diff --git a/.env.example b/.env.example new file mode 100644 index 0000000000000000000000000000000000000000..d64c0e346fffbe09e0bf6deee445ba4cc4cae84c --- /dev/null +++ b/.env.example @@ -0,0 +1,68 @@ +# 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 diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..636f5c34649909eefe8422eaa0d49d7d9d0a303d --- /dev/null +++ b/.gitattributes @@ -0,0 +1,3 @@ +*.png filter=lfs diff=lfs merge=lfs -text +*.jpg filter=lfs diff=lfs merge=lfs -text +*.ico filter=lfs diff=lfs merge=lfs -text diff --git a/.github/prompts/Solution_Architecture.prompt.md b/.github/prompts/Solution_Architecture.prompt.md new file mode 100644 index 0000000000000000000000000000000000000000..852cda4f1deecc1823104cb7ef6d7e9cc1aae152 --- /dev/null +++ b/.github/prompts/Solution_Architecture.prompt.md @@ -0,0 +1,169 @@ +# 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. diff --git a/.github/workflows/sync_to_hf.yml b/.github/workflows/sync_to_hf.yml new file mode 100644 index 0000000000000000000000000000000000000000..3eed44b7f285b47df90f3420ce4150df0912da34 --- /dev/null +++ b/.github/workflows/sync_to_hf.yml @@ -0,0 +1,33 @@ +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 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..961ca9145e38c816b22217400b441e001e7f4ac3 --- /dev/null +++ b/.gitignore @@ -0,0 +1,91 @@ +# ── 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 diff --git a/ARCHITECTURE.md b/ARCHITECTURE.md new file mode 100644 index 0000000000000000000000000000000000000000..82edb72a9c4442125e80fb753ff3c1765b3b0125 --- /dev/null +++ b/ARCHITECTURE.md @@ -0,0 +1,297 @@ +# 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 diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..ab90f3daf214eea14daa338912c442bef12a68c3 --- /dev/null +++ b/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"] diff --git a/QUICKSTART.md b/QUICKSTART.md new file mode 100644 index 0000000000000000000000000000000000000000..eebcff593be3f84721c43243a27f45aa5fee7cb0 --- /dev/null +++ b/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! 🚀 diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..18d71ca47a2648aee008df533e4adb8549383482 --- /dev/null +++ b/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 +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 diff --git a/artifacts/graphify-out/.graphify_analysis.json b/artifacts/graphify-out/.graphify_analysis.json new file mode 100644 index 0000000000000000000000000000000000000000..b63cbb6c265bb179310fa001eccad7665ddca2be --- /dev/null +++ b/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", + 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"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", 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"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" + ], + "9": [ + "core_models_chunk", + "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" + ], + "11": [ + "api_auth_check_scope", + "api_auth_create_access_token", + "api_auth_decode_token", + 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"D:\\Desktop_March_26\\LYZR\\graph-RAG\\src\\graph_rag_service\\retrieval\\report_agent.py", "source_location": "L489"}, {"id": "retrieval_report_agent_reportagent_write_section_with_confidence", "label": "._write_section_with_confidence()", "file_type": "code", "source_file": "D:\\Desktop_March_26\\LYZR\\graph-RAG\\src\\graph_rag_service\\retrieval\\report_agent.py", "source_location": "L504"}, {"id": "retrieval_report_agent_reportagent_write_executive_summary", "label": "._write_executive_summary()", "file_type": "code", "source_file": "D:\\Desktop_March_26\\LYZR\\graph-RAG\\src\\graph_rag_service\\retrieval\\report_agent.py", "source_location": "L536"}, {"id": "retrieval_report_agent_reportagent_compile_markdown", "label": "._compile_markdown()", "file_type": "code", "source_file": "D:\\Desktop_March_26\\LYZR\\graph-RAG\\src\\graph_rag_service\\retrieval\\report_agent.py", "source_location": "L554"}, {"id": "retrieval_report_agent_rationale_1", "label": "ReportAgent \u2014 Full ReACT Analytical Agent Replaces the 72-line stub with a comp", "file_type": "rationale", "source_file": "D:\\Desktop_March_26\\LYZR\\graph-RAG\\src\\graph_rag_service\\retrieval\\report_agent.py", "source_location": "L1"}, {"id": "retrieval_report_agent_rationale_49", "label": "Broad-spectrum hybrid retriever: merges vector similarity + graph neighborh", "file_type": "rationale", "source_file": "D:\\Desktop_March_26\\LYZR\\graph-RAG\\src\\graph_rag_service\\retrieval\\report_agent.py", "source_location": "L49"}, {"id": "retrieval_report_agent_rationale_147", "label": "Macro-level entity sweep: returns all entities of a given type with statist", "file_type": "rationale", "source_file": "D:\\Desktop_March_26\\LYZR\\graph-RAG\\src\\graph_rag_service\\retrieval\\report_agent.py", "source_location": "L147"}, {"id": "retrieval_report_agent_rationale_163", "label": "Return entities of the given type with their summaries.", "file_type": "rationale", "source_file": "D:\\Desktop_March_26\\LYZR\\graph-RAG\\src\\graph_rag_service\\retrieval\\report_agent.py", "source_location": "L163"}, {"id": "retrieval_report_agent_rationale_197", "label": "Fast single-entity lookup by name with direct 1-hop relationships. Useful f", "file_type": "rationale", "source_file": "D:\\Desktop_March_26\\LYZR\\graph-RAG\\src\\graph_rag_service\\retrieval\\report_agent.py", "source_location": "L197"}, {"id": "retrieval_report_agent_rationale_212", "label": "Look up an entity and return its profile + connections.", "file_type": "rationale", "source_file": "D:\\Desktop_March_26\\LYZR\\graph-RAG\\src\\graph_rag_service\\retrieval\\report_agent.py", "source_location": "L212"}, {"id": "retrieval_report_agent_rationale_272", "label": "Full ReACT analytical reporting agent. Workflow: DECOMPOSE \u2192 Brea", "file_type": "rationale", "source_file": "D:\\Desktop_March_26\\LYZR\\graph-RAG\\src\\graph_rag_service\\retrieval\\report_agent.py", "source_location": "L272"}, {"id": "retrieval_report_agent_rationale_310", "label": "Generate an analytical report on the given topic. 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"source_location": "L507"}, {"id": "retrieval_report_agent_rationale_539", "label": "Synthesize all sections into a 3-sentence executive summary.", "file_type": "rationale", "source_file": "D:\\Desktop_March_26\\LYZR\\graph-RAG\\src\\graph_rag_service\\retrieval\\report_agent.py", "source_location": "L539"}], "edges": [{"source": "d_desktop_march_26_lyzr_graph_rag_src_graph_rag_service_retrieval_report_agent_py", "target": "logging", "relation": "imports", "context": "import", "confidence": "EXTRACTED", "source_file": "D:\\Desktop_March_26\\LYZR\\graph-RAG\\src\\graph_rag_service\\retrieval\\report_agent.py", "source_location": "L12", "weight": 1.0}, {"source": "d_desktop_march_26_lyzr_graph_rag_src_graph_rag_service_retrieval_report_agent_py", "target": "asyncio", "relation": "imports", "context": "import", "confidence": "EXTRACTED", "source_file": "D:\\Desktop_March_26\\LYZR\\graph-RAG\\src\\graph_rag_service\\retrieval\\report_agent.py", "source_location": "L16", "weight": 1.0}, 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"source_location": "L199", + "id": "core_models_rationale_199", + "community": 10, + "norm_label": "community summary for lazygraphrag (gap #2)" + }, + { + "label": "neo4j_store.py", + "file_type": "code", + "source_file": "core/neo4j_store.py", + "source_location": "L1", + "id": "core_neo4j_store_py", + "community": 4, + "norm_label": "neo4j_store.py" + }, + { + "label": "GraphStore", + "file_type": "code", + "source_file": "", + "source_location": "", + "id": "graphstore", + "community": 18, + "norm_label": "graphstore" + }, + { + "label": "VectorStore", + "file_type": "code", + "source_file": "", + "source_location": "", + "id": "vectorstore", + "community": 18, + "norm_label": "vectorstore" + }, + { + "label": "._create_vector_index()", + "file_type": "code", + "source_file": "core/neo4j_store.py", + "source_location": "L63", + "id": "core_neo4j_store_neo4jstore_create_vector_index", + "community": 28, + "norm_label": "._create_vector_index()" + }, + { + "label": "._create_fulltext_index()", + "file_type": "code", + "source_file": "core/neo4j_store.py", + "source_location": "L92", + "id": "core_neo4j_store_neo4jstore_create_fulltext_index", + "community": 28, + "norm_label": "._create_fulltext_index()" + }, + { + "label": "._create_constraints()", + "file_type": "code", + "source_file": "core/neo4j_store.py", + "source_location": "L108", + "id": "core_neo4j_store_neo4jstore_create_constraints", + "community": 28, + "norm_label": "._create_constraints()" + }, + { + "label": ".bm25_search()", + "file_type": "code", + "source_file": "core/neo4j_store.py", + "source_location": "L324", + "id": "core_neo4j_store_neo4jstore_bm25_search", + "community": 4, + "norm_label": ".bm25_search()" + }, + { + "label": ".get_communities()", + "file_type": "code", + "source_file": "core/neo4j_store.py", + "source_location": "L367", + "id": "core_neo4j_store_neo4jstore_get_communities", + "community": 4, + "norm_label": ".get_communities()" + }, + { + "label": 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"source_location": "L109", + "id": "core_neo4j_store_rationale_109", + "community": 28, + "norm_label": "create constraints and indexes for performance" + }, + { + "label": "Create an entity node in the graph with temporal + tenant support", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L130", + "id": "core_neo4j_store_rationale_130", + "community": 3, + "norm_label": "create an entity node in the graph with temporal + tenant support" + }, + { + "label": "Create a relationship between entities with temporal support (Gap #5)", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L166", + "id": "core_neo4j_store_rationale_166", + "community": 3, + "norm_label": "create a relationship between entities with temporal support (gap #5)" + }, + { + "label": "Execute a Cypher query with timeout", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L220", + "id": "core_neo4j_store_rationale_220", + "community": 4, + "norm_label": "execute a cypher query with timeout" + }, + { + "label": "Get neighboring entities", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L270", + "id": "core_neo4j_store_rationale_270", + "community": 3, + "norm_label": "get neighboring entities" + }, + { + "label": "Merge duplicate entities", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L295", + "id": "core_neo4j_store_rationale_295", + "community": 3, + "norm_label": "merge duplicate entities" + }, + { + "label": "BM25 fulltext (Lucene) search over chunk text. Returns results with BM2", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L331", + "id": "core_neo4j_store_rationale_331", + "community": 4, + "norm_label": "bm25 fulltext (lucene) search over chunk text. returns results with bm2" + }, + { + "label": "Get community groupings for a list of entities. Uses community_id prope", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L372", + "id": "core_neo4j_store_rationale_372", + "community": 4, + "norm_label": "get community groupings for a list of entities. uses community_id prope" + }, + { + "label": "Get all entities in a community", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L407", + "id": "core_neo4j_store_rationale_407", + "community": 4, + "norm_label": "get all entities in a community" + }, + { + "label": "Server-side community assignment using Neo4j GDS Weakly Connected Components (WC", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L418", + "id": "core_neo4j_store_rationale_418", + "community": 4, + "norm_label": "server-side community assignment using neo4j gds weakly connected components (wc" + }, + { + "label": "Get relationships valid at a specific point in time", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L463", + "id": "core_neo4j_store_rationale_463", + "community": 4, + "norm_label": "get relationships valid at a specific point in time" + }, + { + "label": "Add chunk vectors to Neo4j", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L487", + "id": "core_neo4j_store_rationale_487", + "community": 3, + "norm_label": "add chunk vectors to neo4j" + }, + { + "label": "Vector similarity search using Neo4j vector index", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L536", + "id": "core_neo4j_store_rationale_536", + "community": 3, + "norm_label": "vector similarity search using neo4j vector index" + }, + { + "label": "Fallback search without vector index", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L569", + "id": "core_neo4j_store_rationale_569", + "community": 2, + "norm_label": "fallback search without vector index" + }, + { + "label": "Persist ontology to Neo4j", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L596", + "id": "core_neo4j_store_rationale_596", + "community": 4, + "norm_label": "persist ontology to neo4j" + }, + { + "label": "Load persisted ontology from Neo4j. Returns OntologySchema or None.", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L619", + "id": "core_neo4j_store_rationale_619", + "community": 0, + "norm_label": "load persisted ontology from neo4j. returns ontologyschema or none." + }, + { + "label": "Persist an EvalResult to Neo4j for dashboard trending (Gap #8)", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L647", + "id": "core_neo4j_store_rationale_647", + "community": 4, + "norm_label": "persist an evalresult to neo4j for dashboard trending (gap #8)" + }, + { + "label": "Retrieve eval results for the dashboard (Gap #8)", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L681", + "id": "core_neo4j_store_rationale_681", + "community": 4, + "norm_label": "retrieve eval results for the dashboard (gap #8)" + }, + { + "label": "Find a semantically identical query in the cache", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L696", + "id": "core_neo4j_store_rationale_696", + "community": 4, + "norm_label": "find a semantically identical query in the cache" + }, + { + "label": "Store a query answer in the semantic cache", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L712", + "id": "core_neo4j_store_rationale_712", + "community": 4, + "norm_label": "store a query answer in the semantic cache" + }, + { + "label": "Create a chunk and link it to entities it mentions", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L738", + "id": "core_neo4j_store_rationale_738", + "community": 3, + "norm_label": "create a chunk and link it to entities it mentions" + }, + { + "label": "Create a new user node in the graph", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L788", + "id": "core_neo4j_store_rationale_788", + "community": 4, + "norm_label": "create a new user node in the graph" + }, + { + "label": "Get a user by username", + "file_type": "rationale", + "source_file": "core/neo4j_store.py", + "source_location": "L820", + "id": "core_neo4j_store_rationale_820", + "community": 4, + "norm_label": "get a user by username" + }, + { + "label": "storage.py", + "file_type": "code", + "source_file": "core/storage.py", + "source_location": "L1", + "id": "core_storage_py", + "community": 14, + "norm_label": "storage.py" + }, + { + "label": "StorageProvider", + "file_type": "code", + "source_file": "core/storage.py", + "source_location": "L7", + "id": "core_storage_storageprovider", + "community": 27, + "norm_label": "storageprovider" + }, + { + "label": "._normalize_filename()", + "file_type": "code", + "source_file": "core/storage.py", + "source_location": "L8", + "id": "core_storage_storageprovider_normalize_filename", + "community": 27, + "norm_label": "._normalize_filename()" + }, + { + "label": "LocalStorage", + "file_type": "code", + "source_file": "core/storage.py", + "source_location": "L20", + "id": "core_storage_localstorage", + "community": 27, + "norm_label": "localstorage" + }, + { + "label": ".save_file()", + "file_type": "code", + "source_file": "core/storage.py", + "source_location": "L25", + "id": "core_storage_localstorage_save_file", + "community": 27, + "norm_label": ".save_file()" + }, + { + "label": ".read_file()", + "file_type": "code", + "source_file": "core/storage.py", + "source_location": "L34", + "id": "core_storage_localstorage_read_file", + "community": 27, + "norm_label": ".read_file()" + }, + { + "label": ".delete_file()", + "file_type": "code", + "source_file": "core/storage.py", + "source_location": "L40", + "id": "core_storage_localstorage_delete_file", + "community": 27, + "norm_label": ".delete_file()" + }, + { + "label": "get_storage()", + "file_type": "code", + "source_file": "core/storage.py", + "source_location": "L51", + "id": "core_storage_get_storage", + "community": 27, + "norm_label": "get_storage()" + }, + { + "label": "Returns the appropriate storage provider based on configuration. 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Supports: PDF, TXT, MD, DOCX, CS", + "file_type": "rationale", + "source_file": "ingestion/document_processor.py", + "source_location": "L32", + "id": "ingestion_document_processor_rationale_32", + "community": 8, + "norm_label": "process and chunk documents for ingestion. supports: pdf, txt, md, docx, cs" + }, + { + "label": "Process a document and extract metadata Args: file_path:", + "file_type": "rationale", + "source_file": "ingestion/document_processor.py", + "source_location": "L62", + "id": "ingestion_document_processor_rationale_62", + "community": 8, + "norm_label": "process a document and extract metadata args: file_path:" + }, + { + "label": "Chunk document into smaller pieces Args: document: Docum", + "file_type": "rationale", + "source_file": "ingestion/document_processor.py", + "source_location": "L92", + "id": "ingestion_document_processor_rationale_92", + "community": 8, + "norm_label": "chunk document into smaller pieces args: document: docum" + }, + { + "label": "Extract text from file based on type", + "file_type": "rationale", + "source_file": "ingestion/document_processor.py", + "source_location": "L125", + "id": "ingestion_document_processor_rationale_125", + "community": 8, + "norm_label": "extract text from file based on type" + }, + { + "label": "Extract text from PDF using LlamaParse (if available) or pypdf", + "file_type": "rationale", + "source_file": "ingestion/document_processor.py", + "source_location": "L149", + "id": "ingestion_document_processor_rationale_149", + "community": 8, + "norm_label": "extract text from pdf using llamaparse (if available) or pypdf" + }, + { + "label": "Extract text from TXT/MD file", + "file_type": "rationale", + "source_file": "ingestion/document_processor.py", + "source_location": "L170", + "id": "ingestion_document_processor_rationale_170", + "community": 8, + "norm_label": "extract text from txt/md file" + }, + { + "label": "Extract text from DOCX", + "file_type": "rationale", + "source_file": "ingestion/document_processor.py", + "source_location": "L175", + "id": "ingestion_document_processor_rationale_175", + "community": 8, + "norm_label": "extract text from docx" + }, + { + "label": "Extract CSV as structured text. Each row becomes a natural language sen", + "file_type": "rationale", + "source_file": "ingestion/document_processor.py", + "source_location": "L197", + "id": "ingestion_document_processor_rationale_197", + "community": 8, + "norm_label": "extract csv as structured text. each row becomes a natural language sen" + }, + { + "label": "Extract Excel spreadsheet content. Processes all sheets, converts to st", + "file_type": "rationale", + "source_file": "ingestion/document_processor.py", + "source_location": "L225", + "id": "ingestion_document_processor_rationale_225", + "community": 8, + "norm_label": "extract excel spreadsheet content. processes all sheets, converts to st" + }, + { + "label": "Extract PowerPoint presentation content. Processes each slide: title +", + "file_type": "rationale", + "source_file": "ingestion/document_processor.py", + "source_location": "L264", + "id": "ingestion_document_processor_rationale_264", + "community": 8, + "norm_label": "extract powerpoint presentation content. processes each slide: title +" + }, + { + "label": "Extract JSON content. Flattens nested structures into readable text for", + "file_type": "rationale", + "source_file": "ingestion/document_processor.py", + "source_location": "L306", + "id": "ingestion_document_processor_rationale_306", + "community": 8, + "norm_label": "extract json content. flattens nested structures into readable text for" + }, + { + "label": "Generate unique document ID based on file content", + "file_type": "rationale", + "source_file": "ingestion/document_processor.py", + "source_location": "L345", + "id": "ingestion_document_processor_rationale_345", + "community": 8, + "norm_label": "generate unique document id based on file content" + }, + { + "label": "extractor.py", + "file_type": "code", + "source_file": "ingestion/extractor.py", + "source_location": "L1", + "id": "ingestion_extractor_py", + "community": 18, + "norm_label": "extractor.py" + }, + { + "label": "KnowledgeExtractor", + "file_type": "code", + "source_file": "ingestion/extractor.py", + "source_location": "L18", + "id": "ingestion_extractor_knowledgeextractor", + "community": 9, + "norm_label": "knowledgeextractor" + }, + { + "label": ".extract_from_chunk()", + "file_type": "code", + "source_file": "ingestion/extractor.py", + "source_location": "L33", + "id": "ingestion_extractor_knowledgeextractor_extract_from_chunk", + "community": 9, + "norm_label": ".extract_from_chunk()" + }, + { + "label": "._create_extraction_prompt()", + "file_type": "code", + "source_file": "ingestion/extractor.py", + "source_location": "L190", + "id": "ingestion_extractor_knowledgeextractor_create_extraction_prompt", + "community": 9, + "norm_label": "._create_extraction_prompt()" + }, + { + "label": "._parse_extraction()", + "file_type": "code", + "source_file": "ingestion/extractor.py", + "source_location": "L228", + "id": "ingestion_extractor_knowledgeextractor_parse_extraction", + "community": 18, + "norm_label": "._parse_extraction()" + }, + { + "label": ".generate_embeddings()", + "file_type": "code", + "source_file": "ingestion/extractor.py", + "source_location": "L285", + "id": "ingestion_extractor_knowledgeextractor_generate_embeddings", + "community": 9, + "norm_label": ".generate_embeddings()" + }, + { + "label": "Extract entities and relationships from text chunks Includes hallucination", + "file_type": "rationale", + "source_file": "ingestion/extractor.py", + "source_location": "L19", + "id": "ingestion_extractor_rationale_19", + "community": 9, + "norm_label": "extract entities and relationships from text chunks includes hallucination" + }, + { + "label": "Extract entities and relationships from a single chunk Args:", + "file_type": "rationale", + "source_file": "ingestion/extractor.py", + "source_location": "L38", + "id": "ingestion_extractor_rationale_38", + "community": 9, + "norm_label": "extract entities and relationships from a single chunk args:" + }, + { + "label": "Extract from multiple chunks with entity resolution Args:", + "file_type": "rationale", + "source_file": "ingestion/extractor.py", + "source_location": "L94", + "id": "ingestion_extractor_rationale_94", + "community": 9, + "norm_label": "extract from multiple chunks with entity resolution args:" + }, + { + "label": "Create extraction prompt with ontology constraints", + "file_type": "rationale", + "source_file": "ingestion/extractor.py", + "source_location": "L195", + "id": "ingestion_extractor_rationale_195", + "community": 9, + "norm_label": "create extraction prompt with ontology constraints" + }, + { + "label": "Parse and validate extraction response", + "file_type": "rationale", + "source_file": "ingestion/extractor.py", + "source_location": "L233", + "id": "ingestion_extractor_rationale_233", + "community": 18, + "norm_label": "parse and validate extraction response" + }, + { + "label": "Generate embeddings for chunks Args: chunks: Chu", + "file_type": "rationale", + "source_file": "ingestion/extractor.py", + "source_location": "L289", + "id": "ingestion_extractor_rationale_289", + "community": 9, + "norm_label": "generate embeddings for chunks args: chunks: chu" + }, + { + "label": "ontology_generator.py", + "file_type": "code", + "source_file": "ingestion/ontology_generator.py", + "source_location": "L1", + "id": "ingestion_ontology_generator_py", + "community": 36, + "norm_label": "ontology_generator.py" + }, + { + "label": ".generate_initial_ontology()", + "file_type": "code", + "source_file": "ingestion/ontology_generator.py", + "source_location": "L25", + "id": "ingestion_ontology_generator_ontologygenerator_generate_initial_ontology", + "community": 0, + "norm_label": ".generate_initial_ontology()" + }, + { + "label": ".get_extraction_prompt()", + "file_type": "code", + "source_file": "ingestion/ontology_generator.py", + "source_location": "L195", + "id": "ingestion_ontology_generator_ontologygenerator_get_extraction_prompt", + "community": 36, + "norm_label": ".get_extraction_prompt()" + }, + { + "label": "Ontology generation and evolution LLM-powered automatic ontology discovery with", + "file_type": "rationale", + "source_file": "ingestion/ontology_generator.py", + "source_location": "L1", + "id": "ingestion_ontology_generator_rationale_1", + "community": 36, + "norm_label": "ontology generation and evolution llm-powered automatic ontology discovery with" + }, + { + "label": "Generate and refine ontologies from documents Supports versioning and evolu", + "file_type": "rationale", + "source_file": "ingestion/ontology_generator.py", + "source_location": "L16", + "id": "ingestion_ontology_generator_rationale_16", + "community": 36, + "norm_label": "generate and refine ontologies from documents supports versioning and evolu" + }, + { + "label": "Generate initial ontology from sample chunks Args:", + "file_type": "rationale", + "source_file": "ingestion/ontology_generator.py", + "source_location": "L30", + "id": "ingestion_ontology_generator_rationale_30", + "community": 0, + "norm_label": "generate initial ontology from sample chunks args:" + }, + { + "label": "Refine ontology based on new data or human feedback Args:", + "file_type": "rationale", + "source_file": "ingestion/ontology_generator.py", + "source_location": "L121", + "id": "ingestion_ontology_generator_rationale_121", + "community": 12, + "norm_label": "refine ontology based on new data or human feedback args:" + }, + { + "label": "Generate extraction prompt based on ontology schema Args:", + "file_type": "rationale", + "source_file": "ingestion/ontology_generator.py", + "source_location": "L200", + "id": "ingestion_ontology_generator_rationale_200", + "community": 36, + "norm_label": "generate extraction prompt based on ontology schema args:" + }, + { + "label": "persona_generator.py", + "file_type": "code", + "source_file": "ingestion/persona_generator.py", + "source_location": "L1", + "id": "ingestion_persona_generator_py", + "community": 19, + "norm_label": "persona_generator.py" + }, + { + "label": "PersonaProfile", + "file_type": "code", + "source_file": "ingestion/persona_generator.py", + "source_location": "L18", + "id": "ingestion_persona_generator_personaprofile", + "community": 19, + "norm_label": "personaprofile" + }, + { + "label": ".generate_personas_for_type()", + "file_type": "code", + "source_file": "ingestion/persona_generator.py", + "source_location": "L35", + "id": "ingestion_persona_generator_personagenerator_generate_personas_for_type", + "community": 19, + "norm_label": ".generate_personas_for_type()" + }, + { + "label": "Converts inert graph nodes into living Agent Personas. Runs locally against", + "file_type": "rationale", + "source_file": "ingestion/persona_generator.py", + "source_location": "L26", + "id": "ingestion_persona_generator_rationale_26", + "community": 19, + "norm_label": "converts inert graph nodes into living agent personas. runs locally against" + }, + { + "label": "Finds all entities of a certain type that lack a persona, generates the", + "file_type": "rationale", + "source_file": "ingestion/persona_generator.py", + "source_location": "L36", + "id": "ingestion_persona_generator_rationale_36", + "community": 19, + "norm_label": "finds all entities of a certain type that lack a persona, generates the" + }, + { + "label": "pipeline.py", + "file_type": "code", + "source_file": "ingestion/pipeline.py", + "source_location": "L1", + "id": "ingestion_pipeline_py", + "community": 14, + "norm_label": "pipeline.py" + }, + { + "label": "IngestionPipeline", + "file_type": "code", + "source_file": "ingestion/pipeline.py", + "source_location": "L20", + "id": "ingestion_pipeline_ingestionpipeline", + "community": 12, + "norm_label": "ingestionpipeline" + }, + { + "label": ".initialize()", + "file_type": "code", + "source_file": "ingestion/pipeline.py", + "source_location": "L41", + "id": "ingestion_pipeline_ingestionpipeline_initialize", + "community": 12, + "norm_label": ".initialize()" + }, + { + "label": ".close()", + "file_type": "code", + "source_file": "ingestion/pipeline.py", + "source_location": "L46", + "id": "ingestion_pipeline_ingestionpipeline_close", + "community": 12, + "norm_label": ".close()" + }, + { + "label": ".ingest_document()", + "file_type": "code", + "source_file": "ingestion/pipeline.py", + "source_location": "L51", + "id": "ingestion_pipeline_ingestionpipeline_ingest_document", + "community": 12, + "norm_label": ".ingest_document()" + }, + { + "label": "._store_extraction()", + "file_type": "code", + "source_file": "ingestion/pipeline.py", + "source_location": "L147", + "id": "ingestion_pipeline_ingestionpipeline_store_extraction", + "community": 12, + "norm_label": "._store_extraction()" + }, + { + "label": "End-to-end ingestion pipeline: 1. 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Workflow: DECOMPOSE \u2192 Brea", + "file_type": "rationale", + "source_file": "retrieval/report_agent.py", + "source_location": "L272", + "id": "retrieval_report_agent_rationale_272", + "community": 5, + "norm_label": "full react analytical reporting agent. workflow: decompose \u2192 brea" + }, + { + "label": "Generate an analytical report on the given topic. Args:", + "file_type": "rationale", + "source_file": "retrieval/report_agent.py", + "source_location": "L310", + "id": "retrieval_report_agent_rationale_310", + "community": 10, + "norm_label": "generate an analytical report on the given topic. args:" + }, + { + "label": "Ask LLM to decompose the topic into sub-questions.", + "file_type": "rationale", + "source_file": "retrieval/report_agent.py", + "source_location": "L375", + "id": "retrieval_report_agent_rationale_375", + "community": 10, + "norm_label": "ask llm to decompose the topic into sub-questions." + }, + { + "label": "Run a ReACT iteration for one sub-question. Returns (section_content, c", + "file_type": "rationale", + "source_file": "retrieval/report_agent.py", + "source_location": "L412", + "id": "retrieval_report_agent_rationale_412", + "community": 5, + "norm_label": "run a react iteration for one sub-question. returns (section_content, c" + }, + { + "label": "Decide which tool to call next, or return DONE.", + "file_type": "rationale", + "source_file": "retrieval/report_agent.py", + "source_location": "L461", + "id": "retrieval_report_agent_rationale_461", + "community": 5, + "norm_label": "decide which tool to call next, or return done." + }, + { + "label": "Dispatch tool call and return results.", + "file_type": "rationale", + "source_file": "retrieval/report_agent.py", + "source_location": "L492", + "id": "retrieval_report_agent_rationale_492", + "community": 5, + "norm_label": "dispatch tool call and return results." + }, + { + "label": "Generate a report section from retrieved contexts and provide a structured confi", + 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"norm_label": "hybridsearchtool" + }, + { + "label": ".run()", + "file_type": "code", + "source_file": "retrieval/tools.py", + "source_location": "L38", + "id": "retrieval_tools_hybridsearchtool_run", + "community": 13, + "norm_label": ".run()" + }, + { + "label": "._rrf_fuse()", + "file_type": "code", + "source_file": "retrieval/tools.py", + "source_location": "L105", + "id": "retrieval_tools_hybridsearchtool_rrf_fuse", + "community": 35, + "norm_label": "._rrf_fuse()" + }, + { + "label": "VectorSearchTool", + "file_type": "code", + "source_file": "retrieval/tools.py", + "source_location": "L134", + "id": "retrieval_tools_vectorsearchtool", + "community": 15, + "norm_label": "vectorsearchtool" + }, + { + "label": "CommunitySummaryTool", + "file_type": "code", + "source_file": "retrieval/tools.py", + "source_location": "L163", + "id": "retrieval_tools_communitysummarytool", + "community": 29, + "norm_label": "communitysummarytool" + }, + { + "label": "._get_redis()", + "file_type": "code", + "source_file": "retrieval/tools.py", + "source_location": "L181", + "id": "retrieval_tools_communitysummarytool_get_redis", + "community": 29, + "norm_label": "._get_redis()" + }, + { + "label": "._find_relevant_entities()", + "file_type": "code", + "source_file": "retrieval/tools.py", + "source_location": "L238", + "id": "retrieval_tools_communitysummarytool_find_relevant_entities", + "community": 29, + "norm_label": "._find_relevant_entities()" + }, + { + "label": "._get_community_summary()", + "file_type": "code", + "source_file": "retrieval/tools.py", + "source_location": "L259", + "id": "retrieval_tools_communitysummarytool_get_community_summary", + "community": 29, + "norm_label": "._get_community_summary()" + }, + { + "label": "GraphTraversalTool", + "file_type": "code", + "source_file": "retrieval/tools.py", + "source_location": "L321", + "id": "retrieval_tools_graphtraversaltool", + "community": 15, + "norm_label": "graphtraversaltool" + }, + { + "label": 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"._validate_cypher()" + }, + { + "label": "._correct_cypher()", + "file_type": "code", + "source_file": "retrieval/tools.py", + "source_location": "L479", + "id": "retrieval_tools_cyphergenerationtool_correct_cypher", + "community": 13, + "norm_label": "._correct_cypher()" + }, + { + "label": "._correct_cypher_with_error()", + "file_type": "code", + "source_file": "retrieval/tools.py", + "source_location": "L496", + "id": "retrieval_tools_cyphergenerationtool_correct_cypher_with_error", + "community": 13, + "norm_label": "._correct_cypher_with_error()" + }, + { + "label": "MetadataFilterTool", + "file_type": "code", + "source_file": "retrieval/tools.py", + "source_location": "L518", + "id": "retrieval_tools_metadatafiltertool", + "community": 15, + "norm_label": "metadatafiltertool" + }, + { + "label": "LLMJudge", + "file_type": "code", + "source_file": "retrieval/tools.py", + "source_location": "L567", + "id": "retrieval_tools_llmjudge", + "community": 15, + "norm_label": "llmjudge" + 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Args", + "file_type": "rationale", + "source_file": "retrieval/tools.py", + "source_location": "L46", + "id": "retrieval_tools_rationale_46", + "community": 13, + "norm_label": "run both bm25 and vector search in parallel, then fuse with rrf. args" + }, + { + "label": "Weighted Reciprocal Rank Fusion. Returns sorted list of (id, score) tup", + "file_type": "rationale", + "source_file": "retrieval/tools.py", + "source_location": "L113", + "id": "retrieval_tools_rationale_113", + "community": 35, + "norm_label": "weighted reciprocal rank fusion. returns sorted list of (id, score) tup" + }, + { + "label": "Vector similarity search tool \u2014 pure dense retrieval", + "file_type": "rationale", + "source_file": "retrieval/tools.py", + "source_location": "L135", + "id": "retrieval_tools_rationale_135", + "community": 15, + "norm_label": "vector similarity search tool \u2014 pure dense retrieval" + }, + { + "label": "LazyGraphRAG-style community summarization. Detects entity clusters, genera", + "file_type": "rationale", + "source_file": "retrieval/tools.py", + "source_location": "L164", + "id": "retrieval_tools_rationale_164", + "community": 29, + "norm_label": "lazygraphrag-style community summarization. detects entity clusters, genera" + }, + { + "label": "Lazily initialize Redis connection for caching", + "file_type": "rationale", + "source_file": "retrieval/tools.py", + "source_location": "L182", + "id": "retrieval_tools_rationale_182", + "community": 29, + "norm_label": "lazily initialize redis connection for caching" + }, + { + "label": "1. Find relevant entities via hybrid search 2. Group by community_id", + "file_type": "rationale", + "source_file": "retrieval/tools.py", + "source_location": "L197", + "id": "retrieval_tools_rationale_197", + "community": 13, + "norm_label": "1. find relevant entities via hybrid search 2. group by community_id" + }, + { + "label": "Find entity names most relevant to the query via BM25", + "file_type": "rationale", + "source_file": "retrieval/tools.py", + "source_location": "L239", + "id": "retrieval_tools_rationale_239", + "community": 29, + "norm_label": "find entity names most relevant to the query via bm25" + }, + { + "label": "Generate or fetch cached LLM summary for a community", + "file_type": "rationale", + "source_file": "retrieval/tools.py", + "source_location": "L265", + "id": "retrieval_tools_rationale_265", + "community": 29, + "norm_label": "generate or fetch cached llm summary for a community" + }, + { + "label": "Graph traversal and path finding tool", + "file_type": "rationale", + "source_file": "retrieval/tools.py", + "source_location": "L322", + "id": "retrieval_tools_rationale_322", + "community": 15, + "norm_label": "graph traversal and path finding tool" + }, + { + "label": "Extract entity names from natural language query", + "file_type": "rationale", + "source_file": "retrieval/tools.py", + "source_location": "L360", + "id": "retrieval_tools_rationale_360", + "community": 13, + "norm_label": "extract entity names from natural language query" + }, + { + "label": "Text-to-Cypher tool with hallucination guards. Generates Cypher queries fro", + "file_type": "rationale", + "source_file": "retrieval/tools.py", + "source_location": "L384", + "id": "retrieval_tools_rationale_384", + "community": 13, + "norm_label": "text-to-cypher tool with hallucination guards. generates cypher queries fro" + }, + { + "label": "Filter-based retrieval using metadata constraints", + "file_type": "rationale", + "source_file": "retrieval/tools.py", + "source_location": "L519", + "id": "retrieval_tools_rationale_519", + "community": 15, + "norm_label": "filter-based retrieval using metadata constraints" + }, + { + "label": "LLM-as-a-Judge for real confidence scoring. Replaces the fake len(contexts)", + "file_type": "rationale", + "source_file": "retrieval/tools.py", + "source_location": "L568", + "id": "retrieval_tools_rationale_568", + "community": 15, + "norm_label": "llm-as-a-judge for real confidence scoring. replaces the fake len(contexts)" + }, + { + "label": "Evaluate how well the answer is grounded in the retrieved contexts. R", + "file_type": "rationale", + "source_file": "retrieval/tools.py", + "source_location": "L582", + "id": "retrieval_tools_rationale_582", + "community": 15, + "norm_label": "evaluate how well the answer is grounded in the retrieved contexts. r" + }, + { + "label": "RAGAS-style evaluation metrics for the quality dashboard. 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+import re +import string + +def normalize_answer(s): + """Lower text and remove punctuation, articles and extra whitespace.""" + def remove_articles(text): + return re.sub(r'\b(a|an|the)\b', ' ', text) + def white_space_fix(text): + return ' '.join(text.split()) + def remove_punc(text): + exclude = set(string.punctuation) + return ''.join(ch for ch in text if ch not in exclude) + def lower(text): + return text.lower() + return white_space_fix(remove_articles(remove_punc(lower(s)))) + +def exact_match(prediction, ground_truth): + return normalize_answer(prediction) == normalize_answer(ground_truth) + +def token_f1(prediction, ground_truth): + prediction_tokens = normalize_answer(prediction).split() + ground_truth_tokens = normalize_answer(ground_truth).split() + common = set(prediction_tokens) & set(ground_truth_tokens) + num_same = len(common) + if num_same == 0: + return 0.0 + precision = 1.0 * num_same / len(prediction_tokens) + recall = 1.0 * num_same / len(ground_truth_tokens) + f1 = (2 * precision * recall) / (precision + recall) + return f1 + +# Mock datasets for benchmarking fallback +HOTPOT_QA_SAMPLE = [ + { + "question": "What is the capital of the country where the city of Lyon is located?", + "ground_truth": "Paris", + "context": "Lyon is a city in France. The capital of France is Paris.", + "type": "multi-hop" + }, + { + "question": "Which company acquired the startup that developed the Siri virtual assistant?", + "ground_truth": "Apple", + "context": "Siri was originally developed by Siri Inc. Apple acquired Siri Inc. in 2010.", + "type": "multi-hop" + } +] + +class BenchmarkConfig(BaseModel): + base_url: str = "http://localhost:7860" + modes: List[str] = ["naive", "hybrid", "hippo", "global_community"] + dataset: str = "hotpot_qa" + num_samples: int = 10 + +def load_hf_dataset(config: BenchmarkConfig) -> List[Dict[str, str]]: + try: + from datasets import load_dataset # type: ignore + print(f"Loading {config.dataset} from Hugging Face datasets...") + + if config.dataset == "hotpot_qa": + ds = load_dataset("hotpot_qa", "distractor", split="validation", streaming=True) + elif config.dataset == "musique": + ds = load_dataset("bdsaglam/musique", split="validation", streaming=True) + else: + ds = load_dataset(config.dataset, split="validation", streaming=True) + + samples = [] + for item in ds: + if len(samples) >= config.num_samples: + break + + # Extract context text + context_text = "" + if "context" in item: + # HotpotQA format: lists of titles and sentences + if isinstance(item["context"], dict) and "sentences" in item["context"]: + for sentences in item["context"]["sentences"]: + context_text += " ".join(sentences) + " " + else: + context_text = str(item["context"]) + + samples.append({ + "question": item.get("question", ""), + "ground_truth": item.get("answer", ""), + "context": context_text, + "type": item.get("level", "unknown") + }) + return samples + except ImportError: + print("HF 'datasets' library not installed. Falling back to mock dataset.") + return HOTPOT_QA_SAMPLE + except Exception as e: + print(f"Failed to load dataset from HF: {e}. Falling back to mock dataset.") + return HOTPOT_QA_SAMPLE + +async def create_benchmark_tenant(client: httpx.AsyncClient, config: BenchmarkConfig) -> str: + timestamp = int(time.time()) + username = f"benchmark_user_{timestamp}" + tenant_id = f"benchmark_run_{timestamp}" + password = "password123" + + register_url = f"{config.base_url}/api/auth/register" + payload = { + "username": username, + "password": password, + "email": f"{username}@example.com", + "full_name": "Benchmark User", + "scopes": ["read", "write"], + "tenant_id": tenant_id + } + + try: + res = await client.post(register_url, json=payload, timeout=10.0) + res.raise_for_status() + + login_url = f"{config.base_url}/api/auth/login" + login_payload = {"username": username, "password": password} + login_res = await client.post(login_url, json=login_payload, timeout=10.0) + login_res.raise_for_status() + print(f" Created isolated tenant: {tenant_id}") + return login_res.json()["access_token"] + except Exception as e: + print(f" Failed to create isolated tenant: {e}. Falling back to default admin.") + return await authenticate(client, config) + +async def authenticate(client: httpx.AsyncClient, config: BenchmarkConfig) -> str: + login_url = f"{config.base_url}/api/auth/login" + try: + response = await client.post(login_url, json={"username": "admin", "password": "admin"}, timeout=10.0) + response.raise_for_status() + return response.json().get("access_token", "") + except Exception as e: + print(f"Failed to authenticate with backend: {e}. Some endpoints may be inaccessible.") + return "test-token" + +async def ingest_context(client: httpx.AsyncClient, config: BenchmarkConfig, token: str, q: Dict[str, str]): + if not q.get("context"): + return + + update_url = f"{config.base_url}/api/graph/update" + headers = {"Authorization": f"Bearer {token}"} + payload = { + "text": q["context"], + "source_label": "benchmark_ingest" + } + + try: + response = await client.post(update_url, json=payload, headers=headers, timeout=30.0) + response.raise_for_status() + except Exception as e: + print(f" [Warning] Failed to ingest context: {e}") + +async def evaluate_question(client: httpx.AsyncClient, config: BenchmarkConfig, token: str, mode: str, q: Dict[str, str]) -> Dict[str, Any]: + query_url = f"{config.base_url}/api/query" + payload = { + "query": q["question"], + "top_k": 5, + "mode": mode, + "streaming": False + } + + start_time = time.time() + try: + headers = {"Authorization": f"Bearer {token}"} + + response = await client.post(query_url, json=payload, headers=headers, timeout=60.0) + response.raise_for_status() + data = response.json() + + duration = time.time() - start_time + answer = data.get("answer", "") + + em = exact_match(answer, q["ground_truth"]) + f1 = token_f1(answer, q["ground_truth"]) + is_correct = em or f1 > 0.6 # Use relaxed F1 threshold for general correctness flag + + return { + "question": q["question"], + "mode": mode, + "duration": duration, + "is_correct": is_correct, + "exact_match": em, + "f1_score": f1, + "generated_answer": answer, + "confidence": data.get("confidence", 0.0), + "sources_count": len(data.get("sources", [])) + } + except Exception as e: + return { + "question": q["question"], + "mode": mode, + "duration": time.time() - start_time, + "is_correct": False, + "error": str(e) + } + +async def run_benchmark(): + config = BenchmarkConfig() + dataset = load_hf_dataset(config) + + print(f"Starting benchmark on {config.dataset} with {len(dataset)} questions...") + print(f"Modes to test: {config.modes}") + + results = [] + + async with httpx.AsyncClient() as client: + # Authenticate first + # Create isolated tenant for benchmark + print("\nCreating isolated benchmark tenant...") + token = await create_benchmark_tenant(client, config) + + for i, q in enumerate(dataset): + print(f"\nEvaluating Question {i+1}/{len(dataset)}: {q['question']}") + + # Ingest context into knowledge graph + print(f" Ingesting context into knowledge graph...") + await ingest_context(client, config, token, q) + + # Allow Neo4j indexing to catch up + await asyncio.sleep(1) + + for mode in config.modes: + print(f" Running mode: {mode}...") + res = await evaluate_question(client, config, token, mode, q) + results.append(res) + status = "PASS" if res.get("is_correct") else "FAIL" + print(f" [{status}] Time: {res['duration']:.2f}s | Sources: {res.get('sources_count', 0)}") + + summary = {} + for r in results: + m = r["mode"] + if m not in summary: + summary[m] = {"correct": 0, "total": 0, "time": 0.0} + + summary[m]["total"] += 1 + if r.get("is_correct"): + summary[m]["correct"] += 1 + summary[m]["time"] += r["duration"] + + print("\n=== BENCHMARK RESULTS ===") + for m, stats in summary.items(): + accuracy = (stats["correct"] / stats["total"]) * 100 if stats["total"] > 0 else 0 + avg_time = stats["time"] / stats["total"] if stats["total"] > 0 else 0 + print(f"Mode: {m:<15} | Accuracy: {accuracy:>5.1f}% | Avg Time: {avg_time:>5.2f}s") + +if __name__ == "__main__": + asyncio.run(run_benchmark()) diff --git a/data/uploads/.gitkeep b/data/uploads/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..fdf46cb225600706e735e36f4374da3aadf8ddac --- /dev/null +++ b/data/uploads/.gitkeep @@ -0,0 +1 @@ +# This file ensures the data/uploads directory is created in git diff --git a/fix_datetime.py b/fix_datetime.py new file mode 100644 index 0000000000000000000000000000000000000000..e3ce831d8c758f1bc539b8941ec3aec45f943919 --- /dev/null +++ b/fix_datetime.py @@ -0,0 +1,21 @@ +import os + +target_dir = r"D:\Desktop_March_26\LYZR\graph-RAG\src\graph_rag_service" + +for root, dirs, files in os.walk(target_dir): + for f in files: + if f.endswith(".py"): + p = os.path.join(root, f) + with open(p, "r", encoding="utf-8") as file: + content = file.read() + + if "datetime.utcnow()" in content: + content = content.replace("datetime.utcnow()", "datetime.now(timezone.utc).replace(tzinfo=None)") + if "from datetime import datetime" in content and "timezone" not in content: + content = content.replace("from datetime import datetime", "from datetime import datetime, timezone") + elif "import datetime" in content and "timezone" not in content: + content = "from datetime import timezone\n" + content + + with open(p, "w", encoding="utf-8") as file: + file.write(content) + print(f"Updated {p}") diff --git a/fix_default_factory.py b/fix_default_factory.py new file mode 100644 index 0000000000000000000000000000000000000000..9d6ad417f6499ad280562cf30017ed67aecd328e --- /dev/null +++ b/fix_default_factory.py @@ -0,0 +1,12 @@ +import os +import glob + +for f in glob.glob(r'D:\Desktop_March_26\LYZR\graph-RAG\src\**\*.py', recursive=True): + with open(f, 'r', encoding='utf-8') as file: + content = file.read() + + if 'default_factory=datetime.utcnow' in content: + new_content = content.replace('default_factory=datetime.utcnow', 'default_factory=lambda: datetime.now(timezone.utc).replace(tzinfo=None)') + with open(f, 'w', encoding='utf-8') as file: + file.write(new_content) + print(f"Fixed {f}") diff --git a/fix_print_statements.py b/fix_print_statements.py new file mode 100644 index 0000000000000000000000000000000000000000..456aab29716f8a525b18937944317aa2f4662132 --- /dev/null +++ b/fix_print_statements.py @@ -0,0 +1,21 @@ +import os +import glob +import re + +for f in glob.glob(r'D:\Desktop_March_26\LYZR\graph-RAG\src\**\*.py', recursive=True): + with open(f, 'r', encoding='utf-8') as file: + content = file.read() + + if 'print(' in content: + # Add import logging and logger if not exists + if 'import logging' not in content: + content = "import logging\nlogger = logging.getLogger(__name__)\n" + content + elif 'logger = ' not in content: + content = content.replace('import logging\n', 'import logging\nlogger = logging.getLogger(__name__)\n', 1) + + # Replace print( with logger.info( + content = re.sub(r'\bprint\(', 'logger.info(', content) + + with open(f, 'w', encoding='utf-8') as file: + file.write(content) + print(f"Fixed {f}") diff --git a/fix_timezone_imports.py b/fix_timezone_imports.py new file mode 100644 index 0000000000000000000000000000000000000000..45d0188fcab9633cf36f70b9d5f6f27027d9339b --- /dev/null +++ b/fix_timezone_imports.py @@ -0,0 +1,33 @@ +import os + +files = [ + r"D:\Desktop_March_26\LYZR\graph-RAG\src\graph_rag_service\api\auth.py", + r"D:\Desktop_March_26\LYZR\graph-RAG\src\graph_rag_service\api\routers\ontology.py", + r"D:\Desktop_March_26\LYZR\graph-RAG\src\graph_rag_service\api\routers\system.py", + r"D:\Desktop_March_26\LYZR\graph-RAG\src\graph_rag_service\ingestion\document_processor.py", + r"D:\Desktop_March_26\LYZR\graph-RAG\src\graph_rag_service\ingestion\ontology_generator.py", + r"D:\Desktop_March_26\LYZR\graph-RAG\src\graph_rag_service\retrieval\report_agent.py", + r"D:\Desktop_March_26\LYZR\graph-RAG\src\graph_rag_service\services\graph_memory_updater.py", + r"D:\Desktop_March_26\LYZR\graph-RAG\src\graph_rag_service\services\ontology_drift_detector.py", + r"D:\Desktop_March_26\LYZR\graph-RAG\src\graph_rag_service\workers\simulation_runner.py" +] + +for f in files: + with open(f, 'r', encoding='utf-8') as file: + content = file.read() + + # Check if 'from datetime import datetime' exists + if "from datetime import datetime\n" in content: + content = content.replace("from datetime import datetime\n", "from datetime import datetime, timezone\n") + elif "from datetime import datetime," in content: + if "timezone" not in content: + content = content.replace("from datetime import datetime,", "from datetime import datetime, timezone,") + elif "import datetime\n" in content: + content = content.replace("import datetime\n", "import datetime\nfrom datetime import timezone\n") + else: + # Just put it at the top + content = "from datetime import timezone\n" + content + + with open(f, 'w', encoding='utf-8') as file: + file.write(content) + print(f"Fixed {f}") diff --git a/frontend-react/.gitignore b/frontend-react/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..a547bf36d8d11a4f89c59c144f24795749086dd1 --- /dev/null +++ b/frontend-react/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/frontend-react/README.md b/frontend-react/README.md new file mode 100644 index 0000000000000000000000000000000000000000..7dbf7ebf3b2a3d84ad526bc47810d1d211331b8b --- /dev/null +++ b/frontend-react/README.md @@ -0,0 +1,73 @@ +# React + TypeScript + Vite + +This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules. + +Currently, two official plugins are available: + +- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Oxc](https://oxc.rs) +- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) + +## React Compiler + +The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation). + +## Expanding the ESLint configuration + +If you are developing a production application, we recommend updating the configuration to enable type-aware lint rules: + +```js +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + // Other configs... + + // Remove tseslint.configs.recommended and replace with this + tseslint.configs.recommendedTypeChecked, + // Alternatively, use this for stricter rules + tseslint.configs.strictTypeChecked, + // Optionally, add this for stylistic rules + tseslint.configs.stylisticTypeChecked, + + // Other configs... + ], + languageOptions: { + parserOptions: { + project: ['./tsconfig.node.json', './tsconfig.app.json'], + tsconfigRootDir: import.meta.dirname, + }, + // other options... + }, + }, +]) +``` + +You can also install [eslint-plugin-react-x](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-x) and [eslint-plugin-react-dom](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-dom) for React-specific lint rules: + +```js +// eslint.config.js +import reactX from 'eslint-plugin-react-x' +import reactDom from 'eslint-plugin-react-dom' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + // Other configs... + // Enable lint rules for React + reactX.configs['recommended-typescript'], + // Enable lint rules for React DOM + reactDom.configs.recommended, + ], + languageOptions: { + parserOptions: { + project: ['./tsconfig.node.json', './tsconfig.app.json'], + tsconfigRootDir: import.meta.dirname, + }, + // other options... + }, + }, +]) +``` diff --git a/frontend-react/eslint.config.js b/frontend-react/eslint.config.js new file mode 100644 index 0000000000000000000000000000000000000000..5e6b472f583e34a1cca751440d4f241495475723 --- /dev/null +++ b/frontend-react/eslint.config.js @@ -0,0 +1,23 @@ +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import tseslint from 'typescript-eslint' +import { defineConfig, globalIgnores } from 'eslint/config' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + js.configs.recommended, + tseslint.configs.recommended, + reactHooks.configs.flat.recommended, + reactRefresh.configs.vite, + ], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser, + }, + }, +]) diff --git a/frontend-react/index.html b/frontend-react/index.html new file mode 100644 index 0000000000000000000000000000000000000000..c270cc86c8c4129d902d988fc9cb7711cf4d59b7 --- /dev/null +++ b/frontend-react/index.html @@ -0,0 +1,13 @@ + + + + + + + frontend-react + + +
+ + + diff --git a/frontend-react/package-lock.json b/frontend-react/package-lock.json new file mode 100644 index 0000000000000000000000000000000000000000..03c92b67b33c76b17fd59a5864e923fe0eb6e51b --- /dev/null +++ b/frontend-react/package-lock.json @@ -0,0 +1,6053 @@ +{ + "name": "frontend-react", + "version": "0.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "frontend-react", + "version": "0.0.0", + "dependencies": { + "@heroicons/react": "^2.2.0", + "@types/d3": "^7.4.3", + "d3": "^7.9.0", + "lucide-react": "^1.7.0", + "react": "^19.2.4", + "react-dom": "^19.2.4", + "react-markdown": "^10.1.0", + "react-router-dom": "^7.13.2", + "remark-gfm": "^4.0.1" + }, + "devDependencies": { + "@eslint/js": "^9.39.4", + "@tailwindcss/vite": "^4.2.4", + "@types/node": "^24.12.0", + "@types/react": "^19.2.14", + "@types/react-dom": "^19.2.3", + "@vitejs/plugin-react": "^6.0.1", + "autoprefixer": "^10.5.0", + "concurrently": "^9.2.1", + "eslint": "^9.39.4", + 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No newline at end of file diff --git a/frontend-react/public/favicon.svg b/frontend-react/public/favicon.svg new file mode 100644 index 0000000000000000000000000000000000000000..6893eb13237060adc0c968a690149a49faa2d7d3 --- /dev/null +++ b/frontend-react/public/favicon.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend-react/public/icons.svg b/frontend-react/public/icons.svg new file mode 100644 index 0000000000000000000000000000000000000000..e9522193d9f796a9748e9ad8c952a5df73c87db9 --- /dev/null +++ b/frontend-react/public/icons.svg @@ -0,0 +1,24 @@ + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/frontend-react/public/thumbnail.png b/frontend-react/public/thumbnail.png new file mode 100644 index 0000000000000000000000000000000000000000..e7710d434efabfad76c943ddb61aa42c70a56581 --- /dev/null +++ b/frontend-react/public/thumbnail.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9c0fd3b4ca20ef442918b0cd3504825b0a966e94d681732c036298395d5a773a +size 545039 diff --git a/frontend-react/src/App.css b/frontend-react/src/App.css new file mode 100644 index 0000000000000000000000000000000000000000..456ff12b618716728a022ab654c8a837b9cd74e9 --- /dev/null +++ b/frontend-react/src/App.css @@ -0,0 +1 @@ +/* Empty file to override default vite styles */ diff --git a/frontend-react/src/App.tsx b/frontend-react/src/App.tsx new file mode 100644 index 0000000000000000000000000000000000000000..198dca67a098ea8a5fde2d96981d512f154882da --- /dev/null +++ b/frontend-react/src/App.tsx @@ -0,0 +1,179 @@ +import React from 'react'; +import { BrowserRouter, Routes, Route, Navigate, NavLink } from 'react-router-dom'; +import { AuthProvider, useAuth } from './context/AuthContext'; + +import Login from './views/Login'; +import Home from './views/Home'; +import Process from './views/Process'; +import InteractionView from './views/InteractionView'; +import SimulationRunView from './views/SimulationRunView'; +import Ontology from './views/Ontology'; +import InsightsView from './views/InsightsView'; +import AdminDashboard from './views/AdminDashboard'; + +const ProtectedRoute = ({ children }: { children: React.ReactNode }) => { + const { isAuthenticated } = useAuth(); + if (!isAuthenticated) return ; + return children; +}; + +const Navigation: React.FC = () => { + const { logout, user } = useAuth(); + return ( + + ); +}; + +const Layout = ({ children }: { children: React.ReactNode }) => ( + <> + + {children} + +); + +function App() { + return ( + + + + } /> + + } /> + } /> + } /> + } /> + } /> + } /> + } /> + + + + ); +} + +export default App; diff --git a/frontend-react/src/assets/hero.png b/frontend-react/src/assets/hero.png new file mode 100644 index 0000000000000000000000000000000000000000..2d58a13c6c916ee1d261fc82517761fa3acf2c8d --- /dev/null +++ b/frontend-react/src/assets/hero.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:72a860570eddf1dd9988f26c7106c67be286bc9f2fd3303c465ce87edb1ae6cd +size 44919 diff --git a/frontend-react/src/assets/react.svg b/frontend-react/src/assets/react.svg new file mode 100644 index 0000000000000000000000000000000000000000..6c87de9bb3358469122cc991d5cf578927246184 --- /dev/null +++ b/frontend-react/src/assets/react.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend-react/src/assets/vite.svg b/frontend-react/src/assets/vite.svg new file mode 100644 index 0000000000000000000000000000000000000000..5101b674df391399da71c767aa5c976426c9dc7a --- /dev/null +++ b/frontend-react/src/assets/vite.svg @@ -0,0 +1 @@ +Vite diff --git a/frontend-react/src/components/GraphCanvas.tsx b/frontend-react/src/components/GraphCanvas.tsx new file mode 100644 index 0000000000000000000000000000000000000000..a172f585f4f8761f53e0bd223cc97d8e2e6f594b --- /dev/null +++ b/frontend-react/src/components/GraphCanvas.tsx @@ -0,0 +1,570 @@ +import { useEffect, useRef, forwardRef, useImperativeHandle, useState } from 'react'; +import * as d3 from 'd3'; +import { X, Tag, FileText, Database } from 'lucide-react'; + +// 12-color categorical palette for node types +const TYPE_COLORS = [ + '#e63946', '#457b9d', '#2a9d8f', '#e9c46a', '#f4a261', + '#6a4c93', '#1982c4', '#8ac926', '#ff595e', '#6a994e', + '#bc4749', '#a8dadc' +]; + +export interface GraphOptions { + colorByType: boolean; + showLabels: boolean; + showEdgeLabels: boolean; + nodeRadius: number; + linkDistance: number; + chargeStrength: number; + showCurvedEdges: boolean; + nodeSizeByDegree: boolean; + centerGravity: number; // 0 = no gravity, 0.1 = default +} + +export const DEFAULT_OPTIONS: GraphOptions = { + colorByType: true, + showLabels: true, + showEdgeLabels: false, + nodeRadius: 16, + linkDistance: 120, + chargeStrength: -300, + showCurvedEdges: false, + nodeSizeByDegree: false, + centerGravity: 0.05, +}; + +interface GraphCanvasProps { + data: { nodes: any[]; edges: any[] }; + onNodeUpdate?: (nodeId: string, newName: string) => void; + options?: GraphOptions; + highlightNodeIds?: Set; // nodes to highlight (from search) +} + +export interface GraphCanvasHandle { + exportPNG: () => void; + exportSVG: () => void; + fitView: () => void; + highlightNode: (id: string) => void; +} + +const GraphCanvas = forwardRef( + ({ data, onNodeUpdate, options = DEFAULT_OPTIONS, highlightNodeIds }, ref) => { + const [activeNode, setActiveNode] = useState(null); + const containerRef = useRef(null); + const svgRef = useRef(null); + const zoomRef = useRef | null>(null); + const gRef = useRef | null>(null); + const simulationRef = useRef | null>(null); + const typeColorMap = useRef>(new Map()); + + // ── Imperative API ───────────────────────────────────────────────────── + useImperativeHandle(ref, () => ({ + exportPNG() { + if (!svgRef.current) return; + const svgEl = svgRef.current; + const serializer = new XMLSerializer(); + const svgStr = serializer.serializeToString(svgEl); + const canvas = document.createElement('canvas'); + canvas.width = svgEl.clientWidth * 2; + canvas.height = svgEl.clientHeight * 2; + const ctx = canvas.getContext('2d')!; + const img = new Image(); + const blob = new Blob([svgStr], { type: 'image/svg+xml;charset=utf-8' }); + const url = URL.createObjectURL(blob); + img.onload = () => { + ctx.fillStyle = '#fff'; + ctx.fillRect(0, 0, canvas.width, canvas.height); + ctx.scale(2, 2); + ctx.drawImage(img, 0, 0); + URL.revokeObjectURL(url); + const a = document.createElement('a'); + a.download = 'graph.png'; + a.href = canvas.toDataURL('image/png'); + a.click(); + }; + img.src = url; + }, + exportSVG() { + if (!svgRef.current) return; + const serializer = new XMLSerializer(); + const svgStr = serializer.serializeToString(svgRef.current); + const blob = new Blob([svgStr], { type: 'image/svg+xml;charset=utf-8' }); + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.download = 'graph.svg'; + a.href = url; + a.click(); + URL.revokeObjectURL(url); + }, + fitView() { + if (!svgRef.current || !zoomRef.current) return; + d3.select(svgRef.current).transition().duration(600).call( + zoomRef.current.transform, d3.zoomIdentity + ); + }, + highlightNode(id: string) { + if (!svgRef.current || !zoomRef.current) return; + const node = simulationRef.current?.nodes().find((n: any) => n.id === id); + if (!node || node.x === undefined) return; + const svg = d3.select(svgRef.current); + const w = svgRef.current.clientWidth; + const h = svgRef.current.clientHeight; + const t = d3.zoomIdentity.translate(w / 2 - node.x, h / 2 - node.y); + svg.transition().duration(700).call(zoomRef.current.transform, t); + } + })); + + // ── Main D3 render effect ────────────────────────────────────────────── + useEffect(() => { + if (!data.nodes.length || !containerRef.current || !svgRef.current) return; + + const width = containerRef.current.clientWidth; + const height = containerRef.current.clientHeight; + + // Build type→color map (stable) + const types = [...new Set(data.nodes.map(n => n.type || 'Unknown'))]; + types.forEach((t, i) => { + if (!typeColorMap.current.has(t)) { + typeColorMap.current.set(t, TYPE_COLORS[i % TYPE_COLORS.length]); + } + }); + + const svg = d3.select(svgRef.current); + svg.selectAll('*').remove(); + + const nodes: any[] = data.nodes.map(d => ({ ...d })); + const nodeIds = new Set(nodes.map(n => n.id)); + const links: any[] = data.edges + .filter(d => nodeIds.has(d.source) && nodeIds.has(d.target)) + .map(d => ({ ...d })); + + // Degree map for node-size-by-degree + const degreeMap = new Map(); + nodes.forEach(n => degreeMap.set(n.id, 0)); + links.forEach(l => { + const sid = typeof l.source === 'object' ? l.source.id : l.source; + const tid = typeof l.target === 'object' ? l.target.id : l.target; + degreeMap.set(sid, (degreeMap.get(sid) || 0) + 1); + degreeMap.set(tid, (degreeMap.get(tid) || 0) + 1); + }); + const maxDegree = Math.max(1, ...degreeMap.values()); + + const nodeR = (d: any) => { + if (!options.nodeSizeByDegree) return options.nodeRadius; + const deg = degreeMap.get(d.id) || 0; + return Math.max(8, options.nodeRadius * (0.5 + 1.0 * (deg / maxDegree))); + }; + + // ── Defs: arrowhead markers ────────────────────────────────────────── + const defs = svg.append('defs'); + if (options.colorByType) { + types.forEach(t => { + const color = typeColorMap.current.get(t) || '#000'; + defs.append('marker') + .attr('id', `arrow-${t.replace(/\s+/g, '_')}`) + .attr('viewBox', '-0 -5 10 10').attr('refX', options.nodeRadius + 10) + .attr('refY', 0).attr('orient', 'auto') + .attr('markerWidth', 6).attr('markerHeight', 6) + .append('path').attr('d', 'M 0,-5 L 10,0 L 0,5').attr('fill', color); + }); + } else { + defs.append('marker') + .attr('id', 'arrow-default') + .attr('viewBox', '-0 -5 10 10').attr('refX', options.nodeRadius + 10) + .attr('refY', 0).attr('orient', 'auto') + .attr('markerWidth', 6).attr('markerHeight', 6) + .append('path').attr('d', 'M 0,-5 L 10,0 L 0,5').attr('fill', '#666'); + } + + // ── Force simulation ────────────────────────────────────────────────── + const sim = d3.forceSimulation(nodes) + .force('link', d3.forceLink(links).id((d: any) => d.id).distance(options.linkDistance)) + .force('charge', d3.forceManyBody().strength(options.chargeStrength)) + .force('center', d3.forceCenter(width / 2, height / 2).strength(options.centerGravity)) + .force('collide', d3.forceCollide().radius((d: any) => nodeR(d) + 14)); + + simulationRef.current = sim; + + const g = svg.append('g').attr('class', 'graph-root'); + gRef.current = g; + + // ── Tooltip ─────────────────────────────────────────────────────────── + const tooltip = d3.select(containerRef.current) + .selectAll('.graph-tooltip').data([null]).join('div') + .attr('class', 'graph-tooltip') + .style('position', 'absolute') + .style('pointer-events', 'none') + .style('background', '#000') + .style('color', '#fff') + .style('padding', '6px 12px') + .style('font-family', '"JetBrains Mono", monospace') + .style('font-size', '11px') + .style('line-height', '1.5') + .style('opacity', 0) + .style('border', '1px solid #333') + .style('z-index', '999') + .style('max-width', '220px') + .style('word-break', 'break-word'); + + // ── Links ──────────────────────────────────────────────────────────── + const linkG = g.append('g').attr('class', 'links'); + + // Adjacency set for hover highlight + const adjacentIds = new Set(); + + // Straight lines or curved paths + const linkEl = options.showCurvedEdges + ? linkG.selectAll('path').data(links).enter().append('path') + .attr('fill', 'none') + .attr('stroke', (d: any) => { + if (!options.colorByType) return '#aaa'; + const srcNode = nodes.find(n => n.id === (typeof d.source === 'object' ? d.source.id : d.source)); + return srcNode ? (typeColorMap.current.get(srcNode.type) || '#aaa') : '#aaa'; + }) + .attr('stroke-width', 1.5) + .attr('stroke-opacity', 0.55) + .attr('marker-end', (d: any) => { + if (!options.colorByType) return 'url(#arrow-default)'; + const srcNode = nodes.find(n => n.id === (typeof d.source === 'object' ? d.source.id : d.source)); + const t = srcNode?.type?.replace(/\s+/g, '_') || 'Unknown'; + return `url(#arrow-${t})`; + }) + : linkG.selectAll('line').data(links).enter().append('line') + .attr('stroke', (d: any) => { + if (!options.colorByType) return '#aaa'; + const srcNode = nodes.find(n => n.id === (typeof d.source === 'object' ? d.source.id : d.source)); + return srcNode ? (typeColorMap.current.get(srcNode.type) || '#aaa') : '#aaa'; + }) + .attr('stroke-width', 1.5) + .attr('stroke-opacity', 0.55) + .attr('marker-end', (d: any) => { + if (!options.colorByType) return 'url(#arrow-default)'; + const srcNode = nodes.find(n => n.id === (typeof d.source === 'object' ? d.source.id : d.source)); + const t = srcNode?.type?.replace(/\s+/g, '_') || 'Unknown'; + return `url(#arrow-${t})`; + }); + + // ── Nodes ───────────────────────────────────────────────────────────── + const node = g.append('g').attr('class', 'nodes') + .selectAll('g').data(nodes).enter().append('g') + .call(d3.drag() + .on('start', (event, d) => { if (!event.active) sim.alphaTarget(0.3).restart(); d.fx = d.x; d.fy = d.y; }) + .on('drag', (event, d) => { d.fx = event.x; d.fy = event.y; }) + .on('end', (event, d) => { if (!event.active) sim.alphaTarget(0); d.fx = null; d.fy = null; }) + ) + .on('mouseover', (_, d: any) => { + // Build adjacent set + adjacentIds.clear(); + adjacentIds.add(d.id); + links.forEach(l => { + const sid = typeof l.source === 'object' ? l.source.id : l.source; + const tid = typeof l.target === 'object' ? l.target.id : l.target; + if (sid === d.id) adjacentIds.add(tid); + if (tid === d.id) adjacentIds.add(sid); + }); + // Dim non-adjacent + node.select('circle') + .style('opacity', (n: any) => adjacentIds.has(n.id) ? 1 : 0.15) + .style('stroke-width', (n: any) => n.id === d.id ? 4 : 2); + (linkEl as any) + .style('stroke-opacity', (l: any) => { + const sid = typeof l.source === 'object' ? l.source.id : l.source; + const tid = typeof l.target === 'object' ? l.target.id : l.target; + return (sid === d.id || tid === d.id) ? 0.9 : 0.04; + }); + tooltip + .style('opacity', 1) + .html(`${d.label}
ID: ${d.id}
Type: ${d.type || '—'}
Degree: ${degreeMap.get(d.id) || 0}`); + }) + .on('mousemove', (event) => { + const rect = containerRef.current!.getBoundingClientRect(); + tooltip + .style('left', (event.clientX - rect.left + 14) + 'px') + .style('top', (event.clientY - rect.top - 32) + 'px'); + }) + .on('mouseout', () => { + adjacentIds.clear(); + node.select('circle') + .style('opacity', (n: any) => { + if (!highlightNodeIds || highlightNodeIds.size === 0) return 1; + return highlightNodeIds.has(n.id) ? 1 : 0.2; + }) + .style('stroke-width', (n: any) => highlightNodeIds?.has(n.id) ? 4 : 2); + (linkEl as any).style('stroke-opacity', 0.55); + tooltip.style('opacity', 0); + }) + .on('click', (event, d: any) => { + setActiveNode(d); + // Zoom to node on single click + if (!svgRef.current || !zoomRef.current) return; + const w = svgRef.current.clientWidth; + const h = svgRef.current.clientHeight; + const t = d3.zoomIdentity.translate(w / 2 - d.x, h / 2 - d.y).scale(1.4); + d3.select(svgRef.current).transition().duration(500).call(zoomRef.current.transform, t); + event.stopPropagation(); + }) + .on('dblclick', (event, d: any) => { + const newName = window.prompt('Update entity name:', d.label); + if (newName && newName.trim() && newName.trim() !== d.label) { + const updated = newName.trim(); + d.label = updated; + d3.select(event.currentTarget).select('text.node-label').text( + updated.length > 18 ? updated.substring(0, 16) + '…' : updated + ); + if (onNodeUpdate) onNodeUpdate(d.id, updated); + } + }); + + // Circle + node.append('circle') + .attr('r', (d: any) => nodeR(d)) + .attr('fill', (d: any) => options.colorByType + ? (typeColorMap.current.get(d.type) || '#ccc') + : '#fff') + .attr('stroke', (d: any) => { + if (highlightNodeIds && highlightNodeIds.size > 0) { + return highlightNodeIds.has(d.id) ? '#ff0' : (options.colorByType + ? d3.color(typeColorMap.current.get(d.type) || '#ccc')!.darker(1).toString() + : '#000'); + } + return options.colorByType + ? d3.color(typeColorMap.current.get(d.type) || '#ccc')!.darker(1).toString() + : '#000'; + }) + .attr('stroke-width', (d: any) => highlightNodeIds?.has(d.id) ? 4 : 2) + .style('opacity', (d: any) => { + if (!highlightNodeIds || highlightNodeIds.size === 0) return 1; + return highlightNodeIds.has(d.id) ? 1 : 0.2; + }) + .style('filter', 'drop-shadow(1px 2px 3px rgba(0,0,0,0.15))') + .style('cursor', 'pointer'); + + // Type abbreviation inside circle + node.append('text') + .attr('class', 'node-type-badge') + .text((d: any) => (d.type || '?').substring(0, 2).toUpperCase()) + .attr('text-anchor', 'middle').attr('dy', '0.35em') + .style('font-family', '"JetBrains Mono", monospace') + .style('font-size', (d: any) => `${Math.max(8, nodeR(d) - 6)}px`) + .style('font-weight', '700') + .style('fill', (d: any) => { + if (!options.colorByType) return '#000'; + const c = d3.color(typeColorMap.current.get(d.type) || '#ccc'); + if (!c) return '#000'; + const { r, g: gv, b } = c.rgb(); + return (r * 0.299 + gv * 0.587 + b * 0.114) > 150 ? '#111' : '#fff'; + }) + .style('pointer-events', 'none'); + + // Node name label below circle + if (options.showLabels) { + node.append('text') + .attr('class', 'node-label') + .text((d: any) => d.label && d.label.length > 18 ? d.label.substring(0, 16) + '…' : d.label) + .attr('text-anchor', 'middle') + .attr('dy', (d: any) => nodeR(d) + 14) + .style('font-family', '"JetBrains Mono", monospace') + .style('font-size', '10px') + .style('font-weight', '600') + .style('fill', '#222') + .style('pointer-events', 'none'); + } + + // Edge labels + let edgeLabel: d3.Selection | null = null; + if (options.showEdgeLabels) { + edgeLabel = g.append('g').attr('class', 'edge-labels') + .selectAll('text').data(links).enter().append('text') + .text((d: any) => d.type || '') + .style('font-family', '"JetBrains Mono", monospace') + .style('font-size', '9px') + .style('fill', '#777') + .style('text-anchor', 'middle') + .style('pointer-events', 'none') + .attr('dy', -5); + } + + // ── Tick ────────────────────────────────────────────────────────────── + sim.on('tick', () => { + if (options.showCurvedEdges) { + (linkEl as d3.Selection) + .attr('d', (d: any) => { + const sx = d.source.x, sy = d.source.y; + const tx = d.target.x, ty = d.target.y; + const dx = tx - sx, dy = ty - sy; + const dr = Math.sqrt(dx * dx + dy * dy) * 0.8; + return `M${sx},${sy}A${dr},${dr} 0 0,1 ${tx},${ty}`; + }); + } else { + (linkEl as d3.Selection) + .attr('x1', (d: any) => d.source.x).attr('y1', (d: any) => d.source.y) + .attr('x2', (d: any) => d.target.x).attr('y2', (d: any) => d.target.y); + } + node.attr('transform', (d: any) => `translate(${d.x},${d.y})`); + if (edgeLabel) { + edgeLabel + .attr('x', (d: any) => (d.source.x + d.target.x) / 2) + .attr('y', (d: any) => (d.source.y + d.target.y) / 2); + } + }); + + // ── Zoom / Pan ──────────────────────────────────────────────────────── + const zoom = d3.zoom() + .scaleExtent([0.03, 8]) + .on('zoom', (event) => g.attr('transform', event.transform)); + + svg.call(zoom); + // Click on SVG background resets highlighting and active node + svg.on('click', () => { + setActiveNode(null); + node.select('circle').style('opacity', 1).style('stroke-width', 2); + (linkEl as any).style('stroke-opacity', 0.55); + }); + + zoomRef.current = zoom; + + return () => { sim.stop(); }; + }, [data, options, highlightNodeIds]); + + return ( +
+ + + {/* ── Node details modal ────────────────────────────────────────────── */} + {activeNode && ( +
+
+
+

NODE DETAILS

+ + {activeNode.type || 'Unknown'} + +
+ +
+ +
+
+ Name: + {activeNode.label || '—'} +
+
+ UUID: + {activeNode.id} +
+ + {activeNode.properties && Object.keys(activeNode.properties).length > 0 && ( + <> +
+
PROPERTIES
+
+ {Object.entries(activeNode.properties).map(([k, v]) => ( +
+ {k}: + {String(v)} +
+ ))} +
+ + )} + + {activeNode.description && ( + <> +
+
DESCRIPTION / SUMMARY
+
+ {activeNode.description} +
+ + )} +
+
+ )} + + +
+ ); + } +); + +GraphCanvas.displayName = 'GraphCanvas'; +export default GraphCanvas; diff --git a/frontend-react/src/context/AuthContext.tsx b/frontend-react/src/context/AuthContext.tsx new file mode 100644 index 0000000000000000000000000000000000000000..9071d31872550cdad81d0d7800d27c37bf5e6c79 --- /dev/null +++ b/frontend-react/src/context/AuthContext.tsx @@ -0,0 +1,64 @@ +import React, { createContext, useContext, useState, useEffect } from 'react'; + +// This acts as our authentication context for the React application + +interface User { + username: string; + email?: string; + full_name?: string; + scopes: string[]; +} + +interface AuthContextType { + token: string | null; + user: User | null; + login: (token: string, user: User) => void; + logout: () => void; + isAuthenticated: boolean; +} + +const AuthContext = createContext(undefined); + +export const AuthProvider: React.FC<{ children: React.ReactNode }> = ({ children }) => { + const [token, setToken] = useState(localStorage.getItem('token')); + const [user, setUser] = useState(null); + + useEffect(() => { + const storedUser = localStorage.getItem('user'); + if (storedUser) { + try { + setUser(JSON.parse(storedUser)); + } catch (e) { + console.error('Failed to parse stored user', e); + } + } + }, []); + + const login = (newToken: string, newUser: User) => { + localStorage.setItem('token', newToken); + localStorage.setItem('user', JSON.stringify(newUser)); + setToken(newToken); + setUser(newUser); + }; + + const logout = () => { + localStorage.removeItem('token'); + localStorage.removeItem('user'); + setToken(null); + setUser(null); + }; + + return ( + + {children} + + ); +}; + +export const useAuth = () => { + const context = useContext(AuthContext); + if (context === undefined) { + throw new Error('useAuth must be used within an AuthProvider'); + } + return context; +}; diff --git a/frontend-react/src/index.css b/frontend-react/src/index.css new file mode 100644 index 0000000000000000000000000000000000000000..76a507ce7f82b0753729ef58b3f76e508eb22d77 --- /dev/null +++ b/frontend-react/src/index.css @@ -0,0 +1,494 @@ +@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500;700&family=Noto+Sans+SC:wght@400;500;700&family=Space+Grotesk:wght@300;400;500;600;700&display=swap'); +@import "tailwindcss"; + +/* ── Design Tokens ─────────────────────────────────────────────────────── */ +:root { + --bg-color: #ffffff; + --text-color: #000000; + --border-color: #000000; + --surface-color: #f5f5f5; /* subtle off-white for secondary surfaces */ + --hover-bg: #f0f0f0; + --muted-color: #666666; + --success-color: #16a34a; + --error-color: #dc2626; + --warning-color: #d97706; + + --font-sans: 'Inter', 'Noto Sans SC', sans-serif; + --font-display: 'Space Grotesk', 'Inter', sans-serif; + --font-mono: 'JetBrains Mono', monospace; + + --transition-speed: 0.18s; + --nav-height: 60px; +} + +/* ── Reset ─────────────────────────────────────────────────────────────── */ +*, *::before, *::after { + box-sizing: border-box; + margin: 0; + padding: 0; +} + +body { + font-family: var(--font-sans); + background-color: var(--bg-color); + color: var(--text-color); + line-height: 1.6; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + min-height: 100vh; +} + +h1, h2, h3, h4, h5, h6 { + font-family: var(--font-display); + font-weight: 600; + margin-bottom: 0.75rem; + line-height: 1.2; +} + +code, pre, .technical { font-family: var(--font-mono); } + +/* ── Links ─────────────────────────────────────────────────────────────── */ +a { + color: var(--text-color); + text-decoration: underline; + text-underline-offset: 3px; + transition: background var(--transition-speed), color var(--transition-speed); +} +a:hover { + background-color: var(--text-color); + color: var(--bg-color); +} + +/* ── Buttons ───────────────────────────────────────────────────────────── */ +button, .app-btn { + font-family: var(--font-display); + background-color: var(--bg-color); + color: var(--text-color); + border: 2px solid var(--border-color); + padding: 0.5rem 1rem; + font-weight: 600; + cursor: pointer; + transition: background var(--transition-speed) ease, color var(--transition-speed) ease; + font-size: 0.9rem; + text-transform: uppercase; + letter-spacing: 0.8px; + display: inline-flex; + align-items: center; + gap: 0.4rem; +} +button:hover, .app-btn:hover { + background-color: var(--text-color); + color: var(--bg-color); +} +button:disabled, .app-btn:disabled { + opacity: 0.45; + cursor: not-allowed; + pointer-events: none; +} + +/* Text-only button (no border, no background) */ +.text-btn { + background: none !important; + border: none !important; + color: var(--text-color); + text-decoration: underline; + text-underline-offset: 3px; + cursor: pointer; + font-family: var(--font-mono); + font-size: 0.8rem; + font-weight: 600; + transition: background var(--transition-speed), color var(--transition-speed); + padding: 0.2rem 0.4rem; +} +.text-btn:hover { + background: var(--text-color) !important; + color: var(--bg-color) !important; +} + +/* ── Toast dismiss button ──────────────────────────────────────────────── */ +/* Prevents global button:hover from flipping the close button inside toasts */ +.toast-dismiss-btn { + background: transparent !important; + border: none !important; + color: inherit !important; + cursor: pointer !important; + margin-left: auto !important; + padding: 0 0.3rem !important; + font-size: 1.2rem !important; + line-height: 1 !important; + opacity: 0.7; + flex-shrink: 0; + transition: opacity 0.12s ease !important; +} +.toast-dismiss-btn:hover { + background: transparent !important; + color: inherit !important; + opacity: 1 !important; +} + +/* ── Status toast ──────────────────────────────────────────────────────── */ +.status-toast { + position: fixed; + bottom: 2rem; + right: 2rem; + background: var(--text-color); + color: var(--bg-color); + padding: 0.9rem 1.2rem; + border-left: 5px solid #000; + box-shadow: 4px 4px 0 rgba(0,0,0,0.3); + display: flex; + align-items: center; + gap: 0.75rem; + z-index: 9999; + font-family: var(--font-mono); + font-weight: 700; + font-size: 0.85rem; + max-width: 420px; + animation: slideUpToast 0.28s ease-out both; + will-change: transform; +} +.status-toast.error { border-left-color: var(--error-color); } +.status-toast.success { border-left-color: var(--success-color); } + +/* ── Form Controls ─────────────────────────────────────────────────────── */ +input, textarea, select { + font-family: var(--font-sans); + background-color: var(--bg-color); + color: var(--text-color); + border: 2px solid var(--border-color); + padding: 0.5rem 0.75rem; + font-size: 0.95rem; + transition: box-shadow var(--transition-speed) ease; +} +input:focus, textarea:focus, select:focus { + outline: none; + box-shadow: 3px 3px 0 var(--border-color); +} + +/* ── Scrollbars ────────────────────────────────────────────────────────── */ +::-webkit-scrollbar { width: 8px; height: 8px; } +::-webkit-scrollbar-track { background: #f9f9f9; border-left: 1px solid #e5e5e5; } +::-webkit-scrollbar-thumb { background: #ccc; } +::-webkit-scrollbar-thumb:hover { background: #999; } + +/* ── Layout Utilities ──────────────────────────────────────────────────── */ +.container { + max-width: 1280px; + margin: 0 auto; + padding: 2rem; +} + +.card { + border: 2px solid var(--border-color); + padding: 1.5rem; + background-color: var(--bg-color); + transition: transform var(--transition-speed) ease, box-shadow var(--transition-speed) ease; +} +.card:hover { + transform: translateY(-2px); + box-shadow: 5px 5px 0 var(--border-color); +} + +.mono-text { font-family: var(--font-mono); font-size: 0.88em; } + +.flex-center { + display: flex; + align-items: center; + justify-content: center; +} + +.flex-between { + display: flex; + align-items: center; + justify-content: space-between; + flex-wrap: wrap; + gap: 1rem; +} + +.full-width { width: 100%; } + +.page-header { + border-bottom: 3px solid var(--border-color); + padding-bottom: 1rem; + margin-bottom: 2rem; +} + +/* ── Keyframes ─────────────────────────────────────────────────────────── */ +@keyframes fadeIn { + from { opacity: 0; transform: translateY(8px); } + to { opacity: 1; transform: translateY(0); } +} + +@keyframes slideUpToast { + from { transform: translateY(80px); opacity: 0; } + to { transform: translateY(0); opacity: 1; } +} + +@keyframes slideUp { + from { transform: translateY(20px); opacity: 0; } + to { transform: translateY(0); opacity: 1; } +} + +@keyframes spin { + 100% { transform: rotate(360deg); } +} + +.fade-in { animation: fadeIn 0.4s ease both; } + +/* ── Status indicators ─────────────────────────────────────────────────── */ +.status-dot { + display: inline-block; + width: 8px; + height: 8px; + border-radius: 50%; + flex-shrink: 0; +} +.status-dot.online { background: var(--success-color); } +.status-dot.offline { background: var(--error-color); } + +/* ── Responsive ────────────────────────────────────────────────────────── */ +@media (max-width: 768px) { + .container { padding: 1rem; } + .card { padding: 1rem; } +} + +/* ── Admin / shared aliases ─────────────────────────────────────────────── */ +/* These classes are referenced in AdminDashboard and other views. */ +/* They map onto the existing design system so everything is uniform. */ + +/* Base btn — same as global button but without uppercase transform */ +.btn { + font-family: var(--font-display); + background-color: var(--bg-color); + color: var(--text-color); + border: 2px solid var(--border-color); + padding: 0.5rem 1rem; + font-weight: 600; + cursor: pointer; + font-size: 0.85rem; + letter-spacing: 0.5px; + display: inline-flex; + align-items: center; + gap: 0.4rem; + transition: background var(--transition-speed) ease, color var(--transition-speed) ease; +} +.btn:hover:not(:disabled) { + background-color: var(--text-color); + color: var(--bg-color); +} +.btn:disabled { + opacity: 0.45; + cursor: not-allowed; + pointer-events: none; +} + +/* Primary button — filled black */ +.btn-primary { + background-color: var(--text-color); + color: var(--bg-color); + border: 2px solid var(--text-color); +} +.btn-primary:hover:not(:disabled) { + background-color: #333; + border-color: #333; +} + +/* Outline / ghost button */ +.btn-outline { + background: transparent; + color: var(--text-color); + border: 2px solid var(--border-color); +} +.btn-outline:hover:not(:disabled) { + background: var(--text-color); + color: var(--bg-color); +} + +/* Danger button */ +.btn-danger { + color: var(--error-color); + border-color: var(--error-color); +} +.btn-danger:hover:not(:disabled) { + background: var(--error-color); + color: #fff; +} + +/* Uniform search / select input (matches global input but explicit class) */ +.search-input { + font-family: var(--font-sans); + background-color: var(--bg-color); + color: var(--text-color); + border: 2px solid var(--border-color); + padding: 0.5rem 0.75rem; + font-size: 0.9rem; + transition: box-shadow var(--transition-speed) ease; + width: 100%; +} +.search-input:focus { + outline: none; + box-shadow: 3px 3px 0 var(--border-color); +} + +/* Status / metric cards */ +.status-card { + border: 2px solid var(--border-color); + padding: 1.25rem 1.5rem; + background: var(--bg-color); +} + +.metric-value { + font-family: var(--font-mono); + font-size: 2rem; + font-weight: 900; + line-height: 1; + margin-top: 0.25rem; +} + +.status-label { + font-family: var(--font-mono); + font-size: 0.68rem; + font-weight: 700; + color: var(--muted-color); + letter-spacing: 1px; + text-transform: uppercase; +} + +/* Online / offline indicator dots */ +.indicator { + width: 10px; + height: 10px; + border-radius: 50%; + flex-shrink: 0; +} +.indicator.online { background: var(--success-color); } +.indicator.offline { background: var(--error-color); } +.indicator.pending { background: var(--warning-color); } + +/* Typography */ +.title-lg { + font-family: var(--font-display); + font-size: 1.8rem; + font-weight: 700; + line-height: 1.1; +} +.title-md { + font-family: var(--font-display); + font-size: 1.15rem; + font-weight: 700; +} +.title-sm { + font-family: var(--font-mono); + font-size: 0.72rem; + font-weight: 700; + letter-spacing: 1px; + text-transform: uppercase; + color: var(--muted-color); +} + +/* Data table */ +.data-table { + width: 100%; + border-collapse: collapse; + font-size: 0.9rem; +} +.data-table thead tr { + background: var(--surface-color); + border-bottom: 2px solid var(--border-color); +} +.data-table th, +.data-table td { + padding: 0.75rem 1rem; + text-align: left; + border-bottom: 1px solid #eaeaea; +} +.data-table th { + font-family: var(--font-mono); + font-size: 0.72rem; + font-weight: 700; + letter-spacing: 0.8px; + text-transform: uppercase; + color: var(--muted-color); +} +.data-table tbody tr:hover { + background: var(--surface-color); +} +.data-table tbody tr:last-child td { + border-bottom: none; +} + +/* Tag / badge chips */ +.chip { + display: inline-flex; + align-items: center; + gap: 4px; + border: 1.5px solid var(--border-color); + padding: 2px 8px; + font-family: var(--font-mono); + font-size: 0.72rem; + font-weight: 700; +} +.chip.success { border-color: var(--success-color); color: var(--success-color); } +.chip.error { border-color: var(--error-color); color: var(--error-color); } +.chip.warning { border-color: var(--warning-color); color: var(--warning-color); } +.chip.filled { background: var(--text-color); color: var(--bg-color); } + +/* Empty state */ +.empty-state { + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; + padding: 4rem 2rem; + color: var(--muted-color); + gap: 0.75rem; + border: 2px dashed var(--border-color); + font-family: var(--font-mono); + font-size: 0.85rem; + letter-spacing: 0.5px; + text-align: center; +} + +/* Help tooltip */ +.help-tooltip { + position: relative; + display: inline-flex; + align-items: center; +} +.help-tooltip [data-tip] { + cursor: help; + color: var(--muted-color); +} +.help-tooltip [data-tip]:hover::after { + content: attr(data-tip); + position: absolute; + bottom: 125%; + left: 50%; + transform: translateX(-50%); + background: var(--text-color); + color: var(--bg-color); + padding: 0.4rem 0.75rem; + font-family: var(--font-mono); + font-size: 0.75rem; + white-space: nowrap; + z-index: 9999; + pointer-events: none; + max-width: 260px; + white-space: normal; + text-align: center; +} + +/* Page info bar */ +.page-info-bar { + background: var(--surface-color); + border-left: 4px solid var(--border-color); + padding: 0.75rem 1rem; + font-size: 0.82rem; + line-height: 1.6; + margin-bottom: 1.5rem; + display: flex; + align-items: flex-start; + gap: 0.75rem; + color: var(--muted-color); +} +.page-info-bar strong { color: var(--text-color); font-family: var(--font-mono); } diff --git a/frontend-react/src/main.tsx b/frontend-react/src/main.tsx new file mode 100644 index 0000000000000000000000000000000000000000..bef5202a32cbd0632c43de40f6e908532903fd42 --- /dev/null +++ b/frontend-react/src/main.tsx @@ -0,0 +1,10 @@ +import { StrictMode } from 'react' +import { createRoot } from 'react-dom/client' +import './index.css' +import App from './App.tsx' + +createRoot(document.getElementById('root')!).render( + + + , +) diff --git a/frontend-react/src/types/api.ts b/frontend-react/src/types/api.ts new file mode 100644 index 0000000000000000000000000000000000000000..f4866a0c138be56dd392aa93f67743e17843587d --- /dev/null +++ b/frontend-react/src/types/api.ts @@ -0,0 +1,183 @@ +export interface LoginRequest { + username: string; + password: string; +} + +export interface RegisterRequest { + username: string; + password: string; + email?: string; + full_name?: string; + scopes?: string[]; + tenant_id?: string; +} + +export interface TokenResponse { + access_token: string; + token_type: string; +} + +export interface DocumentUploadResponse { + document_id: string; + filename: string; + size_bytes: number; + task_id?: string; + message: string; +} + +export interface ScrapeRequest { + url: string; +} + +export interface CrawlRequest { + url: string; + max_depth?: number; + max_pages?: number; +} + +export interface IngestionStatusResponse { + task_id: string; + status: string; + progress?: Record; + result?: Record; +} + +export interface DocumentInfo { + id: string; + filename: string; + file_type: string; + size_bytes: number; + upload_date: string; +} + +export interface DocumentListResponse { + documents: DocumentInfo[]; + total: number; +} + +export interface QueryRequest { + query: string; + top_k?: number; + streaming?: boolean; + document_id?: string; + conversation_id?: string; + use_got?: boolean; + at_time?: string; +} + +export interface ConfidenceJudgmentResponse { + score: number; + reasoning: string; + grounded_claims: number; + ungrounded_claims: number; + hallucination_risk: 'low' | 'medium' | 'high'; +} + +export interface QueryResponse { + answer: string; + sources: Array>; + reasoning_chain: string[]; + confidence: number; + confidence_judgment?: ConfidenceJudgmentResponse; + retrieval_method: string; + processing_time_seconds: number; + conversation_id?: string; + drift_expanded?: boolean; + total_sub_queries?: number; +} + +export interface EvalResultData { + overall_score?: number; + faithfulness: number; + answer_relevancy?: number; + relevancy?: number; + context_precision?: number; + precision?: number; +} + +export interface Message { + id?: string; + role: string; + content: string; + reasoning?: string[]; + sources?: Array>; + confidence?: number | null; + hallucination_risk?: string; + confidence_reasoning?: string; + created_at?: string; + eval_result?: EvalResultData; + evaluating?: boolean; + drift_expanded?: boolean; +} + +export interface Conversation { + id: string; + title: string; + created_at: string; + updated_at: string; + messages?: Message[]; +} + +export interface ConversationListResponse { + conversations: Conversation[]; +} + +export interface OntologyResponse { + version: string; + entity_types: string[]; + relationship_types: string[]; + properties: Record; + created_at: string; + approved: boolean; +} + +export interface OntologyUpdateRequest { + entity_types?: string[]; + relationship_types?: string[]; + properties?: Record; + approved?: boolean; +} + +export interface GraphNode { + id: string; + label: string; + type: string; + description?: string; + properties: Record; + community_id?: number; + valid_from?: string; + valid_until?: string; +} + +export interface GraphEdge { + source: string; + target: string; + type: string; + properties: Record; + valid_from?: string; + confidence?: number; +} + +export interface GraphVisualizationResponse { + nodes: GraphNode[]; + edges: GraphEdge[]; +} + +export interface SystemHealthResponse { + status: string; + version: string; + neo4j_connected: boolean; + redis_connected: boolean; + workers_active: number; + timestamp: string; +} + +export interface SystemStatsResponse { + documents_count: number; + entities_count: number; + relationships_count: number; + chunks_count: number; + ontology_version: string; +} + +export interface DriftReport { id: string; detected_at: string; new_entity_types: string[]; new_relationship_types: string[]; removed_entity_types: string[]; removed_relationship_types: string[]; sample_size: number; drift_score: number; status: string; approved_by?: string; approved_at?: string; } diff --git a/frontend-react/src/views/AdminDashboard.tsx b/frontend-react/src/views/AdminDashboard.tsx new file mode 100644 index 0000000000000000000000000000000000000000..a08ea36bb7b2fa807029d93e126a41101f14ce32 --- /dev/null +++ b/frontend-react/src/views/AdminDashboard.tsx @@ -0,0 +1,762 @@ +import React, { useState, useEffect, useCallback } from 'react'; +import { useAuth } from '../context/AuthContext'; +import { + BarChart2, Cpu, Users, Database, Settings, + Trash2, Check, X, Play, RefreshCw, Shield, + AlertTriangle, Zap, GitBranch, Info +} from 'lucide-react'; + +const API_BASE = import.meta.env.VITE_API_BASE_URL || 'http://localhost:8000/api'; + +// ─── Helpers ────────────────────────────────────────────────────────────────── +const Spinner = () => ( + +); + +// ─── Tab Components ────────────────────────────────────────────────────────── + +const OverviewTab = ({ stats, health, onRefresh }: { stats: Partial; health: import('../types/api').SystemHealthResponse | any; onRefresh: () => void }) => ( +
+ {/* KPI Grid */} +
+ {[ + { label:'Graph Nodes', value: stats?.graph?.nodes ?? stats?.total_nodes ?? '—', icon: }, + { label:'Relationships', value: stats?.graph?.relationships ?? stats?.total_relationships ?? '—', icon: }, + { label:'Documents', value: stats?.documents?.total ?? stats?.total_documents ?? '—', icon: }, + { label:'Est. LLM Cost', value: `$${(stats?.costs?.total_estimated_usd ?? 0).toFixed(4)}`, icon: }, + ].map(c => ( +
+
+
{c.label}
+ {c.icon} +
+
{c.value}
+
+ ))} +
+ + {/* System health */} +
+
+

System Health

+ +
+ {health ? ( +
+ {Object.entries(health).map(([k, v]: [string, any]) => { + const isOk = v === true || v === 'ok' || v === 'connected' || v === 'healthy'; + const isErr = v === false || v === 'error' || v === 'disconnected'; + return ( +
+ +
+
{k.toUpperCase()}
+
+ {typeof v === 'object' ? JSON.stringify(v) : String(v)} +
+
+
+ ); + })} +
+ ) : ( +
+ Loading health data… +
+ )} +
+ + {/* Provider info */} + {stats?.system && ( +
+

LLM Provider

+
+ {Object.entries(stats.system).map(([k, v]: any) => ( +
{k}: {String(v)}
+ ))} +
+
+ )} +
+); + +const UsersTab = ({ token }: { token: string | null }) => { + const [users, setUsers] = useState([]); + const [loading, setLoading] = useState(true); + const [msg, setMsg] = useState(''); + + const fetchUsers = useCallback(async () => { + setLoading(true); + try { + const res = await fetch(`${API_BASE}/admin/users`, { headers: { Authorization: `Bearer ${token}` } }); + if (res.ok) { + const json = await res.json(); + setUsers(json.users || []); + } + } finally { setLoading(false); } + }, [token]); + + useEffect(() => { fetchUsers(); }, [fetchUsers]); + + const updateRole = async (username: string, scopes: string) => { + const res = await fetch(`${API_BASE}/admin/users/${username}/role`, { + method: 'PUT', + headers: { Authorization: `Bearer ${token}`, 'Content-Type': 'application/json' }, + body: JSON.stringify({ scopes: [scopes] }) + }); + if (res.ok) { + setUsers(u => u.map(usr => usr.username === username ? { ...usr, scopes: [scopes] } : usr)); + setMsg(`Role updated for ${username}`); + setTimeout(() => setMsg(''), 3000); + } else { + setMsg(`Failed to update role for ${username}`); + setTimeout(() => setMsg(''), 3000); + } + }; + + return ( +
+
+

Registered Users

+ +
+ {msg &&
{msg}
} + {loading ? ( +
+ ) : ( +
+ + + + {users.map(u => { + const isAdminUser = u.username === 'admin'; + const currentScope = u.scopes?.includes('admin') ? 'admin' : (u.scopes?.includes('write') ? 'write' : 'read'); + return ( + + + + + + ); + })} + {users.length === 0 && ( + + )} + +
UsernameScopeChange Role
+ {u.username} + {isAdminUser && PROTECTED} + {u.scopes?.join(', ') || 'none'} + {isAdminUser ? ( + — + ) : ( + + )} +
No users found.
+
+ )} +
+ ); +}; + +const DocumentsTab = ({ token }: { token: string | null }) => { + const [docs, setDocs] = useState([]); + const [loading, setLoading] = useState(true); + const [msg, setMsg] = useState(''); + + const fetchDocs = useCallback(async () => { + setLoading(true); + try { + const res = await fetch(`${API_BASE}/admin/documents`, { headers: { Authorization: `Bearer ${token}` } }); + if (res.ok) { + const json = await res.json(); + setDocs(json.documents || []); + } + } finally { setLoading(false); } + }, [token]); + + useEffect(() => { fetchDocs(); }, [fetchDocs]); + + const deleteDoc = async (id: string, filename: string) => { + if (!window.confirm(`Delete "${filename}" and all its graph data? This cannot be undone.`)) return; + const res = await fetch(`${API_BASE}/admin/documents/${id}`, { method: 'DELETE', headers: { Authorization: `Bearer ${token}` } }); + if (res.ok) { + setDocs(d => d.filter(doc => doc.id !== id)); + setMsg('Document deleted.'); + setTimeout(() => setMsg(''), 3000); + } + }; + + const reIngestDoc = async (id: string, filename: string) => { + setMsg(`Re-ingesting "${filename}"...`); + try { + const res = await fetch(`${API_BASE}/admin/documents/${id}/reingest`, { + method: 'POST', + headers: { Authorization: `Bearer ${token}` } + }); + if (res.ok) { + const data = await res.json(); + setMsg(`Re-ingestion queued for "${filename}". Task: ${data.task_id?.slice(0,8)}…`); + setTimeout(() => { setMsg(''); fetchDocs(); }, 5000); + } else { + setMsg(`Failed to re-ingest "${filename}"`); + setTimeout(() => setMsg(''), 3000); + } + } catch { + setMsg('Network error during re-ingest.'); + setTimeout(() => setMsg(''), 3000); + } + }; + + return ( +
+
+

Document Vault

+ {docs.length} documents +
+ {msg &&
{msg}
} + {loading ? ( +
+ ) : ( +
+ + + + {docs.map(d => ( + + + + + + + ))} + {docs.length === 0 && ( + + )} + +
IDFilenameStatusActions
{d.id?.substring(0,12)}…{d.filename} + + {d.status || 'unknown'} + + + {(d.status === 'failed' || d.status === 'pending') && ( + + )} + +
No documents uploaded yet.
+
+ )} +
+ ); +}; + +const GraphCRUDTab = ({ token }: { token: string | null }) => { + const [nodes, setNodes] = useState([]); + const [query, setQuery] = useState(''); + const [loading, setLoading] = useState(false); + const [msg, setMsg] = useState(''); + + const search = async (e?: React.FormEvent) => { + e?.preventDefault(); + setLoading(true); + try { + const res = await fetch(`${API_BASE}/admin/graph/nodes?query=${encodeURIComponent(query)}&limit=100`, { + headers: { Authorization: `Bearer ${token}` } + }); + if (res.ok) setNodes((await res.json()).nodes || []); + } finally { setLoading(false); } + }; + + const deleteNode = async (id: string) => { + if (!window.confirm('Detach and delete this node?')) return; + const res = await fetch(`${API_BASE}/admin/graph/nodes/${id}`, { + method: 'DELETE', headers: { Authorization: `Bearer ${token}` } + }); + if (res.ok) { + setNodes(n => n.filter(nd => nd.id !== id)); + setMsg('Node deleted.'); + setTimeout(() => setMsg(''), 3000); + } + }; + + return ( +
+

Graph Node Browser

+
+ + Search and inspect nodes directly in Neo4j. Use label names or property values. DELETE detaches all relationships before removing the node. +
+ {msg &&
{msg}
} +
+ setQuery(e.target.value)} + placeholder="Search node labels or properties…" className="search-input flex-1"/> + +
+
+ + + + {nodes.map((n, i) => ( + + + + + + + ))} + {nodes.length === 0 && ( + + )} + +
IDLabelsPropertiesAction
{n.id}{n.labels?.join(', ')} + {JSON.stringify(n.properties)} + + +
+ {loading ? 'Searching…' : 'Enter a search term above to browse nodes.'} +
+
+
+ ); +}; + +const OntologyGovernanceTab = ({ token }: { token: string | null }) => { + const [proposals, setProposals] = useState([]); + const [driftReports, setDriftReports] = useState([]); + const [loading, setLoading] = useState(true); + const [detectLoading, setDetectLoading] = useState(false); + const [msg, setMsg] = useState(''); + + const fetchData = useCallback(async () => { + setLoading(true); + try { + const [propRes, driftRes] = await Promise.all([ + fetch(`${API_BASE}/admin/ontology/pending`, { headers: { Authorization: `Bearer ${token}` } }), + fetch(`${API_BASE}/ontology/drift`, { headers: { Authorization: `Bearer ${token}` } }), + ]); + if (propRes.ok) setProposals((await propRes.json()).proposals || []); + if (driftRes.ok) setDriftReports((await driftRes.json()).reports || []); + } finally { setLoading(false); } + }, [token]); + + useEffect(() => { fetchData(); }, [fetchData]); + + const handleProposal = async (id: string, action: 'approve' | 'reject') => { + const res = await fetch(`${API_BASE}/admin/ontology/${action}/${id}`, { + method: 'POST', headers: { Authorization: `Bearer ${token}` } + }); + if (res.ok) { + setProposals(p => p.filter(o => o.id !== id)); + setMsg(`Proposal ${action}d.`); + setTimeout(() => setMsg(''), 3000); + } + }; + + const handleDrift = async (id: string, action: 'approve' | 'reject') => { + const res = await fetch(`${API_BASE}/ontology/drift/${id}/${action}`, { + method: 'POST', headers: { Authorization: `Bearer ${token}` } + }); + if (res.ok) { + setDriftReports(d => d.filter(r => r.id !== id)); + setMsg(`Drift report ${action}d.`); + setTimeout(() => setMsg(''), 3000); + } + }; + + const detectDrift = async () => { + setDetectLoading(true); + try { + const res = await fetch(`${API_BASE}/ontology/drift/detect`, { + method: 'POST', headers: { Authorization: `Bearer ${token}` } + }); + if (res.ok) { + setMsg('Drift detection complete. Refreshing…'); + await fetchData(); + } + } finally { + setDetectLoading(false); + setTimeout(() => setMsg(''), 4000); + } + }; + + return ( +
+ {msg &&
{msg}
} + + {/* Drift detection */} +
+
+

Ontology Drift Reports

+ +
+

+ Drift detection samples recent data and suggests additions or changes to the graph schema. + Review and approve or reject proposals below. +

+ {loading ?
: ( +
+ {driftReports.map(r => ( +
+
+
+ {r.status || 'pending'} + {r.new_entity_types?.length || 0} new types +
+

+ {r.summary || 'Drift report — review suggested schema changes.'} +

+
+
+ + +
+
+ ))} + {driftReports.length === 0 && ( +
No pending drift reports. Run drift detection above.
+ )} +
+ )} +
+ + {/* Manual proposals */} +
+

Manual Schema Proposals

+
+ {proposals.map(o => ( +
+
+ {o.type} + {o.name} +
+
+ + +
+
+ ))} + {proposals.length === 0 && ( +
No pending manual proposals.
+ )} +
+
+
+ ); +}; + +const WorkersTab = ({ token }: { token: string | null }) => { + const [tasks, setTasks] = useState(null); + const [health, setHealth] = useState(null); + const [loading, setLoading] = useState(true); + + const fetchAll = useCallback(async () => { + setLoading(true); + try { + const [taskRes, healthRes] = await Promise.all([ + fetch(`${API_BASE}/admin/tasks`, { headers: { Authorization: `Bearer ${token}` } }), + fetch(`${API_BASE}/system/health`, { headers: { Authorization: `Bearer ${token}` } }), + ]); + if (taskRes.ok) setTasks(await taskRes.json()); + if (healthRes.ok) setHealth(await healthRes.json()); + } finally { setLoading(false); } + }, [token]); + + useEffect(() => { fetchAll(); }, [fetchAll]); + + return ( +
+
+
+

Celery Worker Status

+ +
+ {loading ?
: ( +
+ {[ + { label:'Active Tasks', value: tasks?.active_tasks ?? tasks?.active ?? 0 }, + { label:'Queued Tasks', value: tasks?.queued_tasks ?? tasks?.reserved ?? 0 }, + { label:'Completed', value: tasks?.completed_tasks ?? tasks?.total ?? 0 }, + { label:'Failed', value: tasks?.failed_tasks ?? 0 }, + ].map(m => ( +
+
{m.label}
+
{m.value}
+
+ ))} +
+ )} +
+ {health && ( +
+

Service Health

+
+ {Object.entries(health).map(([k, v]: any) => { + const ok = v === true || v === 'ok' || v === 'connected' || v === 'healthy'; + return ( +
+ +
+
{k}
+
{String(v)}
+
+
+ ); + })} +
+
+ )} +
+ ); +}; + +const EnrichmentTab = ({ token }: { token: string | null }) => { + const [loading, setLoading] = useState(false); + const [result, setResult] = useState(null); + const [batchSize, setBatchSize] = useState(20); + const [minConnections, setMinConnections] = useState(1); + const [driftLoading, setDriftLoading] = useState(false); + const [driftResult, setDriftResult] = useState(null); + + const runEnrichment = async () => { + setLoading(true); setResult(null); + try { + const res = await fetch(`${API_BASE}/entities/enrich`, { + method:'POST', + headers:{ Authorization:`Bearer ${token}`, 'Content-Type':'application/json' }, + body: JSON.stringify({ batch_size: batchSize, min_connections: minConnections }) + }); + if (res.ok) setResult(await res.json()); + } finally { setLoading(false); } + }; + + const runDrift = async () => { + setDriftLoading(true); setDriftResult(null); + try { + const res = await fetch(`${API_BASE}/ontology/drift/detect`, { + method:'POST', headers:{ Authorization:`Bearer ${token}` } + }); + if (res.ok) setDriftResult(await res.json()); + } finally { setDriftLoading(false); } + }; + + return ( +
+ {/* Entity Enrichment */} +
+

Entity Enrichment

+

+ Synthesize rich LLM-generated profiles for all eligible entities by scanning their neighborhood context in the graph. +

+
+
+ + setBatchSize(Number(e.target.value))} + className="search-input w-full"/> +
+
+ + setMinConnections(Number(e.target.value))} + className="search-input w-full"/> +
+
+ + {result && ( +
+ ✓ {result.message || `Enriched ${result.enriched_count ?? '?'} entities`} +
+ )} +
+ + {/* Drift Detection */} +
+

Ontology Drift Detection

+

+ Analyse recent data samples to detect schema evolution and generate a drift report for admin review. +

+ + {driftResult && ( +
+ ✓ Drift report created. ID: {driftResult.report_id || driftResult.id || '—'} → Review in Ontology Governance tab. +
+ )} +
+
+ ); +}; + +const SandboxTab = ({ token }: { token: string | null }) => { + const [loading, setLoading] = useState(false); + const [msg, setMsg] = useState(''); + + const trigger = async (endpoint: string, label: string) => { + setLoading(true); setMsg(''); + try { + const res = await fetch(`${API_BASE}${endpoint}`, { + method:'POST', headers:{ Authorization:`Bearer ${token}` } + }); + setMsg(res.ok ? `✓ ${label} dispatched to Celery worker.` : `✗ Failed to trigger ${label}.`); + } catch { + setMsg(`✗ Network error.`); + } finally { setLoading(false); } + }; + + return ( +
+

MiroFish God-Mode Sandbox

+

+ Control the simulation loops that connect Knowledge Graph entities into living agents. +

+ {msg && ( +
+ {msg} +
+ )} +
+ {[ + { endpoint:'/v1/simulation/generate_personas', label:'Generate Agent Personas', icon:, + desc:'Converts raw graph nodes into living psychological profiles for agent simulation.' }, + { endpoint:'/v1/simulation/tick', label:'Force Simulation Tick', icon:, + desc:'Forces agents to read their local graph memory and output a new interaction edge.' }, + ].map(item => ( +
+ +

{item.desc}

+
+ ))} +
+
+ ); +}; + +// ─── Main Dashboard ─────────────────────────────────────────────────────────── +export default function AdminDashboard() { + const { token, user } = useAuth(); + const [activeTab, setActiveTab] = useState('overview'); + const [stats, setStats] = useState(null); + const [health, setHealth] = useState(null); + const [error, setError] = useState(null); + + const fetchOverview = useCallback(async () => { + if (!token) return; + try { + const [statsRes, healthRes] = await Promise.all([ + fetch(`${API_BASE}/admin/stats`, { headers: { Authorization: `Bearer ${token}` } }), + fetch(`${API_BASE}/system/health`, { headers: { Authorization: `Bearer ${token}` } }), + ]); + if (statsRes.ok) setStats(await statsRes.json()); + if (healthRes.ok) setHealth(await healthRes.json()); + } catch (err: any) { + setError(err.message); + } + }, [token]); + + useEffect(() => { fetchOverview(); }, [fetchOverview]); + + if (user && user.username !== 'admin' && !user.scopes?.includes('admin')) { + return ( +
+ +

Access Denied

+

You need administrative privileges to view this page.

+
+ ); + } + + const TABS = [ + { id:'overview', label:'Overview', icon: }, + { id:'users', label:'Users', icon: }, + { id:'documents', label:'Documents', icon: }, + { id:'graph', label:'Graph CRUD', icon: }, + { id:'ontology', label:'Ontology', icon: }, + { id:'workers', label:'Workers', icon: }, + { id:'enrichment', label:'Enrichment / Drift', icon: }, + { id:'sandbox', label:'God-Mode Sandbox', icon: }, + ]; + + return ( +
+ {/* Header */} +
+
+

Admin Control Center

+

Manage graph data, workers, users, and platform configuration

+
+ {error && ( +
+ {error} +
+ )} +
+ +
+ {/* Sidebar nav */} +
+ +
+ + {/* Main content */} +
+ {activeTab === 'overview' && } + {activeTab === 'users' && } + {activeTab === 'documents' && } + {activeTab === 'graph' && } + {activeTab === 'ontology' && } + {activeTab === 'workers' && } + {activeTab === 'enrichment' && } + {activeTab === 'sandbox' && } +
+
+ + +
+ ); +} diff --git a/frontend-react/src/views/Home.tsx b/frontend-react/src/views/Home.tsx new file mode 100644 index 0000000000000000000000000000000000000000..e072bdae30227ec69ca323e991a12ac1b9b00d38 --- /dev/null +++ b/frontend-react/src/views/Home.tsx @@ -0,0 +1,692 @@ +import React, { useEffect, useState, useRef } from 'react'; +import { useAuth } from '../context/AuthContext'; +import { Network, Server, Cpu, Database, Activity, ArrowRight, Zap, GitBranch, MessageSquare, TrendingUp, RefreshCw } from 'lucide-react'; + +const API_BASE = import.meta.env.VITE_API_BASE_URL || 'http://localhost:8000/api'; + +// Animated counter hook +function useCountUp(target: number, duration = 1200) { + const [val, setVal] = useState(0); + const prev = useRef(0); + useEffect(() => { + if (target === 0) { setVal(0); return; } + const start = prev.current; + const diff = target - start; + const startTime = performance.now(); + const tick = (now: number) => { + const t = Math.min((now - startTime) / duration, 1); + const ease = 1 - Math.pow(1 - t, 3); + setVal(Math.round(start + diff * ease)); + if (t < 1) requestAnimationFrame(tick); + else prev.current = target; + }; + requestAnimationFrame(tick); + }, [target]); + return val; +} + +const StatCounter: React.FC<{ value: number | string; label: string; suffix?: string }> = ({ value, label, suffix = '' }) => { + const numVal = typeof value === 'number' ? value : parseInt(String(value)) || 0; + const animated = useCountUp(numVal); + return ( +
+
{typeof value === 'number' ? animated : value}{suffix}
+
{label}
+
+ ); +}; + +const Home: React.FC = () => { + const { token, logout, user } = useAuth(); + const [health, setHealth] = useState(null); + const [stats, setStats] = useState(null); + const [myStats, setMyStats] = useState(null); + const [loading, setLoading] = useState(true); + const [lastRefresh, setLastRefresh] = useState(new Date()); + + const fetchData = async () => { + setLoading(true); + try { + const [healthRes, statsRes, myStatsRes] = await Promise.all([ + fetch(`${API_BASE}/system/health`), + fetch(`${API_BASE}/system/stats`, { headers: { Authorization: `Bearer ${token}` } }), + fetch(`${API_BASE}/system/my-stats`, { headers: { Authorization: `Bearer ${token}` } }).catch(() => null), + ]); + if (statsRes.status === 401) { logout(); return; } + if (healthRes.ok) setHealth(await healthRes.json()); + if (statsRes.ok) setStats(await statsRes.json()); + if (myStatsRes?.ok) setMyStats(await myStatsRes.json()); + } catch (err) { + console.error('Failed to fetch system data', err); + } finally { + setLoading(false); + setLastRefresh(new Date()); + } + }; + + useEffect(() => { + fetchData(); + const interval = setInterval(fetchData, 30000); + return () => clearInterval(interval); + }, [token]); + + const isOnline = (v: boolean | undefined) => v === true; + + const systemOk = health ? (isOnline(health.neo4j_connected) && isOnline(health.redis_connected)) : false; + + return ( +
+ + {/* ── Hero ─────────────────────────────────────── */} +
+
+
CORTEX PLATFORM
+

+ Agentic Knowledge
+ Intelligence +

+

+ Production-grade knowledge graph · Real-time extraction · Multi-hop reasoning +

+ +
+ +
+
+ + + {loading ? 'CHECKING...' : systemOk ? 'SYSTEM OPERATIONAL' : 'SYSTEM DEGRADED'} + + +
+ +
+ {[ + { label: 'NEO4J', ok: isOnline(health?.neo4j_connected) }, + { label: 'REDIS', ok: isOnline(health?.redis_connected) }, + { label: `${health?.workers_active ?? 0} WORKERS`, ok: true, neutral: true }, + { label: 'API', ok: true }, + ].map(s => ( +
+ + {s.label} + + {s.neutral ? 'ACTIVE' : s.ok ? 'CONNECTED' : 'OFFLINE'} + +
+ ))} +
+
+
+ + {/* ── Platform Metrics ─────────────────────────── */} +
+
PLATFORM METRICS
+
+ +
+ +
+ +
+ +
+
+
+ {stats?.ontology_version ?? '—'} +
+
ONTOLOGY VER
+
+
+
+ + {/* ── Main Grid ────────────────────────────────── */} +
+ + {/* Quick Actions */} +
+
+ QUICK ACTIONS +
+
+ {[ + { href: '/process', icon: , label: 'INGEST DOCUMENTS', desc: 'Upload PDFs, text, or crawl URLs' }, + { href: '/interact', icon: , label: 'QUERY KNOWLEDGE', desc: 'Ask questions across the graph' }, + { href: '/simulate', icon: , label: 'EXPLORE NODES', desc: 'Interactive D3 force visualization' }, + { href: '/ontology', icon: , label: 'MANAGE ONTOLOGY', desc: 'Edit schema & run AI refinement' }, + { href: '/insights', icon: ,label: 'INSIGHTS', desc: 'Quality metrics & AI reports' }, + ].map(a => ( + + {a.icon} +
+
{a.label}
+
{a.desc}
+
+ + + ))} +
+
+ + {/* Right column: My Activity + Feature cards */} +
+ + {/* User Activity */} +
+
+ MY ACTIVITY + {user && @{user.username}} +
+
+
+
{myStats?.conversation_count ?? '—'}
+
CONVERSATIONS
+
+
+
{myStats?.message_count ?? '—'}
+
QUERIES SENT
+
+
+
{myStats?.last_active ? new Date(myStats.last_active).toLocaleDateString() : '—'}
+
LAST ACTIVE
+
+
+ {!myStats && ( +
+ Start querying the graph to build your activity history. +
+ )} +
+ + {/* Graph intelligence card */} +
+
+ KNOWLEDGE GRAPH +
+
+ Neo4j-powered semantic knowledge graph. Multi-hop reasoning, entity enrichment, and community detection built in. +
+
+ {['Entities', 'Relationships', 'Communities', 'Graph Export'].map(tag => ( + {tag} + ))} +
+
+
+
+ + {/* ── Feature Showcase ─────────────────────────── */} +
+
PLATFORM CAPABILITIES
+
+ {[ + { + icon: , + title: 'DOCUMENT INGESTION', + desc: 'Ingest PDFs, text files, Markdown, and web URLs. Celery workers extract entities and relationships into the knowledge graph automatically via LLM pipelines.', + color: '#2563eb', + }, + { + icon: , + title: 'GRAPH INTELLIGENCE', + desc: 'Neo4j-powered knowledge graph with rich entity relationships. Query across documents globally or per-source with full ontology control.', + color: '#7c3aed', + }, + { + icon: , + title: 'AGENTIC LOGIC', + desc: 'Multi-step ReACT reasoning agent that searches the graph, retrieves relevant chunks, and streams answers with confidence scoring in real time.', + color: '#059669', + }, + { + icon: , + title: 'LLM-AS-JUDGE', + desc: 'Inline faithfulness evaluation using heuristic scoring. Detects hallucination risk, context precision, and answer quality on every response.', + color: '#d97706', + }, + { + icon: , + title: 'LIVE SIMULATION', + desc: 'Interactive D3 force graph with color-coded entity types, physics controls, fullscreen mode, PNG export, and node detail modals.', + color: '#dc2626', + }, + { + icon: , + title: 'ONTOLOGY DRIFT', + desc: 'Automated schema drift detection that spots when new data no longer fits the current ontology. Propose and approve schema expansions.', + color: '#0891b2', + }, + ].map(f => ( +
+
{f.icon}
+
{f.title}
+
{f.desc}
+
+ ))} +
+
+ + {/* Footer bar */} +
+ CORTEX_PLATFORM + + Last refreshed: {lastRefresh.toLocaleTimeString()} + + + v{health ? '1.0' : '—'} · Neo4j + Redis + Celery + +
+ + +
+ ); +}; + +export default Home; diff --git a/frontend-react/src/views/InsightsView.tsx b/frontend-react/src/views/InsightsView.tsx new file mode 100644 index 0000000000000000000000000000000000000000..bdbdebf952a41640f1d61c0fa7eefca15d6e96cd --- /dev/null +++ b/frontend-react/src/views/InsightsView.tsx @@ -0,0 +1,890 @@ +import React, { useState, useEffect, useCallback } from 'react'; +import { useAuth } from '../context/AuthContext'; +import { BarChart2, TrendingUp, AlertTriangle, CheckCircle, RefreshCw, Zap, FileText, GitCommit, MessageSquare } from 'lucide-react'; + +const API_BASE = import.meta.env.VITE_API_BASE_URL || 'http://localhost:8000/api'; + +interface EvalDashboard { + total_evaluations: number; + avg_overall_score: number; + avg_faithfulness: number; + avg_relevancy: number; + hallucination_rate: number; + trend_data: TrendPoint[]; +} + +interface TrendPoint { + timestamp: string; + overall_score: number; + faithfulness: number; + answer_relevancy: number; + hallucination_detected: boolean; + document_id?: string; +} + +interface EvalForm { + question: string; + answer: string; + contexts: string; +} + +// Helper: build markdown from report result +function buildReportMarkdown(result: any, topic: string): string { + const lines: string[] = []; + lines.push(`# ${result.topic || topic}`); + lines.push(''); + if (result.confidence !== undefined) { + lines.push(`**Confidence:** ${(result.confidence * 100).toFixed(1)}%`); + } + if (result.tool_calls_made !== undefined) { + lines.push(`**Tool calls:** ${result.tool_calls_made}`); + } + lines.push(`**Generated:** ${new Date().toLocaleString()}`); + lines.push(''); + + if (result.executive_summary) { + lines.push('## Executive Summary'); + lines.push(''); + lines.push(result.executive_summary); + lines.push(''); + } + + if (result.sections && typeof result.sections === 'object' && !Array.isArray(result.sections)) { + Object.entries(result.sections).forEach(([title, content], i) => { + lines.push(`## ${i + 1}. ${title}`); + lines.push(''); + lines.push(String(content)); + lines.push(''); + }); + } else if (Array.isArray(result.sections)) { + result.sections.forEach((s: any, i: number) => { + lines.push(`## ${i + 1}. ${s.title || ''}`); + lines.push(''); + lines.push(s.content || ''); + lines.push(''); + }); + } else if (!result.sections) { + lines.push(result.report || result.content || result.markdown || JSON.stringify(result, null, 2)); + lines.push(''); + } + + if (result.key_entities && result.key_entities.length > 0) { + lines.push('## Key Entities'); + lines.push(''); + lines.push(result.key_entities.join(', ')); + } + + return lines.join('\n'); +} + +const InsightsView: React.FC = () => { + const { token } = useAuth(); + const [dashboard, setDashboard] = useState(null); + const [loading, setLoading] = useState(false); + const [evalForm, setEvalForm] = useState({ question: '', answer: '', contexts: '' }); + const [evalResult, setEvalResult] = useState(null); + const [evalLoading, setEvalLoading] = useState(false); + const [communities, setCommunities] = useState([]); + const [communityLoading, setCommunityLoading] = useState(false); + const [activeTab, setActiveTab] = useState<'metrics' | 'evaluate' | 'communities' | 'export' | 'report' | 'graph-update' | 'entity-chat'>('metrics'); + + // Report Agent state + const [reportTopic, setReportTopic] = useState(''); + const [reportDepth, setReportDepth] = useState(3); + const [reportResult, setReportResult] = useState(null); + const [reportLoading, setReportLoading] = useState(false); + const [reportTimestamp, setReportTimestamp] = useState(null); + const [copyDone, setCopyDone] = useState(false); + + // Graph Update state + const [updateText, setUpdateText] = useState(''); + const [updateResult, setUpdateResult] = useState(null); + const [updateLoading, setUpdateLoading] = useState(false); + + // Entity Chat state + const [entities, setEntities] = useState([]); + const [selectedEntity, setSelectedEntity] = useState(''); + const [entityContext, setEntityContext] = useState(''); + const [chatMsg, setChatMsg] = useState(''); + const [chatHistory, setChatHistory] = useState<{role: string; content: string}[]>([]); + const [chatLoading, setChatLoading] = useState(false); + + const fetchDashboard = useCallback(async () => { + setLoading(true); + try { + const res = await fetch(`${API_BASE}/eval/dashboard?limit=200`, { + headers: { Authorization: `Bearer ${token}` } + }); + if (res.ok) setDashboard(await res.json()); + } catch (e) { console.error(e); } + setLoading(false); + }, [token]); + + const fetchCommunities = useCallback(async () => { + setCommunityLoading(true); + try { + const res = await fetch(`${API_BASE}/graph/communities?limit=30`, { + headers: { Authorization: `Bearer ${token}` } + }); + if (res.ok) { + const data = await res.json(); + setCommunities(data.communities || []); + } + } catch (e) { console.error(e); } + setCommunityLoading(false); + }, [token]); + + const fetchEntities = useCallback(async () => { + try { + const res = await fetch(`${API_BASE}/admin/graph/nodes?query=&limit=200`, { + headers: { Authorization: `Bearer ${token}` } + }); + if (res.ok) { + const data = await res.json(); + setEntities((data.nodes || []).slice(0, 100)); + } + } catch {} + }, [token]); + + useEffect(() => { + fetchDashboard(); + fetchCommunities(); + fetchEntities(); + }, [fetchDashboard, fetchCommunities, fetchEntities]); + + const runEval = async () => { + if (!evalForm.question || !evalForm.answer || !evalForm.contexts) return; + setEvalLoading(true); + setEvalResult(null); + try { + const res = await fetch(`${API_BASE}/eval/score`, { + method: 'POST', + headers: { 'Content-Type': 'application/json', Authorization: `Bearer ${token}` }, + body: JSON.stringify({ + question: evalForm.question, + answer: evalForm.answer, + contexts: evalForm.contexts.split('\n---\n').map(c => c.trim()).filter(Boolean) + }) + }); + if (res.ok) { + setEvalResult(await res.json()); + fetchDashboard(); // Refresh metrics + } + } catch (e) { console.error(e); } + setEvalLoading(false); + }; + + const runReport = async () => { + if (!reportTopic.trim()) return; + setReportLoading(true); setReportResult(null); + try { + const res = await fetch(`${API_BASE}/report`, { + method: 'POST', + headers: { 'Content-Type': 'application/json', Authorization: `Bearer ${token}` }, + body: JSON.stringify({ topic: reportTopic, depth: reportDepth }) + }); + if (res.ok) { + setReportResult(await res.json()); + setReportTimestamp(new Date()); + } + } catch (e) { console.error(e); } + setReportLoading(false); + }; + + const runGraphUpdate = async () => { + if (!updateText.trim()) return; + setUpdateLoading(true); setUpdateResult(null); + try { + const res = await fetch(`${API_BASE}/graph/update`, { + method: 'POST', + headers: { 'Content-Type': 'application/json', Authorization: `Bearer ${token}` }, + body: JSON.stringify({ text: updateText }) + }); + if (res.ok) setUpdateResult(await res.json()); + } catch (e) { console.error(e); } + setUpdateLoading(false); + }; + + const sendEntityChat = async () => { + if (!selectedEntity || !chatMsg.trim()) return; + const userMsg = { role: 'user', content: chatMsg }; + setChatHistory(h => [...h, userMsg]); + setChatMsg(''); + setChatLoading(true); + try { + const res = await fetch(`${API_BASE}/entities/${encodeURIComponent(selectedEntity)}/chat`, { + method: 'POST', + headers: { 'Content-Type': 'application/json', Authorization: `Bearer ${token}` }, + body: JSON.stringify({ message: userMsg.content }) + }); + if (res.ok) { + const data = await res.json(); + setChatHistory(h => [...h, { role: 'assistant', content: data.response || data.answer || JSON.stringify(data) }]); + } + } catch (e) { console.error(e); } + setChatLoading(false); + }; + + const assignCommunities = async () => { + setCommunityLoading(true); + try { + const res = await fetch(`${API_BASE}/graph/communities/assign`, { + method: 'POST', + headers: { Authorization: `Bearer ${token}` } + }); + if (res.ok) { + const data = await res.json(); + alert(`✓ ${data.message}`); + fetchCommunities(); + } + } catch (e) { console.error(e); } + setCommunityLoading(false); + }; + + const exportGraph = async (fmt: string) => { + const res = await fetch(`${API_BASE}/graph/export?format=${fmt}`, { + headers: { Authorization: `Bearer ${token}` } + }); + if (!res.ok) return; + const blob = await res.blob(); + const ext = fmt === 'json' ? 'json' : fmt === 'cypher' ? 'cypher' : 'graphml'; + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; a.download = `graph_export.${ext}`; a.click(); + URL.revokeObjectURL(url); + }; + + const handleCopyReport = () => { + if (!reportResult) return; + const text = buildReportMarkdown(reportResult, reportTopic); + navigator.clipboard.writeText(text).then(() => { + setCopyDone(true); + setTimeout(() => setCopyDone(false), 2000); + }); + }; + + const handleDownloadReport = () => { + if (!reportResult) return; + const text = buildReportMarkdown(reportResult, reportTopic); + const blob = new Blob([text], { type: 'text/markdown' }); + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; + a.download = `report_${reportTopic.slice(0, 30).replace(/\s+/g, '_')}.md`; + a.click(); + URL.revokeObjectURL(url); + }; + + const score2color = (s: number) => { + if (s >= 0.8) return '#16a34a'; + if (s >= 0.6) return '#d97706'; + return '#dc2626'; + }; + const score2label = (s: number) => s >= 0.8 ? 'HIGH' : s >= 0.6 ? 'MEDIUM' : 'LOW'; + + const ScoreBar: React.FC<{ label: string; value: number }> = ({ label, value }) => ( +
+
+ {label} + {(value * 100).toFixed(1)}% +
+
+
+
+
+ ); + + const confidenceBadgeColor = (c: number) => + c >= 0.8 ? '#16a34a' : c >= 0.6 ? '#d97706' : '#dc2626'; + + return ( +
+
+
+

INSIGHTS HQ

+

QUALITY METRICS // EVAL DASHBOARD // GRAPH INTELLIGENCE

+
+
+ +
+
+ + {/* Tab Nav */} +
+ {([ + { id: 'metrics', label: 'METRICS' }, + { id: 'evaluate', label: 'EVALUATE' }, + { id: 'communities', label: 'COMMUNITIES' }, + { id: 'export', label: 'EXPORT' }, + { id: 'report', label: '⚡ REPORT' }, + { id: 'graph-update', label: '↑ LIVE UPDATE' }, + { id: 'entity-chat', label: '💬 ENTITY CHAT' }, + ] as const).map(t => ( + + ))} +
+ + {/* ── METRICS TAB ── */} + {activeTab === 'metrics' && ( +
+ {loading ? ( +
LOADING METRICS...
+ ) : !dashboard || dashboard.total_evaluations === 0 ? ( +
+ +

NO EVALUATION DATA YET

+

+ Use the EVALUATE tab to score Q&A pairs and build your quality history. +

+
+ ) : ( + <> + {/* KPI Cards */} +
+ {[ + { label: 'TOTAL EVALS', value: dashboard.total_evaluations, raw: true, icon: }, + { label: 'AVG QUALITY', value: dashboard.avg_overall_score, icon: }, + { label: 'FAITHFULNESS', value: dashboard.avg_faithfulness, icon: }, + { label: 'HALLUCINATION RATE', value: dashboard.hallucination_rate, invert: true, icon: }, + ].map(card => ( +
0.3 : false) ? '#fff5f5' : '#fff' }}> +
+ {card.label} + {card.icon} +
+
+ {card.raw ? card.value : `${((card.value as number) * 100).toFixed(1)}%`} +
+
+ ))} +
+ + {/* Score Breakdown */} +
+

METRIC BREAKDOWN

+ + + + +
+ + {/* Trend Table */} + {dashboard.trend_data.length > 0 && ( +
+

EVALUATION HISTORY (LATEST {Math.min(dashboard.trend_data.length, 20)})

+
+ + + + {['TIMESTAMP', 'QUALITY', 'FAITHFULNESS', 'RELEVANCY', 'HALLUCINATION'].map(h => ( + + ))} + + + + {dashboard.trend_data.slice(0, 20).map((p, i) => ( + + + + + + + + ))} + +
{h}
{p.timestamp}{(p.overall_score * 100).toFixed(1)}%{(p.faithfulness * 100).toFixed(1)}%{(p.answer_relevancy * 100).toFixed(1)}% + + {p.hallucination_detected ? 'YES' : 'NO'} + +
+
+
+ )} + + )} +
+ )} + + {/* ── EVALUATE TAB ── */} + {activeTab === 'evaluate' && ( +
+
+

+ + SCORE A Q&A PAIR +

+

+ Paste a question, its generated answer, and the retrieved context chunks (separate chunks with "---"). +

+ +
+
+ +