Index data from Azure Blob Storage

In this article, learn how to configure an indexer that imports content from Azure Blob Storage and makes it searchable in Azure AI Search. Inputs to the indexer are your blobs, in a single container. Output is a search index with searchable content and metadata stored in individual fields.

To configure and run the indexer, you can use:

• Search Service REST API, any version.
• An Azure SDK package, any version.
• Import data wizard in the Azure portal.
• Import and vectorize data wizard in the Azure portal.

This article uses the REST APIs to illustrate each step.

Prerequisites

• Azure Blob Storage, Standard performance (general-purpose v2).

• Access tiers include hot, cool, cold, and archive. Indexers can retrieve blobs on hot, cool, and cold access tiers.

• Blobs providing text content and metadata. If blobs contain binary content or unstructured text, consider adding AI enrichment for image and natural language processing. Blob content can’t exceed the indexer limits for your search service tier.

• A supported network configuration and data access. At a minimum, you need read permissions in Azure Storage. A storage connection string that includes an access key gives you read access to storage content. If instead you're using Microsoft Entra logins and roles, make sure the search service's managed identity has Storage Blob Data Reader permissions.

  By default, both search and storage accept requests from public IP addresses. If network security isn't an immediate concern, you can index blob data using just the connection string and read permissions. When you're ready to add network protections, see Indexer access to content protected by Azure network security features for guidance about data access.

• Use a REST client to formulate REST calls similar to the ones shown in this article.

Supported tasks

You can use this indexer for the following tasks:

• Data indexing and incremental indexing: The indexer can index files and associated metadata from blob containers and folders. It detects new and updated files and metadata through built-in change detection. You can configure data refresh on a schedule or on demand.
• Deletion detection: The indexer can detect deletions through native soft delete or through custom metadata.
• Applied AI through skillsets: Skillsets are fully supported by the indexer. This includes key features like integrated vectorization that adds data chunking and embedding steps.
• Parsing modes: The indexer supports JSON parsing modes if you want to parse JSON arrays or lines into individual search documents. It also supports Markdown parsing mode.
• Compatibility with other features: The indexer is designed to work seamlessly with other indexer features, such as debug sessions, indexer cache for incremental enrichments, and knowledge store.

Supported document formats

The blob indexer can extract text from the following document formats:

• CSV (see Indexing CSV blobs)
• EML
• EPUB
• GZ
• HTML
• JSON (see Indexing JSON blobs)
• KML (XML for geographic representations)
• Microsoft Office formats: DOCX/DOC/DOCM, XLSX/XLS/XLSM, PPTX/PPT/PPTM, MSG (Outlook emails), XML (both 2003 and 2006 WORD XML)
• Open Document formats: ODT, ODS, ODP
• PDF
• Plain text files (see also Indexing plain text)
• RTF
• XML
• ZIP

Determine which blobs to index

Before you set up indexing, review your source data to determine whether any changes should be made up front. An indexer can index content from one container at a time. By default, all blobs in the container are processed. You have several options for more selective processing:

• Place blobs in a virtual folder. An indexer data source definition includes a "query" parameter that can take a virtual folder. If you specify a virtual folder, only those blobs in the folder are indexed.

• Include or exclude blobs by file type. The supported document formats list can help you determine which blobs to exclude. For example, you might want to exclude image or audio files that don't provide searchable text. This capability is controlled through configuration settings in the indexer.

• Include or exclude arbitrary blobs. If you want to skip a specific blob for whatever reason, you can add the following metadata properties and values to blobs in Blob Storage. When an indexer encounters this property, it skips the blob or its content in the indexing run.

  Property name	Property value	Explanation
  "AzureSearch_Skip"	"true"	Instructs the blob indexer to completely skip the blob. Neither metadata nor content extraction is attempted. This is useful when a particular blob fails repeatedly and interrupts the indexing process.
  "AzureSearch_SkipContent"	"true"	Skips content and extracts just the metadata. this is equivalent to the "dataToExtract" : "allMetadata" setting described in configuration settings , just scoped to a particular blob.

If you don't set up inclusion or exclusion criteria, the indexer reports an ineligible blob as an error and move on. If enough errors occur, processing might stop. You can specify error tolerance in the indexer configuration settings.

An indexer typically creates one search document per blob, where the text content and metadata are captured as searchable fields in an index. If blobs are whole files, you can potentially parse them into multiple search documents. For example, you can parse rows in a CSV file to create one search document per row.

A compound or embedded document (such as a ZIP archive, a Word document with embedded Outlook email containing attachments, or an .MSG file with attachments) is also indexed as a single document. For example, all images extracted from the attachments of an .MSG file will be returned in the normalized_images field. If you have images, consider adding AI enrichment to get more search utility from that content.

Textual content of a document is extracted into a string field named "content". You can also extract standard and user-defined metadata.

Indexing blob metadata

Blob metadata can also be indexed, and that's helpful if you think any of the standard or custom metadata properties are useful in filters and queries.

User-specified metadata properties are extracted verbatim. To receive the values, you must define field in the search index of type Edm.String, with same name as the metadata key of the blob. For example, if a blob has a metadata key of Sensitivity with value High, you should define a field named Sensitivity in your search index and it will be populated with the value High.

Standard blob metadata properties can be extracted into similarly named and typed fields, as listed below. The blob indexer automatically creates internal field mappings for these blob metadata properties, converting the original hyphenated name ("metadata-storage-name") to an underscored equivalent name ("metadata_storage_name").

You still have to add the underscored fields to the index definition, but you can omit field mappings because the indexer make the association automatically.

• metadata_storage_name (Edm.String) - the file name of the blob. For example, if you have a blob /my-container/my-folder/subfolder/resume.pdf, the value of this field is resume.pdf.

• metadata_storage_path (Edm.String) - the full URI of the blob, including the storage account. For example, https://myaccount.blob.core.windows.net/my-container/my-folder/subfolder/resume.pdf

• metadata_storage_content_type (Edm.String) - content type as specified by the code you used to upload the blob. For example, application/octet-stream.

• metadata_storage_last_modified (Edm.DateTimeOffset) - last modified timestamp for the blob. Azure AI Search uses this timestamp to identify changed blobs, to avoid reindexing everything after the initial indexing.

• metadata_storage_size (Edm.Int64) - blob size in bytes.

• metadata_storage_content_md5 (Edm.String) - MD5 hash of the blob content, if available.

• metadata_storage_sas_token (Edm.String) - A temporary SAS token that can be used by custom skills to get access to the blob. This token shouldn't be stored for later use as it might expire.

Lastly, any metadata properties specific to the document format of the blobs you're indexing can also be represented in the index schema. For more information about content-specific metadata, see Content metadata properties.

It's important to point out that you don't need to define fields for all of the above properties in your search index - just capture the properties you need for your application.

Currently, indexing blob index tags isn't supported by this indexer.

Define the data source

The data source definition specifies the data to index, credentials, and policies for identifying changes in the data. A data source is defined as an independent resource so that it can be used by multiple indexers.

1. Create or update a data source to set its definition:

   {
       "name" : "my-blob-datasource",
       "type" : "azureblob",
       "credentials" : { "connectionString" : "DefaultEndpointsProtocol=https;AccountName=<account name>;AccountKey=<account key>;" },
       "container" : { "name" : "my-container", "query" : "<optional-virtual-directory-name>" }
   }
   

2. Set "type" to "azureblob" (required).

3. Set "credentials" to an Azure Storage connection string. The next section describes the supported formats.

4. Set "container" to the blob container, and use "query" to specify any subfolders.

A data source definition can also include soft deletion policies, if you want the indexer to delete a search document when the source document is flagged for deletion.

Supported credentials and connection strings

Indexers can connect to a blob container using the following connections.

Add search fields to an index

In a search index, add fields to accept the content and metadata of your Azure blobs.

1. Create or update an index to define search fields that will store blob content and metadata:

   POST https://[service name].search.windows.net/indexes?api-version=2024-07-01
   {
       "name" : "my-search-index",
       "fields": [
           { "name": "ID", "type": "Edm.String", "key": true, "searchable": false },
           { "name": "content", "type": "Edm.String", "searchable": true, "filterable": false },
           { "name": "metadata_storage_name", "type": "Edm.String", "searchable": false, "filterable": true, "sortable": true  },
           { "name": "metadata_storage_size", "type": "Edm.Int64", "searchable": false, "filterable": true, "sortable": true  },
           { "name": "metadata_storage_content_type", "type": "Edm.String", "searchable": false, "filterable": true, "sortable": true },        
       ]
   }
   

2. Create a document key field ("key": true). For blob content, the best candidates are metadata properties.

   • metadata_storage_path (default) full path to the object or file. The key field ("ID" in this example) will be populated with values from metadata_storage_path because it's the default.

   • metadata_storage_name, usable only if names are unique. If you want this field as the key, move "key": true to this field definition.

   • A custom metadata property that you add to blobs. This option requires that your blob upload process adds that metadata property to all blobs. Since the key is a required property, any blobs that are missing a value will fail to be indexed. If you use a custom metadata property as a key, avoid making changes to that property. Indexers will add duplicate documents for the same blob if the key property changes.

   Metadata properties often include characters, such as / and -, which are invalid for document keys. However, the indexer automatically encodes the key metadata property, with no configuration or field mapping required.

3. Add a "content" field to store extracted text from each file through the blob's "content" property. You aren't required to use this name, but doing so lets you take advantage of implicit field mappings.

4. Add fields for standard metadata properties. The indexer can read custom metadata properties, standard metadata properties, and content-specific metadata properties.

Configure and run the blob indexer

Once the index and data source have been created, you're ready to create the indexer. Indexer configuration specifies the inputs, parameters, and properties controlling run time behaviors. You can also specify which parts of a blob to index.

1. Create or update an indexer by giving it a name and referencing the data source and target index:

   POST https://[service name].search.windows.net/indexers?api-version=2024-07-01
   {
     "name" : "my-blob-indexer",
     "dataSourceName" : "my-blob-datasource",
     "targetIndexName" : "my-search-index",
     "parameters": {
         "batchSize": null,
         "maxFailedItems": null,
         "maxFailedItemsPerBatch": null,
         "configuration": {
             "indexedFileNameExtensions" : ".pdf,.docx",
             "excludedFileNameExtensions" : ".png,.jpeg",
             "dataToExtract": "contentAndMetadata",
             "parsingMode": "default"
         }
     },
     "schedule" : { },
     "fieldMappings" : [ ]
   }
   

2. Set batchSize if the default (10 documents) is either underutilizing or overwhelming available resources. Default batch sizes are data source specific. Blob indexing sets batch size at 10 documents in recognition of the larger average document size.

3. Under "configuration", control which blobs are indexed based on file type, or leave unspecified to retrieve all blobs.

   For "indexedFileNameExtensions", provide a comma-separated list of file extensions (with a leading dot). Do the same for "excludedFileNameExtensions" to indicate which extensions should be skipped. If the same extension is in both lists, it will be excluded from indexing.

4. Under "configuration", set "dataToExtract" to control which parts of the blobs are indexed:

   • "contentAndMetadata" specifies that all metadata and textual content extracted from the blob are indexed. This is the default value.

   • "storageMetadata" specifies that only the standard blob properties and user-specified metadata are indexed.

   • "allMetadata" specifies that standard blob properties and any metadata for found content types are extracted from the blob content and indexed.

5. Under "configuration", set "parsingMode". The default parsing mode is one search document per blob. If blobs are plain text, you can get better performance by switching to plain text parsing. If you need more granular parsing that maps blobs to multiple search documents, specify a different mode. One-to-many parsing is supported for blobs consisting of:

   • JSON documents
   • CSV files

6. Specify field mappings if there are differences in field name or type, or if you need multiple versions of a source field in the search index.

   In blob indexing, you can often omit field mappings because the indexer has built-in support for mapping the "content" and metadata properties to similarly named and typed fields in an index. For metadata properties, the indexer will automatically replace hyphens - with underscores in the search index.

7. See Create an indexer for more information about other properties. For the full list of parameter descriptions, see REST API.

An indexer runs automatically when it's created. You can prevent this by setting "disabled" to true. To control indexer execution, run an indexer on demand or put it on a schedule.

Indexing data from multiple Azure Blob containers to a single index

Keep in mind that an indexer can only index data from a single container. If your requirement is to index data from multiple containers and consolidate it into a single AI Search index, this can be achieved by configuring multiple indexers, all directed to the same index. Please be aware of the maximum number of indexers available per SKU.

To illustrate, let's consider an example of two indexers, pulling data from two distinct data sources, named my-blob-datasource1 and my-blob-datasource2. Each data source points to a separate Azure Blob container, but both direct to the same index named my-search-index.

First indexer definition example:

POST https://[service name].search.windows.net/indexers?api-version=2024-07-01
{
  "name" : "my-blob-indexer1",
  "dataSourceName" : "my-blob-datasource1",
  "targetIndexName" : "my-search-index",
  "parameters": {
      "batchSize": null,
      "maxFailedItems": null,
      "maxFailedItemsPerBatch": null,
      "configuration": {
          "indexedFileNameExtensions" : ".pdf,.docx",
          "excludedFileNameExtensions" : ".png,.jpeg",
          "dataToExtract": "contentAndMetadata",
          "parsingMode": "default"
      }
  },
  "schedule" : { },
  "fieldMappings" : [ ]
}

Second indexer definition that runs in parallel example:

POST https://[service name].search.windows.net/indexers?api-version=2024-07-01
{
  "name" : "my-blob-indexer2",
  "dataSourceName" : "my-blob-datasource2",
  "targetIndexName" : "my-search-index",
  "parameters": {
      "batchSize": null,
      "maxFailedItems": null,
      "maxFailedItemsPerBatch": null,
      "configuration": {
          "indexedFileNameExtensions" : ".pdf,.docx",
          "excludedFileNameExtensions" : ".png,.jpeg",
          "dataToExtract": "contentAndMetadata",
          "parsingMode": "default"
      }
  },
  "schedule" : { },
  "fieldMappings" : [ ]
}

Check indexer status

To monitor the indexer status and execution history, send a Get Indexer Status request:

GET https://myservice.search.windows.net/indexers/myindexer/status?api-version=2024-07-01
  Content-Type: application/json  
  api-key: [admin key]

The response includes status and the number of items processed. It should look similar to the following example:

    {
        "status":"running",
        "lastResult": {
            "status":"success",
            "errorMessage":null,
            "startTime":"2022-02-21T00:23:24.957Z",
            "endTime":"2022-02-21T00:36:47.752Z",
            "errors":[],
            "itemsProcessed":1599501,
            "itemsFailed":0,
            "initialTrackingState":null,
            "finalTrackingState":null
        },
        "executionHistory":
        [
            {
                "status":"success",
                "errorMessage":null,
                "startTime":"2022-02-21T00:23:24.957Z",
                "endTime":"2022-02-21T00:36:47.752Z",
                "errors":[],
                "itemsProcessed":1599501,
                "itemsFailed":0,
                "initialTrackingState":null,
                "finalTrackingState":null
            },
            ... earlier history items
        ]
    }

Execution history contains up to 50 of the most recently completed executions, which are sorted in the reverse chronological order so that the latest execution comes first.

Handle errors

Errors that commonly occur during indexing include unsupported content types, missing content, or oversized blobs.

By default, the blob indexer stops as soon as it encounters a blob with an unsupported content type (for example, an audio file). You could use the "excludedFileNameExtensions" parameter to skip certain content types. However, you might want to indexing to proceed even if errors occur, and then debug individual documents later. For more information about indexer errors, see Indexer troubleshooting guidance and Indexer errors and warnings.

There are five indexer properties that control the indexer's response when errors occur.

PUT /indexers/[indexer name]?api-version=2024-07-01
{
  "parameters" : { 
    "maxFailedItems" : 10, 
    "maxFailedItemsPerBatch" : 10,
    "configuration" : { 
        "failOnUnsupportedContentType" : false, 
        "failOnUnprocessableDocument" : false,
        "indexStorageMetadataOnlyForOversizedDocuments": false
      }
    }
}

See also

• Change detection and deletion detection
• Index large data sets
• Indexer access to content protected by Azure network security features