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  • Azure AI Search: Outperforming vector search with hybrid retrieval and . . .
    This approach finds relevant passages using semantic similarity We fully support this pattern in Azure AI Search (formerly Azure Cognitive Search) and offer additional capabilities that complement and build on vector search to deliver markedly improved relevance
  • Microsoft Introduces the Public Preview of Vector Search . . . - InfoQ
    At its annual Inspire conference, Microsoft recently announced the public preview of Vector search in Azure Cognitive Search, a capability for building applications powered by large language
  • Create a Vector Query - Azure AI Search | Microsoft Learn
    Learn how to create vector queries that target an index in Azure AI Search
  • General availability of vector search and semantic ranker in Azure AI . . .
    Azure Cognitive Search is now Azure AI Search, and semantic search is now semantic ranker See below for more details Over the past few months, we have delivered new capabilities as part of our goal to ensure our customers find the best the market has to offer in Azure AI Search when it comes to retrieval systems for generative AI applications Today, we are pleased to announce vector search
  • Unlocking the Power of Azure Cognitive Search: A Dive into Semantic . . .
    Azure Cognitive Search, with its semantic, vector, geography-based search capabilities, and integration of geohashing, extends its reach to applications where location is a critical factor
  • Boosting Search Performance: Vector Search in Azure Cognitive Search
    Azure Cognitive Search supports various data types, including text, images, documents, and structured data How can Vector Search improve search accuracy? Vector Search uses vector embeddings to represent data and performs similarity-based search, resulting in more accurate search results
  • Azure AI Search FAQ - Azure AI Search | Microsoft Learn
    Azure AI Search provides a dedicated search engine and persistent storage of your searchable content for agentic, full-text, and vector search scenarios It also includes optional integrated AI to extract text and structure from raw content and to chunk and vectorize content for vector search
  • Vector Search with Azure SQL Database and Azure AI Search
    With public preview of integrated vectorization, a ground-breaking capability of vector search in Azure AI Search (previously Azure Cognitive Search), you can do vector search with data stored in Azure SQL Database easily This feature is designed to streamline the process of chunking, generating, storing, and querying vectors for vector search in Azure AI Search This feature significantly
  • Supercharge Your Search: Implement Vector Search with Azure AI
    Learn how to implement vector search using Azure AI Search Step-by-step guide covers embeddings, indexing, querying, boosting semantic search results
  • Azure AI Search documentation - Azure AI Search | Microsoft Learn
    Learn how to use Azure AI Search for information retrieval at scale, with support for text, vector, and multimodal content in traditional and generative search scenarios
  • Features and Capabilities - Azure AI Search | Microsoft Learn
    Azure AI Search provides information retrieval and uses optional AI integration to extract more value from text and vector content The following table summarizes features by category There's feature parity in all Azure public, private, and sovereign clouds, but some features aren't supported in specific regions or specific tiers
  • Vector Search for AI Apps in Azure Databases
    In this article, we will discuss several Azure services that support vector search, including Azure Database for PostgreSQL Flexible Server, Azure Cosmos DB, and Azure Cognitive Search Each of these services offers unique features and capabilities that make them suitable for implementing vector search in various applications
  • Perform vector search and retrieval in Azure AI Search
    Explore how to perform vector search and retrieval using Azure AI Search for more intuitive and efficient search results
  • Quickstart: Vector Search in the Azure portal - Azure AI Search
    In this quickstart, you use the Import data wizard in the Azure portal to get started with integrated vectorization The wizard chunks your content and calls an embedding model to vectorize the chunks at indexing and query time This quickstart uses text-based PDFs and simple images from the azure-search-sample-data repo However, you can use different files and still complete this quickstart





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