easy-fnc 0.1.5

This package hopes to provide a modular and highly extendable interface to interact with LLMs via (multiple) function calling, easily.

easy_fnc

The easy_fnc package provides a framework for generating responses using AI models and executing user-defined functions. It allows users to define their own functions and integrate them with AI models to create interactive and customizable applications.

Installation

To install the easy_fnc package, run the following command:

pip install easy_fnc

Usage

Defining User Functions

User-defined functions can be provided to the package in two ways:

1. Function File: Create a Python file (e.g., functions.py) and define your functions there. The package will automatically import the functions from the specified file.

2. List of Functions: Pass a list of function objects directly to the get_user_defined_functions function.

Example:

from easy_fnc.functions import get_user_defined_functions

# Using a function file
user_functions = get_user_defined_functions("path/to/functions.py")

# Using a list of functions
def func1():
    pass

def func2():
    pass

user_functions = get_user_defined_functions([func1, func2])

Core Utility Functions

The package provides a set of core utility functions that can be used in conjunction with user-defined functions. These functions are defined in the core_utils.py file and can be accessed using the get_core_utils function.

Example:

from easy_fnc.core_utils import get_core_utils

core_utils = get_core_utils()

Models

The package provides an abstract base class EasyFNCModel in the model.py file. This class defines the interface for implementing AI models that can generate responses based on user input and execute function calls.

To create a custom model, subclass EasyFNCModel and implement the required methods:

• generate(self, user_input: str) -> dict: Generate a response based on the user input.
• get_function_calls(self, user_input: str, verbose: bool = False) -> list[dict]: Extract function calls from the model output.

Example:

from easy_fnc.models.model import EasyFNCModel

class CustomModel(EasyFNCModel):
    def generate(self, user_input: str) -> dict:
        # Implement response generation logic
        pass

    def get_function_calls(self, user_input: str, verbose: bool = False) -> list[dict]:
        # Implement function call extraction logic
        pass

Ollama Model

The package includes an implementation of the EasyFNCModel using the Ollama model. The OllamaModel class is defined in the ollama.py file.

To use the Ollama model:

1. Pull the model from Ollama using the ollama pull command. For example:

ollama pull adrienbrault/nous-hermes2pro-llama3-8b:f16

2. Then, the usage is as follows if you're using the nous-hermes2pro-llama3-8b model:

from easy_fnc.models.ollama import OllamaModel

model = OllamaModel(model_name="model_name", functions=[...])
response = model.generate(user_input="user input")
function_calls = model.get_function_calls(user_input="user input", verbose=True)

Templates

The easy_fnc package uses JSON templates to format user input and model responses. The OllamaModel class accepts both a string and a dictionary as parameters for the template.

To use a custom template, you have two options:

1. Provide the name of a JSON template file located in the easy_fnc/models/templates/ directory. The OllamaModel will automatically load the template using the load_template method.

model = OllamaModel(model_name="model_name", functions=[...], template="custom_template")

2. Provide a dictionary containing the template structure directly.

custom_template = {
    "function_call_prompt": {
        "beginning": "...",
        "system_prompt_end": "...",
        "prompt_end": "..."
    },
    "function_response_prompt": {
        "beginning": "...",
        "middle": "...",
        "end": "..."
    }
}

model = OllamaModel(model_name="model_name", functions=[...], template=custom_template)

The load_template method in utils.py can be used to load a template from a JSON file and convert it to a dictionary format that can be passed to the OllamaModel.

from easy_fnc.utils import load_template

template_dict = load_template("path/to/custom_template.json")
model = OllamaModel(model_name="model_name", functions=[...], template=template_dict)

Users have the flexibility to use either a predefined template from the easy_fnc/models/templates/ directory or create their own custom template and provide it as a dictionary or load it from a JSON file using the load_template method.

Contributing

Contributions to the easy_fnc package are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request on the package's GitHub repository.

License

The easy_fnc package is open-source and released under the MIT License.

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This documentation provides an overview of how to use the easy_fnc package based on the provided files. It covers the key components, including user-defined functions, core utility functions, models, configuration, and templates. Users can refer to this documentation to understand how to integrate their own functions, create custom models, and utilize the package effectively in their applications.