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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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Manage Costs of Azure AI Search Solution

By Ashwin Venugopal -
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  Manage Costs Of An Azure AI Search Solution The capacity and features you utilize determine how much an Azure AI Search solution will cost to operate. Estimate Your Search Solutions Baseline Costs An excellent tool for estimating the cost of utilizing any Azure service is the Azure pricing calculator. Make use of it to establish a baseline for your search service requirements. Browse to the Azure AI Search pricing calculator. Choose your region, currency, and hour or monthly pricing. Optimizing Azure AI Search's capacity, including the tier you require, the data you're searching, and the features you utilize, is a crucial part of maintaining an affordable Azure AI Search solution. Understand Billing Model Azure AI Search is invoiced similarly to other Azure resources. You are not charged for the number of search queries, responses, or documents ingested. Tips To Reduce Cost Of Your Search Solution These tips can help you reduce the cost of running your search solution: Minim...
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Azure AI Search Solution: Optimize Your Index Size And Schema

By Ashwin Venugopal -
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  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...
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