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Update readme and AutoGen docs (#1183)

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* Update readme and AutoGen docs

* Update Autogen#notebook-examples, Add link to AutoGen arxiv

* Update website/docs/Use-Cases/Autogen.md

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* Update link

---------

Co-authored-by: Chi Wang <wang.chi@microsoft.com>
Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>
This commit is contained in:
Li Jiang
2023-08-29 21:52:33 +08:00
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parent 3a3e11535f
commit f0731e2240
3 changed files with 6 additions and 1 deletions
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README.md
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@@ -20,7 +20,7 @@
:fire: [autogen](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen) is released with support for ChatGPT and GPT-4, based on [Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference](https://arxiv.org/abs/2303.04673).
:fire: FLAML supports AutoML and Hyperparameter Tuning features in [Microsoft Fabric](https://learn.microsoft.com/en-us/fabric/get-started/microsoft-fabric-overview) private preview. Sign up for these features at: https://aka.ms/fabric/data-science/sign-up.
:fire: FLAML supports Code-First AutoML & Tuning – Private Preview in [Microsoft Fabric Data Science](https://learn.microsoft.com/en-us/fabric/data-science/).
## What is FLAML

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website/docs/Examples/AutoGen-AgentChat.md
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# AutoGen - Automated Multi Agent Chat
<!-- Keep aligned with notebooks in docs/Use-Cases/Autogen#notebook-examples -->
`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance via multi-agent conversation.
Please find documentation about this feature [here](/docs/Use-Cases/Autogen#agents).
@@ -13,3 +14,4 @@ Links to notebook examples:
* [Automated Chess Game Playing & Chitchatting by GPT-4 Agents](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_chess.ipynb)
* [Automated Task Solving by Group Chat](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_groupchat.ipynb)
* [Automated Continual Learning from New Data](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_stream.ipynb)
* [Automated Code Generation and Question Answering with Retrieval Augemented Agents](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_RetrieveChat.ipynb)

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website/docs/Use-Cases/Autogen.md
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```
### Notebook Examples
<!-- Keep aligned with notebooks in docs/Examples/AutoGen-AgentChat.md -->
*Interested in trying it yourself? Please check the following notebook examples:*
* [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_auto_feedback_from_code_execution.ipynb)
@@ -157,6 +158,7 @@ user_proxy.initiate_chat(
* [Automated Chess Game Playing & Chitchatting by GPT-4 Agents](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_chess.ipynb)
* [Automated Task Solving by Group Chat](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_groupchat.ipynb)
* [Automated Continual Learning from New Data](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_stream.ipynb)
* [Automated Code Generation and Question Answering with Retrieval Augemented Agents](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_RetrieveChat.ipynb)
## Enhanced Inference
@@ -550,3 +552,4 @@ The compact history is more efficient and the individual API call history contai
*Interested in the research that leads to this package? Please check the following papers.*
* [Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference](https://arxiv.org/abs/2303.04673). Chi Wang, Susan Xueqing Liu, Ahmed H. Awadallah. ArXiv preprint arXiv:2303.04673 (2023).
* [An Empirical Study on Challenging Math Problem Solving with GPT-4](https://arxiv.org/abs/2306.01337). Yiran Wu, Feiran Jia, Shaokun Zhang, Hangyu Li, Erkang Zhu, Yue Wang, Yin Tat Lee, Richard Peng, Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2306.01337 (2023).
* [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework](https://arxiv.org/abs/2308.08155). Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, Chi Wang. ArXiv preprint arXiv:2308.08155 (2023).