> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Azure Blob Storage Content Source for Knowledge

> Load files and folders from Azure Blob Storage containers into your Knowledge base.

Load files and folders from Azure Blob Storage containers into your Knowledge base. Supports Service Principal and SAS (Shared Access Signature) token authentication.

<Tabs>
  <Tab title="Service Principal">
    ```python theme={null}
    """
    Azure Blob Storage Content Source for Knowledge
    ================================================

    Load files and folders from Azure Blob Storage containers into your Knowledge base.
    Uses Azure AD client credentials flow for authentication.

    Features:
    - Load single blobs or entire prefixes (folders) recursively
    - Supports any Azure Storage Account
    - Automatic file type detection and reader selection
    - Rich metadata stored for each file (storage account, container, path)

    Requirements:
    - Azure AD App Registration with:
      - Application (client) ID
      - Client secret
      - Storage Blob Data Reader role on the storage account
    - Storage account name and container name

    Setup:
        1. Register an app in Azure AD (portal.azure.com)
        2. Assign "Storage Blob Data Reader" role to the app on your storage account
        3. Create a client secret
        4. Set environment variables (see below)

    Environment Variables:
        AZURE_TENANT_ID           - Azure AD tenant ID
        AZURE_CLIENT_ID           - App registration client ID
        AZURE_CLIENT_SECRET       - App registration client secret
        AZURE_STORAGE_ACCOUNT_NAME - Storage account name (without .blob.core.windows.net)
        AZURE_CONTAINER_NAME      - Container name

    Run this cookbook:
        python cookbook/07_knowledge/cloud/azure_blob.py
    """

    from os import getenv

    from agno.knowledge.knowledge import Knowledge
    from agno.knowledge.remote_content import AzureBlobConfig
    from agno.vectordb.pgvector import PgVector

    # Configure Azure Blob Storage content source
    # All credentials should come from environment variables
    azure_config = AzureBlobConfig(
        id="company-docs",
        name="Company Documents",
        tenant_id=getenv("AZURE_TENANT_ID"),
        client_id=getenv("AZURE_CLIENT_ID"),
        client_secret=getenv("AZURE_CLIENT_SECRET"),
        storage_account=getenv("AZURE_STORAGE_ACCOUNT_NAME"),
        container=getenv("AZURE_CONTAINER_NAME"),
    )

    # Create Knowledge with Azure Blob Storage as a content source
    knowledge = Knowledge(
        name="Azure Blob Knowledge",
        vector_db=PgVector(
            table_name="azure_blob_knowledge",
            db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        ),
        content_sources=[azure_config],
    )

    if __name__ == "__main__":
        # Insert a single file from Azure Blob Storage
        print("Inserting single file from Azure Blob Storage...")
        knowledge.insert(
            name="DeepSeek Paper",
            remote_content=azure_config.file("DeepSeek_R1.pdf"),
        )

        # Insert an entire folder (prefix)
        print("Inserting folder from Azure Blob Storage...")
        knowledge.insert(
            name="Research Papers",
            remote_content=azure_config.folder("testfolder/"),
        )

        # Search the knowledge base
        print("Searching knowledge base...")
        results = knowledge.search("What is DeepSeek?")
        for doc in results:
            print(f"- {doc.name}: {doc.content[:100]}...")
    ```
  </Tab>

  <Tab title="SAS Token">
    ```python theme={null}
    """
    Azure Integration: Blob Storage (SAS Token)
    =============================================
    Load files and folders from Azure Blob Storage containers using SAS token authentication.

    Features:
    - Load single files or entire prefixes (folders)
    - Uses SAS (Shared Access Signature) token for authentication

    Requirements:
    - Azure Storage Account with a SAS token

    Environment Variables:
        AZURE_SAS_TOKEN            - SAS token
        AZURE_STORAGE_ACCOUNT_NAME - Storage account name
        AZURE_CONTAINER_NAME       - Container name

    Run `uv pip install azure-storage-blob` to install dependencies.
    """

    import asyncio
    from os import getenv

    from agno.knowledge.knowledge import Knowledge
    from agno.knowledge.remote_content import AzureBlobConfig
    from agno.vectordb.chroma import ChromaDb

    azure_blob = AzureBlobConfig(
        id="company-blob-sas",
        name="Company Blob Storage SAS",
        sas_token=getenv("AZURE_SAS_TOKEN"),
        storage_account=getenv("AZURE_STORAGE_ACCOUNT_NAME"),
        container=getenv("AZURE_CONTAINER_NAME"),
    )

    knowledge = Knowledge(
        name="Azure Blob Knowledge (SAS)",
        vector_db=ChromaDb(
            collection="azure_blob_knowledge_sas",
        ),
        content_sources=[azure_blob],
    )

    if __name__ == "__main__":

        async def main():
            await knowledge.ainsert(
                name="Report",
                remote_content=azure_blob.file("reports/annual-report.pdf"),
            )

            await knowledge.ainsert(
                name="All Docs",
                remote_content=azure_blob.folder("documents/"),
            )

            results = knowledge.search("What were the annual results?")
            for doc in results:
                print("- %s" % doc.name)

        asyncio.run(main())
    ```
  </Tab>
</Tabs>

## Run the Example

```bash theme={null}
# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/07_knowledge/cloud

# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate

# Optiona: Run PgVector (needs docker)
./cookbook/scripts/run_pgvector.sh

python azure_blob.py
```