Shopify ChatBot

An intelligent commerce assistant built for seamless Shopify storefront integration, leveraging advanced NLP and hybrid retrieval systems for enhanced customer experiences.

Features

• 🤖 AI-Powered Conversations
  OpenAI LLM integration for natural, context-aware dialogue with customers.

• 🔍 Hybrid Search
  FAISS semantic search combined with Elasticsearch lexical matching for highly accurate product discovery.

• 🛒 Real-time Shopify Integration
  Live access to products, customer accounts, and orders with seamless sync.

• 🧩 Agentic Shopping Assistant
  Smart, autonomous actions to:

  • Create, add, edit, and delete products from the cart
  • Finalize checkout flows
  • Handle customer account creation & authentication
  • Manage order status, updates, and tracking

• 💾 Persistent Sessions
  Redis for active user sessions and MongoDB for chat history to ensure continuity across conversations.

• 🎨 Theme Integration
  Native Shopify theme extension for an on-brand, seamless customer experience.

• 🐳 Containerized Deployment
  Docker-based infrastructure with CI/CD pipelines for reliable, scalable deployment.

Tech Stack

Backend

• Language: Python 3.10+ (with async support)
• Framework: FastAPI / Async ( Async support for high-concurrency operations )
• Data Validation: Pydantic
• Database: MongoDB (persistence), Redis (sessions)
• Search: FAISS (semantic), Elasticsearch/OpenSearch (lexical)
• Message Queue: RabbitMQ
• API Integration: Shopify REST/GraphQL APIs

Frontend

• Languages: HTML, CSS, JavaScript, Liquid
• Integration: Shopify Theme Extension
• Styling: Custom CSS with responsive design

Infrastructure

• Containerization: Docker, Docker Compose
• CI/CD: GitHub Actions
• Hosting: Ubuntu VM (KVM-based)
• Monitoring: Custom logging with OVH Cloud Monitoring

Architecture

graph TD
    A[User Query] --> B[Shopify Theme UI]
    B --> C[FastAPI Server]
    C --> D[OpenAI LLM]    
    D --> N[MCP Server]
    N --> E[Hybrid Retrieval]
    E --> F[FAISS - Semantic]
    E --> G[Elasticsearch - Lexical]
    N --> H[Shopify API]
    H --> Q[Products Data]
    H --> R[Orders Data]
    H --> S[Customers Data]
    C --> O[Session Management]
    O --> I[Redis Store]
    C --> P[Chat Storage]
    P --> J[MongoDB TimeSeries]
    D --> M[Vector File Store]
    N --> Ai[Agentic Abilities]
    Ai --> AA[- Cart -]
    AA --> AB[Create Cart]
    AA --> AC[Update Cart]
    AA --> AD[Add Cart Items]
    AA --> AE[Remove Cart Items]
    Ai --> AH[- Customer -]
    AH --> AF[Create Customer]
    AH --> AG[Create/Update Customer Orders]

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Quick Start

Prerequisites

• Python 3.10+
• Docker & Docker Compose
• Shopify Partner Account
• OpenAI API Key

Installation

1. Clone the repository

   git clone https://github.com/Mobeen-Dev/chatbot_Shopify.git
   cd chatbot_Shopify

2. Set up environment variables

   cp credentials/.env.example credentials/.env
   # Edit credentials/.env with your API keys and configuration

3. Start with Docker Compose

   docker-compose up -d

4. Install Shopify theme extension

   # Follow Shopify CLI installation guide
   shopify app dev

Configuration

Create a credentials/.env file with the following variables:

# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key

# Shopify Configuration
SHOPIFY_API_KEY=your_shopify_api_key
SHOPIFY_API_SECRET=your_shopify_secret
SHOPIFY_STORE_URL=your_store.myshopify.com

# Database Configuration
MONGODB_URI=mongodb://localhost:27017/chatbot
REDIS_URL=redis://localhost:6379

# Search Configuration
ELASTICSEARCH_URL=http://localhost:9200
FAISS_INDEX_PATH=./data/faiss_index

# Application Settings
APP_ENV=development
LOG_LEVEL=INFO

Project Structure

shopify-chatbot/
├── src/
│   ├── server.py              # Main application entry point
│   ├── config.py              # Configuration management
│   ├── logger.py              # Centralized logging
│   ├── shopify.py             # Shopify API integration
│   ├── embed_and_save_vector.py # Vector embedding utilities
│   └── handle_order.py        # Order processing logic
├── theme/
│   ├── assets/                # CSS, JS, images
│   ├── sections/              # Shopify theme sections
│   └── templates/             # Liquid templates
|
├── docker-compose.yaml        # Container orchestration
├── Dockerfile                 # Application container
├── requirements.txt           # Python dependencies
└── credentials/
    └── .env                   # Environment variables (gitignored)

API Documentation

Core Endpoints

Chat Interaction

POST /api/aync-chat
Content-Type: application/json

{
  "message": "Show me blue dresses under $100",
  "session_id": "c4212586-c01e-4fe9-b884-402747a61ff6"
}

Development

Local Development

# Install dependencies
pip install -r requirements.txt

# Run development server
python src/server.py

# Start supporting services
docker-compose up redis mongodb elasticsearch

Deployment

Production Deployment

1. Build and push Docker image

   docker build -t chatbot_Shopify:latest .
   docker push your-registry/chatbot_Shopify:latest

2. Deploy using GitHub Actions

   • Push to main branch triggers production deployment
   • Push to develop branch triggers staging deployment

3. Manual deployment

   docker-compose -f docker-compose.prod.yaml up -d

Monitoring & Logging

• Application Logs: bucket/app.log
• Log Levels: Configurable via LOG_LEVEL environment variable
• Monitoring: OVH Cloud Monitoring integration
• Error Tracking: GitHub notifications for crashes

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Code Style

• Follow PEP 8 for Python code
• Use meaningful variable and function names
• Add docstrings for public functions
• Write tests for new features

Troubleshooting

Common Issues

Connection Issues

• Verify Shopify API credentials in .env
• Check Redis/MongoDB connection strings
• Ensure RabbitMQ is running for sync operations

Search Performance

• Rebuild FAISS index: python src/embed_and_save_vector.py
• Check Elasticsearch cluster health
• Monitor vector embedding quality

Deployment Issues

• Check Docker container logs: docker-compose logs
• Verify environment variables are set
• Ensure proper network connectivity between services

Security

• API keys stored in environment variables only
• CORS configured for trusted domains
• Role-based access control for admin endpoints
• Input validation and sanitization
• Secure session management with Redis

Performance

• Response Time: < 500ms average
• Concurrent Users: Supports 100+ simultaneous sessions
• Scalability: Horizontal scaling via Docker Swarm/Kubernetes
• Caching: Redis-based session and query caching

License

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

Support

For support and questions:

• Create an issue on GitHub
• Check the troubleshooting section
• Review application logs

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Built with ❤️ for the Shopify ecosystem