RAG Chatbot

Retrieval-Augmented Generation chatbot with knowledge base integration. This chatbot uses RAG (Retrieval-Augmented Generation) architecture to provide accurate answers from a knowledge base. Perfect for building chatbots with domain-specific knowledge.

Features

Core Features

• Knowledge base integration with ChromaDB
• Vector similarity search
• Context retrieval from knowledge base
• Accurate responses using RAG architecture
• Domain-specific knowledge support

Advanced Features

• Conversation History: Maintains context across multiple messages
• Streaming Responses: Real-time streaming of LLM responses
• Hybrid Search: Combines vector similarity with keyword matching
• File Upload: Upload documents directly through the web interface
• Analytics Dashboard: Track queries, sessions, response times, and feedback
• Feedback System: Thumbs up/down for responses
• Chat Export: Export conversations as JSON
• Session Management: Multiple concurrent sessions
• Response Time Tracking: Monitor performance metrics

Technologies

• LangChain
• Vector DB (ChromaDB)
• Python
• OpenAI API
• Embeddings

Installation

1. Clone the repository
2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables:

cp .env.example .env
# Edit .env and add your OpenAI API key

4. Prepare your knowledge base:

python prepare_knowledge_base.py

5. Run the application:

python app.py

Usage

1. Start the Flask server
2. Open your browser and navigate to http://localhost:5000
3. Enter your questions in the chat interface
4. The chatbot will retrieve relevant context from the knowledge base and generate accurate responses

Advanced Features Usage

• Streaming Mode: Toggle streaming on/off in the chat header
• Hybrid Search: Enable hybrid search for better results combining semantic and keyword search
• Upload Documents: Click the upload button to add new documents to the knowledge base
• View Analytics: Click the analytics button to see statistics and insights
• Export Chat: Click export to download your conversation as JSON
• Feedback: Use thumbs up/down buttons on responses to provide feedback

Project Structure

rag-chatbot/
├── app.py                      # Flask application with advanced endpoints
├── chatbot.py                  # RAG chatbot implementation
├── vector_store.py             # Vector database operations
├── embeddings.py                # Embedding utilities
├── conversation_manager.py      # Conversation history management
├── analytics.py                # Analytics and statistics tracking
├── hybrid_search.py            # Hybrid search implementation
├── prepare_knowledge_base.py    # Knowledge base preparation
├── config.py                   # Configuration settings
├── setup.py                    # Setup script
├── templates/
│   └── index.html              # Web interface with advanced UI
├── static/
│   ├── css/
│   │   └── style.css           # Styles with modal and advanced UI
│   └── js/
│       └── app.js              # Frontend JavaScript with all features
├── knowledge_base/             # Knowledge base documents
├── vector_db/                  # Vector database storage
├── conversations/              # Conversation history storage
├── analytics/                  # Analytics data storage
└── requirements.txt            # Python dependencies

License

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