funcchain

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.

Simple Demo

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

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

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

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")