Azure AI Search
Simplify how agents connect to enterprise data. Build high-impact AI apps with one centralized solution for agentic retrieval-augmented generation (RAG) workflows.
Overview
Power agentic RAG, search and retrieval
- Focus on exponential growth with an enterprise-ready vector database that comes with security, compliance, and responsible AI practices built in.
- Build better applications with sophisticated retrieval strategies backed by decades of research and customer validation.
- Accelerate AI app development and simplify your RAG pipeline with seamless platform and data integrations for data sources, AI models, and frameworks.
FEATURES
Deliver relevant responses with search for AI
Simplified data ingestion
Automatically upload data from a wide range of supported Azure and third-party sources.
Streamlined data prep, indexing, and querying for unstructured data
Simplify your RAG pipeline with built-in parsing, chunking, enrichment, and embedding—all in one flow.
Feature-rich vector database
Get support for multivector, hybrid, multilingual, and metadata filtering.
Cutting-edge search and retrieval
Move beyond naive RAG with reranking, query planning, query transformations, and autocomplete.
Data and user protection
Control your data with built-in features including encryption, secure authentication, and network isolation.
Seamless platform and framework integrations
Build and access Azure AI Search via SDKs, Azure AI Foundry, Copilot Studio, or OSS frameworks.
USE CASES
Explore common applications to get started
Generative AI app with real-time audio
Build a VoiceRAG application for speech-to-speech interactions with the Azure OpenAI gpt-4o-real time preview model.
Vector search
Move beyond text to create image search engines and multimodal experiences for images, PDFs, and audio.
AI assistant
Gain better analytics and improve support with an agent that provides relevant information to answer a question fast.
Retrieval-augmented generation (RAG)
Ground your generative AI application with proprietary data.
Security
Embedded security and compliance
34,000
Full-time equivalent engineers dedicated to security initiatives at Microsoft.
15,000
Partners with specialized security expertise.
>100
Compliance certifications, including over 50 specific to global regions and countries.
Pricing
Azure AI Search pricing
Azure AI Search has pricing tiers that scale to meet the storage and throughput needs of large scale RAG applications.
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CUSTOMER STORIES
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RESOURCES
Get the latest Azure AI Search news and resources
FAQ
Frequently asked questions
- Azure AI Search is an enterprise knowledge retrieval 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.
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- RAG excellence: Set the bar for app excellence with the latest rigorously tested AI technology, delivered ahead of the market.
- Velocity: Built for end-to-end RAG development, move from idea to production fast with integrations and features across the toolchain.
- Enterprise scale: Deliver high-performance RAG applications without compromising scale or cost.
- 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 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 and Azure AI 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 is built on an enterprise-ready foundation that supports diverse data types and retrieval methods. It integrates vector, full-text, and hybrid search capabilities, enabling developers to store, index, and deliver search applications over various data formats. The system supports advanced indexing, JSON document indexing, and AI enrichment through cognitive skills, enhancing search relevance with semantic ranking.
- 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.
- 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.
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