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autogen/website/docs/Examples.md
Joshua Kim c603ca434e
Graph group chat (#857) 
* Move contrib-openai.yml

* Moved groupgroupchat

* From #753

* Removed local test references

* Added ignore=test/agentchat/contrib

* Trying to pass contrib-openai tests

* More specific in unit testing.

* Update .github/workflows/contrib-tests.yml

Co-authored-by: Li Jiang <lijiang1@microsoft.com>

* Remove coverage as it is included in test dependencies

* Improved docstring with overview of GraphGroupChat

* Iterate on feedback

* Precommit pass

* user just use pip install pyautogen[graphs]

* Pass precommit

* Pas precommit

* Graph utils an test completed

* Added inversion tests

* Added inversion util

* allow_repeat_speaker can be a list of Agents

* Remove unnessary imports

* Expect ValueError with 1 and 0 agents

* Check that main passes all tests

* Check main

* Pytest all in main

* All done

* pre-commit changes

* noqa E402

* precommit pass

* Removed bin

* Removed old unit test

* Test test_graph_utils

* minor cleanup

* restore tests

* Correct documentation

* Special case of only one agent remaining.

* Improved pytest

* precommit pass

* Delete OAI_CONFIG_LIST_sample copy

* Returns a filtered list for auto to work

* Rename var speaker_order_dict

* To write test cases

* Added check for a list of Agents to repeat

* precommit pass

* Update documentation

* Extract names in allow_repeat_speaker

* Post review changes

* hange "pull_request_target" into "pull_request" temporarily.

* 3 return values from main

* pre-commit changes

* PC edits

* docstr changes

* PC edits

* Rest of changes from main

* Update autogen/agentchat/groupchat.py

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

* Remove unnecessary script files from tracking

* Non empty scripts files from main

* Revert changes in script files to match main branch

* Removed link from website as notebook is removed.

* test/test_graph_utils.py is tested as part of L52 of build.yml

* GroupChat ValueError check

* docstr update

* More clarification in docstr

* Update autogen/agentchat/groupchat.py

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

* Update autogen/agentchat/groupchat.py

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

* Update autogen/agentchat/groupchat.py

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

* Update autogen/agentchat/groupchat.py

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

* 1.add commit to line138 in groupchat.py;2.fix bug if random choice [];3.return selected_agent if len(graph_eligible_agents) is 1;4.replace all speaker_order to speaker_transitions;5.format

* fix graph_modelling notebook in the last cell

* fix failure in test_groupchat.py

* fix agent out of group to initiate a chat like SocietyOfMind

* add a warning rule in graph_utils to check duplicates in any lists

* refactor allowed_or_disallowed_speaker_transitions to Dict[Agent, List[Agent]] and modify the tests and notebook

* delete Rule 4 in graph_utils and related test case. Add a test to resolve 993fd006e9 (r1460726831)

* fix as the final comments

* modify setup option from graphs to graph and add texts in optional-dependencies.md

* Update autogen/graph_utils.py

---------

Co-authored-by: Li Jiang <lijiang1@microsoft.com>
Co-authored-by: Beibin Li <BeibinLi@users.noreply.github.com>
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
Co-authored-by: Qingyun Wu <qingyun0327@gmail.com>
Co-authored-by: Yishen Sun <freedeaths@FREEDEATHS-XPS>
Co-authored-by: freedeaths <register917@gmail.com>
2024-02-06 03:13:18 +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 transition paths specified in a graph - View Notebook
    • Running a group chat as an inner-monolgue via the SocietyOfMindAgent - 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
    • Use Tools via Sync and Async Function Calling - 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
    • Function Inception: Enable AutoGen agents to update/remove functions during conversations. - View Notebook
    • Agent Chat with Whisper - View Notebook
    • Constrained Responses via Guidance - View Notebook
    • Browse the Web with Agents - View Notebook
    • SQL: Natural Language Text to SQL Query using the Spider Text-to-SQL Benchmark - View Notebook

  5. Human Involvement

  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
    • Teach OpenAI Assistants Through GPTAssistantAgent - View Notebook
    • Agent Optimizer: Train Agents in an Agentic Way - 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
    • Automatically Build Multi-agent System from Agent Library - 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: