# llama-personalization

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**Llama Personalization (llama-personalization)** is a toolkit within the LlamaSearch AI ecosystem focused on delivering personalized experiences. It likely involves user profiling, preference learning, and adapting content or recommendations based on individual user data.

## Key Features

- **Personalization Engine:** The core logic for generating personalized results resides here (`engine.py`).
- **User Profiling:** Likely involves mechanisms to build and maintain user profiles.
- **Preference Learning:** May include algorithms to learn user preferences from interactions.
- **Command-Line Interface:** Provides CLI access to personalization functions (`cli.py`).
- **Core Module:** Orchestrates the personalization process (`core.py`).
- **Configurable:** Allows customization of models, data sources, and algorithms (`config.py`).

## Installation

```bash
pip install llama-personalization
# Or install directly from GitHub for the latest version:
# pip install git+https://github.com/llamasearchai/llama-personalization.git
```

## Usage

### Command-Line Interface (CLI)

*(CLI usage examples will be added here.)*

```bash
llama-personalization --user-id 123 get-recommendations --item-type article
```

### Python Client

*(Python client usage examples will be added here.)*

```python
# Placeholder for Python client usage
# from llama_personalization import PersonalizationClient, UserProfile

# client = PersonalizationClient(config_path="config.yaml")

# # Update user profile
# profile = UserProfile(user_id="user456", interests=["ai", "python"])
# client.update_profile(profile)

# # Get personalized recommendations
# recommendations = client.get_recommendations(user_id="user456", context="homepage")
# print(recommendations)
```

## Architecture Overview

```mermaid
graph TD
    A[User Data / Interaction History] --> B{Personalization Engine (engine.py)};
    C[Context (e.g., current page)] --> B;
    B --> D[User Profile Store];
    B --> E[Recommendation / Content Models];
    B --> F[Personalized Output (Recommendations, Content)];

    G{Core Module (core.py)} -- Manages --> B;
    H[CLI (cli.py)] -- Interacts --> G;
    I[Configuration (config.py)] -- Configures --> G;
    I -- Configures --> B;

    style B fill:#f9f,stroke:#333,stroke-width:2px
    style D fill:#ccf,stroke:#333,stroke-width:1px
    style E fill:#ccf,stroke:#333,stroke-width:1px
```

1.  **Input:** Takes user data, interaction history, and current context.
2.  **Engine:** The core personalization engine processes inputs, potentially updating user profiles and querying models.
3.  **Data/Models:** Interacts with user profile storage and recommendation/content generation models.
4.  **Output:** Produces personalized recommendations, content adjustments, or other tailored experiences.
5.  **Core/CLI/Config:** The core module orchestrates the process, accessible via CLI and configured by `config.py`.

## Configuration

*(Details on configuring user data sources, recommendation algorithms, profile storage, etc., will be added here.)*

## Development

### Setup

```bash
# Clone the repository
git clone https://github.com/llamasearchai/llama-personalization.git
cd llama-personalization

# Install in editable mode with development dependencies
pip install -e ".[dev]"
```

### Testing

```bash
pytest tests/
```

### Contributing

Contributions are welcome! Please refer to [CONTRIBUTING.md](CONTRIBUTING.md) and submit a Pull Request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
