llama-ctrl 0.1.0

Control plane, configuration management, and service coordination for LlamaAI Ecosystem

llama-ctrl

Llama Control (llama-ctrl) is a versatile command-line interface (CLI) and Python client library designed for interacting with and managing various LlamaSearch AI operations. It provides a unified entry point for controlling models, managing configurations, and executing tasks within the LlamaSearch ecosystem. The tool is optimized for different hardware, including Apple Silicon (M1/M2/M3), to ensure efficient performance.

Key Features

• Unified CLI Interface: Control various LlamaSearch functions via a single llama-ctrl command.
• Python Client Library: Integrate control functionality directly into your Python applications.
• Hardware Optimization: Includes specific optimizations for Apple Silicon (M-series chips).
• Request Handling: Uses a factory pattern to dynamically select appropriate handlers (e.g., default, chat, REPL).
• Caching: Built-in file-based caching to speed up repeated operations (especially LLM calls).
• Extensible: Designed to be easily extended with new commands and handlers.

Installation

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

Usage

Command-Line Interface (CLI)

The CLI provides various subcommands. Use --help to explore options:

llama-ctrl --help
llama-ctrl <command> --help

(More specific CLI examples will be added as commands are finalized)

Python Client

from llamasearch_ctrl import Client # Assuming Client class exists

# Initialize the client (configuration details might vary)
# client = Client(api_key="YOUR_API_KEY") # Example configuration
client = Client() # Basic initialization

# Example: Process some data or execute a command
# The exact methods will depend on the final API design
# result = client.execute_command("some_command", param="value")
# print(result)

# Example from original README (adjust class/method names if needed)
# from llamasearch_ctrl import LlamaCtrlClient
# client = LlamaCtrlClient(api_key="your-api-key")
# result = client.query("your query")
# print(result)

(Note: The Python client API details need refinement based on the actual implementation in src/llamasearch_ctrl)

Architecture Overview

llama-ctrl follows a modular design:

graph TD
    A[CLI (Typer) / Python Client] --> B{Handler Factory};
    B -- Selects Handler --> C[Default Handler];
    B -- Selects Handler --> D[Chat Handler];
    B -- Selects Handler --> E[REPL Handler];
    B -- Selects Handler --> F[M3 Optimized Handler];
    B -- Selects Handler --> G[... other handlers];
    C --> H(Core Logic / LLM Interaction);
    D --> H;
    E --> H;
    F --> H;
    G --> H;
    H --> I[Cache System];
    A --> J[System Info (OS/CPU Detection)];
    J --> B;
    J --> F;

    style H fill:#f9f,stroke:#333,stroke-width:2px
    style I fill:#ccf,stroke:#333,stroke-width:1px

1. Entry Point: User interacts via the CLI (typer) or Python client.
2. System Info: Detects OS and CPU (e.g., Apple Silicon M3) for potential optimizations.
3. Handler Factory: Selects the appropriate handler based on the command/context and system info.
4. Handlers: Specific handlers (Default, Chat, REPL, M3-optimized, etc.) process the request.
5. Core Logic: Handlers interact with the core LlamaSearch functionalities or LLMs.
6. Caching: Results can be cached to improve performance for subsequent identical requests.

Configuration

(Details on configuration files, environment variables, or API keys needed by llama-ctrl will be added here.)

Development

Setup

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

# Create and activate a virtual environment (recommended)
# python -m venv venv
# source venv/bin/activate # or .\venv\Scripts\activate on Windows

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

Testing

pytest tests/

Contributing

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

License

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