AI Document Search (RAG Chatbot)

A powerful AI-powered document search application that allows you to upload PDF documents and ask questions about their content using Retrieval-Augmented Generation (RAG) technology.

Features

• 📄 PDF Upload: Upload PDF documents for processing
• 🤖 AI Chat: Ask questions about your documents using Mistral AI
• 🔍 Vector Search: Uses FAISS for efficient document retrieval
• 🎨 Modern UI: Clean, responsive interface built with React and Tailwind CSS
• 🐳 Docker Support: Easy deployment with Docker Compose

Tech Stack

Backend

• FastAPI: Modern Python web framework
• LangChain: Framework for building LLM applications
• FAISS: Vector database for similarity search
• Mistral AI: Large language model for question answering
• HuggingFace Embeddings: Sentence transformers for document embeddings

Frontend

• React: JavaScript library for building user interfaces
• Vite: Fast build tool and development server
• Tailwind CSS: Utility-first CSS framework

Prerequisites

• Node.js (v14 or higher) and npm
• Python (3.9 or higher)
• Mistral API key (get one from Mistral AI)
• Docker and Docker Compose (optional, for containerized deployment)

Quick Start

🚀 Easiest Method: Using Batch Scripts (Windows)

1. Clone the repository

   git clone https://github.com/Sayeem3051/Chatbot-RAG-.git
   cd Chatbot-RAG-

2. Get your Mistral API key

   • Sign up at Mistral AI
   • Copy your API key

3. Edit the batch script

   • Open start_backend.bat in a text editor
   • Replace your-mistral-api-key-here with your actual API key

4. Start the application

   # Option A: Start both frontend and backend automatically
   start_all.bat
   
   # Option B: Start them separately
   start_backend.bat
   # Then in another terminal: cd frontend && npm run dev

5. Access the application

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:8000

🐳 Docker Method

1. Set up environment variables

   # Copy the example environment file
   cp env.example .env
   
   # Edit .env and add your Mistral API key
   MISTRAL_API_KEY=your-actual-api-key-here

2. Run with Docker Compose

   docker-compose up --build

3. Access the application

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:8000

Manual Setup (Development)

Backend Setup

1. Navigate to backend directory

   cd backend

2. Create virtual environment

   python -m venv venv
   
   # Activate virtual environment
   # On Windows:
   venv\Scripts\activate
   # On macOS/Linux:
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Set environment variable

   # Windows PowerShell:
   $env:MISTRAL_API_KEY="your-mistral-api-key-here"
   
   # Windows Command Prompt:
   set MISTRAL_API_KEY=your-mistral-api-key-here
   
   # macOS/Linux:
   export MISTRAL_API_KEY="your-mistral-api-key-here"

5. Run the server (IMPORTANT: Must be run from backend directory)

   # Make sure you're in the backend directory!
   uvicorn main:app --reload --host 0.0.0.0 --port 8000

Frontend Setup

1. Navigate to frontend directory

   cd frontend

2. Install dependencies

   npm install

3. Run development server

   npm run dev

⚠️ Important Notes:

• Backend: Always run uvicorn from the backend directory, not the project root
• Frontend: Always run npm run dev from the frontend directory, not the project root
• API Key: Make sure your Mistral API key is set before starting the backend

Usage

1. Upload a PDF: Click "Choose File" and select a PDF document, then click "Upload PDF"
2. Ask Questions: Type your question in the input field and click "Ask"
3. View Responses: The AI will provide answers based on the content of your uploaded document

API Endpoints

POST /upload

Upload a PDF file for processing.

Request: Multipart form data with PDF file Response:

{
  "status": "success",
  "message": "PDF processed successfully."
}

POST /chat

Ask a question about the uploaded document.

Request:

{
  "question": "Your question here"
}

Response:

{
  "answer": "AI-generated answer based on document content"
}

Configuration

Environment Variables

• MISTRAL_API_KEY: Your Mistral AI API key (required)

Security Notes

⚠️ Important Security Information:

• Never commit your actual API key to version control
• The .env file is already included in .gitignore to prevent accidental commits
• Use the env.example file as a template for your environment variables
• Keep your API keys secure and rotate them regularly

Model Configuration

The application uses the following models by default:

• Embeddings: sentence-transformers/all-MiniLM-L6-v2
• LLM: open-mistral-7b

You can modify these in backend/main.py if needed.

Batch Scripts (Windows)

This project includes convenient batch scripts to simplify startup:

start_backend.bat

• Automatically navigates to the backend directory
• Activates the Python virtual environment
• Sets the Mistral API key
• Starts the FastAPI server

start_all.bat

• Starts both backend and frontend servers automatically
• Opens two terminal windows (one for each server)
• Handles all directory navigation and environment setup

To customize the API key:

1. Open start_backend.bat in a text editor
2. Replace your-mistral-api-key-here with your actual Mistral API key
3. Save the file

Usage:

# Start both servers
start_all.bat

# Or start backend only
start_backend.bat

Troubleshooting

Common Issues

1. "Could not import module 'main'" error

   • Cause: Running uvicorn from the wrong directory
   • Solution: Always run uvicorn main:app from the backend directory, not the project root
   • Check: Your terminal should show C:\path\to\project\backend> not C:\path\to\project>

2. "Missing script: 'dev'" error

   • Cause: Running npm run dev from the wrong directory
   • Solution: Always run npm run dev from the frontend directory, not the project root
   • Check: Your terminal should show C:\path\to\project\frontend> not C:\path\to\project>

3. "localhost refused to connect" (ERR_CONNECTION_REFUSED)

   • Backend not running: Check if backend is running on http://localhost:8000
   • Frontend not running: Check if frontend is running on http://localhost:3000
   • Solution: Use start_all.bat to start both servers automatically

4. "No document uploaded yet" error

   • Make sure to upload a PDF file before asking questions

5. API key issues

   • Verify your Mistral API key is correctly set
   • Check that the API key has sufficient credits
   • Make sure the API key is set before starting the backend

6. CORS errors

   • The backend is configured to allow all origins for development
   • For production, update the CORS settings in backend/main.py

Quick Fixes

If you see any import errors:

# Navigate to the correct directory first!
cd backend
uvicorn main:app --host 0.0.0.0 --port 8000

If frontend won't start:

# Navigate to the correct directory first!
cd frontend
npm run dev

Easy solution - Use the batch scripts:

# This handles everything automatically
start_all.bat

Success Indicators

Backend is working correctly when you see:

INFO:     Started server process [XXXX]
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
INFO:     127.0.0.1:XXXXX - "POST /upload HTTP/1.1" 200 OK
INFO:     127.0.0.1:XXXXX - "POST /chat HTTP/1.1" 200 OK

Frontend is working correctly when you see:

VITE v4.5.14  ready in XXX ms
➜  Local:   http://localhost:3000/
➜  Network: http://192.168.1.X:3000/

Normal log messages (not errors):

• "GET / HTTP/1.1" 404 Not Found - This is normal, the root path doesn't exist
• LangChainDeprecationWarning - This is just a warning, the app still works

Performance Tips

• For large PDFs, the processing time may be longer
• The vector database is stored in memory and will be lost when the server restarts
• Consider implementing persistent storage for production use

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is licensed under the MIT License.

Support

For issues and questions, please open an issue on GitHub.