llamapersonalization 0.1.0

User profile management, preference modeling, and personalization features for LlamaAI Ecosystem

llama-personalization

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

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.)

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

Python Client

(Python client usage examples will be added here.)

# 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

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

# 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

pytest tests/

Contributing

Contributions are welcome! Please refer to CONTRIBUTING.md and submit a Pull Request.

License

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