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Azure AI Data

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 import AIClient
from azure.identity import DefaultAzureCredential
from langchain_community.document_loaders import AzureAIDataLoader
API Reference:AzureAIDataLoader
# Create a connection to your project
client = AIClient(
# get the latest version of your data asset
data_asset ="<data_asset_name>", label="latest")
# load the data asset
loader = AzureAIDataLoader(url=data_asset.path)
[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 finegrained 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")
[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)]

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