Azure AI Studio provides the capability to upload data assets to cloud storage and register existing data assets from the following sources:
  • Microsoft OneLake
  • Azure Blob Storage
  • Azure Data Lake gen 2
The benefit of this approach over AzureBlobStorageContainerLoader and AzureBlobStorageFileLoader is that authentication is handled seamlessly to cloud storage. You can use either identity-based data access control to the data or credential-based (e.g. SAS token, account key). In the case of credential-based data access you do not need to specify secrets in your code or set up key vaults - the system handles that for you. This notebook covers how to load document objects from a data asset in AI Studio. 
%pip install --upgrade --quiet azureml-fsspec azure-ai-generative

from azure.ai.resources.client import AIClient
from azure.identity import DefaultAzureCredential
from langchain_community.document_loaders import AzureAIDataLoader

# Create a connection to your project
client = AIClient(
    credential=DefaultAzureCredential(),
    subscription_id="<subscription_id>",
    resource_group_name="<resource_group_name>",
    project_name="<project_name>",
)

# get the latest version of your data asset
data_asset = client.data.get(name="<data_asset_name>", label="latest")

# load the data asset
loader = AzureAIDataLoader(url=data_asset.path)

loader.load()

[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': '/var/folders/y6/8_bzdg295ld6s1_97_12m4lr0000gn/T/tmpaa9xl6ch/fake.docx'}, lookup_index=0)]

​
Specifying a glob pattern

You can also specify a glob pattern for more fine-grained control over what files to load. In the example below, only files with a pdf extension will be loaded. 
loader = AzureAIDataLoader(url=data_asset.path, glob="*.pdf")

loader.load()

[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': '/var/folders/y6/8_bzdg295ld6s1_97_12m4lr0000gn/T/tmpujbkzf_l/fake.docx'}, lookup_index=0)]