Smart Bots

Create and deploy intelligent AI chatbots from your documents using RAG (Retrieval Augmented Generation) technology.

Features

• 📄 Document Upload: Support for PDF, DOCX, and TXT files
• 🤖 AI Training: Automatic document processing and vectorization
• 💬 Smart Chat: Intelligent responses based on your content
• 🌐 Easy Embed: One-line script to embed on any website
• 📊 Analytics: Track usage and performance
• 🔒 Secure: User isolation and data privacy

Tech Stack

• Frontend: Next.js 14, TypeScript, Tailwind CSS
• Backend: Node.js, Express, TypeScript
• Database: MongoDB (user data), PineCone (vector database)
• Queue: Redis + BullMQ
• AI: OpenAI API (GPT models + embeddings)
• Deployment: Docker Compose

Quick Start

Prerequisites

• Docker and Docker Compose
• OpenAI API key
• Node.js 18+ (for development)

1. Clone and Setup

git clone <repository-url>
cd AI-chatbot-generator

2. Environment Configuration

cp env.example .env

Edit .env and add your OpenAI API key:

OPENAI_API_KEY=your_openai_api_key_here
JWT_SECRET=your_jwt_secret_here

3. Start the Application

docker compose up --build

This will start:

• Frontend: http://localhost:3000
• Backend API: http://localhost:8000
• MongoDB: localhost:27017
• Redis: localhost:6379

4. Access the Application

Open http://localhost:3000 in your browser to start creating AI agents!

Usage Guide

Creating an AI Agent

1. Create Agent: Click "Create New AI Agent" and configure:

   • Agent name and personality
   • AI model (GPT-3.5, GPT-4, etc.)
   • Temperature (creativity level)
   • System prompt

2. Upload Documents:

   • Drag & drop PDF, DOCX, or TXT files
   • Maximum 10 files, 5MB each, 50MB total
   • Automatic validation and progress tracking

3. Training:

   • Documents are automatically processed
   • Text is chunked and vectorized
   • Stored in Pinecone for fast retrieval

4. Test in Playground:

   • Chat with your AI agent
   • Test responses based on uploaded documents
   • Adjust settings in real-time

5. Deploy:

   • Generate embed code
   • Add to any website with one line
   • Track usage and conversations

Embedding on Websites

1. Copy the generated script tag:

<script src="http://localhost:8000/embed/agent_id.js"></script>

2. Add to your website's HTML

3. The chatbot will appear as a floating widget

Development

Frontend Development

cd frontend
npm install
npm run dev

Backend Development

cd backend
npm install
npm run dev

Database Access

• MongoDB: Use MongoDB Compass or mongo shell
• Redis: Use redis-cli or RedisInsight

API Endpoints

Agents

• POST /api/agents - Create agent
• GET /api/agents - List agents
• GET /api/agents/:id - Get agent details
• PUT /api/agents/:id - Update agent
• DELETE /api/agents/:id - Delete agent

Files

• POST /api/agents/:id/files - Upload files
• GET /api/agents/:id/files - List files
• DELETE /api/agents/:id/files/:fileId - Delete file

Training

• POST /api/agents/:id/train - Start training
• GET /api/agents/:id/training-status - Get training status

Chat

• POST /api/chat/:agentId - Send message
• GET /api/chat/:agentId/history - Get chat history

Embed

• GET /api/embed/:agentId.js - Get embed script
• POST /api/embed/session - Create embed session

Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Frontend      │    │    Backend      │    │   Databases     │
│   (Next.js)     │◄──►│   (Node.js)     │◄──►│                 │
│                 │    │                 │    │ ┌─────────────┐ │
│ - Agent Mgmt    │    │ - API Routes    │    │ │  MongoDB    │ │
│ - File Upload   │    │ - File Process  │    │ │  (Users,    │ │
│ - Chat UI       │    │ - AI Training   │    │ │  Agents,    │ │
│ - Embed Widget  │    │ - RAG Pipeline  │    │ │  Sessions)  │ │
└─────────────────┘    └─────────────────┘    │ └─────────────┘ │
                                              │ ┌─────────────┐ │
                                              │ │  Pinecone   │ │
                                              │ │ (Vectors)   │ │
                                              │ └─────────────┘ │
                                              │ ┌─────────────┐ │
                                              │ │    Redis    │ │
                                              │ │ (Job Queue) │ │
                                              │ └─────────────┘ │
                                              └─────────────────┘

Data Flow

1. Document Upload: Files → Backend → MongoDB (metadata) + Disk (content)
2. Training: Parse → Chunk → Embed → Pinecone (vectors)
3. Chat: Query → PineCone Search → Context → LLM → Response
4. Embed: Script → iframe → Backend API → Chat Interface

Security Features

• JWT authentication for API access
• User data isolation
• File type and size validation
• Rate limiting on API endpoints
• CORS configuration for embed widgets

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 documentation
• Review the API reference

Built with ❤️ using WEAM