Microsoft Azure AI Search Launches Vector and RAG Powered Search Agents

Modern search technology is moving away from simple keyword matching toward AI‑driven semantic retrieval. Vector search stores document meanings as high‑dimensional embeddings, allowing systems to find related content even when exact words differ. Retrieval‑Augmented Generation (RAG) then pairs these vectors with large language models to produce factual, up‑to‑date answers, keeping responses honest by pulling fresh source snippets.

Two recent implementations illustrate this shift. One article explains how developers can replace traditional SQL databases with dedicated vector stores such as Pinecone, Weaviate, Milvus or Qdrant, which use approximate nearest‑neighbor algorithms like HNSW to deliver sub‑second similarity scores. Another describes a fully‑grounded search agent built on Microsoft Azure services: Azure OpenAI (via Microsoft Foundry) decides when to invoke Azure AI Search for unstructured documents or Azure Cosmos DB for structured data, uses function calling to route queries, and returns citations for verification. The agent maintains multi‑turn conversation state and combines vector‑based semantic ranking with traditional search to answer complex, context‑rich questions.