microsoft / autogen
- пятница, 29 сентября 2023 г. в 12:52:24
Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ
This project is a spinoff from FLAML.
🔥 autogen has graduated from FLAML into a new project.
AutoGen is a framework that enables development of LLM applications using multiple agents that can converse with each other to solve task. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools.
openai.Completion
or openai.ChatCompletion
as an enhanced inference API. It allows easy performance tuning, utilities like API unification & caching, and advanced usage patterns, such as error handling, multi-config inference, context programming etc.AutoGen is powered by collaborative research studies from Microsoft, Penn State University, and University of Washington.
AutoGen requires Python version >= 3.8. It can be installed from pip:
pip install pyautogen
Minimal dependencies are installed without extra options. You can install extra options based on the feature you need.
For example, use the following to install the dependencies needed by the blendsearch
option.
pip install "pyautogen[blendsearch]"
Find more options in Installation.
For LLM inference configurations, check the FAQ.
from autogen import AssistantAgent, UserProxyAgent, config_list_from_json
# Load LLM inference endpoints from an env variable or a file
# See https://microsoft.github.io/autogen/docs/FAQ#set-your-api-endpoints
# and OAI_CONFIG_LIST_sample.json
config_list = config_list_from_json(env_or_file="OAI_CONFIG_LIST")
assistant = AssistantAgent("assistant", llm_config={"config_list": config_list})
user_proxy = UserProxyAgent("user_proxy", code_execution_config={"work_dir": "coding"})
user_proxy.initiate_chat(assistant, message="Plot a chart of NVDA and TESLA stock price change YTD.")
# This initiates an automated chat between the two agents to solve the task
This example can be run with
python test/twoagent.py
After the repo is cloned.
The figure below shows an example conversation flow with AutoGen.
Please find more code examples for this feature.
openai.Completion
or openai.ChatCompletion
with powerful functionalities like tuning, caching, error handling, templating. For example, you can optimize generations by LLM with your own tuning data, success metrics and budgets.# perform tuning
config, analysis = autogen.Completion.tune(
data=tune_data,
metric="success",
mode="max",
eval_func=eval_func,
inference_budget=0.05,
optimization_budget=3,
num_samples=-1,
)
# perform inference for a test instance
response = autogen.Completion.create(context=test_instance, **config)
Please find more code examples for this feature.
You can find a detailed documentation about AutoGen here.
In addition, you can find:
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
If you are new to GitHub here is a detailed help source on getting involved with development on GitHub.
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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
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