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# Azure Resource Manager Azure AI Search client library for Java - version 2.34.0
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Azure AI Search (formerly known as "Azure Cognitive Search") is an AI-powered information retrieval platform that helps developers build rich search experiences and generative AI apps that combine large language models with enterprise data.
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Azure Resource Manager Azure AI Search client library for Java
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This library supports managing Microsoft Azure AI Search resources using the Azure SDK for Java.
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Although Azure AI Search is renamed, many API descriptions continue to use the former name, "Azure Cognitive Search". API string descriptions will get updated over time.
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For documentation on how to use this package, please see [Azure Management Libraries for Java](https://aka.ms/azsdk/java/mgmt).
|[Azure AI Search](search-documents-readme.md)|[azure-search-documents](https://repo1.maven.org/maven2/com/azure/azure-search-documents)|[Github](https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/search/azure-search-documents)|
|[Azure AI Search](search-documents-readme.md)|[azure-search-documents](https://repo1.maven.org/maven2/com/azure/azure-search-documents)|[GitHub](https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/search/azure-search-documents)|
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|[Resource Management - Azure AI Search](resourcemanager-search-readme.md)|[azure-resourcemanager-search](https://repo1.maven.org/maven2/com/azure/resourcemanager/azure-resourcemanager-search)|[GitHub](https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/resourcemanager/azure-resourcemanager-search)|
|Azure AI Search|[azure-search](https://repo1.maven.org/maven2/com/azure/azure-search)|[GitHub](https://github.com/Azure/azure-sdk-for-java/blob/main/)|
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|Management - Azure AI Search|[azure-mgmt-search](https://repo1.maven.org/maven2/com/microsoft/azure/azure-mgmt-search)|[GitHub](https://github.com/Azure/azure-sdk-for-java/blob/main/)|
|[Azure AI Search](search-documents-readme.md)|[azure-search-documents](https://repo1.maven.org/maven2/com/azure/azure-search-documents)|[Github](https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/search/azure-search-documents)|
# Azure Cognitive Search client library for Java - version 11.6.0-beta.10
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This is the Java client library for [Azure Cognitive Search](/azure/search/).
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Azure Cognitive Search service is a search-as-a-service cloud solution that gives developers APIs and tools for adding
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a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.
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The Azure Cognitive Search service is well suited for the following application scenarios:
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* Consolidate varied content types into a single searchable index. To populate an index, you can push JSON documents
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that contain your content, or if your data is already in Azure, create an indexer to pull in data automatically.
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* Attach skillsets to an indexer to create searchable content from images and large text documents. A skillset leverages
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AI from Cognitive Services for built-in OCR, entity recognition, key phrase extraction, language detection, text
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translation, and sentiment analysis. You can also add custom skills to integrate external processing of your content
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during data ingestion.
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* In a search client application, implement query logic and user experiences similar to commercial web search engines.
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Use the Azure Cognitive Search client library to:
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* Submit queries for simple and advanced query forms that include fuzzy search, wildcard search, regular expressions.
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* Implement filtered queries for faceted navigation, geospatial search, or to narrow results based on filter criteria.
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# Azure AI Search client library for Java - version 11.6.0-beta.10
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This is the Java client library for [Azure AI Search](https://docs.microsoft.com/azure/search/) (formerly known as "Azure Cognitive Search"). Azure AI Search service is an AI-powered information retrieval platform that helps developers build rich search experiences and generative AI apps that combine large language models with enterprise data.
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Azure AI Search is well suited for the following application scenarios:
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* Consolidate varied content types into a single searchable index.
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To populate an index, you can push JSON documents that contain your content,
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or if your data is already in Azure, create an indexer to pull in data
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automatically.
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* Attach skillsets to an indexer to create searchable content from images
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and unstructured documents. A skillset leverages APIs from Azure AI Services
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for built-in OCR, entity recognition, key phrase extraction, language
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detection, text translation, and sentiment analysis. You can also add
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custom skills to integrate external processing of your content during
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data ingestion.
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* In a search client application, implement query logic and user experiences
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similar to commercial web search engines and chat-style apps.
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Use the Azure AI Search client library to:
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* Submit queries using vector, keyword, and hybrid query forms.
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* Implement filtered queries for metadata, geospatial search, faceted navigation,
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or to narrow results based on filter criteria.
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* Create and manage search indexes.
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* Upload and update documents in the search index.
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* Create and manage indexers that pull data from Azure into an index.
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*[Java Development Kit (JDK) with version 8 or above][jdk]
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*[Azure subscription][azure_subscription]
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*[Azure Cognitive Search service][search]
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*[Azure AI Search service][search]
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* To create a new search service, you can use the [Azure portal][create_search_service_docs],
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[Azure PowerShell][create_search_service_ps], or the [Azure CLI][create_search_service_cli].
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Here's an example using the Azure CLI to create a free instance for getting started:
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To interact with the Search service, you'll need to create an instance of the appropriate client class: `SearchClient`
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for searching indexed documents, `SearchIndexClient` for managing indexes, or `SearchIndexerClient` for crawling data
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sources and loading search documents into an index. To instantiate a client object, you'll need an **endpoint** and
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**API key**. You can refer to the documentation for more information on [supported authenticating approaches](https://learn.microsoft.com/azure/search/search-security-overview#authentication)
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sources and loading search documents into an index.
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To instantiate a client object, you'll need an **endpoint** and **Azure roles** or an **API key**. You can refer to the documentation for more information on [supported authenticating approaches](https://learn.microsoft.com/azure/search/search-security-overview#authentication)
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with the Search service.
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#### Get an API Key
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An API key can be an easier approach to start with because it doesn't require pre-existing role assignments.
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You can get the **endpoint** and an **API key** from the Search service in the [Azure Portal](https://portal.azure.com/).
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Please refer the [documentation](/azure/search/search-security-api-keys) for instructions on
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how to get an API key.
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#### Create a SearchIndexClient
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To create a `SearchIndexClient/SearchIndexAsyncClient`, you will need the values of the Azure Cognitive Search service
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To create a `SearchIndexClient/SearchIndexAsyncClient`, you will need the values of the Azure AI Search service
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URL endpoint and admin key.
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```java readme-sample-createIndexClient
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#### Create a SearchIndexerClient
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To create a `SearchIndexerClient/SearchIndexerAsyncClient`, you will need the values of the Azure Cognitive Search service
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To create a `SearchIndexerClient/SearchIndexerAsyncClient`, you will need the values of the Azure AI Search service
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URL endpoint and admin key.
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```java readme-sample-createIndexerClient
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#### Create a SearchClient
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Once you have the values of the Azure Cognitive Search service URL endpoint and
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Once you have the values of the Azure AI Search service URL endpoint and
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admin key, you can create the `SearchClient/SearchAsyncClient` with an existing index name:
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```java readme-sample-createSearchClient
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.buildAsyncClient();
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```
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#### Create a client using Azure Active Directory authentication
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#### Create a client using Microsoft Entra ID authentication
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You can also create a `SearchClient`, `SearchIndexClient`, or `SearchIndexerClient` using Azure Active Directory (AAD)
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You can also create a `SearchClient`, `SearchIndexClient`, or `SearchIndexerClient` using Microsoft Entra ID
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authentication. Your user or service principal must be assigned the "Search Index Data Reader" role.
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Using the [DefaultAzureCredential](https://github.com/Azure/azure-sdk-for-java/blob/azure-search-documents_11.6.0-beta.10/sdk/identity/azure-identity/README.md#defaultazurecredential)
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you can authenticate a service using Managed Identity or a service principal, authenticate as a developer working on an
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application, and more all without changing code. Please refer the [documentation](https://learn.microsoft.com/azure/search/search-security-rbac?tabs=config-svc-portal%2Croles-portal%2Ctest-portal%2Ccustom-role-portal%2Cdisable-keys-portal)
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for instructions on how to connect to Azure Cognitive Search using Azure role-based access control (Azure RBAC).
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for instructions on how to connect to Azure AI Search using Azure role-based access control (Azure RBAC).
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Before you can use the `DefaultAzureCredential`, or any credential type from [Azure.Identity](https://github.com/Azure/azure-sdk-for-java/blob/azure-search-documents_11.6.0-beta.10/sdk/identity/azure-identity/README.md),
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you'll first need to [install the Azure.Identity package](https://github.com/Azure/azure-sdk-for-java/blob/azure-search-documents_11.6.0-beta.10/sdk/identity/azure-identity/README.md#include-the-package).
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### Send your first search query
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To get running with Azure Cognitive Search first create an index following this [guide][search-get-started-portal].
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To get running with Azure AI Search first create an index following this [guide][search-get-started-portal].
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With an index created you can use the following samples to begin using the SDK.
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## Key concepts
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An Azure Cognitive Search service contains one or more indexes that provide persistent storage of searchable data in
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An Azure AI Search service contains one or more indexes that provide persistent storage of searchable data in
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the form of JSON documents. _(If you're new to search, you can make a very rough analogy between indexes and database
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tables.)_ The `azure-search-documents` client library exposes operations on these resources through two main client types.
and [hybrid queries](https://learn.microsoft.com/azure/search/hybrid-search-how-to-query)
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*[Vector query filters](https://learn.microsoft.com/azure/search/vector-search-filters) and [Text query filters](https://learn.microsoft.com/azure/search/search-filters)
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*[Semantic ranking](https://learn.microsoft.com/azure/search/semantic-how-to-query-request) and [scoring profiles](https://learn.microsoft.com/azure/search/index-add-scoring-profiles) for boosting relevance
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*[Start indexers to automatically crawl data sources](/rest/api/searchservice/indexer-operations)
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*[Define AI powered Skillsets to transform and enrich your data](/rest/api/searchservice/skillset-operations)
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Azure Cognitive Search provides two powerful features:
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Azure AI Search provides two powerful features:
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### Semantic Search
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### Semantic ranking
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Semantic search enhances the quality of search results for text-based queries. By enabling Semantic Search on your
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Semantic ranking enhances the quality of search results for text-based queries. By enabling semantic ranking on your
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search service, you can improve the relevance of search results in two ways:
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- It applies secondary ranking to the initial result set, promoting the most semantically relevant results to the top.
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- It extracts and returns captions and answers in the response, which can be displayed on a search page to enhance the
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user's search experience.
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To learn more about Semantic Search, you can refer to the [documentation](https://learn.microsoft.com/azure/search/vector-search-overview).
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To learn more about semantic ranking, you can refer to the [documentation](https://learn.microsoft.com/azure/search/vector-search-overview).
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### Vector Search
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### Vector search
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Vector Search is an information retrieval technique that overcomes the limitations of traditional keyword-based search.
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Instead of relying solely on lexical analysis and matching individual query terms, Vector Search utilizes machine
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Vector search is an information retrieval technique that overcomes the limitations of traditional keyword-based search.
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Instead of relying solely on lexical analysis and matching individual query terms, vector search utilizes machine
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learning models to capture the contextual meaning of words and phrases. It represents documents and queries as vectors
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in a high-dimensional space called an embedding. By understanding the intent behind the query, Vector Search can deliver
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in a high-dimensional space called an embedding. By understanding the intent behind the query, vector search can deliver
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more relevant results that align with the user's requirements, even if the exact terms are not present in the document.
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Moreover, Vector Search can be applied to various types of content, including images and videos, not just text.
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Moreover, vector search can be applied to various types of content, including images and videos, not just text.
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To learn how to index vector fields and perform vector search, you can refer to the [sample](https://github.com/Azure/azure-sdk-for-java/blob/azure-search-documents_11.6.0-beta.10/sdk/search/azure-search-documents/src/samples/java/com/azure/search/documents/VectorSearchExample.java).
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This sample provides detailed guidance on indexing vector fields and demonstrates how to perform vector search.
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Additionally, for more comprehensive information about Vector Search, including its concepts and usage, you can refer
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Additionally, for more comprehensive information about vector search, including its concepts and usage, you can refer
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to the [documentation](https://learn.microsoft.com/azure/search/vector-search-overview). The documentation provides
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in-depth explanations and guidance on leveraging the power of Vector Search in Azure Cognitive Search.
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in-depth explanations and guidance on leveraging the power of vector search in Azure AI Search.
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## Examples
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### General
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When you interact with Azure Cognitive Search using this Java client library, errors returned by the service correspond
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When you interact with Azure AI Search using this Java client library, errors returned by the service correspond
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to the same HTTP status codes returned for [REST API][rest_api] requests. For example, the service will return a `404`
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error if you try to retrieve a document that doesn't exist in your index.
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## Next steps
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* Samples are explained in detail [here][samples_readme].
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* Watch a [demo or deep dive video](https://azure.microsoft.com/resources/videos/index/?services=search)
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* Read more about the [Azure Cognitive Search service](/azure/search/search-what-is-azure-search)
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* Read more about the [Azure AI Search service](/azure/search/search-what-is-azure-search)
|[Azure AI Search](search-documents-readme.md)|[azure-search-documents](https://repo1.maven.org/maven2/com/azure/azure-search-documents)|[GitHub](https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/search/azure-search-documents)|
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