AI prompts
base on OO for LLMs <div align="center">
<img src="https://raw.githubusercontent.com/google/langfun/main/docs/_static/logo.svg" width="520px" alt="logo"></img>
</div>
# 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='<your-openai-api-key>'),
my_image=image,
)
print(desc)
```
*Output:*
<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/8/83/Solar_system.jpg/1646px-Solar_system.jpg" width="520px" alt="my_image"></img>
```
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-genai | LLMs powered by Google Generative AI API |
| 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.*
", Assign "at most 3 tags" to the expected json: {"id":"11861","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"