lionagi 0.0.106

Towards automated general intelligence.

• PyPI: https://pypi.org/project/lionagi/
• Documentation: https://lionagi.readthedocs.io/en/latest/ （still a lot TODO）
• Website: TODO
• Discord: Join Our Discord

LionAGI

Towards Automated General Intelligence

LionAGI is a Python package that combines data manipulation with AI tools, aiming to simplify the integration of advanced machine learning tools, such as Large Language Models (i.e. OpenAI's GPT), with production level data centric projects.

Install LionAGI with pip:

pip install lionagi

Download the .env_template file, input your OPENAI_API_KEY, save the file, rename as .env and put in your project's root directory.

Features

• Robust performance. LionAGI is written in almost pure python. With minimum external dependency (aiohttp, httpx, python-dotenv, tiktoken)
• Efficient data operations for reading, chunking, binning, writing, storing and managing data.
• Fast interaction with LLM services like OpenAI with configurable rate limiting concurrent API calls for maximum throughput.
• Create a production ready LLM application in hours. Intuitive workflow management to streamline and expedite the process from idea to market.

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Currently, LionAGI only natively support OpenAI API calls, support for other LLM providers as well as open source models will be integrated in future releases. LionAGI is designed to be async only, please check python documentation here

Notice:

• calling API with maximum throughput over large set of data with advanced models i.e. gpt-4 can get EXPENSIVE IN JUST SECONDS,
• please know what you are doing, and check the usage on OpenAI regularly
• default rate limits are set to be tier 1 of OpenAI model gpt-4-1104-preview, please check the OpenAI usage limit documentation you can modify token rate parameters to fit different use cases.
• Documentation is under process

Quick Start

The following example shows how to use LionAGI's Session object to interact with gpt-4 model:

import lionagi as li

# define system messages, context and user instruction
system = "You are a helpful assistant designed to perform calculations."
instruction = {"Addition":"Add the two numbers together i.e. x+y"}
context = {"x": 10, "y": 5}

# Initialize a session with a system message
calculator = li.Session(system=system)

# run a LLM API call
result = await calculator.initiate(instruction=instruction,
                                   context=context,
                                   model="gpt-4-1106-preview")

print(f"Calculation Result: {result}")

Visit our notebooks for our examples.

Community

We encourage contributions to LionAGI and invite you to enrich its features and capabilities. Engage with us and other community members Join Our Discord

Citation

When referencing LionAGI in your projects or research, please cite:

@software{Li_LionAGI_2023,
  author = {Haiyang Li},
  month = {12},
  year = {2023},
  title = {LionAGI: Towards Automated General Intelligence},
  url = {https://github.com/lion-agi/lionagi},
}

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Requirements

Python 3.9 or higher.