llm_wrapper 0.1.9

Run llm functions with just inline documentation and no code

LLM Functions Library Usage Guide

This guide provides a comprehensive overview of how to use the llm_wrapper library, which offers a versatile wrapper, llm_func, designed for seamless interactions with various language learning models (LLMs). The wrapper simplifies model initialization, query execution, and structured output parsing, supporting a wide range of return types including basic data types (int, float, str, bool, set, list, dict) and complex Pydantic BaseModel structures.

Getting Started

from llm_wrapper import llm_func
from langchain_openai import OpenAI

@llm_func
def famous_quote() -> str:
    """Returns a famous quote according to the subject provided."""
    pass

llm = OpenAI()

query = "Peace and War"
quote = famous_quote(llm=llm, query=query)
print(quote)  # Output: "Peace is not a relationship of nations. It is a condition of mind brought about by a serenity of soul. Peace is not merely the absence of war. It is also a state of mind. Lasting peace can come only to peaceful people. - Jawaharlal Nehru

@llm_func
def check_grammar() -> float:
    """Check the grammar of the sentence and return a float number between 0 and 1 reflecting its correctness."""
    pass

query = "I are a student."
correctness = check_grammar(llm=llm, query=query)
print(correctness)  # Output: 0.5
query = "I am a student."
correctness = check_grammar(llm=llm, query=query)
print(correctness)  # Output: 1.0

Installation

Ensure the llm_wrapper library is installed in your environment. You can install it using pip:

pip install llm_wrapper

Importing the Library

Start by importing the necessary components:

from llm_wrapper import llm_func
from pydantic import BaseModel

Initializing Your LLM Object

You'll need to instantiate your preferred LLM object. This library is designed to work flexibly with various LLMs:

llm = YourPreferredLLM()

Using the llm_func Wrapper

The llm_func wrapper is designed to streamline your interaction with LLMs. It automatically handles functions returning basic types (int, float, str, bool) or Pydantic BaseModel instances.

Defining Functions with @llm_func

Annotate your functions with @llm_func and define a clear return type. Here's how to define functions returning basic types and Pydantic models:

Basic Types

@llm_func
def calculate_score() -> int:
    """Returns an integer score based on the input text."""
    pass

@llm_func
def is_valid() -> bool:
    """Determines if the text meets certain criteria, returning True or False."""
    pass

Pydantic BaseModel

class User(BaseModel):
    name: str
    age: int

@llm_func
def get_user_details() -> User:
    """Extracts user details from the text and returns them as a User model."""
    pass

Executing Queries

Pass your query to the function, along with the instantiated LLM object. The wrapper will process the input and return a structured output based on the defined return type.

query = "Calculate the score for the following text..."
score = calculate_score(llm=llm, query=query)
print(score)  # Output will be of type int

query = "Check if the following text is valid..."
validity = is_valid(llm=llm, query=query)
print(validity)  # Output will be of type bool

query = "Extract user details from the following text..."
user_details = get_user_details(llm=llm, query=query)
print(user_details)  # Output will be a User instance

Support and Development

Currently, llm_func supports functions returning basic data types (int, float, str, bool, set, list, dict) and Pydantic BaseModel instances. Support for additional types is under active development, and updates will be released periodically to enhance the library's functionality.

By following these guidelines, you can efficiently use the llm_wrapper library to interact with language models, perform queries, and handle structured outputs, all while writing clear and maintainable code.

Documentation

llm_wrapper Tutorial