Introduction to Azure AI Search

In today's data-driven world, the ability to quickly and efficiently search through massive datasets is paramount. Information retrieval is critical across various scenarios:

• E-commerce websites: Customers expect to find the products they seek quickly and effortlessly.

• Enterprise content management: Employees need to locate specific documents within vast company databases to streamline workflows.

• Software development: Developers rely on efficient search functionalities to identify and resolve bugs within codebases.

Azure AI Search is the best solution to address these challenges. It's a cloud-based search service offered by Microsoft, designed to deliver secure information retrieval at scale for various search applications, including both traditional and those powered by generative AI.

Azure AI Search uses the combined strengths of keyword and similarity search to provide users with highly relevant and personalized results within your applications.

This article dives deep into the core functionalities of Azure AI Search. We'll explore its architecture, guide you through the setup process, and uncover the benefits it offers. Additionally, we'll explore various use cases that showcase the true potential of Azure AI Search in enhancing user experiences and driving productivity.

What is Azure AI Search?

Azure AI Search is a cloud-based search service offered by Microsoft that utilizes artificial intelligence (AI) to deliver intelligent and relevant search functionalities for your applications.

Think of it as a powerful search engine specifically designed for your data.  It goes beyond basic keyword searches and leverages AI to understand the meaning and context within your content. This allows Azure AI Search to return highly relevant results that align with the true intent behind a user's query.

The transition from Azure Cognitive Search to Azure AI Search

Microsoft renamed Azure cognitive search to Azure AI Search in October 2023 aligning it with the broader Azure AI Services suite. This shift reflects the service's core function: providing secure information retrieval at scale across various search applications, including traditional and generative AI-powered search.

Information retrieval is the foundation for any application that surfaces text and vector data. It's crucial for functionalities like catalog or document search, data exploration, and the increasingly popular chat-style apps that rely on proprietary grounding data.

Here's what you can achieve with Azure AI Search:

• Unified Search Engine: Perform vector search, full-text search, and hybrid searches over a comprehensive search index.

• Rich Indexing Capabilities: Leverage built-in features like data chunking, vectorization (preview), analysis for text, and optional AI enrichment for automatic content extraction and transformation.

• Powerful Query Syntax: Craft intricate search queries using vectors, text, or a combination of both. Explore functionalities like fuzzy search, autocomplete, geo-search, and more.

• Unmatched Scalability and Security: Azure AI Search boasts the exceptional security, scalability, and global reach you expect from Azure services.

• Azure Integration: Azure AI Search integrates with the Azure data layer, machine learning layer, Azure AI Services, and Azure OpenAI, empowering a comprehensive search experience.

It is designed to handle massive datasets and high-traffic loads ensuring that your search solution scales effortlessly for your organization's growth. It also utilizes a combination of Microsoft's cutting-edge AI and JSON-based indexes delivering fast and relevant search results, similar to the search functionality you experience on online shopping platforms, but specifically tailored for your unique needs.

Architecture

Your content: This represents the data sources you want to make searchable using Azure AI Search. This data can reside in various Azure cloud storage services or on-premises data sources.

Full Indexing: This stage involves ingesting your data into Azure AI Search. The data is first transformed into a format Azure AI Search understands and then added to a search index.

Azure AI Search: This is the core of the service. It includes capabilities like:

• Indexing Engine: This engine is responsible for ingesting, processing, and storing your data.

• AI Enrichment: Azure AI Search can use built-in or custom AI skills to enrich your data during indexing. This enrichment can involve things like:

• Optical Character Recognition (OCR) to extract text from images

• Extracting key phrases and entities from text documents

• Identifying sentiment in text

• Analyzing images to identify objects and scenes

Query Requests: This represents user queries submitted to Azure AI Search.

Your App: This represents the application that uses Azure AI Search to retrieve information. When a user submits a query through your application, it’s routed to Azure AI Search.

Query Engine: This engine is responsible for processing and evaluating user queries. It uses the search index to identify the most relevant results.

Query Responses: These are the results that Azure AI Search returns to your application in response to a user query. The results can be ranked based on relevance and include additional data extracted from the content during indexing.

Role of Indexing and Querying

Factors	Indexing	Querying
Definition	Indexing is ingesting data into Azure AI Search and making it searchable.	Querying is the process of retrieving information from the search index.
Role	Data Ingestion: Data from various sources is ingested into the search service. Tokenization: The ingested data is broken down into tokens, essentially individual words or terms. Storage: These tokens are then stored in inverted indexes for text and vector indexes for vectors. Index Creation: Indexes are created using indexers, which can be a manual or automated process. Once the index definition exists, loading the index is a separate task.	Query Request: A query request, targeting the searchable text in a search index. Search: The search service searches the index for the requested information. Retrieval: The relevant information is retrieved and returned to the user.
Benefits	It allows Azure AI Search to turn your relational data into JSON format, pick out key phrases, understand and use synonyms, handle geospatial data, and even detect and translate different languages. It simplifies the complexity of data ingestion and search-index creation through integration with Azure Storage solutions and simple RESTful APIs and SDKs.	It allows you to deliver accurate, hyper-personalized responses in your application. It provides contextual and relevant results in your apps using advanced deep-learning models from Microsoft Research and Bing. It enables knowledge mining and summary results, giving users quick snippets without scrolling through numerous results.
Limitations	The Maximum limits on storage, workloads, and quantities of indexes and other objects depend on whether you create Azure AI search at Free, Basic, standard, or storage-optimized pricing tier. Vector index size is measured in bytes and there is no quota at the search-indexed level. Instead, vector quotas are enforced service-wide at the partition level.	You cannot pause a search service. It is not possible to release and reclaim those resources on demand. Service tier, name, and region are fixed for the lifetime of the service.

Both indexing and querying are fundamental processes in Azure AI Search and are used together to provide efficient and effective search functionality. There are some risks associated with parallel indexing.

1. Recall that indexing doesn’t run in the background, increasing the likelihood that queries will be throttled or dropped.

2. Azure AI Search doesn’t lock the index for updates.

3. You can’t join multiple indexes to search content in two or more indexes, but you can query same-name indexes in a considerable search service.

Get started with Azure AI Search

STEP 1: Open Azure Portal. Type "Azure AI Search" in the search bar. Click on "Azure AI Search".

STEP 2: Now, enter the below information:

1. Subscription - Select the subscription from the dropdown list.

2. Resource Group - Select your resource group from the dropdown list or create a new one.

STEP 3: Service Name

Enter a unique name for the service. This name will be used to construct the endpoint URL you'll interact with through API calls.

1. The length should be between 2-60 characters.

2. It should consist of lowercase letters (a - z), digits (0 - 9), or hyphens (-).

3. Don't use hyphens in the first 2 characters or as the last single character.

4. Don't use consecutive hyphens anywhere.

STEP 4: Region

Choose a region from a provided list. Depending on the tier some have 3-7 times more storage per partition.

STEP 5: Pricing tier

Azure AI Search provides multiple pricing tiers. Each tier has its capacity, storage, and limits. Select the one that best suits your needs.

STEP 6: Scale

Adjust the scale according to your needs. This will determine the number of partitions and replicas for your service. If you selected the Free plan then you cannot adjust the scale.

STEP 7: Tags

Enter the tags associated with your service. Tags are name/value pairs that enable you to categorize resources and view consolidated billing by applying the same tag to multiple resources.

STEP 8: Review and create

Review all the entered information and click "Create" to create your Azure AI Search Service.

After that, click on "Notifications" in the upper right corner. Click "Go to resource".

Benefits of Utilizing Azure AI Search:

• Enhanced Search Relevance: AI capabilities ensure your users consistently receive highly relevant results that match the intent behind their queries.

• Improved User Experience: Faster and more accurate search results lead to a more positive user experience within your applications.

• Scalability Search and Secure Search: Azure AI Search is built on the secure and scalable foundation of Microsoft Azure, ensuring your search solution can grow alongside your organization.

• Flexibility: Azure AI Search offers a variety of functionalities and integrates seamlessly with other Azure services, allowing you to tailor the search experience to your specific needs.

Use Case

Azure AI Search shines in application scenarios, empowering you to create intelligent and effective search experiences. Here's a glimpse into its versatility:

1. Traditional & Next-Gen Search:  Azure AI Search integrates traditional full-text and cutting-edge vector similarity search. This empowers you to leverage the strengths of keyword-based and similarity searches, making it ideal for backing your generative AI apps with an information retrieval system that delivers exceptional results.

2. Generative AI Powerhouse:  Azure AI Search can chat with your unique data. Developers can leverage its capabilities to craft rich search experiences and develop generative AI applications that combine large language models with your organization's valuable enterprise data.

3. Supercharge Workplace Search:  Improve internal team collaboration and knowledge sharing through Azure AI Search. It empowers teams to effortlessly explore databases and files, and implement robust search functionalities within mobile or web applications used within your organization.

4. SaaS Search Made Easy: Build intelligent, cutting-edge SaaS applications ready to take on the market with the help of Azure AI Search. It provides a comprehensive search functionality that seamlessly integrates with your Software as a Service (SaaS) offerings.

5. Effortless Website Search:  Deliver an exceptional user experience on your website with Azure AI Search. Visitors can find the information and products they seek quickly and easily with its powerful search capabilities.

6. AI Enrichment for Enhanced Functionality:  The AI enrichment feature of Azure AI Search takes your search experience to the next level. It injects AI processing directly into the indexing pipeline, enabling functionalities like:

   1. Optical Character Recognition (OCR) for scanned documents

   2. Entity recognition and keyphrase extraction to unlock valuable insights from large documents

   3. Multilingual support with language detection and text translation capabilities

   4. Sentiment analysis to understand the emotional tone within your data

Conclusion

Azure AI Search revolutionizes search with its intelligent capabilities. It combines keyword and similarity search for hyper-relevant results, boosting user experience and productivity. From e-commerce to enterprise content management, Azure AI Search empowers you to build powerful search solutions. Get started today and unlock the future of information retrieval!