# Deployment Instructions

This document provides instructions for deploying the chatbot application with Ollama and OpenWebUI integration.

## Prerequisites

- Python 3.8 or higher
- pip
- virtualenv or venv
- Access to OpenWebUI at http://104.225.217.215:8080

## Deployment Steps

1. **Clone the repository**

   ```bash
   git clone <repository-url>
   cd <repository-directory>
   ```

2. **Create and activate a virtual environment**

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

3. **Install dependencies**

   ```bash
   pip install -r requirements.txt
   pip install python-dotenv langchain-text-splitters
   ```

4. **Create a .env file**

   Copy the .env.example file to .env and update the values:

   ```bash
   cp ai_service/.env.example ai_service/.env
   # Edit the .env file with appropriate values
   ```

   Make sure to include the OpenWebUI configuration:

   ```
   # OpenWebUI configuration
   OPENWEBUI_URL=http://104.225.217.215:8080
   OPENWEBUI_API_KEY=GdCU4ieYDqHsLfH2

   # Ollama configuration
   OLLAMA_API_URL=http://104.225.217.215:8080/ollama
   DEFAULT_MODEL=llama3.1
   ```

5. **Run the deployment script**

   For local deployment:
   ```bash
   python -m ai_service.run
   ```

   For server deployment:
   ```bash
   ./ai_service/deploy.sh
   ```

   This will start the application on port 5251 using uvicorn with nohup.

   For remote deployment from your local machine:
   ```bash
   ./remote_deploy.sh 157.157.221.29 user 22 /home/user/ds_zagres_ai
   ```

6. **Verify the application is running**

   ```bash
   curl http://localhost:5251/api/health
   ```

   You should see a response like:
   ```json
   {
     "status": "healthy"
   }
   ```

## Managing the Deployed Application

- **View logs**

  ```bash
  tail -f app.log
  ```

- **Stop the application**

  ```bash
  ps aux | grep uvicorn  # Find the process ID (PID)
  kill <PID>             # Replace <PID> with the actual process ID
  ```

- **Restart the application**

  ```bash
  ./deploy.sh
  ```

## API Endpoints

- `GET /health` - Health check endpoint
- `POST /chats` - Create a new chat
- `POST /chats/{chat_id}/messages` - Send a message to the chatbot
- `GET /chats/{chat_id}` - Get chat history

## Ollama and OpenWebUI Integration

The chatbot now uses Ollama models via OpenWebUI. The following models are available:

- **gemma3**: Google Gemma 3 model
- **llama3.3**: Meta Llama 3 70B model
- **llama3.1**: Meta Llama 3 8B model
- **mistral**: Mistral AI model
- **deepseek**: DeepSeek model

### Document Training

To use RAG with your documents:

1. Go to the OpenWebUI interface at http://104.225.217.215:8080/
2. Navigate to the Knowledge section
3. Upload your documents
4. OpenWebUI will automatically process them for RAG

When using the chatbot API, set `use_rag=True` in your chat requests to enable RAG.