Skip to main content
logo

Langfun

PyPI version codecov pytest

Installation | Getting started | Tutorial

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

pip install langfun

Or install nightly build with:

pip install langfun --pre

Disclaimer: this is not an officially supported Google product.

Metadata

Release files for langfun 0.1.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for langfun 0.1.1
File Size Uploaded
langfun-0.1.1.tar.gz 207.8 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for langfun 0.1.1
File Interpreter ABI Platform
langfun-0.1.1-py3-none-any.whl Python 3 none any Details

Total release size: 505.2 kB

Release files / langfun-0.1.1.tar.gz

Download URL langfun-0.1.1.tar.gz
Size 207.8 kB
Tags Source
SHA-256 checksum
How to use checksums
413685ff6085f8bc9082f01e3f4b6c1522dcb958a94f798c8388d1d8384b3338
BLAKE2b-256 checksum
How to use checksums
e413887945729c5ef622949cb8ddc04c72d1534dcaed5aea4bf8f27638aa0b0c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.1 CPython/3.12.4

Release files / langfun-0.1.1-py3-none-any.whl

Download URL langfun-0.1.1-py3-none-any.whl
Size 297.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
3ce79c14ee052ca9dc801e2a54f99b4b7212f8d38ad5df11228c226e4f2ed9de
BLAKE2b-256 checksum
How to use checksums
56f176da034b10daf56499150181a3c8ab33df19ac9e9defa867907b83f1a9d8
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.1 CPython/3.12.4

Release history Release notifications | RSS feed

This release

0.1.1 This release

2 release files

0.1.0

2 release files

0.0.1

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page