Blogs by Ashwin

  Optimize Your Index Size and Schema The effectiveness of your search queries is closely linked to the size and intricacy of your indexes. The quicker Azure AI Search can answer queries, the more compact and streamlined your indexes are. If you don't pay attention, indexes can grow over time. You should review that all the documents in your index are still relevant and need to be searchable. Consider reviewing all the attributes you've enabled on each field. For example, adding support for filters, facets, and sorting can quadruple the storage needed to support your index. Improve Performance Of Your Queries If you're familiar with the workings of the search service, you can optimize your queries to significantly enhance performance. Use this checklist for writing better queries: Only specify the fields you need to search using the searchFields parameter. As more fields require extra processing. Return the smallest number of fields you need to render on your search results...

  Optimize Performance of an Azure AI Search Solution The size and complexity of your indexes may affect search solutions’ performance. Additionally, You also need to know how to write efficient queries to search it and choose the right service tier. Measure Your Current Search Performance If you don't know how well your search service works, you can't optimize. Establish a baseline performance benchmark so you can verify the advancements you achieve and monitor any decline in performance over time. To start with, enable diagnostic logging using Log Analytics: In the Azure portal, select Diagnostic settings. Give your diagnostic setting a name. Select allLogs and AllMetrics. Select Send to Log Analytics workspace. Choose, or create, your Log Analytics workspace. This diagnostic data must be recorded at the search service level. Your end users or apps may experience performance problems in a number of locations. If you can demonstrate that your search service is operating effici...

  Authenticate Requests To Your Search Solution Your attention may go to how to verify search requests from your users and apps once you have the infrastructure in place to lessen the attack surface of your search solution. Key-based authentication is the default setting when you set up your ACS. There are two different kinds of keys: Admin Keys - grant write permissions and the right to query system information (maximum of 2 admin keys can be created per search service) Query Keys - grant read permissions and are used by your users or apps to query indexes (maximum of 50 query keys can be created per search service) Role-based access control (RBAC) is provided by the Azure platform as a global system to control access to resources. You can use RBAC in Azure AI Search in the following ways: Roles can be granted access to administer the service Define roles with access to create, load, and query indexes The built-in roles you can assign to manage the Azure AI Search serv...

  Introduction Understanding how to manage Azure AI Search's two core workloads, indexing and querying, is essential to running a successful solution. The search solution must be as economical as feasible. Manage Security Of an Azure AI Search Solution Businesses must be able to rely on their search solutions' security. You have control over the security of the data you search for with Azure AI Search. Overview Of Security Approaches AI Search security builds on Azure's existing network security features. When you think about securing your search solution, you can focus on three areas: Inbound search requests made by users to your search solution Outbound requests from your search solution to other servers to index documents Restricting access at the document level per user search request Data Encryption Like all Azure services, the Azure AI Search service uses service controlled keys to encrypt the data it stores while it's at rest. Indexes, data sources, synonym maps,...

  Index Any Data Using Azure AI Search Push API The most adaptable method for adding data to an Azure AI Search index is the REST API. Any programming language or interactive application that can send JSON requests to an endpoint can be used. Supported REST API Operations There are two supported REST APIs provided by AI Search- Search and management APIs. How To Call Search REST API ? If you want to call any of the search APIs you need to: Use the HTTPS endpoint provided by your search service, you must include an api-version in the URI. The request header must include an api-key attribute. To find the endpoint, api-version, and api-key go to the Azure portal. In the portal, navigate to your search service, then select Search explorer. Choose Keys to locate the api-key on the left. If you're doing more than just accessing the index with the REST API, you can use either the primary or secondary admin key. You can make and use query keys if you only need to search an index. An a...

  Index Data From External Data Sources Using Azure Data Factory An organization's search solution frequently has to include external data that isn't stored in Azure. Because it offers numerous methods for creating and pushing data into indexes, Azure AI Search is adaptable. Push Data Into Search Index Using Azure Data Factory (ADF) One method is to use ADF to push data into an index using a zero-code option. Almost 100 distinct data stores are connected to ADF. You can link an infinite number of data stores using connectors like HTTP and REST. These data stores are utilized in pipelines as either a source or a target (referred to as sinks). In a copy activity, the Azure AI Search index connector can be utilized as a sink. Create ADF Pipeline To Push Data Into Search Index The steps you need to take to use and ADF pipeline to push data into a search index are: Create an Azure AI Search index with all the fields you want to store data in. Create a pipeline with a copy data step....