while ! curl -s http://localhost:7474 > /dev/null; do\n\
+    sleep 2\n\
+done\n\
+echo "Neo4j HTTP is up! Waiting for Bolt (7687)..."\n\
+while ! (echo > /dev/tcp/localhost/7687) >/dev/null 2>&1; do\n\
+    sleep 2\n\
+done\n\
+echo "Neo4j is up!"\n\
+\n\
+# Set environment variables for Demo Mode\n\
+export NEO4J_URI=bolt://localhost:7687\n\
+export NEO4J_USER=neo4j\n\
+export NEO4J_PASSWORD=dummy\n\
+export REDIS_HOST=localhost\n\
+export REDIS_PORT=6379\n\
+export REDIS_DB=0\n\
+export DEMO_MODE=true\n\
+export ENVIRONMENT=production\n\
+export SECRET_KEY=demo-secret-key-1234567890\n\
+\n\
+if [ -z "$GOOGLE_API_KEY" ]; then\n\
+    export DEFAULT_LLM_PROVIDER=mock\n\
+    export EMBEDDING_PROVIDER=mock\n\
+    export DEMO_MODE=true\n\
+    echo "[WARNING] GOOGLE_API_KEY is not set. Running in DEMO_MODE with mock LLM provider."\n\
+else\n\
+    export DEFAULT_LLM_PROVIDER=gemini\n\
+    export EMBEDDING_PROVIDER=gemini\n\
+fi\n\
+\n\
+# Create default admin user in Neo4j\n\
+python -c "\n\
+import asyncio\n\
+from src.graph_rag_service.core.neo4j_store import Neo4jStore\n\
+from src.graph_rag_service.api.auth import get_password_hash\n\
+\n\
+async def main():\n\
+    store = Neo4jStore()\n\
+    await store.connect()\n\
+    try:\n\
+        await store.create_user({\n\
+            '\''username'\'': '\''admin'\'',\n\
+            '\''hashed_password'\'': get_password_hash('\''admin'\''),\n\
+            '\''email'\'': '\''admin@example.com'\'',\n\
+            '\''full_name'\'': '\''Demo Admin'\'',\n\
+            '\''disabled'\'': False,\n\
+            '\''scopes'\'': ['\''read'\'', '\''write'\'', '\''admin'\''],\n\
+            '\''tenant_id'\'': '\''demo_tenant'\'',\n\
+        })\n\
+        print('\''Admin user created in Neo4j'\'')\n\
+        \n\
+        # Check GDS\n\
+        gds_res = await store.execute_query('\''RETURN gds.version() as version'\'')\n\
+        print(f'\''GDS Plugin Version: {gds_res[0]["version"] if gds_res else "NOT FOUND"}'\'')\n\
+    except Exception as e:\n\
+        print(f'\''Admin user creation note: {e}'\'')\n\
+        raise e\n\
+    await store.disconnect()\n\
+\n\
+asyncio.run(main())\n\
+"\n\
+if [ $? -ne 0 ]; then\n\
+    echo "Admin seed failed, retrying in 5 seconds..."\n\
+    sleep 5\n\
+    python -c "\n\
+import asyncio\n\
+from src.graph_rag_service.core.neo4j_store import Neo4jStore\n\
+from src.graph_rag_service.api.auth import get_password_hash\n\
+\n\
+async def main():\n\
+    store = Neo4jStore()\n\
+    await store.connect()\n\
+    try:\n\
+        await store.create_user({'username': 'admin', 'hashed_password': get_password_hash('admin'), 'scopes': ['read', 'write', 'admin'], 'tenant_id': 'demo_tenant'})\n\
+    except Exception as e: pass\n\
+    await store.disconnect()\n\
+asyncio.run(main())\n\
+    "\n\
+fi\n\
+\n\
+# Start FastAPI and serve static files (frontend)\n\
+uvicorn src.graph_rag_service.api.server:app --host 0.0.0.0 --port 7860\n\
+' > start.sh && chmod +x start.sh
+
+# HF Spaces requires serving on 7860
+EXPOSE 7860
+
+CMD ["./start.sh"]

QUICKSTART.md

@@ -0,0 +1,265 @@
+# Quick Start Guide
+
+Get the Graph RAG Service up and running in 10 minutes!
+
+## Prerequisites
+
+Before starting, make sure you have:
+- Python 3.12 or higher
+- Neo4j database (running locally or remotely)
+- Redis server (running locally or remotely)
+- UV package manager (will be installed if missing)
+
+## Step 1: Clone and Setup
+
+```bash
+cd graph-RAG
+
+# Install UV if you don't have it
+curl -LsSf https://astral.sh/uv/install.sh | sh
+
+# Install dependencies
+uv sync
+```
+
+## Step 2: Configure Environment
+
+```bash
+# Copy environment template
+cp .env.example .env
+
+# Edit .env with your settings
+# Configure NEO4J_URI, NEO4J_USER, NEO4J_PASSWORD
+# Configure REDIS_URL
+```
+
+## Step 3: Ensure Backend Services Are Running
+
+Make sure Neo4j and Redis are running and accessible:
+- Neo4j should be available at the URI specified in your .env file (default: bolt://localhost:7687)
+- Redis should be available at the URL specified in your .env file (default: redis://localhost:6379)
+
+## Step 4: Start the API Server
+
+### On Windows:
+```bash
+start-server.bat
+```
+
+### On Mac/Linux:
+```bash
+chmod +x start-server.sh
+./start-server.sh
+```
+
+### Or directly (any OS):
+```bash
+uv run python main.py
+```
+
+The API server will start on `http://localhost:8000`
+
+## Step 5: Start Celery Worker
+
+For asynchronous document ingestion, start a worker in a **new terminal**:
+
+### On Windows:
+```bash
+start-worker.bat
+```
+
+### On Mac/Linux:
+```bash
+chmod +x start-worker.sh
+./start-worker.sh
+```
+
+### Or directly:
+```bash
+uv run celery -A src.graph_rag_service.workers.celery_worker worker --loglevel=info
+```
+
+## Step 6: Start the React Frontend
+
+In a **new terminal**:
+
+```bash
+cd frontend-react
+
+# Install Node dependencies (first time only)
+npm install
+
+# Start the dev server
+npm run dev
+```
+
+The React UI will open at `http://localhost:5173`
+
+> **Note:** The project uses a **React/Vite** frontend (`frontend-react/`).  
+> There is no `streamlit run app.py` — any references to Streamlit in older docs are outdated.
+
+Login with: **username** `admin` / **password** `admin`
+
+## Step 7: Test the API
+
+### Using cURL
+
+1. **Get Access Token**
+```bash
+curl -X POST "http://localhost:8000/api/auth/login" \
+  -H "Content-Type: application/json" \
+  -d '{"username": "demo", "password": "demo"}'
+```
+
+Save the `access_token` from the response.
+
+2. **Upload a Document**
+```bash
+curl -X POST "http://localhost:8000/api/documents/upload" \
+  -H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
+  -F "file=@sample.pdf"
+```
+
+3. **Query the Knowledge Base**
+```bash
+curl -X POST "http://localhost:8000/api/query" \
+  -H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "query": "What is this document about?",
+    "top_k": 5
+  }'
+```
+
+### Using the Interactive Docs
+
+Open your browser and go to:
+- Swagger UI: `http://localhost:8000/docs`
+- ReDoc: `http://localhost:8000/redoc`
+
+Click "Authorize" and enter your access token to test endpoints interactively.
+
+## Step 8: Setup Ollama (Optional)
+
+If you want to use local LLMs instead of OpenAI/Anthropic:
+
+```bash
+# Install Ollama (https://ollama.ai)
+# Then pull models:
+ollama pull llama3.2
+ollama pull bge-m3
+```
+
+Update `.env`:
+```
+DEFAULT_LLM_PROVIDER=ollama
+OLLAMA_BASE_URL=http://localhost:11434
+OLLAMA_MODEL=llama3.2
+OLLAMA_EMBEDDING_MODEL=bge-m3
+```
+
+## Common Commands
+
+### Check System Health
+```bash
+curl http://localhost:8000/api/system/health
+```
+
+### Get System Stats
+```bash
+curl -H "Authorization: Bearer YOUR_TOKEN" \
+  http://localhost:8000/api/system/stats
+```
+
+### View Ontology
+```bash
+curl -H "Authorization: Bearer YOUR_TOKEN" \
+  http://localhost:8000/api/ontology
+```
+
+### Visualize Graph
+```bash
+curl -H "Authorization: Bearer YOUR_TOKEN" \
+  "http://localhost:8000/api/graph/visualization?limit=50"
+```
+
+## Troubleshooting
+
+### Neo4j Connection Error
+```bash
+# Verify credentials in .env
+# Check Neo4j is accessible
+
+# Access Neo4j browser
+open http://localhost:7474
+
+# Try connecting with cypher-shell
+cypher-shell -u neo4j -p password
+```
+
+### Redis Connection Error
+```bash
+# Check Redis is running
+redis-cli ping
+
+# Test Redis connection
+redis-cli -h localhost -p 6379 ping
+```
+
+### Worker Not Processing
+```bash
+# Check worker logs
+# If running locally:
+celery -A src.graph_rag_service.workers.celery_worker inspect active
+```
+
+### API Server Won't Start
+```bash
+# Check for port conflicts
+lsof -i :8000  # On Mac/Linux
+netstat -ano | findstr :8000  # On Windows
+
+# View detailed logs
+uv run python main.py
+```
+
+## Next Steps
+
+- Read the [README.md](README.md) for comprehensive documentation
+- Check [ARCHITECTURE.md](ARCHITECTURE.md) for system design details
+- Explore the API docs at `http://localhost:8000/docs`
+
+## Using with Different LLM Providers
+
+### OpenAI
+```bash
+# In .env:
+DEFAULT_LLM_PROVIDER=openai
+OPENAI_API_KEY=sk-your-key-here
+```
+
+### Anthropic
+```bash
+# In .env:
+DEFAULT_LLM_PROVIDER=anthropic
+ANTHROPIC_API_KEY=sk-ant-your-key-here
+```
+
+### Google Gemini
+```bash
+# In .env:
+DEFAULT_LLM_PROVIDER=gemini
+GOOGLE_API_KEY=your-key-here
+```
+
+Services will be available at:
+- API: `http://localhost:8000`
+- Neo4j Browser: `http://localhost:7474`
+
+## Need Help?
+
+- Check the troubleshooting section above
+- Read the full [README.md](README.md)
+- Open an issue on GitHub
+
+Happy building! 🚀

README.md

@@ -0,0 +1,467 @@
+# CORTEX — Agentic Graph RAG Platform
+
+> **CORTEX** is a production-grade, agentic Knowledge Graph platform that transforms unstructured documents and web content into an intelligent, queryable knowledge graph — with a full-featured React UI, streaming AI chat, real-time graph visualization, simulation personas, and deep ontology governance.
+
+---
+
+## ✨ What's Been Built
+
+### 🖥️ Full-Stack Application
+
+| Layer | Stack |
+|---|---|
+| **Backend API** | FastAPI (async) + Python 3.12 |
+| **Task Queue** | Celery + Redis |
+| **Graph + Vector DB** | Neo4j 5.x (unified) |
+| **LLM Layer** | OpenAI, Anthropic, Google Gemini, Ollama |
+| **Frontend** | React 18 + TypeScript + Vite |
+| **Unified Start** | `npm run rag` (concurrently launches all 3 processes) |
+
+---
+
+## 🚀 Features
+
+### 📥 Document Ingestion Pipeline
+
+- **Multi-format ingestion**: PDF, TXT, MD, DOCX, CSV, XLSX, PPTX, JSON
+- **Web scraping**: Single-page scrape via `POST /api/documents/scrape`
+- **Deep web crawling**: Multi-depth Playwright-powered crawler (`POST /api/documents/crawl`) via Crawl4AI
+- **Async Celery workers**: Upload returns instantly with a `task_id`; background workers build the graph
+- **Re-ingest**: Admin can trigger re-processing of any stored document
+- **Document preview & download**: In-browser preview of text/Markdown; PDF download via API
+
+### 🔭 Ontology Management
+
+- **Auto-generation**: LLM analyzes document chunks to propose entity types & relationship types
+- **LLM-powered refinement**: `POST /api/ontology/refine` — refine schema with optional human feedback
+- **Versioning**: Each schema change bumps the version (`v1.0` → `v1.1`, etc.)
+- **Document-scoped stats**: `/api/ontology/stats?document_id=...` returns entity/relationship breakdowns for a specific document
+- **Visual editor**: Ontology view in UI with editable entity types and relationship types
+- **Ontology Drift Detection**: Automated drift detection compares live graph against new chunk samples; exposes pending/approved/rejected drift reports with admin approve/reject workflow
+
+### 🤖 Agentic Retrieval System
+
+- **LangGraph orchestration**: State-machine ReACT agent with multi-step reasoning and fallback mechanisms
+- **Tool routing**: Dynamically selects from Naive Vector, Hybrid, Metadata Filtering, Global Community Search, HippoRAG, Graph of Thoughts (GoT), and Cypher.
+- **Retrieval Modes**: Switch seamlessly between `AUTO`, `HYBRID`, `HIPPO-RAG`, `LOCAL GRAPH`, `GLOBAL COMMUNITY`, `GOT`, `CYPHER`, `NAIVE`, and `SIMULATION`.
+- **Streaming responses**: Server-Sent Events (SSE) with real-time reasoning steps surfaced in the UI
+- **Multi-turn conversations**: Persistent conversation threads stored in Neo4j, per-user
+- **Document-scoped queries**: Filter retrieval to a specific document via `document_id`
+- **Graph of Thoughts (GoT)**: Advanced multi-hop reasoning mode exploring the graph neighborhood
+- **HippoRAG**: Personalized PageRank (PPR) fallback strategies mapped to entity seeding
+- **Global Community Search**: Vector similarity search over pre-computed hierarchical Leiden reports
+- **LLM-as-a-Judge (inline)**: Optional per-response quality scoring with hallucination risk, grounded/ungrounded claims, and confidence reasoning displayed in chat
+- **Confidence display**: Confidence score, hallucination risk, and judge reasoning shown directly in the chat bubble
+
+### 📊 RAGAS Evaluation & Quality Dashboard
+
+- **`POST /api/eval/score`**: Run RAGAS-style evaluation on any Q&A pair (faithfulness, relevancy, context precision, hallucination detection)
+- **`GET /api/eval/dashboard`**: Aggregate evaluation history — avg scores, hallucination rate, trend timeline
+- Results persisted in Neo4j for longitudinal quality tracking
+
+### 🗺️ Graph Intelligence
+
+- **D3 force-directed visualization**: Interactive knowledge graph with zoom, pan, node selection, and a details modal
+- **Graph Export**: Export full or document-scoped graph as JSON, Cypher, or GraphML
+- **Community Detection**: Hierarchical Leiden community clustering with `POST /api/graph/communities/assign`
+- **Community listing**: `GET /api/graph/communities` — top communities by entity count
+- **Temporal Queries**: `GET /api/entities/{entity_name}/at-time` — retrieve entity relationships at a historical point in time
+- **Semantic Entity Deduplication**: Multi-stage entity resolution with configurable similarity thresholds (`POST /api/entities/deduplicate`)
+- **Entity Enrichment**: LLM-synthesized profile summaries for every entity, stored as `e.summary` (`POST /api/entities/enrich`)
+- **Entity Chat (scoped)**: `POST /api/entities/{entity_name}/chat` — multi-turn conversation scoped entirely to a single entity's graph neighborhood
+- **Graph Memory Updater**: Push raw text directly into the live knowledge graph without re-ingesting a document (`POST /api/graph/update`)
+
+### 📝 Analytical Report Agent (ReACT)
+
+- **`POST /api/report`**: ReACT multi-step report agent using InsightForge / PanoramaSearch / QuickSearch tools
+- Decomposes topic into sub-questions → retrieves graph data → synthesizes sections → compiles structured markdown report
+- Exposed in the **Insights** view (copy/download report as Markdown)
+
+### 🎭 Simulation & Persona Engine
+
+- **Persona generation**: Celery task that generates personas from graph entities (`POST /api/v1/simulation/generate_personas`)
+- **Simulation ticks**: Background tick loop (`POST /api/v1/simulation/tick`)
+- **Live persona interview**: `POST /api/v1/simulation/interview` — roleplay chat with any graph entity injecting their Neo4j memory as system context
+- **SimulationRunView**: Dedicated UI view for managing and interacting with simulation personas
+
+### 🛡️ Admin Dashboard
+
+- **System statistics**: Node count, relationship count, LLM provider, environment
+- **User management**: List users, update scopes/roles (RBAC)
+- **Document vault**: View and delete all ingested documents
+- **Graph CRUD**: Search, inspect, and delete graph nodes from the admin panel
+- **Ontology governance**: Review and approve/reject pending ontology proposals
+- **Celery task monitor**: View active and reserved tasks from the admin panel
+- **Self-demotion guard**: Admins cannot demote their own account
+- **Re-ingest button**: Re-queue any stored document from the document vault
+- **User activity metrics**: Per-user conversation count, message count, last active timestamp
+
+### 🔐 Authentication & Security
+
+- **JWT authentication**: Token-based auth with configurable expiry
+- **RBAC scopes**: `read`, `write`, `admin` scopes enforced per endpoint
+- **User registration**: `POST /api/auth/register`
+- **Pydantic validation**: All API inputs validated at the model layer
+- **Cypher injection prevention**: Schema validation and query whitelisting
+- **File upload limits**: File size and MIME type enforcement
+
+### 🌐 Frontend (React/TypeScript)
+
+Seven fully implemented views accessible from the `CORTEX` top navigation bar:
+
+| Route | View | Description |
+|---|---|---|
+| `/` | **Home** | Animated stats dashboard — documents, entities, relationships, graph health |
+| `/process` | **Process** | Upload files or scrape/crawl URLs; view ingestion queue and document list |
+| `/ontology` | **Ontology** | View/edit the live ontology schema; run LLM refinement; inspect entity/relationship stats per doc |
+| `/interact` | **Interact** | Streaming AI chat with reasoning steps, confidence, hallucination risk; conversation history |
+| `/simulate` | **Simulate** | Simulation persona management and live interview interface |
+| `/insights` | **Insights** | Topic-driven analytical report generation with copy/download |
+| `/admin` | **Admin** _(admin-only)_ | Full admin panel for users, docs, tasks, ontology governance |
+
+### 🔭 Observability
+
+- **OpenTelemetry**: Distributed tracing (silenced from console; configured for export)
+- **Health check**: `GET /api/system/health` — Neo4j, Redis, Celery worker status
+- **System stats**: `GET /api/system/stats` — document, entity, relationship, chunk counts
+- **User stats**: `GET /api/system/my-stats` — per-user conversation and message activity
+
+---
+
+## 🏗️ Architecture
+
+```
+┌─────────────────────────────────────────────────────────────────────────────┐
+│                          React Frontend (CORTEX)                             │
+│    Home │ Process │ Ontology │ Interact │ Simulate │ Insights │ Admin        │
+└─────────────────────────────┬───────────────────────────────────────────────┘
+                              │ HTTP / SSE
+┌─────────────────────────────▼───────────────────────────────────────────────┐
+│                     FastAPI Gateway (port 8000)                              │
+│          JWT Auth · RBAC Scopes · CORS · OpenTelemetry                      │
+└──────┬──────────────────────┬──────────────────────┬────────────────────────┘
+       │                      │                      │
+┌──────▼──────┐   ┌───────────▼──────────┐  ┌───────▼────────────────────┐
+│  Ingestion  │   │  ReACT Agent System  │  │  Report Agent (ReACT)      │
+│  Pipeline   │   │  - Vector Search     │  │  - InsightForge            │
+│  - Parser   │   │  - Graph Traversal   │  │  - PanoramaSearch          │
+│  - Ontology │   │  - Cypher Gen (GoT)  │  │  - QuickSearch             │
+│  - Extractor│   │  - Community Search  │  │  - Markdown output         │
+│  - Web      │   │  - Temporal Queries  │  └────────────────────────────┘
+│    Crawler  │   │  - LLM-as-a-Judge    │
+└──────┬──────┘   └─────────┬────────────┘
+       │                    │
+┌──────▼────────────────────▼──────────────────┐
+│              Neo4j 5.x Database               │
+│  Entities · Chunks · Relationships ·          │
+│  Vector Index · Conversations ·               │
+│  EvalResults · DriftReports · Users           │
+└───────────────────────────────────────────────┘
+       │
+┌──────▼──────────────────────┐
+│  Celery Workers (Redis)     │
+│  - Async document ingestion │
+│  - Persona generation       │
+│  - Simulation ticks         │
+└─────────────────────────────┘
+```
+
+---
+
+## 📦 Project Structure
+
+```
+graph-RAG/
+├── src/graph_rag_service/
+│   ├── api/
+│   │   ├── server.py          # Main FastAPI app + all API routes (1900 lines)
+│   │   ├── auth.py            # JWT auth + RBAC helpers
+│   │   ├── admin.py           # Admin sub-router
+│   │   ├── simulation.py      # Simulation / persona interview router
+│   │   └── models.py          # All Pydantic request/response models
+│   ├── core/
+│   │   ├── abstractions.py    # Abstract base classes (GraphStore, VectorStore, LLMProvider)
+│   │   ├── models.py          # Domain data models
+│   │   ├── neo4j_store.py     # Full Neo4j implementation (graph + vector)
+│   │   ├── llm_factory.py     # Multi-LLM provider factory + UnifiedLLMProvider
+│   │   ├── entity_resolver.py # Semantic entity deduplication
+│   │   └── storage.py         # File storage abstraction
+│   ├── ingestion/
+│   │   ├── pipeline.py        # End-to-end ingestion orchestrator
+│   │   ├── document_processor.py  # Multi-format document parsing
+│   │   ├── ontology_generator.py  # LLM ontology generation + refinement
+│   │   ├── extractor.py       # Entity + relationship extraction
+│   │   ├── web_crawler.py     # Playwright-based deep web crawler (Crawl4AI)
+│   │   └── persona_generator.py   # Simulation persona generation
+│   ├── retrieval/
+│   │   ├── agent.py           # LangGraph ReACT retrieval agent
+│   │   ├── tools.py           # Retrieval tools + RAGEvaluator (RAGAS)
+│   │   └── report_agent.py    # ReACT analytical report agent
+│   ├── services/
+│   │   ├── graph_memory_updater.py   # Push raw text → live graph
+│   │   ├── entity_enricher.py        # LLM entity profile summaries
+│   │   └── ontology_drift_detector.py # Automated schema drift detection
+│   ├── workers/
+│   │   └── celery_worker.py   # Celery app + ingest_document_task
+│   ├── observability/
+│   │   └── tracing.py         # OpenTelemetry setup (console suppressed)
+│   ├── config.py              # Pydantic settings (all env vars)
+│   └── main.py                # Uvicorn entry point
+├── frontend-react/
+│   └── src/
+│       ├── views/
+│       │   ├── Home.tsx            # Animated stats dashboard
+│       │   ├── Process.tsx         # Document upload + URL scrape/crawl
+│       │   ├── Ontology.tsx        # Schema editor + stats
+│       │   ├── InteractionView.tsx # Streaming chat + conversation history
+│       │   ├── SimulationRunView.tsx # Persona simulation UI
+│       │   ├── InsightsView.tsx    # Report generation + copy/download
+│       │   ├── AdminDashboard.tsx  # Full admin panel
+│       │   └── Login.tsx           # Login page
+│       ├── components/
+│       │   └── GraphCanvas.tsx     # D3 force-directed graph + node modal
+│       ├── context/
+│       │   └── AuthContext.tsx     # JWT auth context + hooks
+│       └── App.tsx                 # Router + top-nav (CORTEX branding)
+├── tests/                      # Test suite
+├── data/uploads/               # Uploaded documents (local storage)
+├── .env.example                # All configurable environment variables
+├── pyproject.toml              # Python project + uv dependencies
+├── package.json                # Unified start scripts (npm run rag)
+├── ARCHITECTURE.md             # Detailed architecture design doc
+└── QUICKSTART.md               # 5-minute quick start guide
+```
+
+---
+
+## ⚡ Quick Start
+
+### Prerequisites
+
+- Python 3.12+
+- Node.js 18+
+- Neo4j 5.x (running) with **APOC** and **Graph Data Science (GDS)** plugins installed
+- Redis (running)
+- Ollama *(optional, for local LLMs)*
+
+### 1. Clone & Install
+
+```bash
+git clone <repository-url>
+cd graph-RAG
+
+# Installs Python deps (uv), frontend (npm), and Playwright Chromium
+npm install
+```
+
+### 2. Configure Environment
+
+```bash
+cp .env.example .env
+# Fill in NEO4J_URI, NEO4J_PASSWORD, and your LLM API keys
+```
+
+### 3. Start Neo4j (Requires GDS Plugin)
+
+```bash
+docker run -d --name neo4j \
+  -p 7474:7474 -p 7687:7687 \
+  -e NEO4J_AUTH=neo4j/password \
+  -e NEO4J_PLUGINS='["graph-data-science", "apoc"]' \
+  neo4j:5.18.0
+```
+
+### 4. Start Redis
+
+```bash
+docker run -d --name redis -p 6379:6379 redis:alpine
+```
+
+### Hugging Face Spaces / All-in-One Docker
+You can easily deploy CORTEX to Hugging Face Spaces or as a standalone container.
+Our `Dockerfile` automatically installs Python 3.12, Redis, Neo4j, and the GDS plugin to run the entire platform on a single port (`7860`).
+
+### 5. Launch Everything
+
+```bash
+npm run rag
+```
+
+This starts three color-coded processes concurrently:
+
+| Process | URL |
+|---|---|
+| **API Server** | `http://localhost:8000` |
+| **API Docs** | `http://localhost:8000/docs` |
+| **React Frontend** | `http://localhost:5173` |
+
+> Default credentials: `admin` / `admin`
+
+---
+
+## 🔑 Environment Variables
+
+Copy `.env.example` to `.env` and configure:
+
+```env
+# Neo4j
+NEO4J_URI=bolt://localhost:7687
+NEO4J_USER=neo4j
+NEO4J_PASSWORD=password
+
+# Redis
+REDIS_HOST=localhost
+REDIS_PORT=6379
+
+# LLM Provider (openai | anthropic | gemini | ollama)
+DEFAULT_LLM_PROVIDER=gemini
+GOOGLE_API_KEY=your-key-here
+
+# Optional: OpenAI / Anthropic
+OPENAI_API_KEY=sk-...
+ANTHROPIC_API_KEY=sk-ant-...
+
+# Optional: Ollama (local)
+OLLAMA_BASE_URL=http://localhost:11434
+OLLAMA_MODEL=deepseek-r1:7b
+OLLAMA_EMBEDDING_MODEL=nomic-embed-text
+
+# Feature flags
+ENABLE_LLM_JUDGE=true
+
+# Security
+SECRET_KEY=change-this-in-production
+ACCESS_TOKEN_EXPIRE_MINUTES=1440
+```
+
+---
+
+## 🌐 API Reference
+
+### Authentication
+| Method | Endpoint | Description |
+|---|---|---|
+| `POST` | `/api/auth/register` | Register new user |
+| `POST` | `/api/auth/login` | Login → JWT token |
+| `GET` | `/api/auth/me` | Get current user info |
+
+### Documents
+| Method | Endpoint | Description |
+|---|---|---|
+| `POST` | `/api/documents/upload` | Upload file (PDF, DOCX, TXT, MD, CSV, XLSX, PPTX, JSON) |
+| `POST` | `/api/documents/scrape` | Scrape single URL → ingest |
+| `POST` | `/api/documents/crawl` | Deep multi-page Playwright crawl → ingest *(API Only)* |
+| `GET` | `/api/documents` | List all ingested documents |
+| `DELETE` | `/api/documents/{id}` | Delete document + graph chunks |
+| `GET` | `/api/documents/{id}/download` | Download source file |
+| `GET` | `/api/documents/{id}/preview` | Preview text content |
+| `GET` | `/api/documents/status/{task_id}` | Ingestion task status |
+
+### Query & Chat
+| Method | Endpoint | Description |
+|---|---|---|
+| `POST` | `/api/query` | Agentic query (streaming or JSON); supports `document_id`, `use_got` |
+| `GET` | `/api/conversations` | List conversation threads |
+| `GET` | `/api/conversations/{id}` | Get conversation + messages |
+| `DELETE` | `/api/conversations/{id}` | Delete conversation |
+
+### Ontology
+| Method | Endpoint | Description |
+|---|---|---|
+| `GET` | `/api/ontology` | Get current ontology |
+| `PUT` | `/api/ontology` | Update ontology (admin) |
+| `POST` | `/api/ontology/refine` | LLM-powered ontology refinement |
+| `GET` | `/api/ontology/stats` | Entity/relationship counts (optional doc filter) |
+| `POST` | `/api/ontology/drift/detect` | Trigger drift detection |
+| `GET` | `/api/ontology/drift` | List drift reports |
+| `POST` | `/api/ontology/drift/{id}/approve` | Approve drift → merge into ontology |
+| `POST` | `/api/ontology/drift/{id}/reject` | Reject drift report |
+
+### Graph
+| Method | Endpoint | Description |
+|---|---|---|
+| `GET` | `/api/graph/visualization` | Graph nodes + edges for D3 rendering |
+| `GET` | `/api/graph/export` | Export graph (json \| cypher \| graphml) |
+| `POST` | `/api/graph/update` | Push raw text → merge into live graph |
+| `POST` | `/api/graph/communities/assign` | Run Hierarchical Leiden clustering & generate reports |
+| `GET` | `/api/graph/communities` | List top communities |
+
+### Entities
+| Method | Endpoint | Description |
+|---|---|---|
+| `POST` | `/api/entities/deduplicate` | Semantic entity resolution + merge |
+| `POST` | `/api/entities/enrich` | Generate LLM summaries for all entities |
+| `GET` | `/api/entities/{name}/summary` | Get enriched entity profile |
+| `POST` | `/api/entities/{name}/chat` | Multi-turn entity-scoped chat |
+| `GET` | `/api/entities/{name}/at-time` | Temporal query (ISO 8601 date) |
+
+### Reports & Evaluation
+| Method | Endpoint | Description |
+|---|---|---|
+| `POST` | `/api/report` | Generate ReACT analytical report (markdown) |
+| `POST` | `/api/eval/score` | RAGAS evaluation of a Q&A pair |
+| `GET` | `/api/eval/dashboard` | Evaluation history dashboard |
+
+### Simulation
+| Method | Endpoint | Description |
+|---|---|---|
+| `POST` | `/api/v1/simulation/interview` | Live persona interview (in-character LLM) |
+| `GET` | `/api/v1/simulation/report` | Sandbox analytical report *(API Only)* |
+| `POST` | `/api/v1/simulation/generate_personas` | Queue persona generation task *(API Only)* |
+| `POST` | `/api/v1/simulation/tick` | Advance simulation tick *(API Only)* |
+
+### System & Admin
+| Method | Endpoint | Description |
+|---|---|---|
+| `GET` | `/api/system/health` | Neo4j + Redis + Celery health |
+| `GET` | `/api/system/stats` | Document, entity, relationship counts |
+| `GET` | `/api/system/my-stats` | Current user's activity stats |
+| `GET` | `/api/system/formats` | Supported ingestion file formats |
+| `GET` | `/api/admin/stats` | Admin-only system stats |
+| `GET` | `/api/admin/users` | List all users |
+| `PUT` | `/api/admin/users/{username}/role` | Update user scopes |
+| `GET` | `/api/admin/tasks` | View Celery tasks |
+| `GET` | `/api/admin/documents` | Admin document vault |
+| `POST` | `/api/admin/documents/{id}/reingest` | Re-queue document for ingestion |
+| `GET` | `/api/admin/graph/nodes` | Search graph nodes |
+| `DELETE` | `/api/admin/graph/nodes/{id}` | Delete a graph node |
+
+---
+
+## 🧪 Testing & Benchmarking
+
+```bash
+# Run tests
+uv run pytest
+
+# With coverage
+uv run pytest --cov=src/graph_rag_service
+
+# Run SOTA Benchmarks against Hugging Face datasets (e.g. HotpotQA, MuSiQue)
+uv run python benchmarks/run_benchmark.py
+```
+
+---
+
+## 🚀 Production Deployment
+
+| Process | Command |
+|---|---|
+| **API Server** | `uv run python main.py` |
+| **Celery Worker** | `uv run celery -A src.graph_rag_service.workers.celery_worker worker --loglevel=info --concurrency=4 --pool=threads` |
+| **React Build** | `cd frontend-react && npm run build` |
+
+The built React assets can be served directly by FastAPI (static file mount), or deployed to a CDN separately. Neo4j and Redis can be run via Docker, managed cloud services (AuraDB, Redis Cloud), or self-hosted.
+
+---
+
+## 📄 Additional Documentation
+
+- **[ARCHITECTURE.md](./ARCHITECTURE.md)** — Deep dive into the system design, data flow, and component interactions
+- **[QUICKSTART.md](./QUICKSTART.md)** — 5-minute environment setup guide
+- **`/docs`** — Interactive Swagger UI (auto-generated from FastAPI)
+
+---
+
+**Project Status**: Production-grade MVP · Actively developed  
+**License**: Proprietary — all rights reserved

artifacts/graphify-out/.graphify_analysis.json

@@ -0,0 +1,1014 @@
+{
+  "communities": {
+    "0": [
+      "core_llm_factory_llmfactory",
+      "core_llm_factory_rationale_272",
+      "core_models_ontologyschema",
+      "core_models_rationale_121",
+      "core_neo4j_store_neo4jstore_load_ontology",
+      "core_neo4j_store_rationale_619",
+      "ingestion_ontology_generator_ontologygenerator_generate_initial_ontology",
+      "ingestion_ontology_generator_rationale_30",
+      "routers_ontology_approve_drift_report",
+      "routers_ontology_list_drift_reports",
+      "routers_ontology_rationale_199",
+      "routers_ontology_rationale_234",
+      "routers_ontology_rationale_262",
+      "routers_ontology_rationale_285",
+      "routers_ontology_reject_drift_report",
+      "routers_ontology_trigger_drift_detection",
+      "services_ontology_drift_detector_bump_version",
+      "services_ontology_drift_detector_driftreport",
+      "services_ontology_drift_detector_ontologydriftdetector_apply_drift_report",
+      "services_ontology_drift_detector_ontologydriftdetector_compute_diff",
+      "services_ontology_drift_detector_ontologydriftdetector_detect_drift",
+      "services_ontology_drift_detector_ontologydriftdetector_get_drift_report",
+      "services_ontology_drift_detector_ontologydriftdetector_get_random_chunks",
+      "services_ontology_drift_detector_ontologydriftdetector_load_drift_report",
+      "services_ontology_drift_detector_ontologydriftdetector_save_drift_report",
+      "services_ontology_drift_detector_py",
+      "services_ontology_drift_detector_rationale_1",
+      "services_ontology_drift_detector_rationale_136",
+      "services_ontology_drift_detector_rationale_152",
+      "services_ontology_drift_detector_rationale_180",
+      "services_ontology_drift_detector_rationale_186",
+      "services_ontology_drift_detector_rationale_27",
+      "services_ontology_drift_detector_rationale_42",
+      "services_ontology_drift_detector_rationale_62",
+      "services_ontology_drift_detector_rationale_91",
+      "services_ontology_drift_detector_row_to_report"
+    ],
+    "1": [
+      "api_models_communityassignresponse",
+      "api_models_crawlrequest",
+      "api_models_deduplicateresponse",
+      "api_models_documentuploadresponse",
+      "api_models_entitychatrequest",
+      "api_models_entityupdaterequest",
+      "api_models_graphupdaterequest",
+      "api_models_ingestionstatusresponse",
+      "api_models_loginrequest",
+      "api_models_ontologyrefinerequest",
+      "api_models_ontologyrefineresponse",
+      "api_models_ontologyresponse",
+      "api_models_ontologyupdaterequest",
+      "api_models_py",
+      "api_models_queryrequest",
+      "api_models_rationale_1",
+      "api_models_rationale_261",
+      "api_models_rationale_286",
+      "api_models_rationale_331",
+      "api_models_rationale_358",
+      "api_models_registerrequest",
+      "api_models_reportrequest",
+      "api_models_scraperequest",
+      "api_models_systemhealthresponse",
+      "api_models_systemstatsresponse",
+      "api_models_tokenresponse",
+      "basemodel",
+      "core_models_document",
+      "core_models_extractionresult",
+      "core_models_rationale_1",
+      "core_models_rationale_131"
+    ],
+    "2": [
+      "core_neo4j_store_neo4jstore_fallback_search",
+      "core_neo4j_store_rationale_569",
+      "retrieval_agent_agentretrievalsystem",
+      "retrieval_agent_agentretrievalsystem_astream",
+      "retrieval_agent_agentretrievalsystem_build_graph",
+      "retrieval_agent_agentretrievalsystem_cache_get",
+      "retrieval_agent_agentretrievalsystem_cache_set",
+      "retrieval_agent_agentretrievalsystem_community_search",
+      "retrieval_agent_agentretrievalsystem_cypher_query",
+      "retrieval_agent_agentretrievalsystem_decompose_query",
+      "retrieval_agent_agentretrievalsystem_drift_expand",
+      "retrieval_agent_agentretrievalsystem_format_context",
+      "retrieval_agent_agentretrievalsystem_got_explore",
+      "retrieval_agent_agentretrievalsystem_hippo_search",
+      "retrieval_agent_agentretrievalsystem_make_initial_state",
+      "retrieval_agent_agentretrievalsystem_route_query",
+      "retrieval_agent_agentretrievalsystem_score_tool_results",
+      "retrieval_agent_agentretrievalsystem_should_continue",
+      "retrieval_agent_agentretrievalsystem_synthesize_response",
+      "retrieval_agent_rationale_100",
+      "retrieval_agent_rationale_113",
+      "retrieval_agent_rationale_270",
+      "retrieval_agent_rationale_39",
+      "retrieval_agent_rationale_512",
+      "retrieval_agent_rationale_540",
+      "retrieval_agent_rationale_553",
+      "retrieval_agent_rationale_597",
+      "retrieval_agent_rationale_643",
+      "retrieval_agent_rationale_720",
+      "retrieval_agent_rationale_79"
+    ],
+    "3": [
+      "core_abstractions_add_vectors",
+      "core_abstractions_complete",
+      "core_abstractions_complete_structured",
+      "core_abstractions_create_node",
+      "core_abstractions_create_relationship",
+      "core_abstractions_delete_vectors",
+      "core_abstractions_disconnect",
+      "core_abstractions_embed",
+      "core_abstractions_embed_batch",
+      "core_abstractions_find_path",
+      "core_abstractions_get_neighbors",
+      "core_abstractions_merge_entities",
+      "core_abstractions_py",
+      "core_abstractions_rationale_1",
+      "core_abstractions_search",
+      "core_llm_factory_rationale_135",
+      "core_llm_factory_rationale_183",
+      "core_llm_factory_rationale_227",
+      "core_llm_factory_rationale_96",
+      "core_neo4j_store_neo4jstore_create_chunk_with_entities",
+      "core_neo4j_store_rationale_130",
+      "core_neo4j_store_rationale_166",
+      "core_neo4j_store_rationale_270",
+      "core_neo4j_store_rationale_295",
+      "core_neo4j_store_rationale_487",
+      "core_neo4j_store_rationale_536",
+      "core_neo4j_store_rationale_57",
+      "core_neo4j_store_rationale_738",
+      "str"
+    ],
+    "4": [
+      "core_abstractions_execute_query",
+      "core_neo4j_store_neo4jstore_assign_community_ids",
+      "core_neo4j_store_neo4jstore_bm25_search",
+      "core_neo4j_store_neo4jstore_create_user",
+      "core_neo4j_store_neo4jstore_get_communities",
+      "core_neo4j_store_neo4jstore_get_community_entities",
+      "core_neo4j_store_neo4jstore_get_entities_at_time",
+      "core_neo4j_store_neo4jstore_get_eval_results",
+      "core_neo4j_store_neo4jstore_get_semantic_cache",
+      "core_neo4j_store_neo4jstore_get_user",
+      "core_neo4j_store_neo4jstore_save_eval_result",
+      "core_neo4j_store_neo4jstore_save_ontology",
+      "core_neo4j_store_py",
+      "core_neo4j_store_rationale_1",
+      "core_neo4j_store_rationale_220",
+      "core_neo4j_store_rationale_26",
+      "core_neo4j_store_rationale_331",
+      "core_neo4j_store_rationale_372",
+      "core_neo4j_store_rationale_407",
+      "core_neo4j_store_rationale_418",
+      "core_neo4j_store_rationale_463",
+      "core_neo4j_store_rationale_596",
+      "core_neo4j_store_rationale_647",
+      "core_neo4j_store_rationale_681",
+      "core_neo4j_store_rationale_696",
+      "core_neo4j_store_rationale_712",
+      "core_neo4j_store_rationale_788",
+      "core_neo4j_store_rationale_820"
+    ],
+    "5": [
+      "api_simulation_get_global_store",
+      "api_simulation_get_sandbox_report",
+      "api_simulation_interviewrequest",
+      "api_simulation_live_interview_agent",
+      "api_simulation_py",
+      "api_simulation_rationale_109",
+      "api_simulation_rationale_115",
+      "api_simulation_rationale_31",
+      "api_simulation_rationale_93",
+      "api_simulation_start_persona_generation",
+      "api_simulation_start_simulation_tick",
+      "retrieval_report_agent_panoramasearchtool",
+      "retrieval_report_agent_py",
+      "retrieval_report_agent_rationale_1",
+      "retrieval_report_agent_rationale_147",
+      "retrieval_report_agent_rationale_272",
+      "retrieval_report_agent_rationale_412",
+      "retrieval_report_agent_rationale_461",
+      "retrieval_report_agent_rationale_492",
+      "retrieval_report_agent_rationale_507",
+      "retrieval_report_agent_rationale_539",
+      "retrieval_report_agent_reportagent_act",
+      "retrieval_report_agent_reportagent_react_loop",
+      "retrieval_report_agent_reportagent_think",
+      "retrieval_report_agent_reportagent_write_executive_summary",
+      "retrieval_report_agent_reportagent_write_section_with_confidence",
+      "retrieval_report_agent_reportsection"
+    ],
+    "6": [
+      "api_admin_approve_ontology",
+      "api_admin_check_admin_scope",
+      "api_admin_delete_document",
+      "api_admin_delete_node",
+      "api_admin_force_merge_entities",
+      "api_admin_get_admin_stats",
+      "api_admin_get_graph_store",
+      "api_admin_get_pending_ontology",
+      "api_admin_get_review_queue",
+      "api_admin_get_tasks",
+      "api_admin_list_users",
+      "api_admin_py",
+      "api_admin_rationale_111",
+      "api_admin_rationale_121",
+      "api_admin_rationale_135",
+      "api_admin_rationale_19",
+      "api_admin_rationale_32",
+      "api_admin_rationale_56",
+      "api_admin_rationale_89",
+      "api_admin_reject_ontology",
+      "api_admin_search_nodes",
+      "api_admin_systemconfig",
+      "api_admin_taskdashboardresponse",
+      "api_admin_update_config",
+      "api_admin_update_user_role",
+      "routers_documents_rationale_257"
+    ],
+    "7": [
+      "api_init_rationale_1",
+      "core_init_rationale_1",
+      "graph_rag_service_init_rationale_1",
+      "ingestion_init_rationale_1",
+      "init_py",
+      "observability_init_rationale_1",
+      "retrieval_init_rationale_1",
+      "routers_entities_get_entity_summary",
+      "routers_entities_rationale_119",
+      "routers_entities_rationale_89",
+      "routers_entities_trigger_entity_enrichment",
+      "services_entity_enricher_enrichmentresult",
+      "services_entity_enricher_entityenricher_enrich_all_entities",
+      "services_entity_enricher_entityenricher_enrich_entity",
+      "services_entity_enricher_entityenricher_enrich_single",
+      "services_entity_enricher_py",
+      "services_entity_enricher_rationale_1",
+      "services_entity_enricher_rationale_121",
+      "services_entity_enricher_rationale_141",
+      "services_entity_enricher_rationale_155",
+      "services_entity_enricher_rationale_23",
+      "services_entity_enricher_rationale_32",
+      "services_entity_enricher_rationale_57",
+      "services_init_rationale_1",
+      "workers_init_rationale_1"
+    ],
+    "8": [
+      "ingestion_document_processor_documentprocessor_chunk_document",
+      "ingestion_document_processor_documentprocessor_extract_csv",
+      "ingestion_document_processor_documentprocessor_extract_docx",
+      "ingestion_document_processor_documentprocessor_extract_excel",
+      "ingestion_document_processor_documentprocessor_extract_json",
+      "ingestion_document_processor_documentprocessor_extract_pdf",
+      "ingestion_document_processor_documentprocessor_extract_pptx",
+      "ingestion_document_processor_documentprocessor_extract_text",
+      "ingestion_document_processor_documentprocessor_extract_txt",
+      "ingestion_document_processor_documentprocessor_generate_document_id",
+      "ingestion_document_processor_documentprocessor_process_document",
+      "ingestion_document_processor_py",
+      "ingestion_document_processor_rationale_125",
+      "ingestion_document_processor_rationale_149",
+      "ingestion_document_processor_rationale_170",
+      "ingestion_document_processor_rationale_175",
+      "ingestion_document_processor_rationale_197",
+      "ingestion_document_processor_rationale_225",
+      "ingestion_document_processor_rationale_264",
+      "ingestion_document_processor_rationale_306",
+      "ingestion_document_processor_rationale_32",
+      "ingestion_document_processor_rationale_345",
+      "ingestion_document_processor_rationale_62",
+      "ingestion_document_processor_rationale_92"
+    ],
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+      "core_models_rationale_94",
+      "ingestion_extractor_knowledgeextractor",
+      "ingestion_extractor_knowledgeextractor_create_extraction_prompt",
+      "ingestion_extractor_knowledgeextractor_extract_from_chunk",
+      "ingestion_extractor_knowledgeextractor_generate_embeddings",
+      "ingestion_extractor_rationale_19",
+      "ingestion_extractor_rationale_195",
+      "ingestion_extractor_rationale_289",
+      "ingestion_extractor_rationale_38",
+      "ingestion_extractor_rationale_94",
+      "routers_graph_rationale_232",
+      "routers_graph_update_graph_from_text",
+      "services_graph_memory_updater_graphmemoryupdater_get_extractor",
+      "services_graph_memory_updater_graphmemoryupdater_is_fact_assertion",
+      "services_graph_memory_updater_graphupdateresult",
+      "services_graph_memory_updater_py",
+      "services_graph_memory_updater_rationale_1",
+      "services_graph_memory_updater_rationale_174",
+      "services_graph_memory_updater_rationale_23",
+      "services_graph_memory_updater_rationale_33",
+      "services_graph_memory_updater_rationale_59"
+    ],
+    "10": [
+      "core_models_communityreport",
+      "core_models_rationale_199",
+      "retrieval_communities_communitybuilder",
+      "retrieval_communities_communitybuilder_collect_evidence",
+      "retrieval_communities_communitybuilder_create_community_nodes",
+      "retrieval_communities_communitybuilder_embed_report",
+      "retrieval_communities_communitybuilder_run_leiden",
+      "retrieval_communities_py",
+      "retrieval_communities_rationale_110",
+      "retrieval_communities_rationale_134",
+      "retrieval_communities_rationale_155",
+      "retrieval_communities_rationale_21",
+      "retrieval_communities_rationale_244",
+      "retrieval_communities_rationale_31",
+      "retrieval_report_agent_rationale_310",
+      "retrieval_report_agent_rationale_375",
+      "retrieval_report_agent_reportagent_compile_markdown",
+      "retrieval_report_agent_reportagent_decompose_topic",
+      "retrieval_report_agent_reportresult",
+      "routers_report_generate_report",
+      "routers_report_rationale_23"
+    ],
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+      "api_auth_create_access_token",
+      "api_auth_decode_token",
+      "api_auth_get_current_user",
+      "api_auth_get_password_hash",
+      "api_auth_py",
+      "api_auth_rationale_1",
+      "api_auth_rationale_125",
+      "api_auth_rationale_172",
+      "api_auth_rationale_56",
+      "api_auth_rationale_84",
+      "api_auth_token",
+      "api_auth_tokendata",
+      "api_auth_user",
+      "api_auth_verify_password",
+      "routers_auth_get_me",
+      "routers_auth_login",
+      "routers_auth_rationale_60",
+      "routers_auth_rationale_93",
+      "routers_auth_register"
+    ],
+    "12": [
+      "api_server_rationale_91",
+      "api_server_startup_event",
+      "ingestion_ontology_generator_rationale_121",
+      "ingestion_pipeline_ingestionpipeline",
+      "ingestion_pipeline_ingestionpipeline_close",
+      "ingestion_pipeline_ingestionpipeline_ingest_document",
+      "ingestion_pipeline_ingestionpipeline_initialize",
+      "ingestion_pipeline_ingestionpipeline_store_extraction",
+      "ingestion_pipeline_rationale_125",
+      "ingestion_pipeline_rationale_152",
+      "ingestion_pipeline_rationale_21",
+      "ingestion_pipeline_rationale_58",
+      "routers_ontology_get_ontology",
+      "routers_ontology_rationale_103",
+      "routers_ontology_rationale_156",
+      "routers_ontology_rationale_24",
+      "routers_ontology_refine_ontology",
+      "routers_ontology_update_ontology"
+    ],
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+      "retrieval_report_agent_quicksearchtool",
+      "retrieval_report_agent_rationale_163",
+      "retrieval_report_agent_rationale_197",
+      "retrieval_report_agent_rationale_212",
+      "retrieval_report_agent_rationale_49",
+      "retrieval_tools_cyphergenerationtool",
+      "retrieval_tools_cyphergenerationtool_correct_cypher",
+      "retrieval_tools_cyphergenerationtool_correct_cypher_with_error",
+      "retrieval_tools_cyphergenerationtool_generate_cypher",
+      "retrieval_tools_cyphergenerationtool_validate_cypher",
+      "retrieval_tools_graphtraversaltool_extract_entities_from_query",
+      "retrieval_tools_hybridsearchtool_run",
+      "retrieval_tools_rationale_197",
+      "retrieval_tools_rationale_360",
+      "retrieval_tools_rationale_384",
+      "retrieval_tools_rationale_46",
+      "retrieval_tools_rationale_805"
+    ],
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+      "api_dependencies_get_redis_client",
+      "api_dependencies_get_retrieval_agent",
+      "api_dependencies_py",
+      "api_routers_documents_py",
+      "api_routers_entities_py",
+      "api_routers_evaluation_py",
+      "api_routers_graph_py",
+      "api_routers_memory_py",
+      "api_routers_ontology_py",
+      "api_routers_query_py",
+      "api_routers_report_py",
+      "config_py",
+      "core_storage_py",
+      "graph_rag_service_config_redis_url",
+      "ingestion_pipeline_py",
+      "retrieval_agent_py",
+      "routers_documents_rationale_366"
+    ],
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+      "core_llm_factory_unifiedllmprovider_initialize_provider",
+      "retrieval_agent_rationale_525",
+      "retrieval_tools_entitysummarysearchtool",
+      "retrieval_tools_graphtraversaltool",
+      "retrieval_tools_llmjudge",
+      "retrieval_tools_llmjudge_init",
+      "retrieval_tools_llmjudge_score",
+      "retrieval_tools_metadatafiltertool",
+      "retrieval_tools_py",
+      "retrieval_tools_rationale_135",
+      "retrieval_tools_rationale_322",
+      "retrieval_tools_rationale_519",
+      "retrieval_tools_rationale_568",
+      "retrieval_tools_rationale_582",
+      "retrieval_tools_rationale_781",
+      "retrieval_tools_vectorsearchtool"
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+      "workers_celery_worker_cleanup_orphan_nodes_task",
+      "workers_celery_worker_enrich_entities_task",
+      "workers_celery_worker_generate_personas_task",
+      "workers_celery_worker_ingest_document_task",
+      "workers_celery_worker_ingest_documents_batch_task",
+      "workers_celery_worker_init_worker_loop",
+      "workers_celery_worker_py",
+      "workers_celery_worker_rationale_1",
+      "workers_celery_worker_rationale_153",
+      "workers_celery_worker_rationale_206",
+      "workers_celery_worker_rationale_251",
+      "workers_celery_worker_rationale_278",
+      "workers_celery_worker_rationale_311",
+      "workers_celery_worker_rationale_70",
+      "workers_celery_worker_rationale_81",
+      "workers_celery_worker_run_async"
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+      "core_abstractions_resolve",
+      "core_entity_resolver_rationale_108",
+      "core_entity_resolver_rationale_132",
+      "core_entity_resolver_rationale_136",
+      "core_entity_resolver_rationale_171",
+      "core_entity_resolver_rationale_18",
+      "core_entity_resolver_rationale_191",
+      "core_entity_resolver_rationale_34",
+      "core_entity_resolver_rationale_66",
+      "core_entity_resolver_semanticentityresolver",
+      "core_entity_resolver_semanticentityresolver_embedding_similarity",
+      "core_entity_resolver_semanticentityresolver_find_duplicates",
+      "core_entity_resolver_semanticentityresolver_get_entity_embedding",
+      "core_entity_resolver_semanticentityresolver_property_similarity",
+      "core_entity_resolver_semanticentityresolver_string_similarity"
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+      "core_abstractions_rationale_173",
+      "core_abstractions_rationale_245",
+      "core_entity_resolver_rationale_1",
+      "core_models_entity",
+      "core_models_rationale_35",
+      "core_models_rationale_65",
+      "core_models_relationship",
+      "entityresolver",
+      "graphstore",
+      "ingestion_extractor_knowledgeextractor_parse_extraction",
+      "ingestion_extractor_py",
+      "ingestion_extractor_rationale_233",
+      "llmprovider",
+      "vectorstore"
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+      "core_llm_factory_create",
+      "core_llm_factory_create_from_config",
+      "core_llm_factory_py",
+      "core_llm_factory_rationale_33",
+      "core_llm_factory_unifiedllmprovider",
+      "ingestion_persona_generator_personagenerator_generate_personas_for_type",
+      "ingestion_persona_generator_personaprofile",
+      "ingestion_persona_generator_py",
+      "ingestion_persona_generator_rationale_26",
+      "ingestion_persona_generator_rationale_36",
+      "retrieval_hippo_tool_hipporagtool",
+      "retrieval_hippo_tool_py",
+      "retrieval_hippo_tool_rationale_10",
+      "routers_entities_entity_interview",
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+      "observability_tracing_get_meter",
+      "observability_tracing_get_tracer",
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+      "observability_tracing_rationale_28",
+      "observability_tracing_rationale_71",
+      "observability_tracing_rationale_76",
+      "observability_tracing_setup_observability"
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+      "workers_celery_worker_run_simulation_tick_task",
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+      "workers_simulation_runner_rationale_28",
+      "workers_simulation_runner_rationale_35",
+      "workers_simulation_runner_rationale_45",
+      "workers_simulation_runner_rationale_67",
+      "workers_simulation_runner_simulationmanager",
+      "workers_simulation_runner_simulationmanager_get_active_agents",
+      "workers_simulation_runner_simulationmanager_process_agent_turn"
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+      "retrieval_tools_ragevaluator_answer_relevancy",
+      "retrieval_tools_ragevaluator_context_precision",
+      "retrieval_tools_ragevaluator_evaluate",
+      "retrieval_tools_ragevaluator_faithfulness",
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+      "retrieval_tools_rationale_721",
+      "retrieval_tools_rationale_741",
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+      "core_models_confidencejudgment",
+      "core_models_queryresult",
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+      "core_models_rationale_149",
+      "routers_query_query",
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+      "core_models_rationale_20",
+      "core_models_rationale_28",
+      "core_models_relationtype",
+      "core_models_searchmethod",
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+      "ingestion_web_crawler_rationale_9",
+      "ingestion_web_crawler_webcrawler_crawl",
+      "ingestion_web_crawler_webcrawler_crawl_recursive",
+      "ingestion_web_crawler_webcrawler_is_same_domain",
+      "routers_documents_crawl_urls",
+      "routers_documents_rationale_113",
+      "routers_documents_rationale_185",
+      "routers_documents_scrape_url"
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+      "routers_system_get_supported_formats",
+      "routers_system_get_system_stats",
+      "routers_system_health_check",
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+      "routers_system_rationale_150",
+      "routers_system_rationale_75",
+      "workers_celery_worker_rationale_245"
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+      "core_storage_localstorage",
+      "core_storage_localstorage_delete_file",
+      "core_storage_localstorage_read_file",
+      "core_storage_localstorage_save_file",
+      "core_storage_rationale_52",
+      "core_storage_storageprovider",
+      "core_storage_storageprovider_normalize_filename"
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+      "core_neo4j_store_neo4jstore_create_constraints",
+      "core_neo4j_store_neo4jstore_create_fulltext_index",
+      "core_neo4j_store_neo4jstore_create_vector_index",
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+      "core_neo4j_store_rationale_64",
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+      "retrieval_tools_communitysummarytool_get_community_summary",
+      "retrieval_tools_communitysummarytool_get_redis",
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+      "graph_rag_service_config_rationale_179",
+      "graph_rag_service_config_rationale_189",
+      "graph_rag_service_config_settings",
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