🤖 AI Customer Support Bot - MCP Server

🌟 Overview

A Model Context Protocol (MCP) compliant server framework built with modern Python. Designed for developers who want to create intelligent customer support systems without vendor lock-in. Clean architecture, battle-tested patterns, and ready for any AI provider.

graph TB
    Client[HTTP Client] --> API[API Server]
    API --> MW[Middleware Layer]
    MW --> SVC[Service Layer]
    SVC --> CTX[Context Manager]
    SVC --> AI[AI Integration]
    SVC --> DAL[Data Access Layer]
    DAL --> DB[(PostgreSQL)]

✨ Features

🚀 Quick Start

Prerequisites

• Python 3.8+
• PostgreSQL
• Your favorite AI service (OpenAI, Anthropic, etc.)

Installation

# Clone and setup
git clone https://github.com/ChiragPatankar/AI-Customer-Support-Bot--MCP-Server.git
cd AI-Customer-Support-Bot--MCP-Server

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Setup environment
cp .env.example .env
# Edit .env with your configuration

Configuration

# .env file
DATABASE_URL=postgresql://user:password@localhost/customer_support_bot
SECRET_KEY=your-super-secret-key
RATE_LIMIT_REQUESTS=100
RATE_LIMIT_PERIOD=60

Run

# Setup database
createdb customer_support_bot

# Start server
python app.py
# 🚀 Server running at http://localhost:8000

📡 API Reference

GET /mcp/health

POST /mcp/process
Content-Type: application/json
X-MCP-Auth: your-token
X-MCP-Version: 1.0

{
  "query": "How do I reset my password?",
  "priority": "high"
}

POST /mcp/batch
Content-Type: application/json
X-MCP-Auth: your-token

{
  "queries": [
    "How do I reset my password?",
    "What are your business hours?"
  ]
}

{
  "status": "success",
  "data": {
    "response": "Generated AI response",
    "confidence": 0.95,
    "processing_time": "120ms"
  },
  "meta": {
    "request_id": "req_123456",
    "timestamp": "2024-02-14T12:00:00Z"
  }
}

{
  "code": "RATE_LIMIT_EXCEEDED",
  "message": "Rate limit exceeded",
  "details": {
    "retry_after": 60,
    "timestamp": "2024-02-14T12:00:00Z"
  }
}

🏗️ Architecture

Project Structure

📦 AI-Customer-Support-Bot--MCP-Server
├── 🚀 app.py              # FastAPI application
├── 🗄️  database.py         # Database configuration
├── 🛡️  middleware.py       # Auth & rate limiting
├── 📋 models.py          # ORM models
├── ⚙️  mcp_config.py      # MCP protocol config
├── 📄 requirements.txt   # Dependencies
└── 📝 .env.example      # Environment template

Layer Responsibilities

🔌 Extending with AI Services

Add Your AI Provider

1. Install your AI SDK:

pip install openai  # or anthropic, cohere, etc.

2. Configure environment:

# Add to .env
AI_SERVICE_API_KEY=sk-your-api-key
AI_SERVICE_MODEL=gpt-4

3. Implement service integration:

# In service layer
class AIService:
    async def generate_response(self, query: str, context: dict) -> str:
        # Your AI integration here
        return ai_response

🔧 Development

Running Tests

pytest tests/

Code Quality

# Format code
black .

# Lint
flake8

# Type checking
mypy .

Docker Support

# Coming soon - Docker containerization

📊 Monitoring & Observability

Health Metrics

• ✅ Service uptime
• 🔗 Database connectivity
• 📈 Request rates
• ⏱️ Response times
• 💾 Memory usage

Logging

# Structured logging included
{
  "timestamp": "2024-02-14T12:00:00Z",
  "level": "INFO",
  "message": "Query processed",
  "request_id": "req_123456",
  "processing_time": 120
}

🔒 Security

Built-in Security Features

• 🔐 Token Authentication - Secure API access
• 🛡️ Rate Limiting - DoS protection
• ✅ Input Validation - SQL injection prevention
• 📝 Audit Logging - Request tracking
• 🔒 Environment Secrets - Secure config management

🚀 Deployment

Environment Setup

# Production environment variables
DATABASE_URL=postgresql://prod-user:password@prod-host/db
RATE_LIMIT_REQUESTS=1000
LOG_LEVEL=WARNING

Scaling Considerations

• Use connection pooling for database
• Implement Redis for rate limiting in multi-instance setups
• Add load balancer for high availability
• Monitor with Prometheus/Grafana

🤝 Contributing

We love contributions! Here's how to get started:

Development Setup

# Fork the repo, then:
git clone https://github.com/your-username/AI-Customer-Support-Bot--MCP-Server.git
cd AI-Customer-Support-Bot--MCP-Server

# Create feature branch
git checkout -b feature/amazing-feature

# Make your changes
# ...

# Test your changes
pytest

# Submit PR

Contribution Guidelines

• 📝 Write tests for new features
• 📚 Update documentation
• 🎨 Follow existing code style
• ✅ Ensure CI passes

📈 Roadmap

• 🐳 Docker containerization
• 🔄 Redis caching layer
• 📊 Prometheus metrics
• 🔍 OpenTelemetry tracing
• 🌐 GraphQL API
• 📱 WebSocket support

📄 License

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