lionagi 0.0.202

Towards automated general intelligence.

PyPI | Documentation | Discord

LionAGI

Towards Automated General Intelligence

LionAGI is a cutting-edge intelligent agent framework. It integrates data manipulation with advanced machine learning tools, such as Large Language Models (i.e. OpenAI's GPT).

• Designed for data-centric, production-level projects,
• dramatically lowers the barrier in creating intelligent, automated systems
• that can understand and interact meaningfully with large volumes of data.

Install LionAGI with pip:

pip install lionagi

Download the .env_template file, input your appropriate API_KEY, save the file, rename as .env and put in your project's root directory. by default we use OPENAI_API_KEY.

Features

• Robust and scalable. Create a production ready LLM application in hours, with more than 100 models
• Efficient and verstile data operations for reading, chunking, binning, writing, storing data with support for langchain and llamaindex
• Built-in support for chain/graph-of-thoughts, ReAct, Concurrent parallel function calling
• Unified interface with any LLM provider, API or local
  • Fast and concurrent API call with configurable rate limit
  • (Work In Progress) support for models both API and local

---

LionAGI is designed to be asynchronous only, please check python official documentation on how async work: 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.
• if you would like to build from source, please download the latest release, main is under development and will be changed without notice

Quick Start

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

# 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}

# in interactive environment (.ipynb for example)
import lionagi as li

calculator = li.Session(system=system)
result = await calculator.initiate(
  instruction=instruction, context=context, model="gpt-4-1106-preview"
)

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

# or otherwise, you can use
import asyncio
from dotenv import load_dotenv

load_dotenv()

import lionagi as li

async def main():
    calculator = li.Session(system=system)
    result = await calculator.initiate(
      instruction=instruction, context=context, model="gpt-4-1106-preview"
    )
    print(f"Calculation Result: {result}")

if __name__ == "__main__":
    asyncio.run(main())

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},
}

Star History

Requirements

Python 3.9 or higher.