🤖 Free Forever RAG Application – Chat with Your Data

A comprehensive Retrieval Augmented Generation (RAG) application built with Next.js, featuring document processing, website scraping, and AI-powered chat functionality.

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🔐 Security & Privacy

⚡ Important Note:

• Your OpenAI API key and Qdrant credentials are never stored on our servers.
• They are stored securely only on your localhost (or in your environment variables like .env.local).
• This means your credentials always remain under your control and are not shared with any third-party servers.

👉 So, you can safely use your API key without any worries everything stays on your localhost.

Features

• Multiple Data Sources: Support for text input, file uploads (PDF, CSV, TXT), and website scraping
• Vector Database: Qdrant integration for efficient document storage and retrieval
• AI-Powered Chat: OpenAI GPT integration for intelligent responses based on your data
• Modern UI: Dark mode interface with light orange accent colors
• Form Validation: React Hook Form with Zod validation for better user experience
• Real-time Processing: Live feedback and progress indicators
• Flexible Deployment: Support for both local Docker and Qdrant Cloud

Tech Stack

• Frontend: Next.js 14, React, TypeScript
• UI Components: shadcn/ui, Tailwind CSS
• Forms: React Hook Form, Zod validation
• Vector Database: Qdrant (Local Docker or Cloud)
• AI Integration: OpenAI API, LangChain
• File Processing: PDF parsing, CSV processing, web scraping

Prerequisites

• Node.js 18+
• OpenAI API key
• Choose one of the following for vector database:
  • Docker (for local Qdrant instance)
  • Qdrant Cloud account (managed service)

Installation

1. Clone the repository

git clone https://github.com/BCAPATHSHALA/RAGApplication.git
cd RAGApplication

2. Install dependencies

pnpm install

3. Choose your vector database setup:

Option A: Local Docker Setup (Recommended for Development)

3a. Start Qdrant vector database

docker-compose up -d

This will start Qdrant on http://localhost:6333

3b. Set up environment variables Create a .env.local file in the root directory:

QDRANT_URL=http://localhost:6333
OPENAI_API_KEY=your_openai_api_key_here

Option B: Qdrant Cloud Setup (Recommended for Production)

3a. Create a Qdrant Cloud account

• Visit https://cloud.qdrant.io/
• Sign up for a free account
• Create a new cluster
• Get your cluster URL and API key

3b. Set up environment variables Create a .env.local file in the root directory:

QDRANT_URL=https://your-cluster-url.qdrant.io
QDRANT_API_KEY=your_qdrant_api_key
OPENAI_API_KEY=your_openai_api_key_here

4. Run the development server

pnpm run dev

5. Open your browser Navigate to http://localhost:3000

Configuration

In-App Configuration

The application provides an intuitive interface for configuration:

1. OpenAI API Key: Enter your OpenAI API key in the API Key section
2. Qdrant Configuration:
   • For local Docker: Use http://localhost:6333 (no API key needed)
   • For Qdrant Cloud: Enter your cluster URL and API key from the dashboard

Environment Variables (Alternative)

You can also configure via environment variables:

• QDRANT_URL: Qdrant database connection URL
• QDRANT_API_KEY: Qdrant API key (required for cloud, optional for local)
• OPENAI_API_KEY: OpenAI API key for embeddings and chat

Usage

1. Configure Services

• OpenAI API Key: Enter your API key (must start with 'sk-')
• Qdrant Setup: Configure either local Docker or Qdrant Cloud connection

2. Add Data Sources

Text Input:

• Paste text directly into the textarea
• Minimum 10 characters required
• Text will be chunked and indexed automatically

Website Scraping:

• Enter a valid URL (must include http:// or https://)
• The system will scrape and index the website content

File Upload:

• Upload PDF, CSV, or TXT files (max 10MB)
• Files are processed and chunked automatically
• Progress feedback provided during processing

3. Monitor RAG Store

• View real-time statistics of indexed documents
• See total documents and chunks
• Review recent data sources

4. Chat with Your Data

• Use the chat interface to ask questions about your indexed data
• The AI will provide responses based on the most relevant document chunks
• Conversation history is maintained during the session
• Create multiple chat sessions for different topics

API Endpoints

• POST /api/index-text - Index text content
• POST /api/index-website - Scrape and index website
• POST /api/index-file - Process and index uploaded files
• POST /api/chat - Chat with indexed data
• GET /api/rag-store - Get indexed document statistics
• DELETE /api/delete-index - Delete all indexed data

Architecture

Vector Database Options

Local Docker (Development)

• Qdrant: High-performance vector database running in Docker
• Port: 6333 (default)
• Storage: Persistent volume for data retention
• Configuration: No API key required

Qdrant Cloud (Production)

• Managed Service: Fully managed Qdrant instance
• Scalability: Auto-scaling based on usage
• Security: Built-in authentication and encryption
• Global: Multiple regions available
• Configuration: Requires cluster URL and API key

Document Processing

• Text Chunking: Recursive character text splitter (1000 chars, 200 overlap)
• PDF Processing: LangChain PDF loader for text extraction
• CSV Processing: Structured data handling with metadata
• Web Scraping: Cheerio for clean HTML content extraction

AI Integration

• OpenAI GPT-4: For generating contextual responses
• LangChain: Document processing and RAG pipeline orchestration
• Embeddings: text-embedding-3-large for high-quality vector representations

Deployment

Local Development

pnpm run dev

Docker Deployment (with local Qdrant)

1. Build the application:

pnpm run build

2. Start with Docker Compose:

docker-compose up -d

Vercel Deployment (with Qdrant Cloud)

1. Deploy to Vercel:

vercel deploy

2. Set environment variables in Vercel dashboard:
   • QDRANT_URL (your Qdrant Cloud cluster URL)
   • QDRANT_API_KEY (your Qdrant Cloud API key)
   • OPENAI_API_KEY (your OpenAI API key)

Production Considerations

For Qdrant Cloud:

• Use environment variables for sensitive configuration
• Enable API key authentication
• Monitor usage and costs in Qdrant Cloud dashboard
• Consider data residency requirements

For Local Docker:

• Ensure persistent storage for production data
• Configure proper backup strategies
• Monitor resource usage and scaling needs
• Secure network access to Qdrant instance

Troubleshooting

Common Issues

"Collection not found" errors:

• The collection is created automatically when you first index data
• Ensure Qdrant is running and accessible
• Check your Qdrant URL and API key configuration

File upload failures:

• Check file size limits (10MB max)
• Ensure supported file formats (PDF, CSV, TXT)
• Verify OpenAI API key is valid

Chat not working:

• Ensure you have indexed some data first
• Check OpenAI API key configuration
• Verify Qdrant connection is working

Performance Tips

• Use Qdrant Cloud for better performance and reliability
• Index documents in smaller batches for large datasets
• Monitor OpenAI API usage and costs
• Consider chunking strategies for different document types

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

MIT License - see LICENSE file for details

Support

For issues and questions:

• Create an issue on GitHub
• Check the troubleshooting section
• Review the API documentation
• Visit Qdrant Cloud Documentation

Social Links

• LinkedIn
• Twitter