Organize a SharePoint Library

Enrich at the source. This cookbook connects a SharePoint document library, tags its documents with AI-extracted metadata, and writes the results back to the same library as native SharePoint columns. Users keep working where they always have, but now they can filter, sort, and search by contract type, dates, and value. A scheduled workflow keeps the columns current as documents change.

What You’ll Build

Prerequisites

A SharePoint site with a documents library and an Azure app registration (client ID, client secret, tenant ID)
Python 3.9+

pip install unstructured_sdk-*.whl

Step 1. Connect the SharePoint Source

from unstructured import UnstructuredClient

client = UnstructuredClient(
    base_url="https://unstructured.your-company.com/rest/unstructured",
    username="your-username",
    password="your-password",
)

source = client.data_source.create(
    connector_name="legal-library",
    connector_body={
        "type": "SharepointDataSourceManager",
        "name": "legal-library",
        "client_id": "YOUR_CLIENT_ID",
        "client_secret": "YOUR_CLIENT_SECRET",
        "tenant_id": "YOUR_TENANT_ID",
        "sharepoint_site_name": "LegalDocuments",
    },
)
print(f"✓ Connected source: {source.profile_id}")

Step 2. Ingest the Library

Ingestion runs as a background job and automatically captures source metadata (Last Modified, File Type, Created By, Folder Structure) on every document.

import time
import uuid

ingest_job = str(uuid.uuid4())
client.data_source.ingest(
    data_connector_name="legal-library",
    job_id=ingest_job,
)
while client.task_status.get_status(job_id=ingest_job).status == "in_progress":
    time.sleep(10)
print("✓ Ingestion complete")

Step 3. Define the Columns You Want

Each tag becomes a SharePoint column. Classification extracts the values with evidence and confidence.

for tag in [
    {
        "name": "contract_type",
        "description": "Type of contract (NDA, MSA, SLA, Employment, etc.)",
        "output_type": "string",
        "available_values": ["NDA", "MSA", "SLA", "Employment", "Other"],
    },
    {
        "name": "effective_date",
        "description": "When the contract becomes effective",
        "output_type": "date",
    },
    {
        "name": "expiration_date",
        "description": "When the contract expires or terminates",
        "output_type": "date",
    },
    {
        "name": "total_value",
        "description": "Total monetary value of the contract if specified",
        "output_type": "number",
    },
]:
    client.tags.upsert(tag_data=tag)

classify_job = str(uuid.uuid4())
client.metadata.generate.generate_batch(
    data_connector_name="legal-library",
    tag_names=["contract_type", "effective_date", "expiration_date", "total_value"],
    job_id=classify_job,
)
while True:
    progress = client.task_status.get_status(job_id=classify_job)
    if progress.status in ("completed", "failed", "aborted"):
        break
    print(f"  Classification {progress.percent_complete:.0f}%...")
    time.sleep(10)
print(f"✓ Classification {progress.status}")

Step 4. Write the Columns Back

The destination points at the same site. column_store creates one SharePoint column per exported tag.

client.destination.create(
    connector_name="legal-library-columns",
    connector_body={
        "type": "SharepointNodeDestinationManager",
        "name": "legal-library-columns",
        "client_id": "YOUR_CLIENT_ID",
        "client_secret": "YOUR_CLIENT_SECRET",
        "tenant_id": "YOUR_TENANT_ID",
        "sharepoint_site_name": "LegalDocuments",
        "documents_library_folder_name": "Contracts",
    },
)

export_result = client.destination.export(
    destination_name="legal-library-columns",
    data_connector_name="legal-library",
    export_level="file",
    export_metadata=True,
    metadata_format="column_store",
    export_tags=["contract_type", "effective_date", "expiration_date", "total_value"],
)
print(f"✓ Exported {export_result.success} files ({export_result.failed} failed)")

What Users See in SharePoint

Document	Contract Type	Effective Date	Expiration Date	Total Value
Acme-NDA-2024.pdf	NDA	2024-01-15	2026-01-15	-
ServiceAgreement.pdf	MSA	2024-03-01	2027-03-01	$150,000
Employment-JDoe.pdf	Employment	2024-02-01	-	$95,000

Users can filter and sort by any column, build views like “Expiring This Quarter”, set alerts, and use SharePoint’s native search with metadata facets.

Step 5. Maintain It Over Time

New and changed documents should get columns without anyone re-running the flow. Schedule ingest and classify on a nightly cadence, the same shape the app’s predefined workflows use, then re-export on your own trigger or a second workflow stage.

client.workflows.upsert(
    workflow={
        "name": "Nightly maintain: legal-library",
        "description": "Ingest new documents and classify them every night",
        "cadence": "0 0 * * *",
        "stages": [
            {"jobs": [{"endpoint": "/ocr/ingest",
                       "endpoint_request_body": {"data_connector_name": "legal-library"}}]},
            {"jobs": [{"endpoint": "/classify_bulk",
                       "endpoint_request_body": {"data_connector_name": "legal-library"}}]},
        ],
    },
)
print("✓ Nightly maintenance scheduled")

Next Steps

Clean Up a RAG Index

Make an existing vector index AI-ready.

Prepare an AI-Ready Dataset

Gate what gets exported with quality signals.

Protect Sensitive Data

Detect PII before enriching shared libraries.

Workflows

Everything about scheduled maintenance.

Getting Started

Core Concepts

Cookbooks

Integrations

Organize a SharePoint Library

What You’ll Build

Prerequisites

Step 1. Connect the SharePoint Source

Step 2. Ingest the Library

Step 3. Define the Columns You Want

Step 4. Write the Columns Back

What Users See in SharePoint

Step 5. Maintain It Over Time

Next Steps

Clean Up a RAG Index

Prepare an AI-Ready Dataset

Protect Sensitive Data

Workflows

​What You’ll Build

​Prerequisites

​Step 1. Connect the SharePoint Source

​Step 2. Ingest the Library

​Step 3. Define the Columns You Want

​Step 4. Write the Columns Back

​What Users See in SharePoint

​Step 5. Maintain It Over Time

​Next Steps

Clean Up a RAG Index

Prepare an AI-Ready Dataset

Protect Sensitive Data

Workflows

What You’ll Build

Prerequisites

Step 1. Connect the SharePoint Source

Step 2. Ingest the Library

Step 3. Define the Columns You Want

Step 4. Write the Columns Back

What Users See in SharePoint

Step 5. Maintain It Over Time

Next Steps