Azure Updates - Latest from Azure Charts

APIM Support for Foundry Models in Azure AI Search

Thu, 04 Jun 2026 19:30:58 Z

Microsoft Foundry brings Azure API Management support to all Foundry model integrations in Azure AI Search, now in public preview. Enterprise platform engineers and AI solution teams running large-scale RAG pipelines can place Foundry and Azure OpenAI mod
Update Type: Preview, Services: API Management, Azure AI Services, Microsoft Foundry, Azure AI Search, Categories:

Private Connectivity for Azure AI Search and Foundry Knowledge Bases

Thu, 04 Jun 2026 19:15:11 Z

Azure AI Search and Foundry Knowledge Bases now support private, end-to-end network connectivity between search resources and Foundry services. Customers can route ingestion, enrichment, retrieval, and agent traffic over Shared Private Link or Network Sec
Update Type: GA, Services: Azure AI Services, Microsoft Foundry, Azure AI Search, Private Link, Categories:

Content Understanding chunking and image verbalization in Azure AI Search

Thu, 04 Jun 2026 19:00:34 Z

Azure AI Search now supports Content Understanding chunking and image verbalization in public preview, expanding the built-in skill that parses rich documents during indexing. Indexers can split documents into semantically meaningful chunks and generate t
Update Type: Preview, Services: Azure AI Services, Microsoft Foundry, Azure AI Search, Categories:

OneLake catalog integration for Azure AI Search knowledge sources

Tue, 02 Jun 2026 19:00:47 Z

Azure AI Search makes OneLake catalog integration generally available for knowledge sources, so customers can register a OneLake item once and reuse it across multiple knowledge sources and agents. The integration honors existing OneLake item-level permis
Update Type: GA, Services: Azure AI Services, Microsoft Foundry, Azure AI Search, Categories:

Purview admin access auditing for sensitivity-labeled content in Azure AI Search

Tue, 02 Jun 2026 19:00:47 Z

Azure AI Search adds Microsoft Purview admin access auditing for sensitivity-labeled content in public preview. Admin operations that touch Purview-labeled documents inside Azure AI Search, knowledge retrieval, and Foundry IQ knowledge bases now flow into
Update Type: Preview, Services: Azure AI Services, Microsoft Foundry, Azure AI Search, Azure Purview, Categories:

Purview sensitivity labels in Azure AI Search knowledge sources

Tue, 02 Jun 2026 19:00:47 Z

Azure AI Search adds Microsoft Purview sensitivity label support to knowledge sources in public preview. Labels applied at the source system flow through ingestion into Azure AI Search and into the knowledge bases that ground Foundry agents and copilots,
Update Type: Preview, Services: Azure AI Services, Microsoft Foundry, Azure AI Search, Azure Purview, Categories:

Serverless indexers in Azure AI Search

Tue, 02 Jun 2026 19:00:47 Z

Azure AI Search now offers a serverless option for native indexers in public preview, removing the requirement to provision and manage indexer compute for ingestion workflows. Indexer execution scales with workload demand, so customers stop paying for idl
Update Type: Preview, Services: Azure AI Services, Microsoft Foundry, Azure AI Search, Categories:

Incremental SharePoint permissions sync for Azure AI Search and Foundry IQ

Tue, 02 Jun 2026 19:00:47 Z

Azure AI Search and Foundry IQ Knowledge Sources add incremental SharePoint permissions sync in public preview, keeping document-level, site, and library access control lists current as SharePoint permissions change. Enterprises store business-critical co
Update Type: Preview, Services: Azure AI Services, Microsoft Foundry, Azure AI Search, Categories:

Microsoft Purview sensitivity label auditing in Azure AI Search

Tue, 02 Jun 2026 19:00:47 Z

Azure AI Search now emits audit events for Microsoft Purview sensitivity labels carried alongside indexed documents. When the SharePoint in Microsoft 365 indexer is configured to preserve and honor sensitivity labels, query-time filtering decisions and la
Update Type: Preview, Services: Azure AI Services, Microsoft Foundry, Azure AI Search, Azure Purview, Categories:

GenAI prompt skill and chat completion in Azure AI Search knowledge sources

Tue, 02 Jun 2026 19:00:47 Z

Azure AI Search makes the GenAI prompt skill generally available, letting indexers call chat completion models during enrichment without custom code. Authors point the skill at a Foundry-hosted model, write a prompt template that references field values,
Update Type: GA, Services: Logic Apps, Azure AI Services, Microsoft Foundry, Azure AI Search, Categories:

Managed identity for Foundry billing calls from Azure AI Search

Tue, 02 Jun 2026 19:00:47 Z

Azure AI Search now supports managed identity authentication for the billing operations it issues against Microsoft Foundry resources, replacing the prior key-based flow. Customers assign a system-assigned or user-assigned identity to the search service,
Update Type: GA, Services: Azure AI Services, Microsoft Foundry, Azure AI Search, Categories:

Choosing the Right Language Analyzer for Azure AI Search

Fri, 29 May 2026 21:15:00 Z

By Abhishree Shetty, Alec Berntson, Lihang Li A language analyzer is a specific type of text analyzer that performs lexical analysis using the linguistic rules of the target language. It applies l...
Update Type: Announcement, Services: Azure AI Services, Microsoft Foundry, Azure AI Search, Categories:

Foundry IQ by Azure AI Search

Tue, 18 Nov 2025 16:00:16 Z

Foundry IQ, powered by Azure AI Search, is a knowledge system that delivers a smarter way to ground agents in enterprise data. Agents only need to connect to a single knowledge base to access multiple sources, removing the need to manage separate APIs and
Update Type: Preview, Services: Azure AI Services, Azure AI Search, Categories:

Agentic Retrieval Updates in Azure AI Search

Wed, 03 Sep 2025 22:00:00 Z

We’re extending the agentic retrieval API with answer synthesis and knowledge sources so you can ground agents across multiple indexes and generate answers with citations. What are knowledge s...
Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Categories:

Announcing enterprise-grade, Microsoft Entra-based document-level security in Azure AI Search

Thu, 29 May 2025 17:00:00 Z

Learn how Azure AI Search's new native support for Microsoft Entra-based POSIX-style ACLs and RBAC roles simplifies secure document access for AI applications. With enhanced ADLS Gen2 indexers and to...
Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Categories:

Ways to simplify your data ingestion pipeline with Azure AI Search

Wed, 28 May 2025 09:00:00 Z

Azure AI Search introduces new features to simplify RAG (retrieval-augmented generation) data preparation and indexing. Key updates include the GenAI Prompt Skill (in public preview), which leverages...
Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Categories:

Introducing Multi-Vector and Scoring Profile integration with Semantic Ranking in Azure AI Search

Wed, 28 May 2025 08:32:00 Z

We're excited to announce two powerful new enhancements in Azure AI Search: Multi-Vector Field Support and Scoring Profiles Integration with Semantic Ranking. Developed based on your feedback, these ...
Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Categories:

Native Integration of Azure Logic Apps with Azure AI Search for RAG Workflows

Thu, 22 May 2025 20:45:20 Z

Announcing the public preview of native Azure Logic Apps integration with Azure AI Search, delivering a seamless Bring Your Own Search (BYOS) experience for document ingestion in Retrieval-Augmented Generation (RAG) scenarios. This new experience empowers
Update Type: Preview, Services: Logic Apps, Azure AI Services, Azure AI Search, Categories: Features

🎙️ Announcement: Logic Apps connectors in Azure AI Search for Integrated Vectorization

Tue, 20 May 2025 16:56:00 Z

We’re excited to announce that Azure Logic Apps connectors are now supported within AI Search as data sources for ingestion into Azure AI Search vector stores. This unlocks the ability to ingest unst...
Update Type: Announcement, Services: Logic Apps, Azure AI Services, Azure AI Search, Categories:

From diagrams to dialogue: Introducing new multimodal functionality in Azure AI Search

Mon, 19 May 2025 15:45:00 Z

Discover the new multimodal capabilities in Azure AI Search, enabling integration of text and complex image data for enhanced search experiences. With features like image verbalization, multimodal em...
Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Categories:

Introducing agentic retrieval in Azure AI Search

Mon, 19 May 2025 15:45:00 Z

Today we’re announcing agentic retrieval in Azure AI Search, a multiturn query engine that plans and runs its own retrieval strategy for improved answer relevance. Compared to traditional, single-sho...
Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Categories:

Easy service upgrade and change service tier in Azure AI Search

Wed, 23 Apr 2025 16:15:29 Z

Azure AI Search has two new self-serve options that make it easy to change or upgrade your service, directly from Azure portal or the management API. Changing your service tier is now as easy as adding partitions or replicas; no rebuild required. Self-se
Update Type: Preview, Services: Azure AI Services, Azure AI Search, Categories: Features, Services

Azure AI Search: Cut Vector Costs Up To 92.5% with New Compression Techniques

Thu, 17 Apr 2025 12:41:00 Z

TLDR: Key learnings from our compression technique evaluation Cost savings: Up to 92.5% reduction in monthly costs Storage efficiency: Vector index size reduced by up to 99% Speed improvement...
Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Categories:

Building an Interactive Feedback Review Agent with Azure AI Search and Haystack

Wed, 16 Apr 2025 22:38:00 Z

We’re excited to announce the integration of Haystack with Azure AI Search! To demonstrate its capabilities, we’ll walk you through building an interactive review agent to efficiently retrieve and an...
Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Categories:

New enhanced navigation in Azure AI Search

Thu, 10 Apr 2025 18:51:00 Z

Faceted navigation is a key component of search experiences, helping users intuitively drill down through large sets of search results by refining their queries quickly and efficiently. We are ann...
Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Categories:

New scale options in Azure AI Search: change your pricing tier and service upgrade

Wed, 09 Apr 2025 20:13:00 Z

Discover two powerful new preview features in Azure AI Search: change your service’s pricing tier and upgrade existing services to unlock higher storage limits with no downtime or reindexing.
Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Categories:

New enhanced navigation in Azure AI Search

Wed, 26 Mar 2025 15:50:00 Z

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Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Categories:

LlamaIndex TypeScript now supports Azure AI Search as a vector store

Tue, 07 Jan 2025 17:05:00 Z

Learn how JavaScript and TypeScript developers can integrate LlamaIndex with Azure AI Search to build smarter apps that find and use relevant data for better responses
Update Type: Announcement, Services: Azure AI Search, Azure AI Services, Categories:

Case Study: Efficient Faceted Navigation Solution Using Azure AI Search

Thu, 19 Dec 2024 16:07:00 Z

Update Type: Announcement, Services: Azure AI Search, Categories:

Case Study: Microsoft Careers Portal – Efficient Faceted Navigation with Azure AI Search

Thu, 19 Dec 2024 16:07:00 Z

Update Type: Announcement, Services: Azure AI Search, Categories:

Demo: Enriching Data in Azure AI Search Indexing Pipeline Using Azure AI LLMs/SLMs for RAG Apps

Thu, 19 Dec 2024 01:30:00 Z

Enhance your RAG applications with enriched data context using Azure AI's LLMs/SLMs. This demo shows how to integrate custom skills in the Azure AI Search indexing pipeline, adapting prompts to impro...
Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Categories:

Multimodal parsing for RAG: Azure OpenAI GPT-4o, LlamaParse and Azure AI Search

Mon, 25 Nov 2024 18:35:00 Z

Azure OpenAI endpoints are now available in LlamaParse. Extract unstructured data with Azure OpenAI’s GPT-4o model family in LlamaParse, and build a RAG app with Azure AI Search and LlamaCloud.
Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Azure AI Foundry, Categories:

Best Practices for Using Azure AI Search for Natural Language to SQL Generation with Generative AI

Thu, 21 Nov 2024 03:57:00 Z

Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Categories:

Prep your Data for RAG with Azure AI Search: Content Layout, Markdown Parsing & Improved Security

Tue, 19 Nov 2024 13:52:00 Z

Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Categories: Security

Azure AI Search October Updates: Nearly 100x Compression with Minimal Quality Loss

Tue, 08 Oct 2024 17:35:43 Z

In our continued effort to equip developers and organizations with advanced search tools, we are thrilled to announce the launch of several new features in the latest Preview API for Azure AI Search. These enhancements are designed to optimize vector index size and provide more granular control and understanding of your search index to build Retrieval-Augmented Generation (RAG) applications. MRL Support for Quantization Matryoshka Representation Learning (MRL) is a new technique that introduces a different form of vector compression, which complements and works independently of existing quantization methods. MRL enables the flexibility to truncate embeddings without significant semantic loss, offering a balance between vector size and information retention. This technique works by training embedding models so that information density increases towards the beginning of the vector. As a result, even when using only a prefix of the original vector, much of the key information is preserved, allowing for shorter vector representations without a substantial drop in performance. OpenAI has integrated MRL into their 'text-embedding-3-small' and 'text-embedding-3-large' models, making them adaptable for use in scenarios where compressed embeddings are needed while maintaining high retrieval accuracy. You can read more about the underlying research in the official paper [1] or learn about the latest OpenAI embedding models in their blog. Storage Compression Comparison Table 1.1 below highlights the different configurations for vector compression, comparing standard uncompressed vectors, Scalar Quantization (SQ), and Binary Quantization (BQ) with and without MRL. The compression ratio demonstrates how efficiently the vector index size can be optimized, yielding significant cost savings. You can find more about our Vector Index Size Limits here: Service limits for tiers and skus - Azure AI Search | Microsoft Learn. Table 1.1: Vector Index Size Compression
Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Categories: Features, Services

VoiceRAG: An App Pattern for RAG + Voice Using Azure AI Search and the GPT-4o Realtime API for Audio

Tue, 01 Oct 2024 20:19:27 Z

The new Azure OpenAI gpt-4o-realtime-preview model opens the door for even more natural application user interfaces with its speech-to-speech capability. This new voice-based interface also brings an interesting new challenge with it: how do you implement retrieval-augmented generation (RAG), the prevailing pattern for combining language models with your own data, in a system that uses audio for input and output? In this blog post we present a simple architecture for voice-based generative AI applications that enables RAG on top of the real-time audio API with full-duplex audio streaming from client devices, while securely handling access to both model and retrieval system. Architecting for real-time voice + RAG Supporting RAG workflows We use two key building blocks to make voice work with RAG: Function calling: the gpt-4o-realtime-preview model supports function calling, allowing us to include “tools” for searching and grounding in the session configuration. The model listens to audio input and directly invokes these tools with parameters that describe what it’s looking to retrieve from the knowledge base. Real-time middle tier: we need to separate what needs to happen in the client from what cannot be done client-side. The full-duplex, real-time audio content needs to go to/from the client device’s speakers/microphone. On the other hand, the model configuration (system message, max tokens, temperature, etc.) and access to the knowledge base for RAG needs to be handled on the server, since we don’t want the client to have credentials for these resources, and don’t want to require the client to have network line-of-sight to these components. To accomplish this, we introduce a middle tier component that proxies audio traffic, while keeping aspects such as model configuration and function calling entirely on the backend. These two building blocks work in coordination: the real-time API knows not to move a conversation forward if there are outstanding
Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Azure AI Foundry, Categories:

Integrated vectorization with Azure OpenAI for Azure AI Search now generally available

Thu, 22 Aug 2024 16:00:00 Z

We're excited to announce the general availability of integrated vectorization with Azure OpenAI embeddings in Azure AI Search. This marks an important milestone in our ongoing mission to streamline and expedite data preparation and index creation for Retrieval-Augmented Generation (RAG) and traditional applications. Integrated vectorization simplifies RAG pipelines Why is vectorization important? Vectorization is the process of transforming data into embeddings (vector representations) in order to perform vector search. Vector search aids in identifying similarities and differences in data, enabling businesses to deliver more accurate and relevant search results. Getting your data prepared for vectorization and indexed also involves various steps, including cracking, enrichment and chunking. The way you perform each of these steps offers opportunities to make your retrieval system more efficient and effective. Take a look at the blog post Outperforming vector search with hybrid retrieval and ranking capabilities that showcases the configurations that would work better depending on the scenario. What is integrated vectorization? Integrated vectorization, a feature of Azure AI Search, streamlines indexing pipelines and RAG workflows from source file to index query. It incorporates data chunking and text/image vector conversions into one flow, enabling vector search across your proprietary data with minimal friction. Integration vectorization simplifies the steps required to prepare and process your data for vector retrieval. As part of the indexing pipeline, it handles the splitting of original documents into chunks, automatically creates embeddings with its Azure OpenAI integration, and maps the newly vectorized chunks to an Azure AI Search index. It also enables the automated vectorization of user queries sent to the AI Search index. This index can be used as your retrieval system whe
Update Type: GA, Services: Azure AI Services, Azure AI Search, Categories:

Binary quantization in Azure AI Search: optimized storage and faster search

Thu, 22 Aug 2024 15:18:50 Z

As organizations continue to harness the power of Generative AI for building Retrieval-Augmented Generation (RAG) applications and agents, the need for efficient, high-performance, and scalable solutions has never been greater. Today, we're excited to introduce Binary Quantization, a new feature that reduces vector sizes by up to 96% while reducing search latency by up to 40%. What is Binary Quantization? Binary Quantization (BQ) is a technique that compresses high-dimensional vectors by representing each dimension as a single bit. This method drastically reduces the memory footprint of a vector index and accelerates vector comparison operations at the cost of recall. The loss of recall can be compensated for with two techniques called oversampling and reranking, giving you tools to choose what to prioritize in your application: recall, speed, or cost. Why should I use Binary Quantization? Binary quantization is most applicable to customers who want to store a very large number of vectors at a low cost. Azure AI Search keeps the vector indexes in memory to offer the best possible search performance. Binary Quantization (BQ) allows to reduce the size of the in-memory vector index, which in turn reduces the number of Azure AI Search partitions you need to fit your data, leading to cost reductions. Binary quantization reduces the size of the in-memory vector index by converting 32-bit floating point numbers into 1-bit values, can achieve up to a 28x reduction in vector index size (slightly less than the theoretical 32x due to overheads introduced by the index data structures). The table below shows the impact of binary quantization on vector index size and storage use. Table 1.1: Vector Index Storage Benchmarks Compression Configuration Document Count Vector Index Size (GB) Total Storage Size (GB) % Vector Index Savings % Storage Savings Uncompressed 1M 5.77 24.77 SQ 1M 1.48 20.48 74% 17% BQ 1M 0.235 19.23 96% 22% Table 1.1 compares t
Update Type: Announcement, Services: Azure AI Search, Categories:

Boost RAG Performance: Enhance Vector Search with Metadata Filters in Azure AI Search

Sat, 03 Aug 2024 11:37:31 Z

In a Retrieval-Augmented Generation (RAG) setup, user-specified filters, whether implied or explicit, can often be overlooked during vector searches, as the vector search primarily focuses on semantic similarity. In some scenarios, it’s essential to ensure that specific queries are answered exclusively using a predefined (sub)set of the documents. By using “metadata” or tags, you can enforce the type of documents that should be used for each type of user query. This can even turn into a security overlay policy when each users queries are tagged with their credentials / auth level with filters so that their queries are answered with documents at their auth level. When RAG data consists of numerous separate data objects (e.g., files), each data object can be tagged with a predefined set of metadata. These tags then can serve as filters during vector or hybrid search. Metadata can be incorporated into the search index alongside vector embeddings and subsequently used as filters. In this blog, we will demonstrate an example implementation… For the sake of demonstration, in this blogpost will use Wikipedia articles of movies as our documents. We will than tag these movie files with metadata such as genre, releaseYear, and director, and later use this metadata to filter on RAG generations. Please note that an LLM can also be used to “classify” the documents before they are uploaded to the search index for deployment at a larger scale. When a user enters a prompt, we can use an additional LLM call to classify the user prompt (match a set of metadata) and later use it to filter out results. Blogpost demonstrates a simpler use-case where RAG documents (the wikipedia pages saves as pdf files and pre-tagged with the movie metadata… 1. Classify documents and tag with metadata movies = [ {"id": "1", "title": "The Shawshank Redemption", "genre": "Drama", "releaseYear": 1994, "director": "Frank Darabont"}, {
Update Type: Announcement, Services: Azure AI Search, Categories: Security

Announcement!! Azure OpenAI and Azure AI Search connectors are now Generally Available (GA)

Wed, 19 Jun 2024 23:29:26 Z

Announcing General Availability: Azure OpenAI and AI Search Connectors for Logic Apps We are thrilled to announce the General Availability of the Azure OpenAI and AI Search connectors for Logic Apps. These new connectors integrate the power of Azure Open AI's natural language processing with Azure AI Search's intelligent search capabilities, enabling developers to build intelligent, AI-driven applications seamlessly. Innovate where you Integrate Data is the cornerstone of any AI application, unique to each organization. Business processes, whether in the cloud or within a VNET, rely on this data and can be managed by modern or legacy applications. Regardless of where your data resides, Azure Logic Apps offers the ability to easily infuse AI into both new and existing business processes. With over 1000 connectors to various applications and services, Logic Apps simplifies the integration of AI, enabling the development of Retrieval-Augmented Generation (RAG) applications. This seamless integration enhances the functionality and intelligence of your business processes, ensuring that your applications are both innovative and efficient. By leveraging these connectors alongside AI services, organizations can transform their operations and generate intelligent insights like never before. Whether it's automating routine tasks, enhancing customer interactions and support, or generating insights, Azure Logic Apps provides a robust platform for embedding AI into your enterprise's fabric. RAG-based Patterns for AI Applications using Azure Logic Apps Using Logic Apps and these AI connectors, you can quickly build and productionize AI applications based on RAG pattern. Retrieval-Augmented Generation (RAG) combines retrieval and generative models to improve the accuracy and relevance of AI-generated content. This pattern is particularly useful in scenarios where precise information retrieval is crucial, such as: Customer Support Automation Automate customer su
Update Type: GA, Services: Logic Apps, Azure AI Services, Azure AI Search, Virtual Network, Categories: Services

Using Cohere Binary Embeddings in Azure AI Search and Command R/R+ Model via Azure AI Studio

Tue, 04 Jun 2024 11:33:11 Z

In April 2024, we proudly announced our partnership with Cohere, allowing customers to seamlessly leverage Cohere models via the Azure AI Studio Model Catalog, as part of the Models as a Service (MaaS) offering. At Build 2024, Azure AI Search launched support for Binary Vectors. In this blog, we are excited to continue from our previous discussion on int8 embeddings and highlight two powerful new capabilities: utilizing Cohere Binary Embeddings in Azure AI Search for optimized storage and search, and employing the Cohere Command R+ model as a Large Language Model (LLM) for Retrieval-Augmented Generation (RAG). Cohere Binary Embeddings via Azure AI Studio Binary vector embeddings use a single bit per dimension, making them much more compact than vectors using floats or int8, while still yielding surprisingly good quality given the size reduction. Cohere's binary embeddings offer substantial efficiency, enabling you to store and search vast datasets more cost-effectively. This capability can achieve significant memory reduction, allowing more vectors to fit within Azure AI Search units or enabling the use of lower SKUs, thus improving cost efficiency and supporting larger indexes. "Cohere's binary embeddings available in Azure AI Search provide a powerful combination of memory efficiency and search quality, ideal for advanced AI applications." - Nils Reimers, Cohere's Director of Machine Learning. With int8 and binary embeddings, customers can achieve up to a 32x reduction in vector size under optimal conditions, translating to improved cost efficiency and the ability to handle larger datasets. Read the full announcement from Cohere here: Cohere int8 & binary Embeddings - Scale Your Vector Database to Large Datasets Cohere Command R+ Model for RAG The Cohere Command R+ model is a state-of-the-art language model that can be used for Retrieval-Augmented Generation (RAG). This approach combines retrieval of relevant documents with the generation capab
Update Type: Announcement, Services: Azure AI Foundry, Azure AI Search, Categories: Services

Azure AI Search Database Selection: Optimizing Performance and Scalability for Your Business

Fri, 31 May 2024 00:26:10 Z

In today's data-driven world, selecting the right database for your specific use case is crucial for optimizing performance, ensuring scalability, and maintaining security. Azure AI Search, combined with Azure OpenAI and the Retrieval Augmented Generation (RAG) pattern, provides a powerful framework for building advanced generative AI applications. These solutions are tailored to meet diverse needs across different industries. In this blog, we'll explore the use cases for various databases available through Azure AI Search and highlight their applications in real-world scenarios across different verticals such as nonprofits, healthcare, financial services, retail, automotive, and more. Decision-Making Flowchart To assist in selecting the most suitable database for your specific needs, refer to the Database Selection Flowchart below. This guide helps determine the optimal database solution provided by Azure AI Search based on your unique use case requirements, particularly in the context of RAG applications supported by Azure OpenAI. Decision Points To effectively manage diverse data types and meet varying organizational needs, Azure AI Search provides a range of database solutions. Each database type is tailored to specific use cases, ensuring optimal performance, scalability, and security. The decision points below will guide you in selecting the most suitable database for your needs based on your data structure, access patterns, and application requirements. Simple, Scalable Storage for Unstructured Data Azure Storage Accounts: Ideal for cost-effective storage of unstructured data. Nonprofits and conservation organizations, for instance, often deal with vast amounts of documents and media files that need scalable and economical storage solutions. Azure Storage Accounts provide a reliable way to store and access these files, supporting the efficient management of data without breaking the budget. Relational Database with Built-in Intelligence, Scalability
Update Type: Announcement, Services: Azure AI Services, Azure AI Search, Azure Storage, Categories: Security, Services, Management

What’s new in Azure Data, AI, and Digital Applications: Harness the power of intelligent apps

Thu, 02 May 2024 09:00:00 -0700

Sharing insights on technology transformation along with important updates and resources about the data, AI, and digital application solutions that make Microsoft Azure the platform for the era of AI.
Update Type: Announcement, Services: Machine Learning, Azure AI Search, Azure DevOps, Categories:

Announcing updates to Azure AI Search to help organizations build and scale generative AI applications

Thu, 04 Apr 2024 09:05:00 -0700

Today, we are announcing that Azure AI Search, a modern retrieval system for AI applications, now has drastically larger vector capacity and compute without any increase in price, so customers can run RAG at any scale, at a lower cost.
Update Type: Announcement, Services: Azure AI Search, Categories:

Azure Search – New Storage Optimized service tiers available in preview

Fri, 29 Mar 2019 00:00:00 -0700

Azure Search is an AI-powered cloud search service for modern mobile and web app development. Azure Search is the only cloud search service with built-in artificial intelligence (AI) capabilities that enrich all types of information to easily identify and explore relevant content at scale.
Update Type: Announcement, Services: Azure AI Search, Categories:

Azure Search – Announcing the general availability of synonyms

Mon, 02 Jul 2018 00:00:00 -0700

Today we are announcing the general availability of synonyms. Synonyms allows Azure Search to associate equivalent terms that implicitly expand the scope of a query, without the user having to…
Update Type: Announcement, Services: Azure AI Search, Categories:

Autocomplete in Azure Search now in public preview

Thu, 28 Jun 2018 00:00:00 -0700

Today, we are happy to announce public preview support for autocomplete in Azure Search, one of our most requested features on UserVoice. Autocomplete, also called “type-ahead search”, can enhance…
Update Type: Announcement, Services: Azure AI Search, Categories:

Announcing Cognitive Search: Azure Search + cognitive capabilities

Mon, 07 May 2018 00:00:00 -0700

Today we are announcing Cognitive Search, an AI-first approach to content understanding.
Update Type: Announcement, Services: Azure AI Search, Categories:

Azure Search service upgrades: New hardware, unlimited document counts, and more!

Wed, 07 Feb 2018 00:00:00 -0800

Starting in late 2017, all new paid Azure Search services started using brand new, more powerful underlying hardware in select regions. The upgraded search services offer significantly higher performance and remove document count limits, at no additional cost...
Update Type: Announcement, Services: Azure AI Search, Categories:

Azure Search enterprise security: Data encryption and user-identity access control

Wed, 24 Jan 2018 00:00:00 -0800

Effective immediately, Azure Search now supports encryption at rest for all incoming data indexed on or after January 24, 2018…
Update Type: Announcement, Services: Azure AI Search, Categories:

Azure Search releases support for synonyms (public preview)

Mon, 17 Apr 2017 00:00:00 -0700

Azure Search releases Public Preview availability for multi-word synonyms. Synonyms functionality allows for Azure Search to not only return results which match the query terms that were typed into the search box, but also return results which match customer-defined synonyms of the query terms.
Update Type: Announcement, Services: Azure AI Search, Categories: