minillmlib 0.1.0

Minimalist Library for programming with LLMs

MiniLLMLib

A Python library for interacting with various LLM providers (OpenAI, Anthropic, Mistral, HuggingFace, through URL).

Author: Quentin Feuillade--Montixi

Installation

From Source

git clone https://github.com/qfeuilla/MiniLLMLib.git
cd MiniLLMLib
pip install -e .  # Install in editable mode

Usage

import minillmlib as mll

# Create a GeneratorInfo for your model/provider
import os

gi = mll.GeneratorInfo(
    model="gpt-4",
    _format="openai",
    api_key=os.getenv("OPENAI_API_KEY")  # Recommended: use env var for secrets
)

# Create a chat node (conversation root)
chat = mll.ChatNode(content="Hello!", role="user")

# Synchronous completion
response = chat.complete_one(gi)
print(response.content)

# Or asynchronous version
# response = await chat.complete_one_async(gi)

Features

• Unified interface for major LLM providers:
  • OpenAI, Anthropic, Mistral, HuggingFace (local), custom URL (e.g. OpenRouter)
• Thread (linear) and loom (tree/branching) conversation modes
• Synchronous & asynchronous API
• Audio completions (OpenAI audio models, beta)
• Flexible parameter/config management via GeneratorInfo and GeneratorCompletionParameters
• Save/load conversation trees
• Extensible: add new models/providers easily

Documentation

• See the Usage Guide for advanced usage, parameter tables, and branching/loom semantics.
• See the Provider Matrix for supported models and configuration tips.
• See Troubleshooting for common issues and debugging.

Configuration

• Set API keys as environment variables for security (see the Configuration Guide).

Development & Contribution

• Run tests with:

  pytest tests/
  

• See Contributing for contribution guidelines.

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For more, see the full documentation at minillmlib.readthedocs.io or open an issue on GitHub if you need help.