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Linxin Song 83f0c744b2
Testing AutoBuild (#846) 
* init notebook for agent set up

* complete AgentCreator

* AgentCreator first step features completed.

* update AgentCreator

* update AgentCreator

* update AgentCreator (modify params of build)

* [update AgentCreator] add auto judgement of coding

* add autobuild

* rename autobuild notebook

* Add step-by-step command.

* modify name

* fix bugs

* update for new openai api

* add example

* add load_config, save_config, and add task in build and start

* modify notebook

* rewrite save and load function; update notebook

* update description

* update description

* update description

* change member variable of agent_creator.py

* update notebook

* new feature: auto-generate agent name and system message

* new feature: add gpts support

* update notebook

* update notebook

* beautify; add docstring for build

* Update notebook; PR version

* typo

* update notebook

* fix typo

* try to run llama

* try to run llama

* switch api_base to base_url

* add details for Step 6; add print in clear_all_agents()

* Change description of Step 5

* delete ASSISTANT_CONFIG_LIST

* add Linxin to blog authors

* add blog

* Update index.mdx

* add test; add user proxy constrain; change show case in notebook

* modify blog for test

* change test file name; modify test

* modify test

* modify test

* add try/catch for dependency

* add version requirement for openai

* add exception for DistributionNotFound error

* add requirement

* change assertion

* rename test; modify index.mdx

* change config file name

* Update agent_chat.md

* Update agent_chat.md

* Update AgentChat.md

* complete blog; fix typos in notebook

* add autobuild banner

* try to merge

* Update Examples.md

* update test

* skip if openai not installed

* pre-commit

* Update website/blog/2023-11-26-Agent-AutoBuild/index.mdx

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

* update contrib-openai.yml

* change pull_request_target to pull_request

* disable other openai tests

* address issues from ekzhu; modify notebook; modify blog; modify test

* update test example

* update test

* Update agent_chat.md

* Update Examples.md

* address issues from qingyun; update agent_builder.py; update notebook

* recover contrib-openai.yml

* pre-commit solve

---------

Co-authored-by: Jieyu Zhang <jieyuz2@cs.washington.edu>
Co-authored-by: JieyuZ2 <jieyuzhang97@gmail.com>
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>
2023-12-04 02:12:26 +00:00

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Examples

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
    • 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 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
    • Automated Task Solving with agents divided into 2 groups - View Notebook
    • Automated Task Solving with transition paths specified in a graph - 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 Answering 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
    • Function Inception: Enable AutoGen agents to update/remove functions during conversations. - View Notebook

  5. Human Involvement

    • Auto Code Generation, Execution, Debugging and Human Feedback - View Notebook
    • Automated Task Solving with GPT-4 + Multiple Human Users - View Notebook
    • Agent Chat with Async Human Inputs - View Notebook

  6. 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

  7. Multi-Agent Chat with OpenAI Assistants in the loop

  8. Multimodal Agent

  9. Long Context Handling

    • Conversations with Chat History Compression Enabled - View Notebook

  10. Evaluation and Assessment

    • AgentEval: A Multi-Agent System for Assess Utility of LLM-powered Applications - View Notebook

  11. Automatic Agent Building

    • Automatically Build Multi-agent System with AgentBuilder - View Notebook

Enhanced Inferences

Utilities

Inference Hyperparameters Tuning

AutoGen offers a cost-effective hyperparameter optimization technique EcoOptiGen for tuning Large Language Models. The research study finds that tuning hyperparameters can significantly improve the utility of them. Please find documentation about this feature here.

Links to notebook examples: