Multi-Agent-Customer-Service-Chatbot

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Multi-Agent-Customer-Service-Chatbot

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This project is a multi-agent customer service chatbot designed for an e-commerce platform. The chatbot employ specialized agents handle distinct tasks to ensure efficient and accurate interactions. The chatbot aims to enhance user experience by streamlining order processing, answering FAQs, and providing personalized recommendations.

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# E-Commerce Customer Service Chatbot

A multi-agent customer service chatbot for an e-commerce platform that can filter messages, classify user intent, guide users through ordering, retrieve product information, and offer personalized recommendations.

## Architecture

The system follows a modular pipeline structure:

```
User ->
Guard Agent ->
Classification Agent ->
   -> Order Taking Agent
   -> Details Agent
   -> Recommendation Agent
Back to User
```

## Features

- **Guard Agent**: Filters out irrelevant or inappropriate messages before processing
- **Classification Agent**: Accurately classifies user intent and routes queries to specialized agents
- **Order Taking Agent**: Guides users through the ordering process using step-by-step reasoning
- **Details Agent**: Retrieves and responds with detailed product information (RAG System)
- **Recommendation Agent**: Offers personalized product recommendations based on user preferences

## Tech Stack

### Backend
- Python 3.9+
- FastAPI
- SQLAlchemy
- PostgreSQL
- FAISS for vector search
- Hugging Face Transformers

### Frontend
- React
- React Router
- Axios

## Project Structure

```
├── backend/             # Backend code
│   ├── app/            # FastAPI application
│   │   ├── agents/     # Agent implementations
│   │   ├── database/   # Database models and connection
│   │   ├── faiss/      # Vector search implementation
│   │   ├── routes/     # API endpoints
│   │   ├── services/   # Business logic services
│   │   └── utils/      # Utility functions
│   ├── main.py         # Entry point for the backend
│   └── requirements.txt # Python dependencies
│
└── frontend/           # React frontend
    ├── public/         # Static files
    ├── src/            # React source code
    │   ├── components/ # Reusable UI components
    │   ├── pages/      # Page components
    │   └── services/   # API services
    └── package.json    # Node.js dependencies
```

## Setup and Installation

### Prerequisites
- Python 3.9+
- Node.js and npm
- PostgreSQL

### Environment Setup

1. Clone the repository:
```bash
git clone https://github.com/yourusername/customer-service-chatbot.git
cd customer-service-chatbot
```

2. Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
```

3. Install Python dependencies:
```bash
cd backend
pip install -r requirements.txt
```

4. Set up environment variables by creating a `.env` file in the backend directory:
```
DATABASE_URL=postgresql://username:password@localhost:5432/ecommerce_chatbot
```

### Database Setup

1. Create a PostgreSQL database:
```bash
createdb ecommerce_chatbot
```

2. Run database migrations (if applicable)

### Running the Application

You can use the provided scripts to start the application:

#### On Linux/Mac:

```bash
# Start both backend and frontend
./run.sh

# Start only the backend
./run.sh backend

# Start only the frontend
./run.sh frontend

# Build the frontend
./run.sh build
```

#### On Windows:

```cmd
# Start both backend and frontend
run.bat

# Start only the backend
run.bat backend

# Start only the frontend
run.bat frontend

# Build the frontend
run.bat build
```

Alternatively, you can start the services manually:

#### Backend

```bash
cd backend
python -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
```

The API will be available at http://localhost:8000

#### Frontend

```bash
cd frontend
npm install
npm run dev
```

The frontend will be available at http://localhost:5173

### Building for Production

1. Build the React frontend:
```bash
cd frontend
npm run build
```

2. Start the production server:
```bash
cd backend
python -m uvicorn app.main:app
```

The application will serve both the API and the frontend from http://localhost:8000

## API Endpoints

- `/api/chat/` - Process chat messages
- `/api/order/` - Handle order operations
- `/api/recommend/` - Get product recommendations
- `/api/faq/` - Access frequently asked questions

## License

[MIT License](LICENSE)

Multi-Agent-Customer-Service-Chatbot

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