Destinations

Destinations are target systems where enriched metadata and document data can be exported. While Data Connectors bring documents into the platform, Destinations push enriched data out to vector databases, document management systems, and databases for downstream applications.

Supported Destinations

PostgreSQL

Relational database with vector support for hybrid search.

SharePoint

Enrich original documents with metadata columns in Microsoft 365.

OneDrive

Push enriched documents and metadata to OneDrive libraries.

Amazon S3

Write enriched output to S3 buckets.

Google Cloud Storage

Write enriched output to GCS buckets.

Qdrant

Serve curated chunks to RAG pipelines via slice export.

Configuration Details

Destination Type	Description	Key Configuration	Ideal Use Case
PostgreSQL	Relational database with vector support	Host URL, Port, Database, Collection, Credentials	Structured analytics, hybrid search
SharePoint	Microsoft 365 document management	Client ID/Secret, Tenant ID, Site Name	Enriching original documents with metadata columns
OneDrive	Microsoft OneDrive cloud storage	Client ID/Secret, Tenant ID, Site, Library folder	Team drives in Microsoft 365
Amazon S3	AWS cloud object storage	Bucket name, Access Key, Secret Key	Cloud-native archives and pipelines
Google Cloud Storage	GCP cloud object storage	Bucket, Service Account	GCP-native archives and pipelines
Qdrant	Vector database	URL, API Key, Collection	RAG serving collections

See the Integrations Overview for the complete matrix of sources, destinations, and supported file types.

To export a data slice into a vector database (e.g. Qdrant) for RAG pipelines, use client.data_slice.export_vdb(...). See Data Slices.

Export Options

Option	Description	Values
Export Level	What data granularity to export	`file` (document-level), `chunk` (segment-level), `both`
Export Tags	Specific metadata tags to include	List of tag names, or empty for all
Export Nodes	Include vector embeddings	`true` / `false`
Export Metadata	Include extracted metadata	`true` / `false`
Metadata Format	How metadata is stored	`column_store` (separate columns), `json_store` (single JSON column)

Export Processing:

Small Exports (< 100 files): Processed synchronously with immediate results
Large Exports (≥ 100 files): Processed in the background with progress tracking via tracker_id

How Destinations Work

Create a Destination

Configure the target system with the required credentials.

Select Export Options

Choose what data to export (file-level, chunk-level, specific tags).

Choose Metadata Format

Decide between column store (separate columns) or JSON store (single JSON column).

Run Export

The platform sends enriched data to your destination system.

Python SDK

Create Destination
Export Data
List Destinations
Delete Destination

from unstructured import UnstructuredClient

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

# Create a PostgreSQL destination
destination = client.destination.create(
    connector_name="my-postgres-destination",
    connector_body={
        "type": "PostgresNodeDestinationManager",
        "name": "my-postgres-destination",
        "url": "your-db-host.example.com",
        "port": 5432,
        "database_name": "analytics",
        "collection_name": "documents",
        "db_user": "postgres",
        "password": "YOUR_DB_PASSWORD",
    },
)
print(f"Created destination: {destination.profile_id}")

# Export enriched data to a destination
result = client.destination.export(
    destination_name="my-postgres-destination",
    data_connector_name="my-s3-bucket",
    export_level="chunk",       # "file", "chunk", or "both"
    export_metadata=True,       # Include extracted metadata
    metadata_format="json_store",
)
print(f"Exported {result.success} files ({result.failed} failed)")

# For large exports, track progress
if result.tracker_id:
    status = client.task_status.get_status(job_id=result.tracker_id)
    print(f"Export {status.percent_complete:.0f}% complete ({status.status})")

# List all destinations (returned as a dict keyed by destination name)
destinations = client.destination.list()
for name, config in destinations.connectors.items():
    print(name)

# Delete a destination
client.destination.delete(connector_name="my-postgres-destination")
print("Destination deleted")

API Reference

Create Destination

Create a new destination

List Destinations

List all your destinations

Delete Destination

Remove a destination

Export to Destination

Export enriched data to a destination

Getting Started

Core Concepts

Cookbooks

Integrations

Supported Destinations

PostgreSQL

SharePoint

OneDrive

Amazon S3

Google Cloud Storage

Qdrant

Configuration Details

Export Options

How Destinations Work

Python SDK

API Reference

Create Destination

List Destinations

Delete Destination

Export to Destination

​Supported Destinations

PostgreSQL

SharePoint

OneDrive

Amazon S3

Google Cloud Storage

Qdrant

​Configuration Details

​Export Options

​How Destinations Work

​Python SDK

​API Reference

Create Destination

List Destinations

Delete Destination

Export to Destination

Supported Destinations

Configuration Details

Export Options

How Destinations Work

Python SDK

API Reference