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Azure AI Document

By Ashwin Venugopal -
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  Introduction Azure AI Document Intelligence analyzes scanned form content and transforms it into data using Azure AI Services. It is capable of identifying text values in both standard forms and forms specific to your company. Imagine that you are employed by a company that polls political parties and private businesses. Responses are submitted by participants via online PDFs or paper forms. Currently, typing these answers into databases costs you a lot of time and money. To determine whether you can utilize Azure AI Document Intelligence to expedite this process, you should evaluate it. Understand AI Document Intelligence Although Azure AI Document Intelligence is simple to use, you must comprehend its objects, such as models, APIs, and tools, in order to develop a dependable solution. You are evaluating Azure AI Document Intelligence at your polling company to see whether it can improve the efficiency of your data entry process. You must comprehend how your development team wil...
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Set Up Semantic Ranking

By Ashwin Venugopal -
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  Set Up Semantic Ranking Semantic ranking is available for all indexes and is automatically enabled for the Azure AI Search service at the service level. It is not possible to enable or disable semantic ranking per index. Configure Semantic Ranking Before you configure semantic ranking, you must have an Azure AI Search service with at least one index. Not every area has access to semantic ranking. Make sure your AI Search service's region supports semantic ranking before setting it up. To choose the semantic ranking plan in the Azure portal, perform the following steps: Open the Azure portal and sign in. Select All resources and select your search service. In the navigation pane, select Settings and select Semantic ranker. Select the appropriate service plan. You can alter the service plan after deployment. You can con figure semantic ranking on a per-ind ex basis. You can have multiple semantic configurations on each index. To configure semantic ranking, follow these steps: From ...
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How Semantic Ranking Works ?

By Ashwin Venugopal -
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  Introduction The top 50 results from the BM25 ranking are used for semantic ranking. A semantic setup divides the findings into several fields. The fields are cut to 256 distinct tokens after being transformed into text strings. In the document, a token is basically equal to a word. After the strings are ready, machine reading comprehension models are used to identify the words and sentences that best fit the query. This summarization sentence produces a semantic caption and, if desired, a semantic response. The semantic relevance of the caption is now used to rate the semantic captions. After that, the results are given back in decreasing relevancy order. This secondary ranking uses multi-lingual, deep learning models adapted from Microsoft Bing to promote the most semantically relevant results. Semantic Ranking Advantages Semantic ranking has two key advantages over traditional search results: Semantic ranking can rank results to more closely match the semantics of the original...
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Semantic Ranking In Azure AI Search

By Ashwin Venugopal -
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  Introduction Imagine you are employed by a company creating an app aimed at local tourists. You possess a vast amount of data regarding local attractions, yet when users look for information, they occasionally do not see the most pertinent articles at the forefront of their search results. In order to enhance the user experience of your app, you have to explore methods to provide users with more relevant results through the use of Azure AI Search. What is Semantic Ranking? Semantic ranking is a feature in Azure AI Search designed to enhance the order of search results. It leverages language understanding to more effectively align the context of the user's original query with the ranking of the search results. BM25 Ranking Function Azure AI Search, by default, utilizes the BM25 ranking function. The BM25 ranking function evaluates search results based on how often the search term is found in a document. Typically, the BM25 ranking function produces high-quality rankings, as docume...
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Azure Portal

By Ashwin Venugopal -
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  Debug Search Issues Using Azure Portal When you initially set up your search service, you need to establish certain assumptions regarding the data you will be indexing. You decide on the structure of the index and the method for ingesting that data. Yet, you won't be able to confirm whether all your decisions were correct until you execute your built indexer. Explore How To Use Debug Session Tool In Azure AI Search The Debug Session tool is an interactive visual editor that allows you to navigate through the enrichment pipeline of a document during its enrichment process. You can enter each specific skill, apply modifications and corrections, and then execute the indexer in real-time. After addressing any problems, you can refresh and republish the indexer so it can be re-executed to enrich all documents within your index. Once you have assigned a name to your debug session and selected the index for debugging, the search service transfers all necessary components to an Azure Sto...
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Monitor An Azure AI Search Solution

By Ashwin Venugopal -
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  Monitor an Azure AI Search Solution Azure Monitor may provide you with information on the performance and usage of your search service. Additionally, you can get notifications to let you know about problems before they happen. Monitor Azure AI Search in Azure Monitor You may view your current search latency, queries per second, and the percentage of throttledqueries as soon as you start up your Azure AI Search service. The Monitoring tab of the Overview page displays this data. You can also check what resources your search solution is using on the Usage tab. You can monitor with additional setup, but this basic information is an excellent place to start.You will be aware that Azure Monitor can be utilized for all of your Azure resources if you are accustomed to maintaining other Azure platform resources. Once you have started using Log Analytics, you get access to performance and diagnostic data in these log tables: AzureActivity - Shows you tasks that have been executed like sca...
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Improve Reliability Of Azure AI Search Solution

By Ashwin Venugopal -
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  Make Your Search Solution Highly Available One straightforward method to enhance the availability of your search solution is by increasing the quantity of replicas. The sole choice is to maintain more than one within the paid search service tiers. The Azure AI Search service has availability guarantees based on the number of replicas you have: Two replicas guarantee 99.9% availability for your queries Three or more replicas guarantee 99.9% availability for both queries and indexing The alternative method to incorporate redundancy into your search solution is by utilizing Availability Zones. This approach necessitates the use of at least a standard tier. When you create replicas, you have the option to place them in various Availability Zones. The advantage of spreading your replicas this way is that they are situated in separate data centers. Distribute Your Search Solution Globally A single resource group and region is the most economical method to design an Azure AI Search serv...
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