isozero 0.1.0

Enhance LLM Zero-Shot Responses through multi-step reasoning and document analysis

IsoZero: Enhancing LLM Zero-Shot Responses

IsoZero is a powerful SDK designed to enhance Large Language Model (LLM) zero-shot responses through multi-step reasoning and document analysis. By leveraging a step-by-step reasoning process, this SDK helps improve the accuracy and depth of LLM outputs, especially in scenarios where the model hasn't been specifically fine-tuned for the task at hand.

Features

• Multi-step reasoning process to break down complex tasks
• Support for multiple LLM backends:
  • Claude (Anthropic)
  • GPT (OpenAI)
  • Transformer models (Hugging Face)
• Document analysis capabilities for context-aware responses
• Mathematical problem-solving simulation
• Flexible CLI with progress bars and result saving
• Customizable number of reasoning steps

Package Structure

The IsoZero package consists of two main modules:

1. reason_sim: General reasoning and document analysis
2. math_sim: Mathematical problem-solving simulation

Installation

Install IsoZero directly from PyPI:

pip install isozero

For the latest development version:

pip install git+https://github.com/iso-ai/isozero.git

Usage

Command Line Interface

IsoZero provides a flexible CLI for various tasks:

1. General Reasoning Task:

   isozero --mode reasoning --task "Explain the process of photosynthesis" --agent claude --steps 4 --save
   

2. Document Analysis (Question Answering):

   isozero --mode qa --documents https://en.wikipedia.org/wiki/Artificial_intelligence https://en.wikipedia.org/wiki/Machine_learning --questions questions.txt --agent huggingface --model google/flan-t5-large --steps 4
   

3. Math Problem Solving:

   isozero --mode math --task "A train travels at 60 km/h for 2 hours, then at 90 km/h for 3 hours. What's the total distance?" --agent openai --steps 4 --save
   

The --save flag will store the results in a JSON file in the logs folder.

Python API

You can also use IsoZero in your Python scripts:

from isozero.reason_sim import ClaudeAgent, QuestionAnswerer, DocumentLoader
from isozero.reason_sim.reason_simulation import ReasonSimulation
from isozero.reason_sim.simulation_wrapper import SimulationWrapper

# Initialize the agent
agent = ClaudeAgent(api_key="your_api_key_here")

# For reasoning tasks
simulation = ReasonSimulation("Explain the process of photosynthesis", max_steps=4)
wrapper = SimulationWrapper(agent, simulation)

for step in range(4):
    state = wrapper.step()
    print(f"Step {state['text_data']['step']}:", state['text_data']['reasoning'][-1])

# For document analysis
loader = DocumentLoader()
documents = loader.load(["path/to/document.txt"])
qa = QuestionAnswerer(agent)
results = qa.answer_questions(documents, ["Your question here"])

Configuration

Set environment variables for API keys:

export ANTHROPIC_API_KEY=your_anthropic_key_here
export OPENAI_API_KEY=your_openai_key_here

Or use a .env file in your project root.

License

This project is licensed under the Apache License, Version 2.0. See the LICENSE file for details.

Citation

If you use IsoZero in your research, please cite it as follows:

@software{isozero2024,
  author = {Jazmia Henry},
  title = {IsoZero: Enhancing LLM Zero-Shot Responses},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/iso-ai/isozero}}
}