> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-devupd-1765394015-eccef47.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Azure AI Data

> [Azure AI Foundry (formerly Azure AI Studio](https://ai.azure.com/) 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.

```python theme={null}
pip install -qU azureml-fsspec azure-ai-generative
```

```python theme={null}
from azure.ai.resources.client import AIClient
from azure.identity import DefaultAzureCredential
from langchain_community.document_loaders import AzureAIDataLoader
```

```python theme={null}
# Create a connection to your project
client = AIClient(
    credential=DefaultAzureCredential(),
    subscription_id="<subscription_id>",
    resource_group_name="<resource_group_name>",
    project_name="<project_name>",
)
```

```python theme={null}
# get the latest version of your data asset
data_asset = client.data.get(name="<data_asset_name>", label="latest")
```

```python theme={null}
# load the data asset
loader = AzureAIDataLoader(url=data_asset.path)
```

```python theme={null}
loader.load()
```

```output theme={null}
[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.

```python theme={null}
loader = AzureAIDataLoader(url=data_asset.path, glob="*.pdf")
```

```python theme={null}
loader.load()
```

```output theme={null}
[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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