Azure Cognitive Search: Vector Search

Azure Cognitive Search: Vector Search | Two Minute Tuesday with Bala

About vector search
Vector search is a method of information retrieval where documents and queries are represented as vectors instead of plain text. In vector search, machine learning models generate vector representations of source inputs, which can be text, images, audio, or video content. Having a mathematical representation of content provides a common basis for search scenarios. If everything is a vector, a query can find a match in vector space, even if the associated original content is in different media or in a different language than the query.

Why use vector search?
Vectors can overcome the limitations of traditional keyword-based search by using machine learning models to capture the meaning of words and phrases in context, rather than relying solely on lexical analysis and matching of individual query terms. By capturing the intent of the query, vector search can return more relevant results that match the user's needs, even if the exact terms aren't present in the document. Additionally, vector search can be applied to different types of content, such as images and videos, not just text. This enables new search experiences such as multi-modal search or cross-language search.

Vector search in Azure Cognitive Search

Quickstart: Use preview REST APIs for vector search queries

How to query vector data in a search index?