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. Uses Azure AD client credentials flow for authentication.

"""
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]}...")

Run the Example

# 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