FusionChat

An AI-powered chat application with advanced RAG (Retrieval-Augmented Generation) capabilities. Upload documents, build knowledge graphs, and have intelligent conversations powered by vector search and graph-based retrieval.

🚀 Features

• Document Ingestion: Upload PDFs, text files, and markdown documents for AI-powered analysis
• Graph RAG: Combines vector embeddings with knowledge graph relationships for enhanced retrieval
• Multi-Database Architecture: PostgreSQL for metadata, Qdrant for vectors, Neo4j for knowledge graphs
• Real-Time Processing: Live document processing status with visual feedback
• Modern UI: Clean, ChatGPT-inspired interface with smooth animations
• Docker Support: One-command deployment with full containerization

📸 Screenshots

🛠️ Tech Stack

Backend

• FastAPI: High-performance async API framework
• OpenAI GPT-4o-mini: Language model for chat and embeddings
• Qdrant: Vector database for semantic search
• Neo4j: Graph database for entity relationships
• PostgreSQL: Relational database for chat and document metadata
• SQLAlchemy: Async ORM with PostgreSQL
• NLTK: Natural language processing for text chunking

Frontend

• React 19: Modern UI library
• Vite: Fast build tool and dev server
• Framer Motion: Smooth animations and transitions
• Axios: HTTP client for API communication
• Lucide React: Beautiful icon library

📋 Prerequisites

Before installation, ensure you have the following:

• Python 3.11+
• Node.js 20+
• Docker & Docker Compose (for Docker setup)
• OpenAI API Key

🔧 Installation

Option 1: Docker (Recommended)

1. Clone the repository

   git clone https://github.com/hereisSwapnil/FusionChat.git
   cd FusionChat

2. Set up environment variables

   cp .env.example .env

   Edit .env and add your OpenAI API key:

   OPENAI_API_KEY=your_openai_api_key_here

3. Start all services

   docker-compose up -d

4. Access the application

   • Frontend: http://localhost:5173
   • Backend API: http://localhost:8000
   • API Docs: http://localhost:8000/docs

Option 2: Local Development

1. Clone the repository

   git clone https://github.com/hereisSwapnil/FusionChat.git
   cd FusionChat

2. Start databases with Docker

   docker-compose up -d neo4j qdrant postgres

3. Backend Setup

   cd backend
   pip install -r requirements.txt
   
   # Download NLTK data
   python -c "import nltk; nltk.download('punkt'); nltk.download('punkt_tab'); nltk.download('stopwords')"

4. Frontend Setup

   cd frontend
   npm install

5. Environment Variables

   Create backend/.env:

   OPENAI_API_KEY=your_openai_api_key_here

6. Run the application

   Terminal 1 (Backend):

   cd backend
   uvicorn app.main:app --reload

   Terminal 2 (Frontend):

   cd frontend
   npm run dev

📁 Project Structure

FusionChat/
├── backend/
│   ├── app/
│   │   ├── api/endpoints/      # API route handlers
│   │   ├── core/               # Configuration and utilities
│   │   ├── db/                 # Database clients and queries
│   │   ├── models/             # SQLAlchemy models
│   │   ├── schemas/            # Pydantic schemas
│   │   └── services/           # Business logic
│   ├── Dockerfile
│   └── requirements.txt
├── frontend/
│   ├── src/
│   │   ├── App.jsx            # Main application component
│   │   ├── App.css            # Application styles
│   │   └── index.css          # Global styles
│   ├── Dockerfile
│   └── package.json
├── docker-compose.yml          # Multi-container orchestration
├── DOCKER.md                   # Detailed Docker documentation
└── .env.example                # Environment template

🔄 How It Works

1. Create Chat: Start a new conversation or select an existing one
2. Upload Documents: Attach PDFs or text files to enrich the knowledge base
3. Processing Pipeline:
   • Text extraction from documents
   • Semantic chunking with NLTK
   • Embedding generation with OpenAI
   • Vector storage in Qdrant (per-chat collections)
   • Entity and relationship extraction
   • Knowledge graph construction in Neo4j
4. Intelligent Retrieval:
   • Vector similarity search for relevant chunks
   • Graph traversal for related entities
   • Context fusion for comprehensive answers
5. AI Response: GPT-4o-mini generates responses using retrieved context

🔒 Environment Variables

Variable	Description	Default
OPENAI_API_KEY	OpenAI API key (required)	-
NEO4J_URI	Neo4j connection URI	bolt://localhost:7687
NEO4J_USER	Neo4j username	neo4j
NEO4J_PASSWORD	Neo4j password	password
QDRANT_HOST	Qdrant host	localhost
QDRANT_PORT	Qdrant port	6333
POSTGRES_HOST	PostgreSQL host	localhost
POSTGRES_PORT	PostgreSQL port	5432
POSTGRES_DB	PostgreSQL database	fusionchat
POSTGRES_USER	PostgreSQL username	postgres
POSTGRES_PASSWORD	PostgreSQL password	password

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.