llm-runner 0.1.0

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Sublime LLM Communication Core

This project serves as a core engine for communicating with various Large Language Model (LLM) backends, with a primary focus on integration with Sublime Text. Currently, network communication is implemented for the OpenAI gateway, with plans to support OpenAI-alike models and Claude.

Core of OpenAI-sublime-text package.

Overview

• Functionality: This core is designed to interface with LLMs, manage cache entries, handle network requests, and process streaming data.
• Integrations: Primarily targeted at Sublime Text, with support for Python bindings via PyO3 to facilitate usage in Python environments.

Key Modules

Types

Defines core data structures used across the project, including enums, structs for cache management, input processing, and assistant settings. These are serialized/deserialized using serde and interfaced with Python using pyo3.

Runner

Implements the logic to send requests to LLMs and handle responses. It interfaces with network clients and manages cache data for requests.

Worker

A bridge between the frontend (e.g., Sublime Text) and the core functionality, handling request execution and response streaming. Supports cancellations and tracks the status of ongoing operations.

Network Client

Handles HTTP requests to LLM backends using reqwest. Manages headers, payload preparation, and response handling, with specific support for streaming data via SSE.

Cache

Manages the storage and retrieval of request/response history and model-specific data to/from the file system. Ensures efficient access and persistence of session data.

Stream Handler

Processes streamed responses from LLMs, invoking user-defined callback functions to handle live data updates.

Python Worker

Provides a Python interface for the core functionality, allowing interaction using Python scripts via the pyo3 runtime.

OpenAI Network Types

Defines specific request and response structures for OpenAI communication, including message formatting and response deserialization.

Getting Started

Prerequisites

• Rust: Ensure cargo and rustc are installed.
• Python: Python environment for interfacing via PyO3.
• Tokio: For asynchronous operations.
• Serde: For JSON serialization/deserialization.
• Reqwest: For HTTP requests.

Installation

1. Clone the repository:

   git clone [e-mail]:yaroslavyaroslav/ai_helper.git
   cd ai_helper
   

2. Build the project:

[!NOTE] python 3.8

maturin build

3. Install the it as dependency for ST (::under python 3.8 venv::):

[!NOTE] python 3.8

pip install target/wheels/llm_runner-0.1.0-cp38-cp38-macosx_11_0_arm64.whl --target '/path/to/Sublime Text/Lib/python38/' --upgrade

Usage

• Integrate with Sublime Text using the provided Python classes or directly using Rust API.
• Configure and use in your own extensions or projects that require LLM interaction.

Configuration

• Assistant Settings: Modify settings in AssistantSettings struct for your specific LLM configurations and preferences.
• Cache Handling: Manage cache directory and file paths as per your application's needs.

Development

All contributions are welcome! To get started with development:

1. Fork the repository.
2. Make your changes in a new branch.
3. Create a pull request when ready.

Testing

Run tests with:

cargo test

Utilize the extensive test suite included for validation of different components, including network client and cache handling.

Future Plans

• Additional LLM backend support.
• Enhance cache management.
• Expand Python bindings for broader application compatibility.

License

This project is licensed under custom license. See the LICENSE file for more details.