This release is a pre-release and may not be stable for production use.
Langfun
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:
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-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
Disclaimer: this is not an officially supported Google product.
1 maintainer
Metadata
1 maintainer
Release files for langfun 0.1.2.dev202610310804
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| langfun-0.1.2.dev202610310804.tar.gz | 568.0 kB | Without provenance | Details for langfun-0.1.2.dev202610310804.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| langfun-0.1.2.dev202610310804-py3-none-any.whl | Python 3 | none | any | Details for langfun-0.1.2.dev202610310804-py3-none-any.whl |
Total release size: 1.3 MB
Release files / langfun-0.1.2.dev202610310804.tar.gz
| Download URL | langfun-0.1.2.dev202610310804.tar.gz |
|---|---|
| Size | 568.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
722e697fdb0487dd06663835a2e88d2a0bfc89d20d6867239c53b2c541cf8e03
|
|
BLAKE2b-256 checksum How to use checksums |
150356f2bcc2c826ef2230ae2f00396133fe3191f465724f6749a0f6d35365c2
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.15.1
|
Release files / langfun-0.1.2.dev202610310804-py3-none-any.whl
| Download URL | langfun-0.1.2.dev202610310804-py3-none-any.whl |
|---|---|
| Size | 733.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
8fb9e284f8100a3fceaf1262eeadb82701a7590035873750bf6a3e84f065275c
|
|
BLAKE2b-256 checksum How to use checksums |
3fafb0007106a8682b2629edc02d2ee19185bb2adc648252acba79f7de751cd2
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.15.1
|
Security for langfun 0.1.2.dev202610310804
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
