Building a Vector Index in Azure AI Search: HNSW, Profiles,
Summary
This article explains how to build a vector index in Azure AI Search for retrieval-augmented generation. It walks through HNSW-based vector search, vector profiles, embedding generation, and document upload with Python. It also shows how to query by meaning, assemble retrieved context into a prompt, and connect the flow to an LLM. The piece closes with practical guidance on hybrid search, semantic ranking, chunking, and Azure OpenAI embeddings.
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