AI-Support/README.md

# AI-Powered Support System

A hybrid AI-powered customer support system that combines OpenAI's GPT-4 for complex queries with local LLM (Ollama) for simple queries. The system includes sentiment analysis, real-time chat, and a knowledge base for efficient customer support.

## Features

- **Hybrid AI System**: Combines OpenAI's GPT-4 for complex queries with local LLM (Ollama) for simple queries
- **Intelligent Routing**: Automatically routes queries to the appropriate AI model based on complexity
- **Sentiment Analysis**: Analyzes customer sentiment and escalates to human agents when needed
- **Real-time Chat**: WebSocket-based real-time communication with typing indicators and read receipts
- **Knowledge Base**: FAQ system with automatic suggestions based on conversation context
- **User Authentication**: JWT-based authentication with role-based access control
- **Admin Dashboard**: Analytics and management interface for administrators
- **Conversation History**: Search and review past conversations
- **Scalable Architecture**: Built with Go, PostgreSQL, and Redis for high performance

## Technology Stack

### Backend
- **Go**: Programming language
- **Gin**: Web framework
- **PostgreSQL**: Primary database
- **Redis**: Caching and session management
- **JWT**: Authentication
- **GORM**: ORM for database operations
- **Logrus**: Structured logging
- **Viper**: Configuration management

### AI Integration
- **OpenAI API**: For complex queries
- **Ollama**: For local LLM integration
- **Sentiment Analysis**: For analyzing customer sentiment

### Frontend (To be implemented)
- **React/Vue.js**: Frontend framework
- **WebSocket**: Real-time communication
- **Material-UI/Ant Design**: UI components

### Deployment
- **Docker**: Containerization
- **Docker Compose**: Local development
- **Kubernetes**: Production deployment
- **CI/CD**: Automated testing and deployment

## Project Structure

```
support/
├── backend/                 # Backend Go application
│   ├── cmd/                # Application entry point
│   │   └── main.go        # Main application file
│   ├── internal/          # Internal application packages
│   │   ├── ai/            # AI service
│   │   ├── auth/          # Authentication service
│   │   ├── conversation/  # Conversation service
│   │   ├── database/      # Database connection and migrations
│   │   ├── handlers/      # HTTP handlers
│   │   ├── knowledge/     # Knowledge base service
│   │   ├── models/        # Data models
│   │   └── routes/        # Route definitions
│   ├── pkg/               # Public library packages
│   │   ├── config/        # Configuration management
│   │   └── logger/        # Logging utilities
│   ├── go.mod             # Go module file
│   ├── go.sum             # Go module checksums
│   └── Dockerfile         # Docker configuration for backend
├── frontend/              # Frontend React/Vue.js application (to be implemented)
├── docs/                  # Documentation
├── deployment/            # Kubernetes deployment configurations (to be implemented)
├── docker-compose.yml     # Docker Compose configuration
├── .gitignore             # Git ignore file
└── README.md              # This file
```

## Getting Started

### Prerequisites

- Go 1.21 or higher
- Docker and Docker Compose
- PostgreSQL (if not using Docker)
- Redis (if not using Docker)
- OpenAI API key (for GPT-4 integration)

### Installation

1. Clone the repository:
   ```bash
   git clone https://github.com/yourusername/support.git
   cd support
   ```

2. Set up environment variables:
   ```bash
   cp backend/.env.example backend/.env
   ```
   Edit the `.env` file with your configuration, including your OpenAI API key.

3. Start the services using Docker Compose:
   ```bash
   docker-compose up -d
   ```

4. Wait for all services to start up. You can check the logs with:
   ```bash
   docker-compose logs -f
   ```

### Development

#### Backend Development

1. Navigate to the backend directory:
   ```bash
   cd backend
   ```

2. Install dependencies:
   ```bash
   go mod download
   ```

3. Run the application:
   ```bash
   go run cmd/main.go
   ```

The backend API will be available at `http://localhost:8080`.

#### Frontend Development (To be implemented)

1. Navigate to the frontend directory:
   ```bash
   cd frontend
   ```

2. Install dependencies:
   ```bash
   npm install
   ```

3. Run the development server:
   ```bash
   npm run dev
   ```

The frontend will be available at `http://localhost:3000`.

## API Documentation

### Authentication Endpoints

- `POST /api/v1/public/login` - User login
- `POST /api/v1/public/register` - User registration

### User Endpoints

- `GET /api/v1/user/profile` - Get user profile
- `PUT /api/v1/user/profile` - Update user profile
- `PUT /api/v1/user/change-password` - Change password

### Admin Endpoints

- `GET /api/v1/admin/users` - List all users
- `GET /api/v1/admin/users/:id` - Get user by ID
- `PUT /api/v1/admin/users/:id` - Update user
- `DELETE /api/v1/admin/users/:id` - Delete user

### Conversation Endpoints

- `GET /api/v1/conversations` - List conversations
- `POST /api/v1/conversations` - Create new conversation
- `GET /api/v1/conversations/:id` - Get conversation by ID
- `PUT /api/v1/conversations/:id` - Update conversation
- `DELETE /api/v1/conversations/:id` - Delete conversation
- `GET /api/v1/conversations/:id/stats` - Get conversation statistics

### Message Endpoints

- `GET /api/v1/conversations/:id/messages` - Get messages in a conversation
- `POST /api/v1/conversations/:id/messages` - Send a message
- `PUT /api/v1/conversations/:id/messages/:messageId` - Update a message
- `DELETE /api/v1/conversations/:id/messages/:messageId` - Delete a message
- `POST /api/v1/conversations/:id/ai` - Send message with AI response

### Knowledge Base Endpoints

- `GET /api/v1/knowledge` - Get knowledge base entries
- `GET /api/v1/knowledge/search` - Search knowledge base
- `GET /api/v1/knowledge/categories` - Get knowledge categories
- `GET /api/v1/knowledge/tags` - Get knowledge tags
- `GET /api/v1/knowledge/popular` - Get popular knowledge entries
- `GET /api/v1/knowledge/recent` - Get recent knowledge entries
- `GET /api/v1/knowledge/best-match` - Find best match for a query
- `GET /api/v1/knowledge/stats` - Get knowledge base statistics
- `GET /api/v1/knowledge/:id` - Get knowledge base entry by ID
- `POST /api/v1/knowledge/:id/rate` - Rate knowledge base entry

#### Admin Knowledge Base Endpoints

- `POST /api/v1/admin/knowledge` - Create knowledge base entry
- `PUT /api/v1/admin/knowledge/:id` - Update knowledge base entry
- `DELETE /api/v1/admin/knowledge/:id` - Delete knowledge base entry

### AI Endpoints

- `POST /api/v1/ai/query` - Query AI service
- `POST /api/v1/ai/analyze-complexity` - Analyze complexity of a prompt
- `GET /api/v1/ai/models` - Get available AI models
- `POST /api/v1/ai/openai` - Query OpenAI directly
- `POST /api/v1/ai/ollama` - Query Ollama directly

## Testing

### Backend Testing

1. Navigate to the backend directory:
   ```bash
   cd backend
   ```

2. Run tests:
   ```bash
   go test ./...
   ```

### Frontend Testing (To be implemented)

1. Navigate to the frontend directory:
   ```bash
   cd frontend
   ```

2. Run tests:
   ```bash
   npm test
   ```

## Deployment

### Docker Deployment

1. Build the Docker image:
   ```bash
   docker build -t support-backend ./backend
   ```

2. Run the container:
   ```bash
   docker run -p 8080:8080 support-backend
   ```

### Docker Compose Deployment

1. Start all services:
   ```bash
   docker-compose up -d
   ```

2. Stop all services:
   ```bash
   docker-compose down
   ```

### Kubernetes Deployment (To be implemented)

1. Apply the Kubernetes configurations:
   ```bash
   kubectl apply -f deployment/
   ```