FREETEXTTABLE()<\/code> still uses BM25 to rank results by relevance. Vector search may feel new, but relevance-ranked search has been part of the SQL story for decades.<\/p>\n<\/i>The magic of FREETEXTTABLE()<\/strong><\/p>Unlike Microsoft SQL’s `CONTAINSTABLE()`, which searches text using precise terms, phrases, prefixes, proximity, and explicit inflectional forms, `FREETEXTTABLE()` is designed for broad, relevance-ranked retrieval.<\/p>\n <\/p>\n
It automatically applies language-aware word breaking and stemming to expand user input across related word forms. The user queries for “run,” but the engine understands “ran,” “running,” and “runs” without any extra clarification. This makes text search in SQL especially useful when users describe what they want rather than knowing the exact words stored in the data.<\/div><\/p>\n
Sample syntax<\/h4>\nSELECT p.Name, ft.RANK\r\nFROM FREETEXTTABLE(Product, (Description), 'running shoes') ft\r\nJOIN Product p ON p.Id = ft.[KEY]\r\nORDER BY ft.RANK DESC;<\/code><\/pre>\nLearn more about FREETEXTTABLE()<\/a><\/div><\/p>\nVector search<\/h3>\n
In 2025, SQL embraced DiskANN, another innovation born in Microsoft Research. First published in 2019, DiskANN showed that approximate vector search could remain fast and accurate across millions, even billions, of vectors without requiring enormous amounts of memory. The industry noticed, with other databases implementing DiskANN-inspired approaches of their own. Today, DiskANN is part and parcel of SQL’s approach to vector search, bringing that same research directly into the engine.<\/p>\n
kNN, or k-nearest neighbors, is the exact form of vector search. It compares the query vector against the available vectors and returns the closest matches according to the selected distance metric. It is simple and accurate, but the work grows with the size of the data set, which is why ANN becomes more useful at larger scale.<\/p>\n
<\/i>The magic of VECTOR_SEARCH()<\/strong><\/p>Unlike Microsoft SQL’s VECTOR_DISTANCE()<\/code>, which performs an exact comparison and never uses a vector index, VECTOR_SEARCH()<\/code> is designed to retrieve the nearest matches efficiently across large vector sets. Predicates can even participate during the search through iterative filtering, helping SQL return the requested number of relevant rows without oversampling.<\/div><\/p>\nSample syntax<\/h4>\nSELECT TOP (10) WITH APPROXIMATE\r\n p.Id, p.Name, v.distance\r\nFROM VECTOR_SEARCH(\r\n TABLE = ProductEmbedding AS p,\r\n COLUMN = Embedding,\r\n SIMILAR_TO = @Embedding,\r\n METRIC = 'cosine'\r\n) AS v\r\nORDER BY v.distance;<\/code><\/pre>\nLearn more about VECTOR_SEARCH()<\/a><\/div><\/p>\nModel integration<\/h3>\n
In 2022, Azure SQL introduced sp_invoke_external_rest_endpoint<\/code>, giving T-SQL a native way to call REST services directly from the database. Today, that capability underpins SQL’s growing model integration story, including External Models and built-in AI functions like AI_GENERATE_EMBEDDINGS<\/code> that simplify integration. And when these built-in abstractions do not yet cover emerging interactions and models, the direct REST endpoint remains available, making scenarios such as GPT-based intent detection or Cohere reranking part of the same SQL-centered RAG pipeline.<\/p>\n<\/i>Endpoints in Azure SQL database<\/strong><\/p>sp_invoke_external_rest_endpoint<\/code> is implemented a little differently in Azure SQL than in SQL Server. In both, credentials remain protected inside SQL and endpoint access requires explicit permissions. Azure SQL adds another layer of protection with an allowlist of approved service domains, including most major Azure services, Microsoft Graph, Power BI, and – perhaps most important here – Microsoft Foundry. Administrators can validate supported endpoints and use services such as API Management when they need to extend access beyond the built-in list.<\/div><\/p>\nSample syntax<\/h4>\nDECLARE @Response NVARCHAR(MAX);\r\n\r\nEXEC sys.sp_invoke_external_rest_endpoint\r\n @method = 'POST',\r\n @url = 'https:\/\/example.com\/api',\r\n @payload = N'{\"query\":\"running shoes\"}',\r\n @response = @Response OUTPUT;<\/code><\/pre>\n
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