chat2func 0.1.1

Automatically creating functions that LLMs can use.

chat2func

chat2func automatically generates JSON schemas from Python, allowing ChatGPT to talk to your code.

Installation

# Clone the repo
git clone [e-mail]:ttumiel/chat2func.git
cd chat2func
pip install -e .

Quick Start

Annotate your function with @json_schema

from chat2func import json_schema

@json_schema
def my_function(x: float, y: float) -> bool:
    """This is a sample function.

    Args:
        x: The first float.
        y: Another float.
    """
    return x > y

After this, my_function will have an additional .json attribute containing its JSON schema.

print(my_function.json)

{'description': 'This is a sample function.',
 'name': 'my_function',
 'parameters': {'properties': {'x': {'description': 'The first float.', 'type': 'number'},
                               'y': {'description': 'Another float.', 'type': 'number'}},
                'required': ['x', 'y'],
                'type': 'object'}}

Using Custom Classes

json_schema works with classes or dataclasses too. Set descriptions=False to not generate object descriptions from docstrings.

@json_schema(descriptions=False)
@dataclass
class Data:
    a: int = 0

print(Data.json)
{'name': 'Data',
 'parameters': {'type': 'object', 'properties': {'a': {'type': 'integer'}}}}

Calling Functions with JSON Arguments

function_call provides additional functionality for calling functions with JSON arguments. It automatically converts JSON arguments to Python objects and returns the result as JSON. It validates the JSON, raising FunctionCallError if something is unexpected.

import json
from chat2func import function_call, collect_functions

def plusplus(x: float, y: float) -> float:
    "Add two floats."
    return x + y

# Specify the available functions.
# You can also use `collect_functions` to collect all functions within a scope.
functions = {"plusplus": plusplus}

# Arguments are passed as a JSON string.
arguments = json.dumps({"x": 1.0, "y": 2.0})
result = function_call("plusplus", arguments, functions)
print(result) # 3.0

# We can optionally validate the function arguments too. Defaults to on.
arguments = json.dumps({"x": "a", "y": 2.0})
result = function_call("plusplus", arguments, functions)
# FunctionCallError: Function call failed. 1 validation error for plusplus

Creating a ChatGPT Plugin

You can easily create and demo a ChatGPT plugin using the included server. First, install the additional server requirements using pip install -e .[server]. Then, define the functions you want ChatGPT to be able to use, expose them with the server and connect to them from ChatGPT. Visit http://localhost:3333/ to see the available functions.

from chat2func.server import FunctionServer

def addition(x: float, y: float) -> float:
    "Add two floats."
    return x + y

server = FunctionServer({"addition": addition})
server.run()