An AI-powered bookstore application that leverages advanced language models and multi-agent systems to provide personalized book recommendations and intelligent shopping experiences.

The Smart Retail Bookstore is a comprehensive e-commerce platform that combines modern web technologies with cutting-edge AI capabilities. Built on a microservices architecture, it features an intelligent multi-agent system powered by LangChain and OpenAI, providing users with personalized book recommendations, AI-powered chatbot assistance, and seamless order management.

The system follows a modern, scalable architecture with clear separation of concerns:

• Frontend: React-based single-page application with modern UI/UX
• Backend: FastAPI server with AI-powered services and multi-agent orchestration
• Database: PostgreSQL with user preference storage and order management
• AI Services: LangChain integration with OpenAI GPT models for intelligent operations
• External APIs: Hardcover GraphQL API for comprehensive book metadata

• Intelligent Book Matching: Advanced recommendation engine using LangChain and OpenAI
• User Preference Learning: Dynamic preference analysis and adaptation
• Contextual Understanding: Multi-turn conversations for better recommendation accuracy

• Operator Agent: Central orchestrator managing all specialized agents
• Recommendation Agent: Specialized in book discovery and matching
• Order Management Agents: Handle order placement, tracking, and fraud detection
• User Proxy Agent: Manages user interactions and context

• Natural Language Processing: Understands complex book-related queries
• Conversation Memory: Maintains context across multiple interactions
• Multi-Modal Support: Handles recommendations, orders, and general inquiries

• User Authentication: Secure login with Clerk integration
• Shopping Cart: Persistent cart management with user preferences
• Order Management: Complete order lifecycle from placement to tracking
• User Profiles: Personalized dashboards and preference management

• React 18 - Modern UI framework with hooks and functional components
• Vite - Fast build tool and development server
• Tailwind CSS - Utility-first CSS framework for responsive design
• Redux Toolkit - State management with modern Redux patterns
• React Router - Client-side routing and navigation
• Clerk - Authentication and user management
• Lucide React - Beautiful, customizable icons
• React Slick - Carousel and slider components
• Axios - HTTP client for API communication

• FastAPI - High-performance Python web framework
• SQLAlchemy - SQL toolkit and ORM
• PostgreSQL - Robust, open-source database
• LangChain - Framework for developing LLM-powered applications
• OpenAI GPT-4o-mini - Advanced language model integration
• GraphQL - Efficient data querying with external APIs
• Pydantic - Data validation and settings management

• LangChain Agents - Multi-agent orchestration and task delegation
• Conversation Memory - Context-aware conversation management
• Prompt Engineering - Optimized prompts for better AI responses
• LangGraph - State management for multi-agent workflows

smart-retail-bookstore/
├── backend/                          # FastAPI backend services
│   ├── app/
│   │   ├── api/                     # API endpoints and routers
│   │   │   ├── chatbot.py          # Chatbot API endpoints
│   │   │   └── recommendations.py  # Recommendation API endpoints
│   │   ├── core/                    # Core configuration and settings
│   │   │   ├── config.py           # Environment and app configuration
│   │   │   └── memory.py           # Memory management utilities
│   │   ├── database/                # Database configuration and models
│   │   │   ├── database.py         # Database connection and session management
│   │   │   └── initialize.py       # Database initialization scripts
│   │   ├── models/                  # Data models and schemas
│   │   │   ├── orders.py           # Order data models with CRUD operations
│   │   │   └── user.py             # User preference models
│   │   └── services/                # Business logic and AI services
│   │       ├── chatbot_service.py  # Multi-agent chatbot orchestration
│   │       ├── recommendation_agent.py # AI recommendation engine
│   │       ├── recommendation_service.py # Recommendation business logic
│   │       ├── operator_agent.py   # Central agent orchestrator
│   │       ├── order_placement_agent.py # Order processing agent
│   │       ├── order_query_agent.py # Order query and tracking agent
│   │       ├── fraudulent_transaction_agent.py # Fraud detection
│   │       ├── user_proxy_agent.py # User interaction management
│   │       ├── graphql_service.py  # External API integration
│   │       └── utils.py            # Utility functions
│   └── requirements.txt             # Python dependencies
├── frontend/                         # React frontend application
│   ├── src/
│   │   ├── components/              # Reusable UI components
│   │   │   ├── Authors/            # Author-related components
│   │   │   ├── Books/              # Book display components
│   │   │   ├── Chatbot/            # AI chatbot interface
│   │   │   ├── Home/               # Homepage and landing components
│   │   │   ├── Modals/             # Modal and dialog components
│   │   │   └── Profile/            # User profile components
│   │   ├── pages/                  # Main application pages
│   │   │   ├── Dashboard.jsx       # User dashboard with recommendations
│   │   │   ├── Cart.jsx            # Shopping cart management
│   │   │   ├── Login.jsx           # User authentication
│   │   │   ├── Signup.jsx          # User registration
│   │   │   ├── Profile.jsx         # User profile management
│   │   │   ├── OrderHistory.jsx    # Order tracking and history
│   │   │   ├── Settings.jsx        # User settings
│   │   │   ├── Favourite.jsx       # User favorites
│   │   │   ├── AllBooks.jsx        # Browse all books
│   │   │   ├── Authors.jsx         # Author listings
│   │   │   ├── ViewBookDetails.jsx # Book detail views
│   │   │   └── Loader.jsx          # Loading states
│   │   ├── Redux/                  # State management
│   │   │   ├── booksSlice.jsx      # Book state management
│   │   │   └── store.js            # Redux store configuration
│   │   └── main.jsx                # Application entry point
│   ├── package.json                 # Node.js dependencies
│   └── tailwind.config.js          # Tailwind CSS configuration
├── create_tables.py                 # Database initialization script
└── README.md                        # Project documentation

• Python 3.8+
• Node.js 16+
• PostgreSQL 12+
• OpenAI API key
• Hardcover API access

1. Clone the repository

   git clone <repository-url>
   cd smart-retail-bookstore/backend

2. Create virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Environment configuration Create a .env file in the backend directory:

   DATABASE_URL=postgresql://username:password@localhost/bookstore_db
   OPENAI_API_KEY=your_openai_api_key
   HARDCOVER_API_TOKEN=your_hardcover_token

5. Database setup

   python create_tables.py

6. Run the backend

   python -m uvicorn app.app:app --reload --host 0.0.0.0 --port 8000

1. Navigate to frontend directory

   cd ../frontend

2. Install dependencies

   npm install

3. Start development server

   npm run dev

4. Build for production

   npm run build

The application uses PostgreSQL with the following main tables:

• user_preferences: Stores user reading preferences and favorites

  • user_id (String, Primary Key)
  • favorite_books (Text)
  • favorite_authors (Text)
  • preferred_genres (Text)
  • themes_of_interest (Text)
  • reading_level (String)

• orders: Manages order information and status

  • order_id (String, Primary Key)
  • user_id (String, Foreign Key)
  • title (String)
  • price (Float)
  • total_quantity (Integer)
  • street, city, state, zip_code (String)
  • card_number, expiry_date (String)
  • purchase_date, expected_shipping_date (DateTime)

• POST /api/recommendations/initial-recommendations - Get initial book recommendations
• GET /api/recommendations/trending-books - Get trending books
• POST /api/recommendations/preferences - Save user preferences
• GET /api/recommendations/preferences/{user_id} - Get user preferences

• POST /api/chatbot/chat - Process chatbot messages
• POST /api/chatbot/place-order - Handle order placement
• GET /api/chatbot/health - Health check endpoint

The application features a sophisticated multi-agent system:

• Role: Central orchestrator and decision maker
• Responsibilities:
  • Routes user requests to appropriate specialized agents
  • Manages conversation flow and context
  • Coordinates between different AI services
  • Uses LangGraph for state management

• Recommendation Agent: Analyzes user preferences and generates book suggestions
• Order Placement Agent: Handles order creation and processing
• Order Query Agent: Manages order tracking and status inquiries
• Fraudulent Transaction Agent: Detects and prevents fraudulent activities
• User Proxy Agent: Manages user interactions and context

Agents communicate through a state graph managed by LangGraph, ensuring:

• Proper task delegation
• Context preservation
• Efficient resource utilization
• Scalable agent interactions

1. User completes preference questionnaire
2. System analyzes preferences using AI
3. Generates initial book recommendations
4. Stores preferences for future use

1. User logs into dashboard
2. System retrieves stored preferences
3. AI generates contextual recommendations
4. Results displayed with rich metadata

1. User initiates conversation
2. System routes to appropriate agent
3. Agent processes request using LLM
4. Response generated and streamed back

1. User selects books and adds to cart
2. Order placement agent processes request
3. Fraud detection agent validates transaction
4. Order confirmed and tracking provided

• Authentication: Secure user authentication with Clerk
• Authorization: Role-based access control
• Fraud Detection: AI-powered transaction monitoring
• Data Encryption: Secure data transmission and storage
• API Security: Input validation and error handling

• Asynchronous Processing: FastAPI async/await for concurrent requests
• Database Optimization: Efficient queries with SQLAlchemy
• AI Model Optimization: Efficient prompt engineering and response handling
• State Management: Optimized Redux state updates
• Component Optimization: React performance best practices

• Backend runs on http://localhost:8000
• Frontend runs on http://localhost:5173 (Vite default)
• Database runs on local PostgreSQL instance

• Use production-grade database (PostgreSQL with connection pooling)
• Implement proper logging and monitoring
• Set up CI/CD pipelines
• Configure load balancers and CDN
• Implement backup and disaster recovery

• OpenAI for providing the GPT models
• LangChain for the multi-agent framework
• FastAPI for the high-performance backend framework
• React for the frontend framework
• Tailwind CSS for the utility-first CSS framework