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https://github.com/google/langfun
Empower LLMs with Symbols.
https://github.com/google/langfun
framework llms nlp
Last synced: 22 days ago
JSON representation
Empower LLMs with Symbols.
- Host: GitHub
- URL: https://github.com/google/langfun
- Owner: google
- License: apache-2.0
- Created: 2023-08-29T17:58:47.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-03T18:59:47.000Z (4 months ago)
- Last Synced: 2024-07-06T09:27:40.763Z (4 months ago)
- Topics: framework, llms, nlp
- Language: Python
- Homepage:
- Size: 692 KB
- Stars: 86
- Watchers: 7
- Forks: 16
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# Langfun
[](https://badge.fury.io/py/langfun)
[](https://codecov.io/gh/google/langfun)
[**Installation**](#install) | [**Getting started**](#hello-langfun) | [**Tutorial**](https://colab.research.google.com/github/google/langfun/blob/main/docs/notebooks/langfun101.ipynb) | [**Discord community**](https://discord.gg/U6wPN9R68k)
## Introduction
Langfun is a [PyGlove](https://github.com/google/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](https://colab.research.google.com/github/google/langfun/blob/main/docs/notebooks/langfun101.ipynb). 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
```python
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=''),
my_image=image,
)
print(desc)
```
*Output:*
```
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](https://colab.research.google.com/github/google/langfun/blob/main/docs/notebooks/langfun101.ipynb) for more examples.
## Install
Langfun offers a range of features through [Extras](https://packaging.python.org/en/latest/tutorials/installing-packages/#installing-extras), allowing users to install only what they need. The minimal installation of Langfun requires only [PyGlove](https://github.com/google/pyglove), [Jinja2](https://github.com/pallets/jinja/), and [requests](https://github.com/psf/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. |
| llm | All supported LLMs. |
| llm-google | All supported Google-powered LLMs. |
| llm-google-vertexai | LLMs powered by Google Cloud VertexAI |
| llm-google-genai | LLMs powered by Google Generative AI API |
| llm-openai | LLMs powered by OpenAI |
| mime | All MIME supports. |
| mime-auto | Automatic MIME type detection. |
| mime-docx | DocX format support. |
| mime-pil | Image support for PIL. |
| mime-xlsx | XlsX format support. |
| ui | UI enhancements |
For example, to install a nightly build that includes Google-powered LLMs, full modality support, and UI enhancements, use:
```
pip install langfun[llm-google,mime,ui] --pre
```
*Disclaimer: this is not an officially supported Google product.*