A RAG (Retrieval Augmented Generation) based question-answering prrof-of-concept (PoC) system that allows a user to query target documents using natural language. The system uses local LLMs through Ollama for privacy and performance and provides a chat interface for easy interaction. The entire codebase (both backend and frontend are developed using Python3.10).

• Local LLM Integration: Uses Ollama for running models locally, ensuring data privacy
• Vector Search: Efficient document retrieval using FAISS
• Modern Chat Interface: Built with Chainlit for a smooth user experience
• Containerized Services: Easy deployment with Docker Compose
• Async Processing: Built with FastAPI for high performance

        
User ───────────────▶ Chainlit UI       Documents (txt/md/pdf)
                           │                     │
                           │                     │
                    Query  │                     │ Retrieve
                           │                     │ Documents
                           ▼                     ▼
                       FastAPI ◄───────────  RAG Model ◄─────── Local Ollama
                       Backend                   │
                           │                     │
                           └─────────────────────┘
                                Return Answer

• Docker
  • Linux: Follow the official Docker documentation for your distribution.
  • Windows: Download and install Docker Desktop for Windows.
  • MacOS: Download and install Docker Desktop for MacOS.
• Docker Compose
• Ollama (for local model running)
• Python 3.10+

• Install Ollama locally (for Mac):

  brew install ollama
  brew services start ollama

• Install Ollama locally (for Linux):

  curl -fsSL https://ollama.com/install.sh | sh

• Install Ollama locally (for Windows): Download and install Ollama from the official Ollama website.

• Download required models:

  ollama run mistral
  ollama run nomic-embed-text

• Clone the repository:

  git clone https://github.com/shantoroy/rag-chatbot-python-fullstack-template.git
  cd rag-chatbot-python-fullstack-template

• Configure .env file (check details at the Configuration section below)

• Start the services:

  docker-compose build
  docker-compose up -d

• Stop the services:

  docker-compose down

• Access the chat interface at http://localhost:8505
• Keep your files under the documents directory
• Start asking questions about your documents!

rag-chatbot-python-fullstack-template/
├── backend/
│   ├── model.py          # RAG model implementation
│   └── api.py            # FastAPI backend
├── frontend/
│   └── app.py            # Chainlit chat interface
├── docker/
│   ├── backend.Dockerfile
│   └── frontend.Dockerfile
├── requirements/
│   ├── backend_requirements.txt
│   └── frontend_requirements.txt
├── documents/            # Put/organize your documents here
│   ├── test_file_1.txt 
│   └── test_file_2.md
├── .env.example          # Example file, rename to .env
├── .gitignore
├── docker-compose.yml    # Service orchestration
├── requirements.txt      # Python dependencies as a whole (not needed)
├── chainlit.md          # Chainlit configuration
└── README.md

• All processing is done locally through Ollama
• No data leaves your infrastructure
• Authentication can be added as needed

• Don't forget to rename the .env.example file to .env
• Also add your own secret key.

• OLLAMA_URL=http://localhost:11434
• CHAINLIT_AUTH_SECRET=your-secret-key

To generate a CHAINLIT_AUTH_SECRET for your .env file, you can use the following command:

openssl rand -hex 32

This command uses OpenSSL to generate a secure random 32-byte hexadecimal string, which is suitable for use as an authentication secret. After running this command, you'll get a string that looks something like:

3d7c4e608f6df9a0e3e3ded3f1c3f384b9b3a9f9e5c1a0e2b4a8d1e0f2c3b4a7

You would then add this to your .env file:

CHAINLIT_AUTH_SECRET=3d7c4e608f6df9a0e3e3ded3f1c3f384b9b3a9f9e5c1a0e2b4a8d1e0f2c3b4a7

For Kubernetes, you'll need to encode this value as base64 before adding it to your secrets.yaml file:

echo -n "3d7c4e608f6df9a0e3e3ded3f1c3f384b9b3a9f9e5c1a0e2b4a8d1e0f2c3b4a7" | base64

Then use the resulting base64 string in your Kubernetes secrets configuration.

Added sample kubernetes config files under kubernetes-template folder. You need to modify values before production usage. Read the Deployment Steps guide for details.

• Fork the repository
• Create your feature branch (git checkout -b feature/amazing-feature)
• Commit your changes (git commit -m 'Add amazing feature')
• Push to the branch (git push origin feature/amazing-feature)
• Open a Pull Request

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

• Ollama for local LLM support
• LangChain for RAG implementation
• Chainlit for the chat interface
• FastAPI for the backend framework