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Create a Custom Skill For Azure AI Search

By Ashwin Venugopal -
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  Introduction By extracting more information from the source data, you can significantly enhance an index using Azure AI Search's predefined skills. However, there can be times when you have certain data extraction requirements that call for some bespoke functionality and cannot be satisfied by the established abilities. For example: Integrate the Form Recognizer service to extract data from forms Consume an Azure Machine Learning model to integrate predicted values into an index Any other custom logic Custom skills can be implemented as web-hosted services (like Azure Functions) that enable the necessary interface for integration into a skillset in order to support these scenarios. Define Custom Skill Schema The expected structure for input and output data that is required by abilities in an Azure AI Search skillset must be implemented by your custom skill. Input Schema A JSON structure with a record for every document to be handled is defined by the input schema for a custom ski...
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Azure AI Search Solution: Apply Filtering and Sorting

By Ashwin Venugopal -
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  Apply Filtering and Sorting Users frequently like to filter and order query results according to field values in search solutions. Both of these features are supported by Azure AI Search via the search query API. Filtering Results You can apply filters to queries in two ways: By including filter criteria in a simple search expression. By providing an OData filter expression as a $filter parameter with a full syntax search expression. You can apply a filter to any filterable field in the index. Filtering With Facets When presenting users with filtering criteria based on field values in a result set, facets are a helpful tool. They function best when a field contains a limited number of discrete values that can be shown in the user interface as options or links. In order to use facets, you must first define the facetable fields for which you wish to extract the potential values. Sorting Results The query process's relevancy score is used by default to rank the results, with the hig...
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Azure AI Search Solution: Understand Indexing Process

By Ashwin Venugopal -
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  Understand The Indexing Process For every indexed entity, a document is created as part of the indexing process. An enrichment pipeline produces the documents repeatedly during indexing, combining enriched fields retrieved by cognitive skills with metadata from the data source. You can think of each indexed document as a JSON structure, which initially consists of a document with the index fields you have mapped to fields extracted directly from the source data. You can set up the indexer to extract image data from documents in the data source and add each image to a collection called normalized_images. You can use the collection of photographs as an input for skills that extract information from image data by normalizing the image data in this manner. Every talent adds fields to the document. For instance, a skill that determines the language used in a document may store its result in a language field. The document is structured hierarchically, and the skills are applied to a sp...
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Azure AI Search Solution: Understand Search Components

By Ashwin Venugopal -
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  Introduction Each of the several parts that make up an AI search solution is crucial to the extraction, enrichment, indexing, and querying of data. Data Source The majority of search solutions begin with a data source that has the information you are looking for. Azure AI Search is compatible with a variety of data sources, including: Unstructured files in Azure blob storage containers. Tables in Azure SQL Database. Documents in Cosmos DB. Azure AI Search can pull data from these data sources for indexing. Alternatively, applications can push JSON data directly into an index, without pulling it from an existing data store. Skillset You may index the data taken from the data source in a simple search solution. The data source determines the information that can be extracted. For example, while indexing data in a database, the fields in the database tables may be extracted. Similarly, when indexing a collection of documents, the text content of the document many be extracted along...
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Create an Azure AI Search Solution (Part 2)

By Ashwin Venugopal -
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  Manage Capacity You must first build an Azure AI Search resource in your Azure subscription before you can develop an Azure AI Search solution. You might additionally require Azure resources for data storage and other application services, depending on the particular solution you want to develop. Service Tiers and Capacity Management When establishing an Azure AI Search resource, it is necessary to choose a pricing tier. The tier you choose dictates the capacity restrictions of your search service, the configuration options that are accessible to you, and the expense of the service. The available pricing tiers are: Free (F)- Use this tier to explore the service or try the tutorials in the product documentation. Basic (B)- Use this tier for small-scale search solutions that include a maximum of 15 indexes and 2 GB of index data. Standard (S)- Use this tier for enterprise-scale solutions. There are multiple variants of this tier, including S, S2, and S3; which offer increasing c...
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Create an Azure AI Search Solution (Part 1)

By Ashwin Venugopal -
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  Introduction Information is necessary for all companies to make choices, respond to inquiries, and operate effectively. For the majority of businesses, the issue is not a lack of information but rather the difficulty of locating and retrieving it from the vast collection of documents, databases, and other sources that contain it. For example, that Margie's Travel is a travel company that specializes in planning vacations to cities all over the world. The business has accumulated a substantial amount of data over time in papers like brochures and customer-submitted hotel reviews.  Although the sheer amount of data can make it challenging to locate pertinent information to address a particular client query, this data is a significant source of insights for travel agents and customers as they plan trips. To address this challenge, Margie's Travel can implement a solution in which the documents are indexed and made easy to search. This solution enables agents and customers t...
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Synthesize Translations

By Ashwin Venugopal -
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  Introduction The TranslationRecognizer provides transcriptions of spoken input that have been translated, effectively converting spoken language into text. Additionally, you can produce the translation as speech, enabling speech-to-speech translation options. There are two methods available to achieve this. Event-based Synthesis When you aim to conduct a 1:1 translation (translating from one source language directly into a single target language), you can utilize event-driven synthesis to capture the translation as an audio stream. To accomplish this, you must indicate the preferred voice for the translated speech in the TranslationConfig. Develop an event handler for the Synthesizing event of the TranslationRecognizer object. Within the event handler, employ the GetAudio() method from the Result parameter to obtain the byte stream of the translated audio. The exact code for creating an event handler differs depending on the programming language you are using. Manual Synthesis Ma...
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