RAG Chatbot with Google Docs Integration

A powerful Retrieval-Augmented Generation (RAG) chatbot that integrates with Google Drive, allowing users to query their documents using natural language. Built with FastAPI backend and Next.js frontend.

🚀 Features

Core Features

• Google OAuth Authentication - Secure sign-in with Google accounts
• Google Drive Integration - Access and list documents from Google Drive
• Multi-format Support - Works with Google Docs, PDFs, DOCX, TXT, and PPTX files
• RAG Pipeline - Intelligent document querying using ChromaDB and Gemini AI
• Smart Fallback - Falls back to general knowledge when answers aren't found in documents
• Clean UI - Modern, responsive interface built with Next.js and Tailwind CSS

Document Processing

• Text Extraction - Advanced content extraction from various file formats
• Intelligent Chunking - Smart text splitting for optimal retrieval
• Vector Embeddings - Semantic search using sentence transformers
• Knowledge Base Management - Add/remove documents from the AI knowledge base

AI Capabilities

• Natural Language Queries - Ask questions in plain English
• Contextual Responses - Answers based on your specific documents
• Clean Formatting - Well-formatted responses without markdown artifacts
• Multi-document Support - Query across multiple documents simultaneously

🛠️ Tech Stack

Backend

• FastAPI - Modern Python web framework
• ChromaDB - Vector database for embeddings
• Google APIs - Drive and Docs integration
• Gemini 2.0 Flash - Advanced AI model for generation
• Sentence Transformers - Text embedding generation

Frontend

• Next.js 13+ - React framework with App Router
• Tailwind CSS - Utility-first CSS framework
• Shadcn UI - Modern component library
• TypeScript - Type-safe development

Infrastructure

• Docker - Containerized deployment
• Supabase - Optional user management
• Environment Variables - Secure configuration management

📋 Prerequisites

• Python 3.8+
• Node.js 16+
• Google Cloud Console project with APIs enabled
• Gemini API key

🔧 Setup Instructions

1. Clone the Repository

git clone https://github.com/santoshnaya/codemet-rag-chatbot.git
cd codemet-rag-chatbot

2. Google Cloud Console Setup

1. Go to Google Cloud Console

2. Create a new project or select existing one

3. Enable the following APIs:

   • Google Drive API
   • Google Docs API
   • Google+ API

4. Create OAuth 2.0 credentials:

   • Go to APIs & Services > Credentials
   • Click Create Credentials > OAuth 2.0 Client IDs
   • Set Authorized redirect URIs to: http://localhost:3000/auth/callback

3. Environment Configuration

Backend Environment (.env in backend folder):

# Google OAuth Configuration
GOOGLE_CLIENT_ID=your_google_client_id
GOOGLE_CLIENT_SECRET=your_google_client_secret

# Gemini API Configuration
GEMINI_API_KEY=your_gemini_api_key

# Supabase Configuration (Optional)
SUPABASE_URL=your_supabase_url
SUPABASE_SERVICE_ROLE_KEY=your_supabase_key

# Development Settings
ENVIRONMENT=development

Frontend Environment (.env.local in root folder):

# Google OAuth Configuration
NEXT_PUBLIC_GOOGLE_CLIENT_ID=your_google_client_id

# Backend Configuration
NEXT_PUBLIC_BACKEND_URL=http://localhost:8000

# Supabase Configuration (Optional)
NEXT_PUBLIC_SUPABASE_URL=your_supabase_url
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_supabase_anon_key

4. Backend Setup

cd backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

5. Frontend Setup

npm install

6. Run the Application

Start Backend (Terminal 1):

cd backend
source venv/bin/activate
python main.py

Start Frontend (Terminal 2):

npm run dev

7. Access the Application

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

🎯 Usage

1. Sign In: Click "Sign in with Google" and authorize the application
2. Browse Documents: Navigate to the Documents tab to see your Google Drive files
3. Add to Knowledge Base: Select documents and add them to the chatbot's knowledge base
4. Start Chatting: Go to the Chat tab and ask questions about your documents
5. Get Answers: Receive intelligent responses based on your document content

🐳 Docker Deployment

Build and Run with Docker Compose:

docker-compose up --build

Individual Container Build:

Backend:

docker build -f Dockerfile.backend -t rag-chatbot-backend .
docker run -p 8000:8000 rag-chatbot-backend

Frontend:

docker build -f Dockerfile.frontend -t rag-chatbot-frontend .
docker run -p 3000:3000 rag-chatbot-frontend

🚀 Deployment

Render (Backend)

1. Connect your GitHub repository to Render
2. Set the following configuration:
   • Root Directory: backend
   • Build Command: pip install -r requirements.txt
   • Start Command: python main.py
   • Environment Variables: Add all backend environment variables

Vercel (Frontend)

1. Connect your GitHub repository to Vercel
2. Set the following configuration:
   • Framework Preset: Next.js
   • Root Directory: . (root)
   • Build Command: npm run build
   • Environment Variables: Add all frontend environment variables

📚 API Endpoints

Authentication

• POST /auth/google - Google OAuth authentication
• GET /health - Health check

Documents

• GET /documents - List user documents
• POST /documents/add - Add documents to knowledge base
• DELETE /documents/{id} - Remove document from knowledge base

Chat

• POST /chat - Chat with documents
• GET /debug/knowledge-base - Debug knowledge base content

User

• GET /user/profile - Get user profile

🔍 Troubleshooting

Common Issues

1. Authentication Failed

   • Check Google Cloud Console redirect URI: http://localhost:3000/auth/callback
   • Ensure APIs are enabled (Drive, Docs, Google+)
   • Verify client ID and secret in environment variables

2. Documents Not Loading

   • Check Google Drive API permissions
   • Verify access token is valid
   • Ensure proper scopes are requested

3. Chat Not Working

   • Check Gemini API key
   • Verify documents are added to knowledge base
   • Check backend logs for errors

Debug Pages

• /debug - OAuth configuration debug
• /debug-auth - Authentication status and token testing

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit your changes: git commit -m 'Add amazing feature'
4. Push to the branch: git push origin feature/amazing-feature
5. Open a Pull Request

📝 License

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

🙏 Acknowledgments

• Google APIs for document integration
• Gemini AI for intelligent responses
• ChromaDB for vector storage
• Next.js and FastAPI communities

📞 Support

If you encounter any issues or have questions:

1. Check the Issues section
2. Create a new issue with detailed information
3. Include error logs and environment details

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Built with ❤️ using FastAPI, Next.js, and Google AI