AI Persona Lab

A powerful AI-driven application for creating and managing dynamic personas, enabling interactive group chats with AI personalities powered by Ollama.

Key Features

• Dynamic Persona Generation: Create detailed AI personas with unique backgrounds, personalities, and skills
• Customizable Models: Choose different Ollama models for each persona (mistral, llama2, etc.)
• Advanced Settings: Fine-tune temperature and token settings per persona
• Interactive Group Chat: Engage in conversations with multiple AI personas simultaneously
• Persona Management: Add tags, notes, and customize settings for each persona

Requirements

• Python 3.8+
• Ollama server running locally
• Required Python packages (see requirements.txt)

Installation

1. Install Ollama following the instructions at ollama.ai

2. Clone the repository:

   git clone https://github.com/marc-shade/ai-persona-lab.git
   cd ai-persona-lab

3. Install dependencies:

   pip install -r requirements.txt

4. Start Ollama server and pull required models:

   ollama serve  # Start the Ollama server
   ollama pull mistral:instruct  # Pull the default model

Usage

1. Start the application:

   streamlit run app.py

2. Create Personas:

   • Use the quick generate buttons or custom input for new personas
   • Edit persona details in the expandable sections
   • Customize model settings and parameters
   • Add tags and notes for better organization

3. Manage Personas:

   • Edit any persona attribute through the UI
   • Add/remove tags and update notes
   • Customize model settings per persona
   • Toggle personas active/inactive in chat

4. Chat Interface:

   • Select which personas to include in the conversation
   • Start conversations with natural language
   • Watch personas interact based on their unique characteristics
   • Each persona maintains context and personality throughout the chat

Project Structure

ai-persona-lab/
├── app.py              # Main Streamlit application and UI
├── models/
│   └── persona.py      # Persona class and management logic
├── chat/
│   └── interface.py    # Chat interface and message handling
├── data/               # Storage for personas and chat history
├── requirements.txt    # Python dependencies
└── README.md          # Documentation

Configuration

Default Settings

• Ollama API URL: http://localhost:11434/api
• Default Model: mistral:instruct
• Temperature: 0.7
• Max Tokens: 500
• Avatar Size: 200x200

Model Settings

Each persona can be configured with:

• Any Ollama model
• Temperature (0.0 to 1.0)
• Max tokens (50 to 2000)
• Custom system prompts via notes

Data Storage

• Personas are automatically saved to data/personas.json
• Chat history is maintained during the session
• All changes are persisted immediately

Troubleshooting

Common issues and solutions:

1. Ollama Connection Error

   • Ensure Ollama server is running (ollama serve)
   • Check if the API URL is accessible (http://localhost:11434/api)
   • Verify firewall settings

2. Model Loading Issues

   • Pull the model explicitly: ollama pull mistral:instruct
   • Check available models: ollama list
   • Ensure sufficient disk space

3. UI Issues

   • Clear browser cache
   • Restart Streamlit server
   • Check console for JavaScript errors

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Ollama for the amazing local LLM server
• Streamlit for the powerful UI framework
• The open-source AI community