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@@ -18,23 +18,23 @@ Azure Cognitive Search provides a full-text search engine, persistent storage of
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| Category | Features |
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| Data sources |Azure Cognitive Search indexes accept data from any source, provided it is submitted as a JSON data structure. <br/><br/> [**Indexers**](search-indexer-overview.md) automate data ingestion for supported Azure data sources and handle JSON serialization. Connect to [Azure SQL Database](search-howto-connecting-azure-sql-database-to-azure-search-using-indexers.md), [Azure Cosmos DB](search-howto-index-cosmosdb.md), or [Azure Blob storage](search-howto-indexing-azure-blob-storage.md) to extract searchable content in primary data stores. Azure Blob indexers can perform *document cracking* to [extract text from major file formats](search-howto-indexing-azure-blob-storage.md), including Microsoft Office, PDF, and HTML documents. |
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| Hierarchical and nested data structures |[**Complex types**](search-howto-complex-data-types.md) and collections allow you to model virtually any type of JSON structure as an Azure Cognitive Search index. One-to-many and many-to-many cardinality can be expressed natively through collections, complex types, and collections of complex types.|
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| Linguistic analysis | Analyzers are components used for text processing during indexing and search operations. There are two types. <br/><br/>[**Custom lexical analyzers**](index-add-custom-analyzers.md) are used for complex search queries using phonetic matching and regular expressions. <br/><br/>[**Language analyzers**](index-add-language-analyzers.md) from Lucene or Microsoft are used to intelligently handle language-specific linguistics including verb tenses, gender, irregular plural nouns (for example, 'mouse' vs. 'mice'), word de-compounding, word-breaking (for languages with no spaces), and more. <br/><br/>|
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| Data sources | Search indexes can accept text from any source, provided it is submitted as a JSON document. <br/><br/> [**Indexers**](search-indexer-overview.md) automate data ingestion from supported Azure data sources and handle JSON serialization. Connect to [Azure SQL Database](search-howto-connecting-azure-sql-database-to-azure-search-using-indexers.md), [Azure Cosmos DB](search-howto-index-cosmosdb.md), or [Azure Blob storage](search-howto-indexing-azure-blob-storage.md) to extract searchable content in primary data stores. Azure Blob indexers can perform *document cracking* to [extract text from major file formats](search-howto-indexing-azure-blob-storage.md), including Microsoft Office, PDF, and HTML documents. |
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| Hierarchical and nested data structures |[**Complex types**](search-howto-complex-data-types.md) and collections allow you to model virtually any type of JSON structure within a search index. One-to-many and many-to-many cardinality can be expressed natively through collections, complex types, and collections of complex types.|
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| Linguistic analysis | Analyzers are components used for text processing during indexing and search operations. By default, you can use the general-purpose Standard Lucene analyzer, or override the default with a language analyzer, a custom analyzer that you configure, or another predefined analyzer that produces tokens in the format you require. <br/><br/>[**Language analyzers**](index-add-language-analyzers.md) from Lucene or Microsoft are used to intelligently handle language-specific linguistics including verb tenses, gender, irregular plural nouns (for example, 'mouse' vs. 'mice'), word de-compounding, word-breaking (for languages with no spaces), and more. <br/><br/>[**Custom lexical analyzers**](index-add-custom-analyzers.md) are used for complex query forms such as phonetic matching and regular expressions.<br/><br/> |
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## AI enrichment and knowledge mining
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| Category | Features |
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|AI processing during indexing |[**AI enrichment**](cognitive-search-concept-intro.md) for image and text analysis can be applied to an indexing pipeline to extract text information from raw content. A few examples of [built-in skills](cognitive-search-predefined-skills.md) include optical character recognition (making scanned JPEGs searchable), entity recognition (identifying an organization, name, or location), and key phrase recognition. You can also [code custom skills](cognitive-search-create-custom-skill-example.md) to attach to the pipeline. You can also [integrate Azure Machine Learning authored skills](./cognitive-search-tutorial-aml-custom-skill.md). |
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| Storing enriched content for analysis and consumption in non-search scenarios |[**Knowledge store**](knowledge-store-concept-intro.md) is an extension of AI-based indexing. With Azure Storage as a backend, you can save enrichments created during indexing. These artifacts can be used to help you design better skillsets, or create shape and structure out of amorphous or ambiguous data. You can create projections of these structures that target specific workloads or users. You can also directly analyze the extracted data, or load it into other apps.<br/><br/> |
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| Cached content |[**Incremental enrichment (preview)**](cognitive-search-incremental-indexing-conceptual.md) limits processing to just the documents that are changed by specific edit to the pipeline, using cached content for the parts of the pipeline that do not change. |
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| Storing enriched content for analysis and consumption in non-search scenarios |[**Knowledge store**](knowledge-store-concept-intro.md) is an alternative output of an indexing pipeline. Instead of sending tokenized terms to an index, you can send enriched documents created by the indexing pipeline to a knowledge store, resident in either Azure Blob storage or Table storage, depending on the configuration. Knowledge stores are created from AI-based indexing (skillsets). The purpose of a knowledge store is to support downstream analysis or processing. With new information and structures in a knowledge store, you can attach it to a machine learning process or connect from Power BI to explore the data.<br/><br/> |
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| Cached content |[**Incremental enrichment (preview)**](cognitive-search-incremental-indexing-conceptual.md) limits processing to just the documents that are changed by specific edits to the pipeline, using cached content for the parts of the pipeline that do not change. |
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## Query and user experience
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| Category | Features |
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|Free-form text search |[**Full-text search**](search-lucene-query-architecture.md) is a primary use case for most search-based apps. Queries can be formulated using a supported syntax. <br/><br/>[**Simple query syntax**](query-simple-syntax.md) provides logical operators, phrase search operators, suffix operators, precedence operators.<br/><br/>[**Lucene query syntax**](query-lucene-syntax.md) includes all operations in simple syntax, with extensions for fuzzy search, proximity search, term boosting, and regular expressions.|
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|Free-form text search |[**Full-text search**](search-lucene-query-architecture.md) is a primary use case for most search-based apps. Queries can be formulated using a supported syntax. <br/><br/>[**Simple query syntax**](query-simple-syntax.md) provides logical operators, phrase search operators, suffix operators, precedence operators.<br/><br/>[**Full Lucene query syntax**](query-lucene-syntax.md) includes all operations in simple syntax, with extensions for fuzzy search, proximity search, term boosting, and regular expressions.|
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| Relevance |[**Simple scoring**](index-add-scoring-profiles.md) is a key benefit of Azure Cognitive Search. Scoring profiles are used to model relevance as a function of values in the documents themselves. For example, you might want newer products or discounted products to appear higher in the search results. You can also build scoring profiles using tags for personalized scoring based on customer search preferences you've tracked and stored separately. |
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| Geo-search | Azure Cognitive Search processes, filters, and displays geographic locations. It enables users to explore data based on the proximity of a search result to a physical location. [Watch this video](https://channel9.msdn.com/Shows/Data-Exposed/Azure-Search-and-Geospatial-Data) or [review this sample](https://github.com/Azure-Samples/search-dotnet-asp-net-mvc-jobs) to learn more. |
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| Filters and facets |[**Faceted navigation**](search-faceted-navigation.md) is enabled through a single query parameter. Azure Cognitive Search returns a faceted navigation structure you can use as the code behind a categories list, for self-directed filtering (for example, to filter catalog items by price-range or brand). <br/><br/> [**Filters**](query-odata-filter-orderby-syntax.md) can be used to incorporate faceted navigation into your application's UI, enhance query formulation, and filter based on user- or developer-specified criteria. Create filters using the OData syntax. |
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| Category | Features |
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| Tools for prototyping and inspection |[**Import data wizard**](search-import-data-portal.md) creates indexes, indexers, skillsets, and data source definitions. <br/><br/>[**Search explorer**](search-explorer.md) is used to test queries and refine scoring profiles.<br/><br/>[**Create demo app**](search-create-app-portal.md) is used to generate an HTML page that can be used to test the search experience. |
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| Tools for prototyping and inspection |[**Add index**](search-what-is-an-index.md) is an index designer in the portal that you can use to create a basic schema consisting of attributed fields and a few other settings. After saving the index, you can populate it using an SDK or the REST API to provide the data. <br/><br/>[**Import data wizard**](search-import-data-portal.md) creates indexes, indexers, skillsets, and data source definitions. If your data exists in Azure, this wizard can save you significant time and effort, especially for proof-of-concept investigation and exploration. <br/><br/>[**Search explorer**](search-explorer.md) is used to test queries and refine scoring profiles.<br/><br/>[**Create demo app**](search-create-app-portal.md) is used to generate an HTML page that can be used to test the search experience. |
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| Monitoring and diagnostics |[**Enable monitoring features**](search-monitor-usage.md) to go beyond the metrics-at-a-glance that are always visible in the portal. Metrics on queries per second, latency, and throttling are captured and reported in portal pages with no additional configuration required.|
Architecturally, a search service fits in between the external data stores that contain your data, and a client app that sends query requests and handles responses. The two primary workloads of a search service include*indexing* and *querying*.
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Architecturally, a search service sits in between the external data stores that contain your data, and a client app that sends query requests and handles responses. The two primary workloads of a search service are*indexing* and *querying*.
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Indexing adds content to your search service and makes it searchable. Internally, text is processed into tokens and stored in inverted indexes for fast matching. An index schema determines the structure of searchable content. During indexing, you have the option of adding *cognitive skills*, either predefined ones from Microsoft or custom skills that you create. The resulting analysis and transformations can create new information and structures that did not previously exist, providing high utility for many search and knowledge mining scenarios.
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Indexing adds content to your search service and makes it searchable. Internally, inbound text is processed into tokens and stored in inverted indexes for fast matching. An index schema determines the structure of searchable content. During indexing, you have the option of adding *cognitive skills*, either predefined ones from Microsoft or custom skills that you create. The resulting analysis and transformations can create new information and structures that did not previously exist, providing high utility for many search and knowledge mining scenarios.
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Once an index is populated with searchable data, your client app sends query requests to a search service and handles responses. The search experience is defined in your client using APIs from Azure Cognitive Search, and can include relevance tuning, autocomplete, synonym matching, fuzzy matching, pattern matching, filter, and sort.
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