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Copy file name to clipboardExpand all lines: articles/search/search-faq-frequently-asked-questions.yml
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ms.custom:
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- ignite-2023
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ms.topic: faq
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ms.date: 12/12/2023
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ms.date: 02/21/2024
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title: Azure AI Search Frequently Asked Questions
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summary: Find answers to commonly asked questions about Azure AI Search.
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What languages are supported?
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The default analyzer used for tokenization is standard Lucene and its language agnostic. Otherwise, language support is expressed through [language analyzers](index-add-language-analyzers.md#supported-language-analyzers) that apply linguistic rules to inbound (indexing) and outbound (queries) content. Some features, such as [semantic ranking](/rest/api/searchservice/preview-api/search-documents#queryLanguage) and [speller](speller-how-to-add.md#supported-languages), are limited to a subset of languages.
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The default analyzer used for tokenization is standard Lucene and it is language agnostic. Otherwise, language support is expressed through [language analyzers](index-add-language-analyzers.md#supported-language-analyzers) that apply linguistic rules to inbound (indexing) and outbound (queries) content. Some features, such as [speller](speller-how-to-add.md#supported-languages), are limited to a subset of languages.
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How do I integrate search into my solution?
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What does "indexing" mean in Azure AI Search?
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It refers to the ingestion, parsing, and storing of textual content and tokens that populate a search index. Indexing creates inverted indexes and other physical data structures that support information retrieval.
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It refers to the ingestion, parsing, and storing of textual content and tokens that populate a search index. Indexing creates inverted indexes and other physical data structures that support information retrieval. It creates vector indexes if the schema includes vector fields.
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Can I move, backup, and restore indexes?
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There's no native support for index management. Search indexes are considered downstream data structures, accepting content from other data sources that collect operational data. As such, there's no built-in support for backing up and restoring indexes because the expectation is that you would rebuild an index from source data if you deleted it, or wanted to move it.
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There's no native support for porting indexes. Search indexes are considered downstream data structures, accepting content from other data sources that collect operational data. As such, there's no built-in support for backing up and restoring indexes because the expectation is that you would rebuild an index from source data if you deleted it, or wanted to move it.
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However, if you want to move an index between search services, you can try the **index-backup-restore** sample code in this [Azure AI Search .NET sample repo](https://github.com/Azure-Samples/azure-search-dotnet-utilities).
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However, if you want to move an index between search services, you can try the **index-backup-restore** sample code in this [Azure AI Search .NET sample repo](https://github.com/Azure-Samples/azure-search-dotnet-utilities). There's also a [Python version of backup and restore](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/azure-search-backup-and-restore.ipynb).
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Can I restore my index or service once it's deleted?
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What is vector search?
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Vector search is a technique that finds the most similar documents by comparing their vector representations. Since the goal of a vector representation is to capture the essential characteristics of an item in a numerical format, it can capture abstract concepts and identify matches even if there are no explicit matches based on keywords or tags. When a user performs a search, the query is summarized into a vector representation and the vector search engine identifies the most similar documents. To improve efficiency on large databases, vector search often provides the approximate nearest neighbors for a query vector. See [Vector search overview](vector-search-overview.md) for the specifics of Azure AI Search's vector search product offering.
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Vector search is a technique that finds the most similar documents by comparing their vector representations. Since the goal of a vector representation is to capture the essential characteristics of an item in a numerical format, vector queries can identify similar content even if there are no explicit matches based on keywords or tags. When a user performs a search, the query is summarized into a vector representation and the vector search engine identifies the most similar documents. To improve efficiency on large databases, vector search often provides the approximate nearest neighbors for a query vector. See [Vector search overview](vector-search-overview.md) for the specifics of Azure AI Search's vector offering.
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Does Azure AI Search support vector search?
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To enable vector search in an index, you should:
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* Add one or more fields of type `Collection(Edm.Single)`, with a "dimensions" property and an "vectorSearchConfiguration" property.
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* Add one or more vector fields to a field collection.
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* Add a "vectorSearch" section to the index schema specifying the configuration used by vector search fields, including the parameters of the Approximate Nearest Neighbor algorithm used, like HNSW.
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* Use the [**2023-11-01**](/rest/api/searchservice) or an Azure SDK to create or update the index, load documents, and issue queries.
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* Use [**2023-11-01**](/rest/api/searchservice) or an Azure SDK to create or update the index, load documents, and issue queries.
Copy file name to clipboardExpand all lines: articles/search/search-howto-managed-identities-cosmos-db.md
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---
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title: Set up an indexer connection to Azure Cosmos DB via a managed identity
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titleSuffix: Azure AI Search
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description: Learn how to set up an indexer connection to an Azure Cosmos DB account via a managed identity
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description: Learn how to set up an indexer connection to an Azure Cosmos DB account via a managed identity.
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author: gmndrg
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ms.author: gimondra
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ms.service: cognitive-search
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ms.topic: how-to
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ms.date: 09/19/2022
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ms.date: 02/22/2024
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```azurepowershell
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az cosmosdb sql role assignment create --account-name $cosmosdbname --resource-group $resourcegroup --role-definition-id $readOnlyRoleDefinitionId --principal-id $sys_principal --scope $scope
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```
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* For Cosmos DB for NoSQL, you can optionally [Enforcing RBAC as the only authentication method](../cosmos-db/how-to-setup-rbac.md#disable-local-auth)
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* For Cosmos DB for NoSQL, you can optionally [enforce role-based access as the only authentication method](../cosmos-db/how-to-setup-rbac.md#disable-local-auth)
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for data connections by setting `disableLocalAuth` to `true` for your Cosmos DB account.
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**For Gremlin and MongoDB Collections*:
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Indexer support is currently in preview. At this time, a preview limitation exists that requires Azure AI Search to connect using keys. You can still set up a managed identity and role assignment, but Azure AI Search will only use the role assignment to get keys for the connection. This limitation means that you can't configure an [RBAC-only approach](../cosmos-db/how-to-setup-rbac.md#disable-local-auth) if your indexers are connecting to Gremlin or MongoDB using Search with managed identities to connect to Azure Cosmos DB.
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Indexer support is currently in preview. At this time, a preview limitation exists that requires Azure AI Search to connect using keys. You can still set up a managed identity and role assignment, but Azure AI Search will only use the role assignment to get keys for the connection. This limitation means that you can't configure a [role-based approach](../cosmos-db/how-to-setup-rbac.md#disable-local-auth) if your indexers are connecting to Gremlin or MongoDB using Search with managed identities to connect to Azure Cosmos DB.
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* You should be familiar with [indexer concepts](search-indexer-overview.md) and [configuration](search-howto-index-cosmosdb.md).
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When you're connecting with a system-assigned managed identity, the only change to the data source definition is the format of the "credentials" property. You'll provide the database name and a ResourceId that has no account key or password. The ResourceId must include the subscription ID of Azure Cosmos DB, the resource group, and the Azure Cosmos DB account name.
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* For SQL collections, the connection string doesn't require "ApiKind".
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* For SQL collections add "IdentityAuthType=AccessToken" if RBAC is enforced as the only authentication method. It is not applicable for MongoDB and Gremlin collections.
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* For SQL collections, add "IdentityAuthType=AccessToken" if role-based access is enforced as the only authentication method. It isn't applicable for MongoDB and Gremlin collections.
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* For MongoDB collections, add "ApiKind=MongoDb" to the connection string and use a preview REST API.
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* For Gremlin graphs, add "ApiKind=Gremlin" to the connection string and use a preview REST API.
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* First, the format of the "credentials" property is the database name and a ResourceId that has no account key or password. The ResourceId must include the subscription ID of Azure Cosmos DB, the resource group, and the Azure Cosmos DB account name.
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* For SQL collections, the connection string doesn't require "ApiKind".
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* For SQL collections add "IdentityAuthType=AccessToken" if RBAC is enforced as the only authentication method. It is not applicable for MongoDB and Gremlin collections.
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* For SQL collections, add "IdentityAuthType=AccessToken" if role-based access is enforced as the only authentication method. It isn't applicable for MongoDB and Gremlin collections.
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* For MongoDB collections, add "ApiKind=MongoDb" to the connection string
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* For Gremlin graphs, add "ApiKind=Gremlin" to the connection string.
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* Second, you'll add an "identity" property that contains the collection of user-assigned managed identities. Only one user-assigned managed identity should be provided when creating the data source. Set it to type "userAssignedIdentities".
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* Second, you add an "identity" property that contains the collection of user-assigned managed identities. Only one user-assigned managed identity should be provided when creating the data source. Set it to type "userAssignedIdentities".
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Here's an example of how to create an indexer data source object using the [preview Create or Update Data Source](/rest/api/searchservice/preview-api/create-or-update-data-source) REST API:
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An indexer connects a data source with a target search index and provides a schedule to automate the data refresh. Once the index and data source have been created, you're ready to create and run the indexer. If the indexer is successful, the connection syntax and role assignments are valid.
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Here's a [Create Indexer](/rest/api/searchservice/create-indexer) REST API call with an Azure Cosmos DB indexer definition. The indexer will run when you submit the request.
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Here's a [Create Indexer](/rest/api/searchservice/create-indexer) REST API call with an Azure Cosmos DB indexer definition. The indexer runs when you submit the request.
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```http
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POST https://[service name].search.windows.net/indexers?api-version=2020-06-30
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## Troubleshooting
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If you recently rotated your Azure Cosmos DB account keys, you'll need to wait up to 15 minutes for the managed identity connection string to work.
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If you recently rotated your Azure Cosmos DB account keys, you need to wait up to 15 minutes for the managed identity connection string to work.
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Check to see if the Azure Cosmos DB account has its access restricted to select networks. You can rule out any firewall issues by trying the connection without restrictions in place.
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Here's an example of how to create a data source to index data from a storage account using the [Create Data Source](/rest/api/searchservice/create-data-source) REST API and a managed identity connection string. The managed identity connection string format is the same for the REST API, .NET SDK, and the Azure portal.
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```http
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POST https://[service name].search.windows.net/datasources?api-version=2020-06-30
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POST https://[service name].search.windows.net/datasources?api-version=2023-11-01
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Content-Type: application/json
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api-key: [admin key]
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* First, the format of the "credentials" property is an Initial Catalog or Database name and a ResourceId that has no account key or password. The ResourceId must include the subscription ID of Azure SQL Database, the resource group of SQL Database, and the name of the SQL database. This is the same format as the system-assigned managed identity.
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* Second, you'll add an "identity" property that contains the collection of user-assigned managed identities. Only one user-assigned managed identity should be provided when creating the data source. Set it to type "userAssignedIdentities".
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* Second, add an "identity" property that contains the collection of user-assigned managed identities. Only one user-assigned managed identity should be provided when creating the data source. Set it to type "userAssignedIdentities".
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Here's an example of how to create an indexer data source object using the [preview Create orUpdate Data Source](/rest/api/searchservice/preview-api/create-or-update-data-source) REST API:
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Here's a [Create Index](/rest/api/searchservice/create-index) REST API call with a searchable `booktitle` field:
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```http
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POST https://[service name].search.windows.net/indexes?api-version=2020-06-30
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POST https://[service name].search.windows.net/indexes?api-version=2023-11-01
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Content-Type: application/json
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api-key: [admin key]
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An indexer connects a data source with a target search index, and provides a schedule to automate the data refresh. Once the index and data source have been created, you're ready to create the indexer. If the indexer is successful, the connection syntax and role assignments are valid.
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Here's a [Create Indexer](/rest/api/searchservice/create-indexer) REST API call with an Azure SQL indexer definition. The indexer will run when you submit the request.
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Here's a [Create Indexer](/rest/api/searchservice/create-indexer) REST API call with an Azure SQL indexer definition. The indexer runs when you submit the request.
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```http
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POST https://[service name].search.windows.net/indexers?api-version=2020-06-30
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POST https://[service name].search.windows.net/indexers?api-version=2023-11-01
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Content-Type: application/json
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api-key: [admin key]
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{
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"name" : "sql-indexer",
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"dataSourceName" : "sql-datasource",
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"targetIndexName" : "my-target-index"
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}
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```
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## Troubleshooting
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If you get an error when the indexer tries to connect to the data source that says that the client isn't allowed to access the server, take a look at [common indexer errors](./search-indexer-troubleshooting.md).
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You can also rule out any firewall issues by trying the connection with and without restrictions in place.
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