Azure AI Search
In order to assist customers developing generative AI applications that are ready for production, Azure is pleased to announce today some major updates to Azure AI Search. In order to enable customers to run retrieval augmented generation (RAG) at any scale without sacrificing cost or performance, Azure AI Search has significantly increased storage capacity and vector index size at no additional cost.
This article will explain how clients can
- Can use today’s changes to achieve more scalability at a lower cost.
- Put their large RAG workloads in the hands of Azure AI Search.
- To innovate in previously unthinkable ways, use sophisticated search techniques to navigate complex data.
Introducing Azure AI Search, which offers greater performance and scalability at a cheaper price
With much increased vector and storage capacity, Azure AI Search can now provide users with higher scalability, better performance, and more data for their money.
In some regions, the Basic and Standard tiers of Azure AI Search now have more available capacity and compute.
Users are going to see up to a
- 11 times larger vector index.
- A sixfold rise in overall storage.
- Indexing and query throughput improvements of two times.
With these adjustments, clients can provide excellent user and interaction experiences at any size. With just one search instance, users can scale their generative AI applications to a multi-billion vector index without sacrificing efficiency or speed.
Providing a reliable enterprise retrieval system to support sizable RAG-based applications
For the management of their mission-critical enterprise search and generative AI applications, more than half of Fortune 500 companies rely on Azure AI Search. Azure AI Search is used by OpenAI, Otto Group, KPMG, and PETRONAS to support workloads related to retrieval augmented generation (RAG).
OpenAI had to make sure their retrieval system could handle previously unheard-of demand and scale when they unveiled their Assistant API and RAG-powered “GPTs” at OpenAI DevDay 2023. Because of Azure AI Search’s ability to handle their massive, internet-scale RAG workloads, OpenAI turned to it.
Azure AI Search now offers search functionality to products such as the GPT Store and supports RAG capabilities for ChatGPT, GPTs, and the Assistant API. Azure AI Search is the retrieval system that makes these products function whenever someone searches within them or adds a file to them.
As of November 2023, 100 million people visit ChatGPT alone each week, and over 2 million developers use its API to build applications. Three million custom GPTs were generated in the first two months after their announcement. With users from all over the world, these are enormous numbers. Really RAG on a large scale.
Using a cutting-edge, modern retrieval system to create better applications
Using just one search technique, such as vector search, is ineffective for creating generative AI applications that function as intended, as teams in the professional services, healthcare, and telecommunications industries have discovered.
Certain use cases are better served by different retrieval strategies. To cover the range of scenarios that any given application is likely to encounter, high-quality retrieval systems combine multiple techniques.
Developers can accomplish goals more quickly and efficiently by using Azure AI Search to enable applications to apply a range of strategies straight out of the box, such as hybrid retrieval and semantic reranking.
Advanced RAG is used by Telus Health to provide a customer support application
Telus Health is a Canadian-based company that leads the way in offering technology-based services and solutions to insurers, individuals, employers, and healthcare professionals. In order to address user questions regarding particular health plans and provide assistance with using their website, the company launched a customer support platform. All of the requirements could not be met by the first implementation, which was based only on vector search. Telus Health resorted to Azure AI Search as a result, which is renowned for its cutting-edge, extensive suite of search technologies.
The Guide Team at Telus Health played a key role in developing their search approach and making efficient use of AI Search to improve the platform. Telus Health made it possible for the system to effectively handle queries pertaining to client-specific documents as well as those utilising the company website by broadening their retrieval strategy and introducing hybrid search with semantic reranking. This strategic improvement, made possible by Azure AI Search, has greatly increased the platform’s accuracy and responsiveness and demonstrates Telus Health’s dedication to providing top-notch customer service.
NIQ Brandbank uses multi-vector retrieval to enable brands to maximise their online presence
Fast-moving consumer goods (FMCG) brands can outperform their rivals by using NIQ Brandbank’s solutions to provide rich, pertinent content and imagery for their digital shelf.
With data-driven, practical advice and insights that demonstrate how their product content compares to competitors in the market, NIQ Brandbank’s Content Health+ solution enables brands to maximise their online presence.
The application helps brands increase sales, improve product placement across retailer search results, and improve their online presence with its straightforward, user-friendly format.
In order to determine which product attributes affect organic placement on the digital shelf, Content Health+ draws from research conducted by an NIQ Data Impact team. The application uses multi-vector search on the backend to search the research that is stored in both text and images. Search reranking is used to present the most relevant results. This feature makes excellent recommendations about the kind of content that a brand should prioritise in order to boost sales and performance.
Content Health+ was developed using hybrid multi-vector search and semantic ranking to ensure that the application functions as intended. Combining different retrieval techniques allows more ideas and opportunities to be realised for e-commerce and recommendation apps.
Find out more about Azure AI Search
They are facilitating the AI systems’ ability to retrieve information at scale by making these announcements today. With Azure AI Search’s cutting-edge retrieval technology and an enterprise-ready foundation, customers can innovate with confidence.
Leading search and information retrieval platform for RAG Azure AI Search, an AI-powered platform for information retrieval, assists developers in creating generative AI apps and rich search experiences by fusing enterprise data with large language models. Provide search capabilities for all mobile applications, internal search applications, and software as a service (SaaS) apps.
Simplify the creation and provision of search solutions
Simplify the process of creating search indexes and ingesting data by integrating them with Azure storage solutions, RESTful APIs, and SDKs. Implement a search service that is fully configured and offers user-friendly features like synonyms, faceting, scoring, and geo-search. Steer clear of the operational costs associated with debugging index corruption, keeping an eye on service availability, or manually scaling during traffic spikes.
Showcase the most pertinent outcomes for your users
Utilise cutting-edge deep learning models from Bing and Microsoft Research to give your apps results that are pertinent and contextual. Use the semantic search feature to provide customers with substantially better results, gain a deeper understanding of their search terms, and increase customer engagement. Knowledge mining and summary results are also made possible by semantic search, providing your users with quick snippets without making them scroll through a tonne of results.
Use Azure OpenAI Service to develop apps for the next generation
To apply the most sophisticated AI language models to your search solutions that use your own data as the foundation for responses, combine Azure AI Search with Azure OpenAI Service. ChatGPT, an Azure OpenAI service, allows you to retrieve enterprise data from knowledge bases using conversational language.
Adapt search features with AI integrations
Customise the search process to fit your organization’s particular needs. Key phrase extraction, language detection, optical character recognition (OCR), image analysis, translation, and role-based access control (RBAC) are just a few of the customizable features that Azure AI Search provides. Utilize the integration features offered by Azure AI services, such as Speech, Vision, Language, and Azure OpenAI Service, to enhance the conversion of unstructured, raw data into searchable content.
Scale to handle heavy traffic loads and big datasets
Easily index and search through enormous volumes of data, regardless of the size of your company, to provide excellent search results for your users without worrying about infrastructure management. Your search solution will be scalable as your company expands thanks to Azure AI Search’s ability to manage massive data loads and high traffic loads.
Use AI sensibly
With Azure AI Search, you can get access to cloud search tools, guidelines, and other resources to assist you in developing a responsible AI solution. Go through Microsoft’s responsible AI guidelines.
News source:Azure AI Search
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