An intelligent RAG system combining vector search, knowledge graphs, and smart memory for comprehensive document analysis and conversational AI.
✅ Production Ready • Version 2.0.0 • 94.32% F1 Score
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Beautiful theme with smooth transitions, localStorage persistence, and optimized color palette for comfortable viewing. Context-aware conversations that remember previous Q&A, instant answers from cache, 60-70% accuracy in complex queries. Use your own OpenAI API key for complete control over costs and usage. Keys are encrypted at rest with AES encryption and never stored on the server. |
9 natural language operations to create, delete, merge, and connect nodes - no Cypher needed! Pure white text in dark mode, markdown rendering with code blocks, smooth animations throughout. |
Secure API key management - Encrypted at rest, never stored on server
Intuitive UI with drag-and-drop PDF upload and real-time search
Agent intelligently selects tools and streams reasoning steps
Real-time memory state visualization with cost tracking and context utilization
Transparent reasoning process with memory-first strategy for instant answers
Interactive D3.js graph with clickable nodes and relationship exploration
Detailed entity and relationship tables extracted from documents
Unified knowledge graph spanning multiple documents with cross-document connections
Multi-pass enrichment: Initial extraction → Missing entities → Indirect relationships
# 1. Clone repository with submodules
git clone --recurse-submodules <repository-url>
cd LYZR-Hackathon
# If already cloned without submodules:
git submodule update --init --recursive
# 2. Setup environment
cp .env-example .env
# Edit .env with your OpenAI API key
# 3. Start services
docker-compose up --build
# 4. Access the system
# Frontend: http://localhost:3000
# API Docs: http://localhost:8000/docs
# Neo4j: http://localhost:7474Prerequisites: Docker, OpenAI API Key, 8GB+ RAM
--recurse-submodules flag or run git submodule update --init --recursive after cloning.
Complete control over your OpenAI costs:
- Bring Your Own Key: Use your personal OpenAI API key
- Encrypted at Rest: AES encryption with device-specific fingerprinting
- Never Server-Stored: Keys stay in your browser's localStorage
- Zero Trust: Server never persists your key, only uses it for requests
- Easy Setup: Configure via Settings modal in the UI
- Fallback Support: System can use environment key if user key not provided
4 intelligent search tools that auto-select or run in parallel:
- Vector Search - Semantic understanding (5x/3x retrieval multipliers)
- Graph Search - Multi-hop relationship traversal (1-hop, 2-hop)
- Filter Search - Metadata and date filtering via Elasticsearch
- Graph Update - Natural language graph modifications
- Pass 1: Broad entity extraction
- Pass 2: Find missing referenced entities
- Pass 3: Discover indirect relationships
- Result: 30-50% richer knowledge graphs, 25 concurrent chunks
- Memory-First: Checks history before searching documents
- Cost Savings: Instant cached responses
- Performance: 60-70% accuracy in complex needle-in-haystack tests
- Control: One-click memory clearing
| Metric | Score |
|---|---|
| Context Precision | 99.99% |
| Context Recall | 94.32% |
| F1 Score | 94.32% |
| Memory Speed | Instant |
| Query Speed | <2s |
┌────────────────────────────────────────────────────────────────┐
│ React Frontend │
│ • Dark Mode UI • Real-time Streaming • Graph Visualization │
│ • Memory State Display • Token Usage Tracking │
└──────────────────────────────┬─────────────────────────────────┘
│ HTTP/SSE
▼
┌────────────────────────────────────────────────────────────────┐
│ FastAPI Backend (v2.0) │
├────────────────────────────────────────────────────────────────┤
│ ┌──────────────────────────────────────────────────────┐ │
│ │ 🧠 Memory Manager │ │
│ │ • Conversation History • Token Tracking │ │
│ │ • Context Compression • Memory-First Strategy │ │
│ └──────────────────────────┬───────────────────────────┘ │
│ │ │
│ ┌──────────────────────────▼───────────────────────────┐ │
│ │ 🤖 Multi-Tool Search Agent │ │
│ │ ┌────────────┐ ┌──────────┐ ┌──────────┐ ┌───────┐ │ │
│ │ │ Vector │ │ Graph │ │ Filter │ │ Graph │ │ │
│ │ │ Search │ │ Search │ │ Search │ │Update │ │ │
│ │ └────────────┘ └──────────┘ └──────────┘ └───────┘ │ │
│ │ • Smart Tool Selection • MAX_PERFORMANCE Mode │ │
│ └──────────────────────────┬───────────────────────────┘ │
│ │ │
│ ┌──────────────────────────▼───────────────────────────┐ │
│ │ 🕸️ GraphRAG Pipeline (3-Pass) │ │
│ │ Pass 1: Entity Extraction │ │
│ │ Pass 2: Missing Entities │ │
│ │ Pass 3: Indirect Relationships │ │
│ └──────────────────────────┬───────────────────────────┘ │
└────────────────────────────┬┴───────────────────────────────┬─-┘
│ │
┌───────────────────┼────────────────────────────────┼──────┐
│ │ │ │
▼ ▼ ▼ ▼
┌─────────────────┐ ┌──────────────┐ ┌─────────────┐ ┌──────────---┐
│ PostgreSQL │ │ PGVector │ │ Neo4j │ │Elasticsearch│
│ (Metadata + │ │ (Embeddings │ │ (Knowledge │ │ (Metadata │
│ Memory) │ │ 1536-dim) │ │ Graph) │ │ Search) │
└─────────────────┘ └──────────────┘ └─────────────┘ └─────────---─┘
┌─────────────────────┐
│ User Query │
└──────────┬──────────┘
│
▼
┌─────────────────────┐
│ 🧠 Memory Check │
│ (Memory-First) │
└──────────┬──────────┘
│
┌────────────────┴────────────────┐
│ │
▼ Found ▼ Not Found
┌────────────────────┐ ┌─────────────────────┐
│ Return Cached │ │ Document Search │
│ Answer (Instant) │ │ Required │
└────────────────────┘ └──────────┬──────────┘
│
┌─────────────┴─────────────┐
│ │
▼ MAX_PERFORMANCE=true ▼ Standard Mode
┌──────────────────────────┐ ┌──────────────────────┐
│ 🚀 Run All Tools │ │ 🤖 Agent Selects │
│ in Parallel: │ │ Best Tool(s): │
│ • Vector Search │ │ │
│ • Graph Search │ │ Decision Logic: │
│ • Filter Search │ │ │
│ Then synthesize results │ │ ✏️ "Create/Delete" │
└──────────────────────────┘ │ → graph_update │
│ │
│ 🕸️ "Who is X?" │
│ "How X relates Y"│
│ → graph_search │
│ │
│ 📚 "What is X?" │
│ "Explain..." │
│ → vector_search │
│ │
│ 🔍 "Docs from 2023"│
│ → filter_search │
└──────────────────────┘
│
▼
┌──────────────────────┐
│ Synthesize Results │
│ Store in Memory │
│ Stream to Frontend │
└──────────────────────┘
┌──────────────┐
│ Upload PDF │
└──────┬───────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Backend: Document Processing │
├─────────────────────────────────────────────────────────────┤
│ 1. Extract Text (PyMuPDF/Docling) │
│ 2. Chunk Text (1200 chars, 500 overlap) │
│ ↓ │
│ 3. Generate Embeddings (OpenAI text-embedding-3-large) │
│ ↓ │
│ 4. GraphRAG 3-Pass Enrichment │
│ • Pass 1: Extract entities/relationships │
│ • Pass 2: Find referenced entities │
│ • Pass 3: Discover indirect connections │
└────┬────────────────┬──────────────────┬──────────────┬─────┘
│ │ │ │
▼ ▼ ▼ ▼
┌──────────┐ ┌──────────────┐ ┌─────────────┐ ┌──────────────┐
│PostgreSQL│ │ PGVector │ │ Neo4j │ │Elasticsearch │
│ │ │ │ │ │ │ │
│• Metadata│ │• Embeddings │ │• Entities │ │• Text Index │
│• Filename│ │• Chunks │ │• Relations │ │• Metadata │
│• Status │ │• Vectors │ │• Properties │ │• Highlights │
└──────────┘ └──────────────┘ └─────────────┘ └──────────────┘
┌──────────────┐
│ User Query │
└──────┬───────┘
│
▼
┌────────────────────────────────────────────────────────────┐
│ Step 1: Memory Check (PostgreSQL) │
│ • Search conversation history │
│ • Semantic keyword matching │
│ • If found → Return cached answer (FAST PATH) │
└────────┬───────────────────────────────────────────────────┘
│ Not in memory
▼
┌────────────────────────────────────────────────────────────┐
│ Step 2: Tool Execution │
├────────────────────────────────────────────────────────────┤
│ 📚 Vector Search (PGVector + BM25) │
│ • Query embedding → Similarity search │
│ • Retrieve top-k×5 chunks │
│ • Rerank with cross-encoder → top-k×3 │
│ • Expand context (±2 adjacent chunks) │
│ │
│ 🕸️ Graph Search (Neo4j) │
│ • Entity extraction from query │
│ • 1-hop traversal (direct connections) │
│ • 2-hop traversal (indirect connections) │
│ • Return entity network with relationships │
│ │
│ 🔍 Filter Search (Elasticsearch) │
│ • Extract filters (date, author, category) │
│ • Metadata-based search │
│ • Return matching documents with highlights │
│ │
│ ✏️ Graph Update (Neo4j) │
│ • Parse update command │
│ • Execute CRUD operations on graph │
│ • Return success/failure status │
└────────┬───────────────────────────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────────┐
│ Step 3: LLM Synthesis │
│ • Combine results from tools │
│ • Generate comprehensive answer │
│ • Format with proper markdown │
└────────┬───────────────────────────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────────┐
│ Step 4: Memory Storage (PostgreSQL) │
│ • Store query + response │
│ • Track token usage │
│ • Update memory state │
└────────┬───────────────────────────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────────┐
│ Step 5: Stream to Frontend │
│ • SSE events (thinking, tool_start, tool_end) │
│ • Final answer with formatting │
│ • Memory state + token usage │
└────────────────────────────────────────────────────────────┘
# API & Credentials
OPENAI_API_KEY=sk-proj-your-key # Optional: Can be provided by users via UI
POSTGRES_PASSWORD=your_password
NEO4J_AUTH=neo4j/your_password
# Performance Features
MAX_PERFORMANCE=false # Run all tools in parallel
GRAPHRAG_ENABLE_MULTIPASS=true # 3-pass enrichment
# Optimal RAG Settings
CHUNK_SIZE=1200
CHUNK_OVERLAP=500
TOP_K_RESULTS=20Users can provide their own OpenAI API key through the UI:
- Click Settings Icon (⚙️) in the top-right corner
- Enter Your OpenAI API Key in the modal
- Save - Key is encrypted and stored in browser localStorage
- Use the System - All API calls use your key automatically
Security Features:
- 🔐 AES Encryption with browser fingerprint-based key derivation
- 🏠 Client-Side Storage - Keys never leave your browser
- 🔒 Zero Server Persistence - Backend receives keys via headers only
- 🔄 Easy Management - Clear/update key anytime via Settings
# Upload document with your API key
curl -X POST http://localhost:8000/api/rag/upload \
-H "X-OpenAI-API-Key: sk-proj-your-key-here" \
-F "file=@document.pdf"
# Query with your API key
curl -X POST http://localhost:8000/api/rag/query/stream \
-H "Content-Type: application/json" \
-H "X-OpenAI-API-Key: sk-proj-your-key-here" \
-d '{"query": "What is this about?", "document_id": "your-id"}'
# Update graph with your API key
curl -X POST http://localhost:8000/api/rag/query/stream \
-H "Content-Type: application/json" \
-H "X-OpenAI-API-Key: sk-proj-your-key-here" \
-d '{"query": "Create AI node and connect to Python, ML", "document_id": "your-id"}'# Upload document (uses env OPENAI_API_KEY)
curl -X POST http://localhost:8000/api/rag/upload \
-F "file=@document.pdf"
# Query without custom key
curl -X POST http://localhost:8000/api/rag/query/stream \
-H "Content-Type: application/json" \
-d '{"query": "What is this about?", "document_id": "your-id"}'
# Clear memory
curl -X DELETE http://localhost:8000/api/memory/clearNote: The X-OpenAI-API-Key header is optional. If not provided, the system falls back to the OPENAI_API_KEY from environment variables.
Full API documentation: http://localhost:8000/docs
# Run unit tests (27% coverage)
docker exec lyzr-hackathon-backend-1 pytest test/unit_tests/ -v
# Run RAGAS evaluation
docker exec lyzr-hackathon-backend-1 pytest test/integration_tests/ -vSee test/README.md for detailed testing documentation.
LYZR-Hackathon/
├── backend/ # FastAPI + Search Agent + Memory + GraphRAG
├── frontend/ # React + Dark Mode + Graph Visualization
├── memory/ # Git submodule: Long-term memory system (Lyzr)
│ # Source: https://github.com/dev-pratap-singh/memory
├── test/ # Unit tests + RAGAS evaluation
├── docker-compose.yml
└── .env-example
Memory Submodule: The memory/ directory is a git submodule containing the Lyzr long-term memory implementation. It provides:
- Conversation tracking and history management
- Memory facts and user preferences storage
- Training history for model fine-tuning
- Vector-based semantic search for memory retrieval
"Graph traversal timeout":
- Multi-hop traversal can be slow on very large graphs
- Check Neo4j performance
- Consider limiting 2-hop traversal depth
- SLM for Graph Creation: Use Gemma-3-8B to reduce costs
- Microsoft GraphRAG: Full hierarchical clustering implementation
- Visual Image RAG: Late interaction models for image retrieval
- Embedding-based Memory: True semantic search vs keyword matching
- Multi-Document Evolution: Stress test with 100+ documents
Major Features:
- 🔑 Bring Your Own API Key - User-provided OpenAI keys with AES encryption
- 🌙 Complete Dark Mode - Beautiful theme with localStorage persistence
- 🧠 Smart Memory System - Memory-first strategy with instant cached responses
- 🎨 Enhanced UI - Pure white text in dark mode, improved markdown rendering
- 🔒 Zero-Trust Security - API keys encrypted at rest, never stored on server
Natural language graph updates • 9 operations • Batch connections • Real-time refresh
Multi-hop traversal • 3-pass enrichment • MAX_PERFORMANCE mode • F1 Score 94.32%
Initial release • Multi-tool agent • Hybrid search • RAGAS evaluation
Dev Pratap Singh • Senior AI Engineer • IIT Goa
Special thanks to the team for organizing this hackathon. If I don't win, I'd love to meet the team in Bangalore for coffee! ✌️
Last Updated: October 15, 2025 • Status: ✅ Production Ready • Version: 2.0.0