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# Deployment Instructions
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This document provides instructions for deploying the chatbot application with Ollama and OpenWebUI integration.
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## Prerequisites
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- Python 3.8 or higher
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- pip
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- virtualenv or venv
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- Access to OpenWebUI at http://104.225.217.215:8080
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## Deployment Steps
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1. **Clone the repository**
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```bash
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git clone <repository-url>
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cd <repository-directory>
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```
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2. **Create and activate a virtual environment**
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```bash
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python -m venv venv
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source venv/bin/activate # On Windows: venv\Scripts\activate
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```
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3. **Install dependencies**
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```bash
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pip install -r requirements.txt
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pip install python-dotenv langchain-text-splitters
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```
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4. **Create a .env file**
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Copy the .env.example file to .env and update the values:
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```bash
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cp ai_service/.env.example ai_service/.env
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# Edit the .env file with appropriate values
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```
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Make sure to include the OpenWebUI configuration:
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```
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# OpenWebUI configuration
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OPENWEBUI_URL=http://104.225.217.215:8080
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OPENWEBUI_API_KEY=GdCU4ieYDqHsLfH2
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# Ollama configuration
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OLLAMA_API_URL=http://104.225.217.215:8080/ollama
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DEFAULT_MODEL=llama3.1
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```
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5. **Run the deployment script**
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For local deployment:
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```bash
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python -m ai_service.run
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```
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For server deployment:
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```bash
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./ai_service/deploy.sh
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```
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This will start the application on port 5251 using uvicorn with nohup.
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For remote deployment from your local machine:
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```bash
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./remote_deploy.sh 157.157.221.29 user 22 /home/user/ds_zagres_ai
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```
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6. **Verify the application is running**
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```bash
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curl http://localhost:5251/api/health
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```
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You should see a response like:
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```json
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{
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"status": "healthy"
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}
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```
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## Managing the Deployed Application
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- **View logs**
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```bash
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tail -f app.log
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```
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- **Stop the application**
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```bash
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ps aux | grep uvicorn # Find the process ID (PID)
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kill <PID> # Replace <PID> with the actual process ID
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```
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- **Restart the application**
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```bash
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./deploy.sh
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```
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## API Endpoints
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- `GET /health` - Health check endpoint
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- `POST /chats` - Create a new chat
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- `POST /chats/{chat_id}/messages` - Send a message to the chatbot
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- `GET /chats/{chat_id}` - Get chat history
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## Ollama and OpenWebUI Integration
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The chatbot now uses Ollama models via OpenWebUI. The following models are available:
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- **gemma3**: Google Gemma 3 model
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- **llama3.3**: Meta Llama 3 70B model
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- **llama3.1**: Meta Llama 3 8B model
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- **mistral**: Mistral AI model
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- **deepseek**: DeepSeek model
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### Document Training
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To use RAG with your documents:
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1. Go to the OpenWebUI interface at http://104.225.217.215:8080/
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2. Navigate to the Knowledge section
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3. Upload your documents
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4. OpenWebUI will automatically process them for RAG
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When using the chatbot API, set `use_rag=True` in your chat requests to enable RAG.
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