Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/shroominic/funcchain
⛓️ build cognitive systems, pythonic
https://github.com/shroominic/funcchain
funcchain jinja2 langchain langsmith llm minimalistic openai-functions prompt pydantic python-async pythonic
Last synced: 7 days ago
JSON representation
⛓️ build cognitive systems, pythonic
- Host: GitHub
- URL: https://github.com/shroominic/funcchain
- Owner: shroominic
- License: mit
- Created: 2023-08-31T12:28:34.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-30T20:14:43.000Z (7 months ago)
- Last Synced: 2024-10-29T16:41:46.326Z (3 months ago)
- Topics: funcchain, jinja2, langchain, langsmith, llm, minimalistic, openai-functions, prompt, pydantic, python-async, pythonic
- Language: Python
- Homepage: https://shroominic.github.io/funcchain/
- Size: 5.13 MB
- Stars: 324
- Watchers: 12
- Forks: 21
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Roadmap: roadmap.todo
Awesome Lists containing this project
- awesome-langchain-zh - Funcchain
- awesome-langchain - Funcchain
README
# funcchain
[](https://badge.fury.io/py/funcchain)
[](https://github.com/shroominic/funcchain/actions/workflows/code-check.yml)
[](https://discord.gg/TrwWWMXdtR)
[](https://docs.pydantic.dev/latest/contributing/#badges)
[](https://x.com/shroominic)
```bash
pip install funcchain
```
## Introduction
`funcchain` is the *most pythonic* way of writing cognitive systems. Leveraging pydantic models as output schemas combined with langchain in the backend allows for a seamless integration of llms into your apps.
It utilizes OpenAI Functions or LlamaCpp grammars (json-schema-mode) for efficient structured output.
In the backend it compiles the funcchain syntax into langchain runnables so you can easily invoke, stream or batch process your pipelines.
[](https://codespaces.new/ricklamers/funcchain-demo)
## Simple Demo
```python
from funcchain import chain
from pydantic import BaseModel
# define your output shape
class Recipe(BaseModel):
ingredients: list[str]
instructions: list[str]
duration: int
# write prompts utilising all native python features
def generate_recipe(topic: str) -> Recipe:
"""
Generate a recipe for a given topic.
"""
return chain() # <- this is doing all the magic
# generate llm response
recipe = generate_recipe("christmas dinner")
# recipe is automatically converted as pydantic model
print(recipe.ingredients)
```
## Complex Structured Output
```python
from pydantic import BaseModel, Field
from funcchain import chain
# define nested models
class Item(BaseModel):
name: str = Field(description="Name of the item")
description: str = Field(description="Description of the item")
keywords: list[str] = Field(description="Keywords for the item")
class ShoppingList(BaseModel):
items: list[Item]
store: str = Field(description="The store to buy the items from")
class TodoList(BaseModel):
todos: list[Item]
urgency: int = Field(description="The urgency of all tasks (1-10)")
# support for union types
def extract_list(user_input: str) -> TodoList | ShoppingList:
"""
The user input is either a shopping List or a todo list.
"""
return chain()
# the model will choose the output type automatically
lst = extract_list(
input("Enter your list: ")
)
# custom handler based on type
match lst:
case ShoppingList(items=items, store=store):
print("Here is your Shopping List: ")
for item in items:
print(f"{item.name}: {item.description}")
print(f"You need to go to: {store}")
case TodoList(todos=todos, urgency=urgency):
print("Here is your Todo List: ")
for item in todos:
print(f"{item.name}: {item.description}")
print(f"Urgency: {urgency}")
```
## Vision Models
```python
from funcchain import Image
from pydantic import BaseModel, Field
from funcchain import chain, settings
# set global llm using model identifiers (see MODELS.md)
settings.llm = "openai/gpt-4-vision-preview"
# everything defined is part of the prompt
class AnalysisResult(BaseModel):
"""The result of an image analysis."""
theme: str = Field(description="The theme of the image")
description: str = Field(description="A description of the image")
objects: list[str] = Field(description="A list of objects found in the image")
# easy use of images as input with structured output
def analyse_image(image: Image) -> AnalysisResult:
"""
Analyse the image and extract its
theme, description and objects.
"""
return chain()
result = analyse_image(Image.open("examples/assets/old_chinese_temple.jpg"))
print("Theme:", result.theme)
print("Description:", result.description)
for obj in result.objects:
print("Found this object:", obj)
```
## Seamless local model support
```python
from pydantic import BaseModel, Field
from funcchain import chain, settings
# auto-download the model from huggingface
settings.llm = "ollama/openchat"
class SentimentAnalysis(BaseModel):
analysis: str
sentiment: bool = Field(description="True for Happy, False for Sad")
def analyze(text: str) -> SentimentAnalysis:
"""
Determines the sentiment of the text.
"""
return chain()
# generates using the local model
poem = analyze("I really like when my dog does a trick!")
# promised structured output (for local models!)
print(poem.analysis)
```
## Features
- 🐍 pythonic
- 🔀 easy swap between openai or local models
- 🔄 dynamic output types (pydantic models, or primitives)
- 👁️ vision llm support
- 🧠 langchain_core as backend
- 📝 jinja templating for prompts
- 🏗️ reliable structured output
- 🔁 auto retry parsing
- 🔧 langsmith support
- 🔄 sync, async, streaming, parallel, fallbacks
- 📦 gguf download from huggingface
- ✅ type hints for all functions and mypy support
- 🗣️ chat router component
- 🧩 composable with langchain LCEL
- 🛠️ easy error handling
- 🚦 enums and literal support
- 📐 custom parsing types
## Documentation
[Checkout the docs here](https://shroominic.github.io/funcchain/) 👈
Also highly recommend to try and run the examples in the `./examples` folder.
## Contribution
You want to contribute? Thanks, that's great!
For more information checkout the [Contributing Guide](docs/contributing/dev-setup.md).
Please run the dev setup to get started:
```bash
git clone https://github.com/shroominic/funcchain.git && cd funcchain
./dev_setup.sh
```