> ## Documentation Index
> Fetch the complete documentation index at: https://docs.deasylabs.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Organize a SharePoint Library
> Enrich a SharePoint library at the source with AI-extracted metadata columns and keep it maintained over time
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
```mermaid theme={null}
flowchart LR
SP[SharePoint Library
documents] --> DL[Deasy Labs
ingest + classify]
DL -->|column_store export| SP2[Same SharePoint Library
+ metadata columns]
CRON[Nightly workflow] -.->|maintain| DL
```
## Prerequisites
* A SharePoint site with a documents library and an Azure app registration (client ID, client secret, tenant ID)
* Python 3.9+
```bash theme={null}
pip install unstructured_sdk-*.whl
```
## Step 1. Connect the SharePoint Source
```python theme={null}
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.
```python theme={null}
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.
```python theme={null}
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.
```python theme={null}
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.
```python theme={null}
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
Make an existing vector index AI-ready.
Gate what gets exported with quality signals.
Detect PII before enriching shared libraries.
Everything about scheduled maintenance.