Skip to main content

Langfun: Language as Functions.

Project description

logo

Langfun

PyPI version codecov pytest

Installation | Getting started | Tutorial | Discord community

Introduction

Langfun is a PyGlove powered library that aims to make language models (LM) fun to work with. Its central principle is to enable seamless integration between natural language and programming by treating language as functions. Through the introduction of Object-Oriented Prompting, Langfun empowers users to prompt LLMs using objects and types, offering enhanced control and simplifying agent development.

To unlock the magic of Langfun, you can start with Langfun 101. Notably, Langfun is compatible with popular LLMs such as Gemini, GPT, Claude, all without the need for additional fine-tuning.

Why Langfun?

Langfun is powerful and scalable:

  • Seamless integration between natural language and computer programs.
  • Modular prompts, which allows a natural blend of texts and modalities;
  • Efficient for both request-based workflows and batch jobs;
  • A powerful eval framework that thrives dimension explosions.

Langfun is simple and elegant:

  • An intuitive programming model, graspable in 5 minutes;
  • Plug-and-play into any Python codebase, making an immediate difference;
  • Comprehensive LLMs under a unified API: Gemini, GPT, Claude, Llama3, and more.
  • Designed for agile developement: offering intellisense, easy debugging, with minimal overhead;

Hello, Langfun

import langfun as lf
import pyglove as pg

from IPython import display

class Item(pg.Object):
  name: str
  color: str

class ImageDescription(pg.Object):
  items: list[Item]

image = lf.Image.from_uri('https://upload.wikimedia.org/wikipedia/commons/thumb/8/83/Solar_system.jpg/1646px-Solar_system.jpg')
display.display(image)

desc = lf.query(
    'Describe objects in {{my_image}} from top to bottom.',
    ImageDescription,
    lm=lf.llms.Gpt4o(api_key='<your-openai-api-key>'),
    my_image=image,
)
print(desc)

Output:

my_image

ImageDescription(
  items = [
    0 : Item(
      name = 'Mercury',
      color = 'Gray'
    ),
    1 : Item(
      name = 'Venus',
      color = 'Yellow'
    ),
    2 : Item(
      name = 'Earth',
      color = 'Blue and white'
    ),
    3 : Item(
      name = 'Moon',
      color = 'Gray'
    ),
    4 : Item(
      name = 'Mars',
      color = 'Red'
    ),
    5 : Item(
      name = 'Jupiter',
      color = 'Brown and white'
    ),
    6 : Item(
      name = 'Saturn',
      color = 'Yellowish-brown with rings'
    ),
    7 : Item(
      name = 'Uranus',
      color = 'Light blue'
    ),
    8 : Item(
      name = 'Neptune',
      color = 'Dark blue'
    )
  ]
)

See Langfun 101 for more examples.

Install

Langfun offers a range of features through Extras, allowing users to install only what they need. The minimal installation of Langfun requires only PyGlove, Jinja2, and requests. To install Langfun with its minimal dependencies, use:

pip install langfun

For a complete installation with all dependencies, use:

pip install langfun[all]

To install a nightly build, include the --pre flag, like this:

pip install langfun[all] --pre

If you want to customize your installation, you can select specific features using package names like langfun[X1, X2, ..., Xn], where Xi corresponds to a tag from the list below:

Tag Description
all All Langfun features.
vertexai VertexAI access.
mime All MIME supports.
mime-auto Automatic MIME type detection.
mime-pil Image support for PIL.
ui UI enhancements

For example, to install a nightly build that includes VertexAI access, full modality support, and UI enhancements, use:

pip install langfun[vertexai,mime,ui] --pre

Solving import issue with libmagic

Langfun utilizes libmagic for automatic MIME type detection to support multi-modal functionalities. However, pip install libmagic may not work out-of-the-box on all operation systems, sometimes leading to an 'ImportError: failed to find libmagic.' error after Langfun installation.

If you encounter this error, you will need to follow the recommendations below to fix the installation of libmagic library.

OSX

conda install conda-forge::libmagic

Windows:

pip install python-magic
pip uninstall python-magic-bin
pip install python-magic-bin

Disclaimer: this is not an officially supported Google product.

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distribution

langfun-0.1.2.dev202507070805.tar.gz (281.7 kB view details)

Uploaded Source

Built Distribution

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

langfun-0.1.2.dev202507070805-py3-none-any.whl (399.3 kB view details)

Uploaded Python 3

File details

Details for the file langfun-0.1.2.dev202507070805.tar.gz.

File metadata

  • Download URL: langfun-0.1.2.dev202507070805.tar.gz
  • Upload date:
  • Size: 281.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.5

File hashes

Hashes for langfun-0.1.2.dev202507070805.tar.gz
Algorithm Hash digest
SHA256 3e5ebd2f30de5695a37a68281ae68ecb78cdbac97dee45152c3bd9ed3f5a6ee4
MD5 053831fdf70961d2b10e7a8b85b723a1
BLAKE2b-256 28bdfce8d018e008ad8fbb7282d3b44d3d057f6c4ff6088952c60dd1ecfada83

See more details on using hashes here.

File details

Details for the file langfun-0.1.2.dev202507070805-py3-none-any.whl.

File metadata

File hashes

Hashes for langfun-0.1.2.dev202507070805-py3-none-any.whl
Algorithm Hash digest
SHA256 59f24f2585a10093e73081446ac61d2d7937b77672ac6be805fcafd7e943bdb5
MD5 0e82f79b175f2d5f5f47cc46af195b11
BLAKE2b-256 6f689a430d9740303aec03dea03eec9a50f419b7ddab3667f304c52a1684cc76

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