feat: add AG2 multi-agent RAG example with Weaviate#70
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Jupyter notebook and e2e test demonstrating AG2 (formerly AutoGen) multi-agent conversations using Weaviate as the vector store for RAG. - Weaviate Embedded for zero-setup vector search - Automatic embeddings via text2vec-openai module - AG2 GroupChat with Research + Analyst agents - Weaviate search registered as AG2 tool via decorator pattern - Google Colab support
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Hi @databyjp @victorialslocum — could one of you review this when you get a chance? Thanks! |
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To avoid any confusion in the future about your contribution to Weaviate, we work with a Contributor License Agreement. If you agree, you can simply add a comment to this PR that you agree with the CLA so that we can merge. |
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Summary
Add an example for AG2 (formerly AutoGen), a multi-agent conversation framework with 500K+ monthly PyPI downloads, 4,300+ GitHub stars, and 400+ contributors.
What's included
ag2-multiagent-rag/ag2_multiagent_rag_with_weaviate.ipynb— Complete notebookag2-multiagent-rag/test_e2e.py— End-to-end test scriptag2-multiagent-rag/README.md— Setup instructions and overviewHow it works
text2vec-openai)search_documentstool to query WeaviateKey features
text2vec-openaivectorizer, no manual embedding stepRelated