Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use an embedding model . Embedding models convert text into numerical vector representations whose geometric relationships encode semantic similarity.
During indexing, policy documents are normally divided into chunks. Each chunk is submitted to the embedding model and the resulting vector is stored in a vector field in Azure AI Search or another vector store.
At query time, the user ' s natural-language question is submitted to the same embedding model. The resulting query vector can then be compared with the stored document vectors by using a nearest-neighbor similarity algorithm. Semantically similar passages can therefore be retrieved even when the query and policy use different wording.
A large language model or small language model can subsequently use the retrieved text to generate an answer, but the LLM/SLM is not the component specifically intended to create search vectors. An image-generation model creates visual content and is unrelated to textual vector retrieval.
Embedding models are therefore foundational to vector search and many RAG ingestion and retrieval architectures.
Study Guide alignment: configure semantic, hybrid, and vector search for grounding and build retrieval pipelines for internal documents . Microsoft Learn
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