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Azure AI Search

As explained in Generative AI in Action

Azure AI Search is Microsoft's cloud search service for indexing and retrieving content, including through vector and semantic search capabilities.

Azure AI Search is a managed cloud service for enterprise search applications. It supports vector retrieval over embeddings and scales beyond locally hosted Redis for large corpora, indexes, and production workloads. section 8.2

Its search architecture combines several retrieval capabilities:

  • Vector search retrieves objects semantically similar to a query through vector embeddings. section 8.2
  • Hybrid search combines full-text and vector queries against an index containing plain text and generated embeddings. section 8.2
  • Hybrid requests can use filtering, faceting, sorting, scoring profiles, and semantic ranking, then return one result set through reciprocal rank fusion. section 8.2
  • The service indexes structured, semi-structured, and unstructured data and provides enterprise controls including encryption and role-based access control. section 8.2

How Generative AI in Action covers it

How deep it goes
Explains vector and hybrid retrieval, ranking options, indexing scope, and security, but stops short of implementation code.
Examples
Contrasts Azure AI Search with locally containerized Redis for larger corpora, indexes, availability, and product-scale workloads.
What it assumes
Assumes familiarity with vector databases, search indexes, vector embeddings, and retrieving information from indexed data.
What you can do after
Apply it via Azure AI Search when locally hosted vector databases no longer meet scale, availability, or enterprise-security needs.
How it connects
Connects vector search with embeddings, full-text search, hybrid search, semantic ranking, reciprocal rank fusion, and role-based access control.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Generative AI in Action. The section links point to the parts of the book it draws on.