function-schema

Function schema

This is a small utility to generate JSON schemas for python functions. With power of type annotations, it is possible to generate a schema for a function without describing it twice.

At this moment, extracting schema from a function is useful for OpenAI Assistant Toll Calling, OpenAI API function-call, and Anthropic Claude Toll calling feature. And it can be used for other purposes for example to generate documentation in the future.

Installation

pip install function-schema

Usage

from typing import Annotated, Optional
from function_schema import Doc
import enum

def get_weather(
    city: Annotated[str, Doc("The city to get the weather for")],
    unit: Annotated[
        Optional[str],
        Doc("The unit to return the temperature in"),
        enum.Enum("Unit", "celcius fahrenheit")
    ] = "celcius",
) -> str:
    """Returns the weather for the given city."""
    return f"Weather for {city} is 20°C"

Function description is taken from the docstring. Type hinting with typing.Annotated for annotate additional information about the parameters and return type.

Then you can generate a schema for this function:

import json
from function_schema import get_function_schema

schema = get_function_schema(get_weather)
print(json.dumps(schema, indent=2))

Will output:

{
  "name": "get_weather",
  "description": "Returns the weather for the given city.",
  "parameters": {
    "type": "object",
    "properties": {
      "city": {
        "type": "string",
        "description": "The city to get the weather for"
      },
      "unit": {
        "type": "string",
        "description": "The unit to return the temperature in",
        "enum": [
          "celcius",
          "fahrenheit"
        ],
        "default": "celcius"
      }
    },
  }
  "required": [
    "city"
  ]
}

for claude, you should pass 2nd argument as SchemaFormat.claude or claude:

from function_schema import get_function_schema

schema = get_function_schema(get_weather, "claude")

Please refer to the Claude tool use documentation for more information.

You can use any type hinting supported by python for the first argument of Annotated. including: typing.Literal, typing.Optional, typing.Union, and T | None for python 3.10+.
Doc class or plain string in Annotated is used for describe the parameter. Doc metadata is the PEP propose for standardizing the metadata in type hints. currently, implemented in typing-extensions module. Also function_schema.Doc is provided for compatibility.

Enumeratable candidates can be defined with enum.Enum in the argument of Annotated.

import enum

class AnimalType(enum.Enum):
    dog = enum.auto()
    cat = enum.auto()

def get_animal(
    animal: Annotated[str, Doc("The animal to get"), AnimalType],
) -> str:
    """Returns the animal."""
    return f"Animal is {animal.value}"

In this example, each name of AnimalType enums(dog, cat) is used as an enum schema. In shorthand, you can use typing.Literal as the type will do the same thing.

def get_animal(
    animal: Annotated[Literal["dog", "cat"], Doc("The animal to get")],
) -> str:
    """Returns the animal."""
    return f"Animal is {animal}"

Plain String in Annotated

The string value of Annotated is used as a description for convenience.

def get_weather(
    city: Annotated[str, "The city to get the weather for"], # <- string value of Annotated is used as a description
    unit: Annotated[Optional[str], "The unit to return the temperature in"] = "celcius",
) -> str:
    """Returns the weather for the given city."""
    return f"Weather for {city} is 20°C"

But this would create a predefined meaning for any plain string inside of Annotated, and any tool that was using plain strings in them for any other purpose, which is currently allowed, would now be invalid. Please refer to the PEP 0727, Plain String in Annotated for more information.

Usage with OpenAI API

You can use this schema to make a function call in OpenAI API:

import openai
openai.api_key = "sk-..."

# Create an assistant with the function
assistant = client.beta.assistants.create(
    instructions="You are a weather bot. Use the provided functions to answer questions.",
    model="gpt-4-turbo-preview",
    tools=[{
        "type": "function",
        "function": get_function_schema(get_weather),
    }]
)

run = client.beta.messages.create(
    assistant_id=assistant.id,
    messages=[
        {"role": "user", "content": "What's the weather like in Seoul?"}
    ]
)

# or with chat completion

result = openai.chat.completion.create(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "user", "content": "What's the weather like in Seoul?"}
    ],
    tools=[{
      "type": "function",
      "function": get_function_schema(get_weather)
    }],
    tool_call="auto",
)

Usage with Anthropic Claude

import anthropic

client = anthropic.Client()

response = client.beta.tools.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=4096,
    tools=[get_function_schema(get_weather, "claude")],
    messages=[
        {"role": "user", "content": "What's the weather like in Seoul?"}
    ]
)

CLI usage

function_schema mymodule.py my_function | jq

License

MIT License