Skip to main content

Minimalist Library for programming with LLMs

Project description

MiniLLMLib

GitHub stars GitHub forks GitHub issues GitHub last commit

PyPI version Docs License: MIT Python


Installation

pip install minillmlib
# For HuggingFace/local models: (Beta - not well tested)
pip install minillmlib[huggingface]

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

Development & Contribution

  • Run tests with:
    pytest tests/
    
  • See Contributing for contribution guidelines.

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


Release Tagging Reminder

(for maintainers use)

To push a new release tag:

git add <files you changed>
git commit -m "<your message>"
git tag v<NEW_VERSION> -m "Release v<NEW_VERSION>: <short description>"
git push origin main --tags

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

minillmlib-0.2.4.tar.gz (47.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

minillmlib-0.2.4-py3-none-any.whl (27.3 kB view details)

Uploaded Python 3

File details

Details for the file minillmlib-0.2.4.tar.gz.

File metadata

  • Download URL: minillmlib-0.2.4.tar.gz
  • Upload date:
  • Size: 47.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for minillmlib-0.2.4.tar.gz
Algorithm Hash digest
SHA256 497d40b22dd8410a94f22d397521ae414be65ac3c7408ea77fbde01b5de48ac3
MD5 fbc211faf2c6ac6bb4fbb20ae99049e1
BLAKE2b-256 0417149505d46fec0435a28d24ca27c5d9c2104ba1bd311174bf8d5739cecd58

See more details on using hashes here.

File details

Details for the file minillmlib-0.2.4-py3-none-any.whl.

File metadata

  • Download URL: minillmlib-0.2.4-py3-none-any.whl
  • Upload date:
  • Size: 27.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for minillmlib-0.2.4-py3-none-any.whl
Algorithm Hash digest
SHA256 82944aa3079ae5a9b40670ef8342e4af22ebb7b4cf78270ff8c35392794fc1a5
MD5 fbe54296946bb71ad6ddbd79d88c52be
BLAKE2b-256 21d2970481364d52e9569a17ac4cee17e9c277229280429fb614210093ea19d2

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page