Official Technical Resources: Microsoft (page 8)
Official Technical Resources · Microsoft: 312 entries in total; this page lists entries 106–120.
Relevance in Azure AI Search
Learn how relevance and ranking work in Azure AI Search. Explore hybrid search, semantic reranking, agentic retrieval, and scoring profiles to improve results.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Return a semantic answer in Azure AI Search
Describes the composition of a semantic answer and how to obtain answers from a result set.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Configure semantic ranker and return captions in search results
Add a semantic configuration to a search index.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Use scoring profiles with semantic ranker in Azure AI Search
Learn how to combine scoring profiles with semantic ranking in Azure AI Search to optimize final document relevance.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Add semantic ranking to queries in Azure AI Search
Set a semantic query type to attach the deep learning models of semantic ranker.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Rewrite queries with semantic ranker in Azure AI Search (preview)
Learn how to rewrite queries with semantic ranker in Azure AI Search
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Semantic ranking in Azure AI Search
Learn how semantic ranking in Azure AI Search uses deep learning language models to rerank results, improve relevance, and enhance search quality for your users.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Add a filter to a vector query in Azure AI Search
Learn how to add filter expressions to vector queries in Azure AI Search. Configure prefilter, postfilter, and strict postfilter (preview) modes to optimize query performance and improve search results.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Assign narrow data types to vector fields in Azure AI Search
Learn how to assign narrow data types to vector fields to reduce the storage requirements of vector indexes.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Chunk large documents for RAG and vector search in Azure AI Search
Learn strategies for chunking PDFs, HTML files, and other large documents for agentic retrieval and vector search.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Choose an approach for optimizing vector storage and processing
Learn about the vector compression options in Azure AI Search, and how to reduce storage through narrow data types, built-in scalar or quantization, truncated dimensions, and elimination of redundant storage.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Configure a vectorizer in a search index
Add a vectorizer to an Azure AI Search index so the search service converts text queries to vectors at query time using Microsoft-hosted or custom embedding models.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Create a vector index in Azure AI Search
Learn how to create or update a vector index in Azure AI Search by defining vector fields, configuring search algorithms, and setting vector profiles.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Generate embeddings for search queries and documents
Learn how to generate embeddings for downstream indexing into an Azure AI Search index.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Index binary vectors for vector search
Learn how to configure fields for binary vectors and the vector search configuration for querying the fields.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24