Azure AI Search

Azure AI Search is an enterprise knowledge system that powers sophisticated retrieval-augmented generation (RAG) applications and enterprise search engines. With advanced AI and search technology, and seamless platform integrations, Azure AI Search delivers end-to-end RAG systems built for app excellence, enterprise-readiness, and speed to market.

Hybrid search in Azure AI Search executes a query for both text search (or keyword search) and vector search in one request. The results are then merged together using Reciprocal Rank Fusion (RRF). Hybrid search is useful for RAG scenarios—vector search is effective at finding information from queries posed in natural language and full text search is able to find specific data like someone’s name or a product code.

RAG is an AI technique that combines retrieval-based techniques with generative models to produce accurate and contextually relevant responses. It retrieves information from external sources like databases, documents, or the web to enhance the generation of results. This ensures that responses generated by large language models are accurate, relevant, and enriched with the most up-to-date information. Learn more about RAG here and RAG in Azure AI Search here.

Read the Azure Service Level Agreements (SLA).

Azure AI Search is available in more than 30 countries and regions worldwide, with new locations added regularly.

Built-in indexers are available for Azure Cosmos DB, Azure SQL Database, Azure Blob Storage, and Microsoft SQL Server hosted in Azure Virtual Machines. Use Azure Data Factory, with more than 80 connectors, or Azure Logic Apps to connect to your data source. Alternatively, push data into an Azure AI Search index, which has no restrictions on data source type. 

Azure AI Search also supports data connections in knowledge bases, including Sharepoint, public web, and Azure Blob storage.

Refer to the docs for the latest supported knowledge sources.

Knowledge sources and knowledge bases are available in the Microsoft Foundry portal and Azure AI Search.

Azure AI Search works with several popular formats, including Microsoft Word, PowerPoint, and Excel; Adobe PDF; and PNG, RTF, JSON, HTML, and XML.

Azure AI Search is an enterprise retrieval and search engine used in custom apps that supports vector, full-text, and hybrid search over an indexed database. It’s used in RAG-based applications on Azure and integrates with Azure OpenAI service and Foundry models. Microsoft Search enables the ability to search across Microsoft 365 apps, such as Outlook and SharePoint, to help users find relevant information within their organization.

Azure AI Search and Azure OpenAI are two different services.
Azure AI Search is a knowledge base and retrieval system built for RAG and enterprise search applications. Azure OpenAI provides AI models and agent development tools for enterprise applications. Azure AI Search and Azure OpenAI are used together for AI agents that rely on private, enterprise data.

Azure AI Search supports vector search, keyword search, and hybrid search, combining vector and non-vector fields in the same search corpus. It stores the data you query over, allowing it to function as a vector store for applications that require long-term memory, a knowledge base, or grounding data for retrieval-augmented generation (RAG) architecture. This makes Azure AI Search a versatile tool that can be used as a vector database when needed.

Azure AI Search uses advanced algorithms like exhaustive k-nearest neighbors (KNN) and Hierarchical Navigable Small World (HNSW) for approximate nearest neighbor (ANN) search, enabling vector similarity queries to find semantically similar information. It also employs BM25 relevance scoring for full-text search, computing search scores based on the strength of the match.

Azure AI Search provides native indexing support for multiple databases, including SQL databases like Azure SQL Database and Azure SQL Managed Instance, as well as NoSQL databases and various data stores such as Azure Blob Storage, ADLS Gen2, and Azure Tables through built-in indexers. For other data sources, content can be integrated into AI Search indexes using Azure Logic App connectors. The Azure AI Search Push API allows for maximum flexibility by enabling data to be pushed in a specific format regardless of the source.

When selecting knowledge sources for a knowledge base, you can multi-select the sources you wish to include. You can also add multiple knowledge sources from the same service (e.g. multiple indexes in Azure AI Search).

As of November 2023, Azure Cognitive Search is now Azure AI Search. All existing deployments, configurations, and integrations continue to function as they previously did. There are no changes to pricing and no breaking changes to APIs or SDKs.

Foundry IQ is a feature family available in Foundry portal, powered by Azure AI Search and integrated with Foundry Agent Service. As of November 2025, knowledge bases were made available in the Foundry portal as well as in Azure AI Search. Foundry IQ consists of features knowledge bases and knowledge sources by Azure AI Search.

In the Foundry portal, customers can easily connect Agent Service agents to knowledge bases.

An Azure AI Search resource is required to create and manage knowledge bases in Foundry.

Foundry IQ, Fabric IQ and Work IQ are solutions that deliver intelligence from organizational data to teams using AI.