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RAGChat: Optimal retrieval with Azure AI Search

Microsoft Reactor
106K subscribers
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2.8K views Streamed 2 months ago #MicrosoftReactor #learnconnectbuild #RAGDeepDive
In this series, we dive deep into our most popular, fully-featured, and open-source RAG solution: https://aka.ms/ragchat Our RAG solution uses Azure AI Search to find matching documents, using state-of-the-art retrieval mechanisms. We'll dive into the mechanics of vector embeddings, hybrid search with RRF, and semantic ranking. We'll also discuss the data ingestion process, highlighting the differences between manual ingestion and integrated vectorization Get more RAG resources: https://aka.ms/thesource/RAG This session is a part of a series! To learn more, click here: https://aka.ms/RAGDeepDive #MicrosoftReactor #learnconnectbuild #RAGDeepDive [eventID:24572] ...more
...more
RAGChat: Optimal retrieval with Azure AI Search
In this series, we dive deep into our most popular, fully-featured, and open-source RAG solution: https://aka.ms/ragchat Our RAG solution uses Azure AI Search to find matching documents, using state-of-the-art retrieval mechanisms. We'll dive into the mechanics of vector embeddings, hybrid search with RRF, and semantic ranking. We'll also discuss the data ingestion process, highlighting the differences between manual ingestion and integrated vectorization Get more RAG resources: https://aka.ms/thesource/RAG This session is a part of a series! To learn more, click here: https://aka.ms/RAGDeepDive #MicrosoftReactor #learnconnectbuild #RAGDeepDive [eventID:24572]

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Microsoft Reactor

106K subscribers
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