RAG Chatbot Project

Hey, Welcome! This project implements a Retrieval-Augmented Generation (RAG) chatbot that combines the power of modern language models with your own document knowledge base.

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Backend

• Django with REST API endpoints for chat, authentication, and session management
• Supabase for user data, authentication, and vector search
• OpenAI integration for generating smart, context-aware answers
• RAG logic for combining document retrieval with LLM responses

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What’s Inside?

• Document Ingestion & Chunking: Efficiently processes and embeds documents for fast, relevant retrieval.
• Smart Retrieval: Uses vector search (Supabase) to find the most relevant pieces of information for any user query.
• Conversational AI: Integrates with OpenAI’s GPT models to generate helpful, context-aware responses.
• MCP Server Integration: Supports Model Context Protocol for advanced query handling and future scalability.
• User Authentication: Secure endpoints using Supabase JWT.

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Frontend

• Next.js app for a fast, interactive user experience
• User authentication with email and password (Supabase-backed)
• Clean chat interface with chat history, session sidebar, and real-time messaging
• Styled with Tailwind CSS for a modern, soothing look
• TypeScript for type safety and maintainability

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Project Structure

• rag_chatbot_clean/
• ├── chat/ # Django backend app
• ├── config/ # Django settings
• ├── manage.py # Django runner
• └── src/ # Next.js frontend (with API routes and UI)

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Quick Start

• Backend:

  • Configure Supabase and OpenAI keys
  • python manage.py migrate
  • python manage.py runserver

• Frontend:

  • cd src
  • npm install
  • npm run dev

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Happy coding! If you have any questions or suggestions, open an issue or join the discussion.