AI Chatbot with RAG (Retrieval-Augmented Generation)

A modern web application featuring an AI-powered chatbot with RAG capabilities. Upload documents to create a knowledge base, and the chatbot will use them to provide contextual, accurate responses.

Features

• AI-Powered Chatbot: Leverages OpenAI's GPT models for intelligent conversations
• RAG System: Uses ChromaDB vector database for document retrieval
• Document Upload: Support for PDF and TXT files
• Semantic Search: Finds relevant context using sentence embeddings
• Modern UI: Clean, responsive interface built with vanilla JavaScript
• Toggle RAG: Switch between RAG-enhanced and standard chatbot modes
• Real-time Updates: Live document count and system status

Architecture

Backend

• FastAPI: Modern Python web framework
• ChromaDB: Vector database for document storage and retrieval
• Sentence Transformers: For creating document embeddings
• OpenAI API: Powers the chatbot responses

Frontend

• HTML/CSS/JavaScript: No framework dependencies
• Responsive Design: Works on desktop and mobile
• Real-time Communication: Async API calls

Prerequisites

• Python 3.8 or higher
• OpenAI API key
• pip (Python package manager)

Installation

1. Clone the repository:

   git clone <your-repo-url>
   cd Contracting-Government-Expert

2. Install Python dependencies:

   pip install -r requirements.txt

3. Set up environment variables:

   cp .env.example .env

4. Edit .env file and add your OpenAI API key:

   OPENAI_API_KEY=sk-your-api-key-here
   

Usage

Starting the Server

Run the application using Python:

python -m backend.main

Or use the run script:

python run.py

The server will start at http://localhost:8000

Using the Application

1. Open your browser and navigate to http://localhost:8000

2. Upload Documents:

   • Click "Choose File" in the sidebar
   • Select a .txt or .pdf file
   • Click "Upload"
   • The document will be processed and added to the knowledge base

3. Chat with the AI:

   • Type your message in the text area at the bottom
   • Press Enter or click "Send"
   • The AI will respond using context from your uploaded documents (if RAG is enabled)

4. Toggle RAG:

   • Use the toggle switch to enable/disable RAG
   • When disabled, the chatbot responds without document context

5. Clear Documents:

   • Click "Clear All Documents" to remove all uploaded documents
   • This will reset the knowledge base

API Endpoints

Health Check

GET /api/health

Returns system status and document count.

Chat

POST /api/chat
Body: {
  "message": "your question",
  "use_rag": true
}

Send a message and get an AI response.

Upload Document

POST /api/upload
Body: FormData with file

Upload a document to the knowledge base.

Get Document Count

GET /api/documents/count

Get the number of document chunks in the system.

Clear Documents

DELETE /api/documents

Remove all documents from the knowledge base.

Configuration

Edit the .env file to customize:

Variable	Description	Default
OPENAI_API_KEY	Your OpenAI API key	Required
HOST	Server host	0.0.0.0
PORT	Server port	8000
COLLECTION_NAME	ChromaDB collection name	documents
EMBEDDING_MODEL	Sentence transformer model	sentence-transformers/all-MiniLM-L6-v2
CHUNK_SIZE	Document chunk size	1000
CHUNK_OVERLAP	Chunk overlap	200

Project Structure

Contracting-Government-Expert/
├── backend/
│   ├── models/
│   │   └── schemas.py          # Pydantic models
│   ├── routes/
│   │   └── api.py              # API endpoints
│   ├── services/
│   │   ├── chat_service.py     # AI chat logic
│   │   └── rag_service.py      # RAG implementation
│   └── main.py                 # FastAPI application
├── frontend/
│   ├── static/
│   │   ├── css/
│   │   │   └── styles.css      # Styling
│   │   └── js/
│   │       └── app.js          # Frontend logic
│   └── index.html              # Main page
├── documents/                   # Place for sample documents
├── .env                        # Environment variables
├── .env.example                # Example environment file
├── requirements.txt            # Python dependencies
├── run.py                      # Run script
└── README.md                   # This file

How RAG Works

1. Document Upload: When you upload a document, it's split into chunks
2. Embedding: Each chunk is converted to a vector embedding
3. Storage: Embeddings are stored in ChromaDB
4. Query: When you ask a question, it's also converted to an embedding
5. Retrieval: Similar document chunks are retrieved using vector similarity
6. Generation: Retrieved chunks are sent to the AI as context
7. Response: The AI generates a response using the context

Troubleshooting

OpenAI API Error

• Make sure your API key is correct in .env
• Check you have credits in your OpenAI account
• Verify the API key has proper permissions

Port Already in Use

• Change the PORT in .env to a different value
• Or kill the process using port 8000

Module Not Found

• Ensure all dependencies are installed: pip install -r requirements.txt
• Make sure you're running from the project root directory

Document Upload Fails

• Check file format (only .txt and .pdf supported)
• Ensure file size is reasonable
• Check server logs for errors

Development

Adding New Features

1. Backend: Add new routes in backend/routes/api.py
2. Services: Add business logic in backend/services/
3. Frontend: Update frontend/static/js/app.js for new UI features

Testing

Test the API using curl:

# Health check
curl http://localhost:8000/api/health

# Send a chat message
curl -X POST http://localhost:8000/api/chat \
  -H "Content-Type: application/json" \
  -d '{"message":"Hello", "use_rag":true}'

Technologies Used

• FastAPI: Modern, fast web framework
• ChromaDB: Vector database
• OpenAI: GPT models
• Sentence Transformers: Document embeddings
• PyPDF: PDF processing
• Uvicorn: ASGI server

License

This project is open source and available under the MIT License.

Contributing

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

Support

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