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autogen/website/docs/Examples/AgentChat.md
Chi Wang c4f8b1c761
Dev/v0.2 (#393) 
* api_base -> base_url (#383)

* InvalidRequestError -> BadRequestError (#389)

* remove api_key_path; close #388

* close #402 (#403)

* openai client (#419)

* openai client

* client test

* _client -> client

* _client -> client

* extra kwargs

* Completion -> client (#426)

* Completion -> client

* Completion -> client

* Completion -> client

* Completion -> client

* support aoai

* fix test error

* remove commented code

* support aoai

* annotations

* import

* reduce test

* skip test

* skip test

* skip test

* debug test

* rename test

* update workflow

* update workflow

* env

* py version

* doc improvement

* docstr update

* openai<1

* add tiktoken to dependency

* filter_func

* async test

* dependency

* migration guide (#477)

* migration guide

* change in kwargs

* simplify header

* update optigude description

* deal with azure gpt-3.5

* add back test_eval_math_responses

* timeout

* Add back tests for RetrieveChat (#480)

* Add back tests for RetrieveChat

* Fix format

* Update dependencies order

* Fix path

* Fix path

* Fix path

* Fix tests

* Add not run openai on MacOS or Win

* Update skip openai tests

* Remove unnecessary dependencies, improve format

* Add py3.8 for testing qdrant

* Fix multiline error of windows

* Add openai tests

* Add dependency mathchat, remove unused envs

* retrieve chat is tested

* bump version to 0.2.0b1

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Co-authored-by: Li Jiang <bnujli@gmail.com>
2023-11-04 04:01:49 +00:00

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Automated Multi Agent Chat

AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation via multi-agent conversation. Please find documentation about this feature here.

Links to notebook examples:

  1. Code Generation, Execution, and Debugging

    • Automated Task Solving with Code Generation, Execution & Debugging - View Notebook
    • Auto Code Generation, Execution, Debugging and Human Feedback - View Notebook
    • Automated Code Generation and Question Answering with Retrieval Augmented Agents - View Notebook
    • Automated Code Generation and Question Answering with Qdrant based Retrieval Augmented Agents - View Notebook

  2. Multi-Agent Collaboration (>3 Agents)

    • Automated Task Solving with GPT-4 + Multiple Human Users - View Notebook
    • Automated Task Solving by Group Chat (with 3 group member agents and 1 manager agent) - View Notebook
    • Automated Data Visualization by Group Chat (with 3 group member agents and 1 manager agent) - View Notebook
    • Automated Complex Task Solving by Group Chat (with 6 group member agents and 1 manager agent) - View Notebook
    • Automated Task Solving with Coding & Planning Agents - View Notebook

  3. Applications

    • Automated Chess Game Playing & Chitchatting by GPT-4 Agents - View Notebook
    • Automated Continual Learning from New Data - View Notebook
    • OptiGuide - Coding, Tool Using, Safeguarding & Question Anwering for Supply Chain Optimization

  4. Tool Use

    • Web Search: Solve Tasks Requiring Web Info - View Notebook
    • Use Provided Tools as Functions - View Notebook
    • Task Solving with Langchain Provided Tools as Functions - View Notebook
    • RAG: Group Chat with Retrieval Augmented Generation (with 5 group member agents and 1 manager agent) - View Notebook
    • In-depth Guide to OpenAI Utility Functions - View Notebook

  5. Agent Teaching and Learning

    • Teach Agents New Skills & Reuse via Automated Chat - View Notebook
    • Teach Agents New Facts, User Preferences and Skills Beyond Coding - View Notebook