Create a blob knowledge source from Azure Blob Storage and ADLS Gen2

Note

This feature is currently in public preview. This preview is provided without a service-level agreement and isn't recommended for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Azure Previews.

Use a blob knowledge source to index and query Azure blob content in an agentic retrieval pipeline. Knowledge sources are created independently, referenced in a knowledge base, and used as grounding data when an agent or chatbot calls a retrieve action at query time.

Unlike a search index knowledge source, which specifies an existing and qualified index, a blob knowledge source specifies an external data source, models, and properties to automatically generate the following Azure AI Search objects:

  • A data source that represents a blob container.
  • A skillset that chunks and optionally vectorizes multimodal content from the container.
  • An index that stores enriched content and meets the criteria for agentic retrieval.
  • An indexer that uses the previous objects to drive the indexing and enrichment pipeline.

Note

If user access is specified at the document (blob) level in Azure Storage, a knowledge source can carry permission metadata forward to indexed content in Azure AI Search. For more information, see ADLS Gen2 permission metadata or Blob RBAC scopes.

Prerequisites

Note

Although you can use the Azure portal to create blob knowledge sources, the portal uses the 2025-08-01-preview, which uses the previous "knowledge agent" terminology and doesn't support all 2025-11-01-preview features. For help with breaking changes, see Migrate your agentic retrieval code.

Check for existing knowledge sources

A knowledge source is a top-level, reusable object. Knowing about existing knowledge sources is helpful for either reuse or naming new objects.

Run the following code to list knowledge sources by name and type.

// List knowledge sources by name and type
using Azure.Search.Documents.Indexes;

var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
var knowledgeSources = indexClient.GetKnowledgeSourcesAsync();

Console.WriteLine("Knowledge Sources:");

await foreach (var ks in knowledgeSources)
{
    Console.WriteLine($"  Name: {ks.Name}, Type: {ks.GetType().Name}");
}

You can also return a single knowledge source by name to review its JSON definition.

using Azure.Search.Documents.Indexes;
using System.Text.Json;

var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);

// Specify the knowledge source name to retrieve
string ksNameToGet = "earth-knowledge-source";

// Get its definition
var knowledgeSourceResponse = await indexClient.GetKnowledgeSourceAsync(ksNameToGet);
var ks = knowledgeSourceResponse.Value;

// Serialize to JSON for display
var jsonOptions = new JsonSerializerOptions 
{ 
    WriteIndented = true,
    DefaultIgnoreCondition = System.Text.Json.Serialization.JsonIgnoreCondition.Never
};
Console.WriteLine(JsonSerializer.Serialize(ks, ks.GetType(), jsonOptions));

The following JSON is an example response for a blob knowledge source.

{
  "name": "my-blob-ks",
  "kind": "azureBlob",
  "description": "A sample blob knowledge source.",
  "encryptionKey": null,
  "azureBlobParameters": {
    "connectionString": "<REDACTED>",
    "containerName": "blobcontainer",
    "folderPath": null,
    "isADLSGen2": false,
    "ingestionParameters": {
      "disableImageVerbalization": false,
      "ingestionPermissionOptions": [],
      "contentExtractionMode": "standard",
      "identity": null,
      "embeddingModel": {
        "kind": "azureOpenAI",
        "azureOpenAIParameters": {
          "resourceUri": "<REDACTED>",
          "deploymentId": "text-embedding-3-large",
          "apiKey": "<REDACTED>",
          "modelName": "text-embedding-3-large",
          "authIdentity": null
        }
      },
      "chatCompletionModel": {
        "kind": "azureOpenAI",
        "azureOpenAIParameters": {
          "resourceUri": "<REDACTED>",
          "deploymentId": "gpt-5-mini",
          "apiKey": "<REDACTED>",
          "modelName": "gpt-5-mini",
          "authIdentity": null
        }
      },
      "ingestionSchedule": null,
      "assetStore": null,
      "aiServices": {
        "uri": "<REDACTED>",
        "apiKey": "<REDACTED>"
      }
    },
    "createdResources": {
      "datasource": "my-blob-ks-datasource",
      "indexer": "my-blob-ks-indexer",
      "skillset": "my-blob-ks-skillset",
      "index": "my-blob-ks-index"
    }
  }
}

Note

Sensitive information is redacted. The generated resources appear at the end of the response.

Create a knowledge source

Run the following code to create a blob knowledge source.

// Create a blob knowledge source
using Azure.Search.Documents.Indexes;
using Azure.Search.Documents.Indexes.Models;
using Azure.Search.Documents.KnowledgeBases.Models;
using Azure;

var indexClient = new SearchIndexClient(new Uri(searchEndpoint), new AzureKeyCredential(apiKey));

var chatCompletionParams = new AzureOpenAIVectorizerParameters
{
    ResourceUri = new Uri(aoaiEndpoint),
    DeploymentName = aoaiGptDeployment,
    ModelName = aoaiGptModel
};

var embeddingParams = new AzureOpenAIVectorizerParameters
{
    ResourceUri = new Uri(aoaiEndpoint),
    DeploymentName = aoaiEmbeddingDeployment,
    ModelName = aoaiEmbeddingModel
};

var ingestionParams = new KnowledgeSourceIngestionParameters
{
    DisableImageVerbalization = false,
    ChatCompletionModel = new KnowledgeBaseAzureOpenAIModel(azureOpenAIParameters: chatCompletionParams),
    EmbeddingModel = new KnowledgeSourceAzureOpenAIVectorizer
    {
        AzureOpenAIParameters = embeddingParams
    }
};

var blobParams = new AzureBlobKnowledgeSourceParameters(
    connectionString: connectionString,
    containerName: containerName
)
{
    IsAdlsGen2 = false,
    IngestionParameters = ingestionParams
};

var knowledgeSource = new AzureBlobKnowledgeSource(
    name: "my-blob-ks",
    azureBlobParameters: blobParams
)
{
    Description = "This knowledge source pulls from a blob storage container."
};

await indexClient.CreateOrUpdateKnowledgeSourceAsync(knowledgeSource);
Console.WriteLine($"Knowledge source '{knowledgeSource.Name}' created or updated successfully.");

Source-specific properties

You can pass the following properties to create a blob knowledge source.

Name Description Type Editable Required
name The name of the knowledge source, which must be unique within the knowledge sources collection and follow the naming guidelines for objects in Azure AI Search. String No Yes
Description A description of the knowledge source. String Yes No
encryptionKey A customer-managed key to encrypt sensitive information in both the knowledge source and the generated objects. Object Yes No
chatCompletionParams Parameters specific to chat completion models used for query planning and optional answer synthesis when the retrieval reasoning effort is low or medium. Object No
embeddingParams Parameters specific to embedding models used if you want to vectorize chunks of content. Object No
azureBlobParameters Parameters specific to blob knowledge sources: connectionString, containerName, folderPath, and isAdlsGen2. Object No
connectionString A key-based connection string or, if you're using a managed identity, the resource ID. String No Yes
containerName The name of the blob storage container. String No Yes
folderPath A folder within the container. String No No
isAdlsGen2 The default is False. Set to True if you're using an ADLS Gen2 storage account. Boolean No No

ingestionParameters properties

For indexed knowledge sources only, you can pass the following ingestionParameters properties to control how content is ingested and processed.

Name Description Type Editable Required
Identity A managed identity to use in the generated indexer. Object Yes No
DisableImageVerbalization Enables or disables the use of image verbalization. The default is False, which enables image verbalization. Set to True to disable image verbalization. Boolean No No
ChatCompletionModel A chat completion model that verbalizes images or extracts content. Supported models are gpt-4o, gpt-4o-mini, gpt-4.1, gpt-4.1-mini, gpt-4.1-nano, gpt-5, gpt-5-mini, and gpt-5-nano. The GenAI Prompt skill will be included in the generated skillset. Setting this parameter also requires that disable_image_verbalization is set to False. Object Only api_key and deployment_name are editable No
EmbeddingModel A text embedding model that vectorizes text and image content during indexing and at query time. Supported models are text-embedding-ada-002, text-embedding-3-small, and text-embedding-3-large. The Azure OpenAI Embedding skill will be included in the generated skillset, and the Azure OpenAI vectorizer will be included in the generated index. Object Only api_key and deployment_name are editable No
ContentExtractionMode Controls how content is extracted from files. The default is minimal, which uses standard content extraction for text and images. Set to standard for advanced document cracking and chunking using the Azure Content Understanding skill, which will be included in the generated skillset. For standard only, the AiServices and AssetStore parameters are specifiable. String No No
AiServices A Azure Foundry resource to access Azure Content Understanding in Foundry Tools. Setting this parameter requires that ContentExtractionMode is set to standard. Object Only api_key is editable Yes
IngestionSchedule Adds scheduling information to the generated indexer. You can also add a schedule later to automate data refresh. Object Yes No
IngestionPermissionOptions The document-level permissions to ingest from select knowledge sources: either ADLS Gen2. If you specify user_ids, group_ids, or rbac_scope, the generated ADLS Gen2 indexer will include the ingested permissions. Array No No

Check ingestion status

Run the following code to monitor ingestion progress and health, including indexer status for knowledge sources that generate an indexer pipeline and populate a search index.

// Get knowledge source ingestion status
using Azure.Search.Documents.Indexes;
using System.Text.Json;

var indexClient = new SearchIndexClient(new Uri(searchEndpoint), new AzureKeyCredential(apiKey));

// Get the knowledge source status
var statusResponse = await indexClient.GetKnowledgeSourceStatusAsync(knowledgeSourceName);
var status = statusResponse.Value;

// Serialize to JSON for display
var json = JsonSerializer.Serialize(status, new JsonSerializerOptions { WriteIndented = true });
Console.WriteLine(json);

A response for a request that includes ingestion parameters and is actively ingesting content might look like the following example.

{ 
  "synchronizationStatus": "active", // creating, active, deleting 
  "synchronizationInterval" : "1d", // null if no schedule 
  "currentSynchronizationState" : { // spans multiple indexer "runs" 
    "startTime": "2025-10-27T19:30:00Z", 
    "itemUpdatesProcessed": 1100, 
    "itemsUpdatesFailed": 100, 
    "itemsSkipped": 1100, 
  }, 
  "lastSynchronizationState" : {  // null on first sync 
    "startTime": "2025-10-27T19:30:00Z", 
    "endTime": "2025-10-27T19:40:01Z", // this value appears on the activity record on each /retrieve 
    "itemUpdatesProcessed": 1100, 
    "itemsUpdatesFailed": 100, 
    "itemsSkipped": 1100, 
  }, 
  "statistics": {  // null on first sync 
    "totalSynchronization": 25, 
    "averageSynchronizationDuration": "00:15:20", 
    "averageItemsProcessedPerSynchronization" : 500 
  } 
} 

Review the created objects

When you create a blob knowledge source, your search service also creates an indexer, index, skillset, and data source. We don't recommend that you edit these objects, as introducing an error or incompatibility can break the pipeline.

After you create a knowledge source, the response lists the created objects. These objects are created according to a fixed template, and their names are based on the name of the knowledge source. You can't change the object names.

We recommend using the Azure portal to validate output creation. The workflow is:

  1. Check the indexer for success or failure messages. Connection or quota errors appear here.
  2. Check the index for searchable content. Use Search Explorer to run queries.
  3. Check the skillset to learn how your content is chunked and optionally vectorized.
  4. Check the data source for connection details. Our example uses API keys for simplicity, but you can use Microsoft Entra ID for authentication and role-based access control for authorization.

Assign to a knowledge base

If you're satisfied with the knowledge source, continue to the next step: specify the knowledge source in a knowledge base.

After the knowledge base is configured, use the retrieve action to query the knowledge source.

Delete a knowledge source

Before you can delete a knowledge source, you must delete any knowledge base that references it or update the knowledge base definition to remove the reference. For knowledge sources that generate an index and indexer pipeline, all generated objects are also deleted. However, if you used an existing index to create a knowledge source, your index isn't deleted.

If you try to delete a knowledge source that's in use, the action fails and returns a list of affected knowledge bases.

To delete a knowledge source:

  1. Get a list of all knowledge bases on your search service.

    using Azure.Search.Documents.Indexes;
    
    var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
    var knowledgeBases = indexClient.GetKnowledgeBasesAsync();
    
    Console.WriteLine("Knowledge Bases:");
    
    await foreach (var kb in knowledgeBases)
    {
        Console.WriteLine($"  - {kb.Name}");
    }
    

    An example response might look like the following:

     Knowledge Bases:
       - earth-knowledge-base
       - hotels-sample-knowledge-base
       - my-demo-knowledge-base
    
  2. Get an individual knowledge base definition to check for knowledge source references.

    using Azure.Search.Documents.Indexes;
    using System.Text.Json;
    
    var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
    
    // Specify the knowledge base name to retrieve
    string kbNameToGet = "earth-knowledge-base";
    
    // Get a specific knowledge base definition
    var knowledgeBaseResponse = await indexClient.GetKnowledgeBaseAsync(kbNameToGet);
    var kb = knowledgeBaseResponse.Value;
    
    // Serialize to JSON for display
    string json = JsonSerializer.Serialize(kb, new JsonSerializerOptions { WriteIndented = true });
    Console.WriteLine(json);
    

    An example response might look like the following:

     {
       "Name": "earth-knowledge-base",
       "KnowledgeSources": [
         {
           "Name": "earth-knowledge-source"
         }
       ],
       "Models": [
         {}
       ],
       "RetrievalReasoningEffort": {},
       "OutputMode": {},
       "ETag": "\u00220x8DE278629D782B3\u0022",
       "EncryptionKey": null,
       "Description": null,
       "RetrievalInstructions": null,
       "AnswerInstructions": null
     }
    
  3. Either delete the knowledge base or update the knowledge base to remove the knowledge source if you have multiple sources. This example shows deletion.

    using Azure.Search.Documents.Indexes;
    var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
    
    await indexClient.DeleteKnowledgeBaseAsync(knowledgeBaseName);
    System.Console.WriteLine($"Knowledge base '{knowledgeBaseName}' deleted successfully.");
    
  4. Delete the knowledge source.

    await indexClient.DeleteKnowledgeSourceAsync(knowledgeSourceName);
    System.Console.WriteLine($"Knowledge source '{knowledgeSourceName}' deleted successfully.");
    

Note

This feature is currently in public preview. This preview is provided without a service-level agreement and isn't recommended for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Azure Previews.

Use a blob knowledge source to index and query Azure blob content in an agentic retrieval pipeline. Knowledge sources are created independently, referenced in a knowledge base, and used as grounding data when an agent or chatbot calls a retrieve action at query time.

Unlike a search index knowledge source, which specifies an existing and qualified index, a blob knowledge source specifies an external data source, models, and properties to automatically generate the following Azure AI Search objects:

  • A data source that represents a blob container.
  • A skillset that chunks and optionally vectorizes multimodal content from the container.
  • An index that stores enriched content and meets the criteria for agentic retrieval.
  • An indexer that uses the previous objects to drive the indexing and enrichment pipeline.

Note

If user access is specified at the document (blob) level in Azure Storage, a knowledge source can carry permission metadata forward to indexed content in Azure AI Search. For more information, see ADLS Gen2 permission metadata or Blob RBAC scopes.

Prerequisites

Note

Although you can use the Azure portal to create blob knowledge sources, the portal uses the 2025-08-01-preview, which uses the previous "knowledge agent" terminology and doesn't support all 2025-11-01-preview features. For help with breaking changes, see Migrate your agentic retrieval code.

Check for existing knowledge sources

A knowledge source is a top-level, reusable object. Knowing about existing knowledge sources is helpful for either reuse or naming new objects.

Run the following code to list knowledge sources by name and type.

# List knowledge sources by name and type
import requests
import json

endpoint = "{search_url}/knowledgesources"
params = {"api-version": "2025-11-01-preview", "$select": "name, kind"}
headers = {"api-key": "{api_key}"}

response = requests.get(endpoint, params = params, headers = headers)
print(json.dumps(response.json(), indent = 2))

You can also return a single knowledge source by name to review its JSON definition.

# Get a knowledge source definition
import requests
import json

endpoint = "{search_url}/knowledgesources/{knowledge_source_name}"
params = {"api-version": "2025-11-01-preview"}
headers = {"api-key": "{api_key}"}

response = requests.get(endpoint, params = params, headers = headers)
print(json.dumps(response.json(), indent = 2))

The following JSON is an example response for a blob knowledge source.

{
  "name": "my-blob-ks",
  "kind": "azureBlob",
  "description": "A sample blob knowledge source.",
  "encryptionKey": null,
  "azureBlobParameters": {
    "connectionString": "<REDACTED>",
    "containerName": "blobcontainer",
    "folderPath": null,
    "isADLSGen2": false,
    "ingestionParameters": {
      "disableImageVerbalization": false,
      "ingestionPermissionOptions": [],
      "contentExtractionMode": "standard",
      "identity": null,
      "embeddingModel": {
        "kind": "azureOpenAI",
        "azureOpenAIParameters": {
          "resourceUri": "<REDACTED>",
          "deploymentId": "text-embedding-3-large",
          "apiKey": "<REDACTED>",
          "modelName": "text-embedding-3-large",
          "authIdentity": null
        }
      },
      "chatCompletionModel": {
        "kind": "azureOpenAI",
        "azureOpenAIParameters": {
          "resourceUri": "<REDACTED>",
          "deploymentId": "gpt-5-mini",
          "apiKey": "<REDACTED>",
          "modelName": "gpt-5-mini",
          "authIdentity": null
        }
      },
      "ingestionSchedule": null,
      "assetStore": null,
      "aiServices": {
        "uri": "<REDACTED>",
        "apiKey": "<REDACTED>"
      }
    },
    "createdResources": {
      "datasource": "my-blob-ks-datasource",
      "indexer": "my-blob-ks-indexer",
      "skillset": "my-blob-ks-skillset",
      "index": "my-blob-ks-index"
    }
  }
}

Note

Sensitive information is redacted. The generated resources appear at the end of the response.

Create a knowledge source

Run the following code to create a blob knowledge source.

# Create a blob knowledge source
from azure.core.credentials import AzureKeyCredential
from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import AzureBlobKnowledgeSource, AzureBlobKnowledgeSourceParameters, KnowledgeBaseAzureOpenAIModel, AzureOpenAIVectorizerParameters, KnowledgeSourceAzureOpenAIVectorizer, KnowledgeSourceContentExtractionMode, KnowledgeSourceIngestionParameters

index_client = SearchIndexClient(endpoint = "search_url", credential = AzureKeyCredential("api_key"))

knowledge_source = AzureBlobKnowledgeSource(
    name = "my-blob-ks",
    description = "This knowledge source pulls from a blob storage container.",
    encryption_key = None,
    azure_blob_parameters = AzureBlobKnowledgeSourceParameters(
        connection_string = "blob_connection_string",
        container_name = "blob_container_name",
        folder_path = None,
        is_adls_gen2 = False,
        ingestion_parameters = KnowledgeSourceIngestionParameters(
            identity = None,
            disable_image_verbalization = False,
            chat_completion_model = KnowledgeBaseAzureOpenAIModel(
                azure_open_ai_parameters = AzureOpenAIVectorizerParameters(
                    # TRIMMED FOR BREVITY
                )
            ),
            embedding_model = KnowledgeSourceAzureOpenAIVectorizer(
                azure_open_ai_parameters=AzureOpenAIVectorizerParameters(
                    # TRIMMED FOR BREVITY
                )
            ),
            content_extraction_mode = KnowledgeSourceContentExtractionMode.MINIMAL,
            ingestion_schedule = None,
            ingestion_permission_options = None
        )
    )
)

index_client.create_or_update_knowledge_source(knowledge_source)
print(f"Knowledge source '{knowledge_source.name}' created or updated successfully.")

Source-specific properties

You can pass the following properties to create a blob knowledge source.

Name Description Type Editable Required
name The name of the knowledge source, which must be unique within the knowledge sources collection and follow the naming guidelines for objects in Azure AI Search. String No Yes
description A description of the knowledge source. String Yes No
encryption_key A customer-managed key to encrypt sensitive information in both the knowledge source and the generated objects. Object Yes No
azure_blob_parameters Parameters specific to blob knowledge sources: connection_string, container_name, folder_path, and is_adls_gen2. Object No
connection_string A key-based connection string or, if you're using a managed identity, the resource ID. String No Yes
container_name The name of the blob storage container. String No Yes
folder_path A folder within the container. String No No
is_adls_gen2 The default is False. Set to True if you're using an ADLS Gen2 storage account. Boolean No No

ingestionParameters properties

For indexed knowledge sources only, you can pass the following ingestionParameters properties to control how content is ingested and processed.

Name Description Type Editable Required
identity A managed identity to use in the generated indexer. Object Yes No
disable_image_verbalization Enables or disables the use of image verbalization. The default is False, which enables image verbalization. Set to True to disable image verbalization. Boolean No No
chat_completion_model A chat completion model that verbalizes images or extracts content. Supported models are gpt-4o, gpt-4o-mini, gpt-4.1, gpt-4.1-mini, gpt-4.1-nano, gpt-5, gpt-5-mini, and gpt-5-nano. The GenAI Prompt skill will be included in the generated skillset. Setting this parameter also requires that disable_image_verbalization is set to False. Object Only api_key and deployment_name are editable No
embedding_model A text embedding model that vectorizes text and image content during indexing and at query time. Supported models are text-embedding-ada-002, text-embedding-3-small, and text-embedding-3-large. The Azure OpenAI Embedding skill will be included in the generated skillset, and the Azure OpenAI vectorizer will be included in the generated index. Object Only api_key and deployment_name are editable No
content_extraction_mode Controls how content is extracted from files. The default is minimal, which uses standard content extraction for text and images. Set to standard for advanced document cracking and chunking using the Azure Content Understanding skill, which will be included in the generated skillset. For standard only, the ai_services and asset_store parameters are specifiable. String No No
ai_services A Azure Foundry resource to access Azure Content Understanding in Foundry Tools. Setting this parameter requires that content_extraction_mode is set to standard. Object Only api_key is editable Yes
asset_store A blob container to store extracted images. Setting this parameter requires that content_extraction_mode is set to standard. Object No No
ingestion_schedule Adds scheduling information to the generated indexer. You can also add a schedule later to automate data refresh. Object Yes No
ingestion_permission_options The document-level permissions to ingest from select knowledge sources: either ADLS Gen2. If you specify user_ids, group_ids, or rbac_scope, the generated ADLS Gen2 indexer will include the ingested permissions. Array No No

Check ingestion status

Run the following code to monitor ingestion progress and health, including indexer status for knowledge sources that generate an indexer pipeline and populate a search index.

# Check knowledge source ingestion status
import requests
import json

endpoint = "{search_url}/knowledgesources/{knowledge_source_name}/status"
params = {"api-version": "2025-11-01-preview"}
headers = {"api-key": "{api_key}"}

response = requests.get(endpoint, params = params, headers = headers)
print(json.dumps(response.json(), indent = 2))

A response for a request that includes ingestion parameters and is actively ingesting content might look like the following example.

{ 
  "synchronizationStatus": "active", // creating, active, deleting 
  "synchronizationInterval" : "1d", // null if no schedule 
  "currentSynchronizationState" : { // spans multiple indexer "runs" 
    "startTime": "2025-10-27T19:30:00Z", 
    "itemUpdatesProcessed": 1100, 
    "itemsUpdatesFailed": 100, 
    "itemsSkipped": 1100, 
  }, 
  "lastSynchronizationState" : {  // null on first sync 
    "startTime": "2025-10-27T19:30:00Z", 
    "endTime": "2025-10-27T19:40:01Z", // this value appears on the activity record on each /retrieve 
    "itemUpdatesProcessed": 1100, 
    "itemsUpdatesFailed": 100, 
    "itemsSkipped": 1100, 
  }, 
  "statistics": {  // null on first sync 
    "totalSynchronization": 25, 
    "averageSynchronizationDuration": "00:15:20", 
    "averageItemsProcessedPerSynchronization" : 500 
  } 
} 

Review the created objects

When you create a blob knowledge source, your search service also creates an indexer, index, skillset, and data source. We don't recommend that you edit these objects, as introducing an error or incompatibility can break the pipeline.

After you create a knowledge source, the response lists the created objects. These objects are created according to a fixed template, and their names are based on the name of the knowledge source. You can't change the object names.

We recommend using the Azure portal to validate output creation. The workflow is:

  1. Check the indexer for success or failure messages. Connection or quota errors appear here.
  2. Check the index for searchable content. Use Search Explorer to run queries.
  3. Check the skillset to learn how your content is chunked and optionally vectorized.
  4. Check the data source for connection details. Our example uses API keys for simplicity, but you can use Microsoft Entra ID for authentication and role-based access control for authorization.

Assign to a knowledge base

If you're satisfied with the knowledge source, continue to the next step: specify the knowledge source in a knowledge base.

After the knowledge base is configured, use the retrieve action to query the knowledge source.

Delete a knowledge source

Before you can delete a knowledge source, you must delete any knowledge base that references it or update the knowledge base definition to remove the reference. For knowledge sources that generate an index and indexer pipeline, all generated objects are also deleted. However, if you used an existing index to create a knowledge source, your index isn't deleted.

If you try to delete a knowledge source that's in use, the action fails and returns a list of affected knowledge bases.

To delete a knowledge source:

  1. Get a list of all knowledge bases on your search service.

    # Get knowledge bases
    import requests
    import json
    
    endpoint = "{search_url}/knowledgebases"
    params = {"api-version": "2025-11-01-preview", "$select": "name"}
    headers = {"api-key": "{api_key}"}
    
    response = requests.get(endpoint, params = params, headers = headers)
    print(json.dumps(response.json(), indent = 2))
    

    An example response might look like the following:

     {
         "@odata.context": "https://my-search-service.search.azure.cn/$metadata#knowledgebases(name)",
         "value": [
         {
             "name": "my-kb"
         },
         {
             "name": "my-kb-2"
         }
         ]
     }
    
  2. Get an individual knowledge base definition to check for knowledge source references.

    # Get a knowledge base definition
    import requests
    import json
    
    endpoint = "{search_url}/knowledgebases/{knowledge_base_name}"
    params = {"api-version": "2025-11-01-preview"}
    headers = {"api-key": "{api_key}"}
    
    response = requests.get(endpoint, params = params, headers = headers)
    print(json.dumps(response.json(), indent = 2))
    

    An example response might look like the following:

     {
       "name": "my-kb",
       "description": null,
       "retrievalInstructions": null,
       "answerInstructions": null,
       "outputMode": null,
       "knowledgeSources": [
         {
           "name": "my-blob-ks",
         }
       ],
       "models": [],
       "encryptionKey": null,
       "retrievalReasoningEffort": {
         "kind": "low"
       }
     }
    
  3. Either delete the knowledge base or update the knowledge base to remove the knowledge source if you have multiple sources. This example shows deletion.

    # Delete a knowledge base
    from azure.core.credentials import AzureKeyCredential 
    from azure.search.documents.indexes import SearchIndexClient
    
    index_client = SearchIndexClient(endpoint = "search_url", credential = AzureKeyCredential("api_key"))
    index_client.delete_knowledge_base("knowledge_base_name")
    print(f"Knowledge base deleted successfully.")
    
  4. Delete the knowledge source.

    # Delete a knowledge source
    from azure.core.credentials import AzureKeyCredential 
    from azure.search.documents.indexes import SearchIndexClient
    
    index_client = SearchIndexClient(endpoint = "search_url", credential = AzureKeyCredential("api_key"))
    index_client.delete_knowledge_source("knowledge_source_name")
    print(f"Knowledge source deleted successfully.")
    

Note

This feature is currently in public preview. This preview is provided without a service-level agreement and isn't recommended for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Azure Previews.

Use a blob knowledge source to index and query Azure blob content in an agentic retrieval pipeline. Knowledge sources are created independently, referenced in a knowledge base, and used as grounding data when an agent or chatbot calls a retrieve action at query time.

Unlike a search index knowledge source, which specifies an existing and qualified index, a blob knowledge source specifies an external data source, models, and properties to automatically generate the following Azure AI Search objects:

  • A data source that represents a blob container.
  • A skillset that chunks and optionally vectorizes multimodal content from the container.
  • An index that stores enriched content and meets the criteria for agentic retrieval.
  • An indexer that uses the previous objects to drive the indexing and enrichment pipeline.

Note

If user access is specified at the document (blob) level in Azure Storage, a knowledge source can carry permission metadata forward to indexed content in Azure AI Search. For more information, see ADLS Gen2 permission metadata or Blob RBAC scopes.

Prerequisites

Note

Although you can use the Azure portal to create blob knowledge sources, the portal uses the 2025-08-01-preview, which uses the previous "knowledge agent" terminology and doesn't support all 2025-11-01-preview features. For help with breaking changes, see Migrate your agentic retrieval code.

Check for existing knowledge sources

A knowledge source is a top-level, reusable object. Knowing about existing knowledge sources is helpful for either reuse or naming new objects.

Use Knowledge Sources - Get (REST API) to list knowledge sources by name and type.

### List knowledge sources by name and type
GET {{search-url}}/knowledgesources?api-version=2025-11-01-preview&$select=name,kind
api-key: {{api-key}}

You can also return a single knowledge source by name to review its JSON definition.

### Get a knowledge source definition
GET {{search-url}}/knowledgesources/{{knowledge-source-name}}?api-version=2025-11-01-preview
api-key: {{api-key}}

The following JSON is an example response for a blob knowledge source.

{
  "name": "my-blob-ks",
  "kind": "azureBlob",
  "description": "A sample blob knowledge source.",
  "encryptionKey": null,
  "azureBlobParameters": {
    "connectionString": "<REDACTED>",
    "containerName": "blobcontainer",
    "folderPath": null,
    "isADLSGen2": false,
    "ingestionParameters": {
      "disableImageVerbalization": false,
      "ingestionPermissionOptions": [],
      "contentExtractionMode": "standard",
      "identity": null,
      "embeddingModel": {
        "kind": "azureOpenAI",
        "azureOpenAIParameters": {
          "resourceUri": "<REDACTED>",
          "deploymentId": "text-embedding-3-large",
          "apiKey": "<REDACTED>",
          "modelName": "text-embedding-3-large",
          "authIdentity": null
        }
      },
      "chatCompletionModel": {
        "kind": "azureOpenAI",
        "azureOpenAIParameters": {
          "resourceUri": "<REDACTED>",
          "deploymentId": "gpt-5-mini",
          "apiKey": "<REDACTED>",
          "modelName": "gpt-5-mini",
          "authIdentity": null
        }
      },
      "ingestionSchedule": null,
      "assetStore": null,
      "aiServices": {
        "uri": "<REDACTED>",
        "apiKey": "<REDACTED>"
      }
    },
    "createdResources": {
      "datasource": "my-blob-ks-datasource",
      "indexer": "my-blob-ks-indexer",
      "skillset": "my-blob-ks-skillset",
      "index": "my-blob-ks-index"
    }
  }
}

Note

Sensitive information is redacted. The generated resources appear at the end of the response.

Create a knowledge source

Use Knowledge Sources - Create or Update (REST API) to create a blob knowledge source.

PUT {{search-url}}/knowledgesources/my-blob-ks?api-version=2025-11-01-preview
api-key: {{api-key}}
Content-Type: application/json

{
  "name": "my-blob-ks",
  "kind": "azureBlob",
  "description": "This knowledge source pulls from a blob storage container.",
  "encryptionKey": null,
  "azureBlobParameters": {
    "connectionString": "<YOUR AZURE STORAGE CONNECTION STRING>",
    "containerName": "<YOUR BLOB CONTAINER NAME>",
    "folderPath": null,
    "isADLSGen2": false,
    "ingestionParameters": {
        "identity": null,
        "disableImageVerbalization": null,
        "chatCompletionModel": { TRIMMED FOR BREVITY },
        "embeddingModel": { TRIMMED FOR BREVITY },
        "contentExtractionMode": "minimal",
        "ingestionSchedule": null,
        "ingestionPermissionOptions": []
    }
  }
}

Source-specific properties

You can pass the following properties to create a blob knowledge source.

Name Description Type Editable Required
name The name of the knowledge source, which must be unique within the knowledge sources collection and follow the naming guidelines for objects in Azure AI Search. String No Yes
kind The kind of knowledge source, which is azureBlob in this case. String No Yes
description A description of the knowledge source. String Yes No
encryptionKey A customer-managed key to encrypt sensitive information in both the knowledge source and the generated objects. Object Yes No
azureBlobParameters Parameters specific to blob knowledge sources: connectionString, containerName, folderPath, and isADLSGen2. Object No
connectionString A key-based connection string or, if you're using a managed identity, the resource ID. String No Yes
containerName The name of the blob storage container. String No Yes
folderPath A folder within the container. String No No
isADLSGen2 The default is false. Set to true if you're using an ADLS Gen2 storage account. Boolean No No

ingestionParameters properties

For indexed knowledge sources only, you can pass the following ingestionParameters properties to control how content is ingested and processed.

Name Description Type Editable Required
identity A managed identity to use in the generated indexer. Object Yes No
disableImageVerbalization Enables or disables the use of image verbalization. The default is false, which enables image verbalization. Set to true to disable image verbalization. Boolean No No
chatCompletionModel A chat completion model that verbalizes images or extracts content. Supported models are gpt-4o, gpt-4o-mini, gpt-4.1, gpt-4.1-mini, gpt-4.1-nano, gpt-5, gpt-5-mini, and gpt-5-nano. The GenAI Prompt skill will be included in the generated skillset. Setting this parameter also requires that disableImageVerbalization is set to false. Object Only apiKey and deploymentId are editable No
embeddingModel A text embedding model that vectorizes text and image content during indexing and at query time. Supported models are text-embedding-ada-002, text-embedding-3-small, and text-embedding-3-large. The Azure OpenAI Embedding skill will be included in the generated skillset, and the Azure OpenAI vectorizer will be included in the generated index. Object Only apiKey and deploymentId are editable No
contentExtractionMode Controls how content is extracted from files. The default is minimal, which uses standard content extraction for text and images. Set to standard for advanced document cracking and chunking using the Azure Content Understanding skill, which will be included in the generated skillset. For standard only, the aiServices and assetStore parameters are specifiable. String No No
aiServices A Azure Foundry resource to access Azure Content Understanding in Foundry Tools. Setting this parameter requires that contentExtractionMode is set to standard. Object Only apiKey is editable Yes
assetStore A blob container to store extracted images. Setting this parameter requires that contentExtractionMode is set to standard. Object No No
ingestionSchedule Adds scheduling information to the generated indexer. You can also add a schedule later to automate data refresh. Object Yes No
ingestionPermissionOptions The document-level permissions to ingest from select knowledge sources: either ADLS Gen2. If you specify userIds, groupIds, or rbacScope, the generated ADLS Gen2 indexer will include the ingested permissions. Array No No

Check ingestion status

Use Knowledge Sources - Status (REST API) to monitor ingestion progress and health, including indexer status for knowledge sources that generate an indexer pipeline and populate a search index.

### Check knowledge source ingestion status
GET {{search-url}}/knowledgesources/{{knowledge-source-name}}/status?api-version=2025-11-01-preview
api-key: {{api-key}}
Content-Type: application/json 

A response for a request that includes ingestion parameters and is actively ingesting content might look like the following example.

{ 
  "synchronizationStatus": "active", // creating, active, deleting 
  "synchronizationInterval" : "1d", // null if no schedule 
  "currentSynchronizationState" : { // spans multiple indexer "runs" 
    "startTime": "2025-10-27T19:30:00Z", 
    "itemUpdatesProcessed": 1100, 
    "itemsUpdatesFailed": 100, 
    "itemsSkipped": 1100, 
  }, 
  "lastSynchronizationState" : {  // null on first sync 
    "startTime": "2025-10-27T19:30:00Z", 
    "endTime": "2025-10-27T19:40:01Z", // this value appears on the activity record on each /retrieve 
    "itemUpdatesProcessed": 1100, 
    "itemsUpdatesFailed": 100, 
    "itemsSkipped": 1100, 
  }, 
  "statistics": {  // null on first sync 
    "totalSynchronization": 25, 
    "averageSynchronizationDuration": "00:15:20", 
    "averageItemsProcessedPerSynchronization" : 500 
  } 
} 

Review the created objects

When you create a blob knowledge source, your search service also creates an indexer, index, skillset, and data source. We don't recommend that you edit these objects, as introducing an error or incompatibility can break the pipeline.

After you create a knowledge source, the response lists the created objects. These objects are created according to a fixed template, and their names are based on the name of the knowledge source. You can't change the object names.

We recommend using the Azure portal to validate output creation. The workflow is:

  1. Check the indexer for success or failure messages. Connection or quota errors appear here.
  2. Check the index for searchable content. Use Search Explorer to run queries.
  3. Check the skillset to learn how your content is chunked and optionally vectorized.
  4. Check the data source for connection details. Our example uses API keys for simplicity, but you can use Microsoft Entra ID for authentication and role-based access control for authorization.

Assign to a knowledge base

If you're satisfied with the knowledge source, continue to the next step: specify the knowledge source in a knowledge base.

After the knowledge base is configured, use the retrieve action to query the knowledge source.

Delete a knowledge source

Before you can delete a knowledge source, you must delete any knowledge base that references it or update the knowledge base definition to remove the reference. For knowledge sources that generate an index and indexer pipeline, all generated objects are also deleted. However, if you used an existing index to create a knowledge source, your index isn't deleted.

If you try to delete a knowledge source that's in use, the action fails and returns a list of affected knowledge bases.

To delete a knowledge source:

  1. Get a list of all knowledge bases on your search service.

    ### Get knowledge bases
    GET {{search-endpoint}}/knowledgebases?api-version=2025-11-01-preview&$select=name
    api-key: {{api-key}}
    

    An example response might look like the following:

     {
         "@odata.context": "https://my-search-service.search.azure.cn/$metadata#knowledgebases(name)",
         "value": [
         {
             "name": "my-kb"
         },
         {
             "name": "my-kb-2"
         }
         ]
     }
    
  2. Get an individual knowledge base definition to check for knowledge source references.

    ### Get a knowledge base definition
    GET {{search-endpoint}}/knowledgebases/{{knowledge-base-name}}?api-version=2025-11-01-preview
    api-key: {{api-key}}
    

    An example response might look like the following:

     {
       "name": "my-kb",
       "description": null,
       "retrievalInstructions": null,
       "answerInstructions": null,
       "outputMode": null,
       "knowledgeSources": [
         {
           "name": "my-blob-ks",
         }
       ],
       "models": [],
       "encryptionKey": null,
       "retrievalReasoningEffort": {
         "kind": "low"
       }
     }
    
  3. Either delete the knowledge base or update the knowledge base by removing the knowledge source if you have multiple sources. This example shows deletion.

    ### Delete a knowledge base
    DELETE {{search-endpoint}}/knowledgebases/{{knowledge-base-name}}?api-version=2025-11-01-preview
    api-key: {{api-key}}
    
  4. Delete the knowledge source.

    ### Delete a knowledge source
    DELETE {{search-endpoint}}/knowledgesources/{{knowledge-source-name}}?api-version=2025-11-01-preview
    api-key: {{api-key}}