🚀 DSL Chatbot Platform

A production-ready chatbot platform that allows users to define chatbots using a custom Domain-Specific Language (DSL), compile them into optimized JSON configurations, and deploy them instantly with a beautiful embeddable widget. https://buildchatbot.onrender.com

✨ Features

• Custom DSL Compiler: Define chatbots with a simple, intuitive DSL
• MongoDB Storage: Persistent storage with connection pooling
• Runtime Engine: Optimized message matching with multiple strategies
• Embeddable Widget: Beautiful, responsive chat widget for any website
• Web Dashboard: Compile and manage bots through a web interface
• REST API: Full CRUD operations for bot management
• Production Ready: Error handling, validation, rate limiting, and logging

🏗️ Architecture

DSL → Compiler → JSON → MongoDB
                    ↓
Widget ← API ← Runtime Engine

Components

• Compiler Pipeline: Lexer → Parser → Semantic Analyzer → IR Generator
• Runtime Engine: Message matching with keyword indexing and trie-based search
• API Layer: RESTful endpoints with validation and error handling
• Frontend: Vanilla JS widget + HTML dashboard

🚀 Quick Start

Prerequisites

• Node.js 16+
• MongoDB (local or Atlas)

Installation

1. Clone the repository

   git clone <repository-url>
   cd dsl-chatbot-platform

2. Install dependencies

   npm install

3. Configure environment

   # Copy and edit .env
   cp .env.example .env

   Edit .env:

   MONGODB_URI=mongodb://localhost:27017/dsl_chatbot
   DB_NAME=dsl_chatbot
   PORT=5000
   NODE_ENV=production

4. Start the server

   npm start

5. Open the dashboard

   http://localhost:5000
   

📝 DSL Syntax

Define your chatbot using our custom DSL:

bot MyChatbot
domain ecommerce
tone friendly

welcome "Hello! How can I help you today?"

intent product_search
keywords: shirts pants dresses shoes
response "Great choice! What size are you looking for?"

intent order_status
keywords: order status tracking
response "Please provide your order number."

fallback "I'm sorry, I didn't understand. Can you rephrase?"

DSL Reference

• bot <name>: Bot name
• domain <type>: ecommerce, education, saas
• tone <style>: friendly, formal, professional
• welcome "<message>": Welcome message
• intent <name>: Define an intent
• keywords: word1 word2: Keywords for matching
• response "<message>": Bot response
• fallback "<message>": Default response

🔌 API Endpoints

Compile DSL

POST /api/compile
Content-Type: application/json

{
  "dsl": "bot MyBot\ndomain ecommerce..."
}

Chat with Bot

POST /api/chat/{botId}
Content-Type: application/json

{
  "message": "Hello"
}

Get Bot Info

GET /api/bot/{botId}

List All Bots

GET /api/bots

Delete Bot

DELETE /api/bot/{botId}

Health Check

GET /api/health

🎨 Embedding the Widget

Add this script tag to your website:

<script src="https://yourdomain.com/widget.js" data-bot-id="bot_abc123"></script>

🧪 Testing

Using the Web Interface

1. Visit http://localhost:5000
2. Paste DSL code in the left panel
3. Click "Compile Bot"
4. Copy the embed code and use the bot ID

Using cURL

# Compile a bot
curl -X POST http://localhost:5000/api/compile \
  -H "Content-Type: application/json" \
  -d '{
    "dsl": "bot TestBot\ndomain ecommerce\ntone friendly\nwelcome \"Hello!\"\nintent greet\nkeywords: hello hi\nresponse \"Hi there!\"\nfallback \"Sorry\""
  }'

# Chat with the bot
  ## Deploying to Render
  Follow these steps to deploy on Render (https://render.com):

  1. Remove any committed secrets from the repository (run locally):

    ```bash
    git rm --cached .env || true
    git commit -m "Remove local .env from repo"
    ```

  2. Ensure `.env` is listed in `.gitignore` (already configured).

  3. Create a new Web Service on Render and connect your Git repository.

  4. In the Render service settings, set the following Environment variables:

    - `MONGODB_URI` : your MongoDB connection string
    - `DB_NAME` : database name (e.g. `dsl_chatbot`)
    - `NODE_ENV` : `production`
    - `JWT_SECRET` : a secure random string
    - `REDIS_URL` : (optional) Redis connection string

  5. Leave the build command as `npm install` and the start command as `npm start`, or use the provided `render.yaml` or `Dockerfile` for Docker-based deploys.

  6. Optional: Use the included `Dockerfile` for a containerized deployment on Render (recommended for consistent runtime).

  7. After deployment, check the health endpoint at `https://<your-service>.onrender.com/api/health`.

curl -X POST http://localhost:5000/api/chat/bot_abc123 \
  -H "Content-Type: application/json" \
  -d '{"message": "hello"}'

🏃 Development

# Development mode with auto-restart
npm run dev

# Run tests
npm test

# Lint code
npm run lint

# Format code
npm run format

📁 Project Structure

├── src/
│   ├── compiler/          # DSL Compiler Pipeline
│   │   ├── index.js       # Compiler facade
│   │   ├── lexer.js       # Lexical analysis
│   │   ├── parser.js      # Syntax analysis
│   │   ├── semantic.js    # Semantic validation
│   │   └── ir.js          # IR generation
│   ├── engine/            # Runtime Engine
│   │   ├── runtime.js     # Runtime manager
│   │   └── matcher.js     # Message matching
│   ├── storage/           # Database Layer
│   │   ├── mongodb.repository.js  # MongoDB operations
│   │   ├── cache.js       # Runtime cache
│   │   └── initDb.js      # Database setup
│   ├── api/               # API Layer
│   │   ├── routes.js      # Route definitions
│   │   ├── controllers/   # Request handlers
│   │   └── middleware/    # Express middleware
│   ├── errors/            # Error classes
│   └── server.js          # Main server file
├── public/                # Static assets
│   ├── index.html         # DSL Editor UI
│   └── widget.js          # Chat widget
├── tests/                 # Test files and examples
├── templates/             # DSL templates
├── .env                   # Environment variables
├── package.json
└── README.md

🔧 Configuration

Environment Variables

• MONGODB_URI: MongoDB connection string
• DB_NAME: Database name
• PORT: Server port (default: 5000)
• NODE_ENV: Environment (development/production)

MongoDB Indexes

The system automatically creates optimized indexes:

• { user_id: 1, bot_id: 1 } (unique)
• { user_id: 1, created_at: -1 }

🚀 Deployment

Docker

FROM node:16-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
EXPOSE 5000
CMD ["npm", "start"]

Environment Setup

For production:

• Set NODE_ENV=production
• Use a production MongoDB instance
• Configure proper logging
• Set up monitoring

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

Author

Vaishnavi Khandelwal

📄 License

MIT License - see LICENSE file for details.

🆘 Support

• Documentation: [Link]
• Issues: [GitHub Issues]
• Email: support@example.com

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Built with ❤️ using Node.js, Express, MongoDB, and vanilla JavaScript.