# ChatRAG

> The Next.js & AI SDK boilerplate for building production-ready AI chatbots with RAG (Retrieval Augmented Generation). Build, launch, and monetize custom chatbot-based SaaS products in hours, not months.

## Overview

ChatRAG is a complete Next.js boilerplate that enables developers to rapidly build and deploy AI-powered chatbots with document understanding capabilities. It combines modern web technologies with enterprise-grade AI features, including vector databases, multiple LLM providers, and built-in monetization through Polar.

**Key Value Proposition:** Turn data into profit by building custom AI chatbot SaaS products without months of infrastructure development.

## Main Pages

- [Home](https://chatrag.ai/): Full feature overview, pricing, and live demo
- [Documentation](https://chatrag.ai/docs/): Complete setup and integration guides
- [Blog](https://chatrag.ai/blog/): Articles about RAG, AI chatbots, and implementation guides
- [Pricing](https://chatrag.ai/#pricing): Transparent pricing with one-time payment
- [Demo](https://chatrag.ai/#demo): Interactive chatbot demonstration
- [FAQ](https://chatrag.ai/#faq): Common questions and answers


## Core Features

### AI & RAG Capabilities
- **Multiple LLM Support**: OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Groq
- **Advanced RAG System**: Context-aware responses using vector search
- **Document Processing**: Support for PDF, TXT, MD, DOCX, and web scraping
- **Smart Context Management**: Automatic relevance scoring and citation

### Technical Stack
- **Framework**: Next.js 14+ with App Router and Server Actions
- **Database**: Supabase with pgvector for embeddings
- **AI SDKs**: Vercel AI SDK, LlamaIndex, LangChain
- **Storage**: Multiple vector database options (Supabase, LlamaCloud)
- **Authentication**: NextAuth.js with multiple providers

### Business Features
- **Built-in Monetization**: Polar integration for subscriptions and payments
- **Usage Tracking**: Token consumption and analytics
- **Multi-tenancy**: Support for multiple users and workspaces
- **White-label Ready**: Customizable branding and themes

## Documentation Structure

### Getting Started
- [Prerequisites](https://chatrag.ai/docs/prerequisites): System requirements and accounts needed
- [Quick Start](https://chatrag.ai/docs/quick-start): 5-minute setup guide
- [Database Setup](https://chatrag.ai/docs/database-setup): Supabase configuration
- [Authentication](https://chatrag.ai/docs/authentication): User authentication setup
- [API Keys](https://chatrag.ai/docs/api-keys): Configure LLM providers

### Configuration
- [Config UI](https://chatrag.ai/docs/config-ui): Visual configuration interface
- [AI Models](https://chatrag.ai/docs/ai-models): Select and configure LLM providers
- [System Prompt](https://chatrag.ai/docs/system-prompt): Customize chatbot behavior
- [Branding](https://chatrag.ai/docs/branding): White-label customization

### Features
- [RAG System](https://chatrag.ai/docs/rag-system): Document indexing and retrieval
- [Document Processing](https://chatrag.ai/docs/document-processing): Upload and process files
- [Media Generation](https://chatrag.ai/docs/media-generation): AI image generation
- [WhatsApp Integration](https://chatrag.ai/docs/whatsapp): Connect chatbot to WhatsApp

### Advanced
- [MCP Integration](https://chatrag.ai/docs/mcp-integration): Model Context Protocol
- [Payments](https://chatrag.ai/docs/payments): Polar monetization setup
- [Deployment](https://chatrag.ai/docs/deployment): Production deployment guide
- [Troubleshooting](https://chatrag.ai/docs/troubleshooting): Common issues and solutions

## Use Cases

ChatRAG is ideal for building:
- **Customer Support Bots**: AI assistants trained on documentation and help articles
- **Knowledge Base Chatbots**: Internal company knowledge with natural language queries
- **Document Analysis Tools**: Legal, financial, or research document Q&A systems
- **Educational Assistants**: Course materials and textbook-based tutoring bots
- **Sales & Lead Generation**: Product catalog search and recommendation engines
- **Content Creation**: AI writing assistants trained on brand guidelines

## Technology Details

**Frontend:**
- React 18+ with TypeScript
- Tailwind CSS with shadcn/ui components
- Framer Motion for animations
- Dark mode support

**Backend:**
- Next.js Server Actions
- PostgreSQL with pgvector extension
- RESTful API routes
- Webhook support

**AI Infrastructure:**
- Vector embeddings (text-embedding-3-small, etc.)
- Semantic search with cosine similarity
- Streaming responses
- Function calling support

**Developer Experience:**
- Hot reload development
- TypeScript strict mode
- ESLint configuration
- Environment-based configuration

## Pricing & Licensing

- **One-time Payment**: $99 (current price)
- **Lifetime Updates**: Access to all future updates
- **No Recurring Fees**: No subscriptions or hidden costs
- **Commercial Use**: Build unlimited commercial projects

## Support & Community

- **Documentation**: Comprehensive guides at chatrag.ai/docs
- **Email Support**: Direct developer support
- **GitHub**: Issue tracking and feature requests
- **Updates**: Regular feature additions and improvements

## Technical Requirements

- Node.js 18+
- Supabase account (free tier available)
- OpenAI API key (or alternative LLM provider)
- Vercel/Netlify account for deployment (optional)

## Quick Installation

```bash
git clone [repository]
npm install
cp .env.example .env.local
# Configure environment variables
npm run dev
```

## About ChatRAG

Created by Carlos Marcial (@carlosmarcialt), ChatRAG emerged from the need for a production-ready boilerplate that handles the complex infrastructure of AI chatbots. Instead of spending months building RAG systems, authentication, database schemas, and payment integration, developers can launch in hours.

**Mission**: Democratize AI chatbot development by providing enterprise-grade infrastructure as an affordable, easy-to-use boilerplate.

## Related Keywords

AI chatbot builder, RAG implementation, Next.js boilerplate, AI SDK, chatbot SaaS, vector database integration, OpenAI chatbot, Supabase pgvector, LlamaCloud, document Q&A, semantic search, AI monetization, chatbot white-label, Next.js AI starter