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* add agentchat_video_transcript_translate.ipynb * finish the agentchat_video_transcript_translate.ipynb file notebook * modify the recognize_transcript_from_video function * run the script and add the output to the notebook * implement the notebook * add the link to the video clip * rename the file and add the version requirement of each packages * add the new notebook path * add the notebook path to Example.md * add the new notebook path to the new example.md * add the instruction of FFmpeg and video download * Update Examples.md * Update Examples.md * Update Examples.md * Update Examples.md * Delete notebook/agentchat_video_transcript_translate.ipynb * Update Examples.md and add the link --------- Co-authored-by: silver233jpg <60947716+silver233jpg@users.noreply.github.com>
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7.8 KiB
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:
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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
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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
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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
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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
- Function Inception: Enable AutoGen agents to update/remove functions during conversations. - View Notebook
- Agent Chat with Whisper - View Notebook
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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
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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
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Multi-Agent Chat with OpenAI Assistants in the loop
- Hello-World Chat with OpenAi Assistant in AutoGen - View Notebook
- Chat with OpenAI Assistant using Function Call - View Notebook
- Chat with OpenAI Assistant with Code Interpreter - View Notebook
- Chat with OpenAI Assistant with Retrieval Augmentation - View Notebook
- OpenAI Assistant in a Group Chat - View Notebook
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Multimodal Agent
- Multimodal Agent Chat with DALLE and GPT-4V - View Notebook
- Multimodal Agent Chat with Llava - View Notebook
- Multimodal Agent Chat with GPT-4V - View Notebook
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Long Context Handling
- Conversations with Chat History Compression Enabled - View Notebook
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Evaluation and Assessment
- AgentEval: A Multi-Agent System for Assess Utility of LLM-powered Applications - View Notebook
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Automatic Agent Building
- Automatically Build Multi-agent System with AgentBuilder - View Notebook
Enhanced Inferences
Utilities
- API Unification - View Documentation with Code Example
- Utility Functions to Help Managing API configurations effectively - View Notebook
- Cost Calculation - View Notebook
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: