os-llm-agents 0.0.1

Python package that helps to build LLM agents based on open-source models from Huggingface Hub.

OS (Open Source) LLM Agents

A library that helps to build LLM agents based on open-source models from Huggingface Hub.

Installation

From source

Run in the root dir of this repo:

pip install .

Example usage

Import needed packages:

from os_llm_agents.models import CustomLLM
from os_llm_agents.executors import AgentExecutor

import torch
from transformers import pipeline, BitsAndBytesConfig

Optional: initialize quantization config

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

Initialize the model:

llm = CustomLLM(
    model_name="meta-llama/Meta-Llama-3-8B-Instruct",
    quantization_config=quantization_config
)

Define the tool:

def multiply(**kwargs) -> int:
    """Multiply two integers together."""
    n1, n2 = kwargs["n1"], kwargs["n2"]
    return n1 * n2
    
multiply_tool = {
    "name": "multiply",
    "description": "Multiply two numbers",
    "parameters": {
        "type": "object",
        "properties": {
            "n1": {
                "type": "int",
                "description": "Number one",
            },
            "n2": {
                "type": "int",
                "description": "Number two",
            },
        },
        "required": ["n1", "n2"],
    },
    "implementation": multiply,  # Attach the function implementation
}

Initialize the AgentExecutor:

executor = AgentExecutor(llm=llm,
                         tools=[multiply_tool],
                         system_prompt="You are helpful assistant")

Run the agent:

chat_history = None

result = executor.invoke("What can you do for me?")

chat_history = result["chat_history"]
print("Response: ", result["response"].content)

>>> Response:  I'm a helpful assistant! I can help you with a variety of tasks. I have access to a function called "multiply" that allows me to multiply two numbers. I can also provide information and answer questions to the best of my knowledge. If you need help with something specific, feel free to ask!

result = executor.invoke("Multiply 12 by 12", chat_history)

chat_history = result["chat_history"]
print("Response: ", result["response"].content)

>>> Response:  144

print(len(chat_history))

>>> 5