If you’re new to Unstructured, read this note first.Before you can create a destination connector, you must first sign up for Unstructured and get your Unstructured API key. After you sign up, the Unstructured user interface (UI) appears, which you use to get the key. To learn how, watch this 40-second how-to video.After you create the destination connector, add it along with a source connector to a workflow. Then run the worklow as a job. To learn how, try out the hands-on Workflow Endpoint quickstart, go directly to the quickstart notebook, or watch the two 4-minute video tutorials for the Unstructured Python SDK.You can also create destination connectors with the Unstructured user interface (UI). Learn how.If you need help, reach out to the community on Slack, or contact us directly.You are now ready to start creating a destination connector! Keep reading to learn how.
Send processed data from Unstructured to Qdrant. The requirements are as follows. The following video shows how to set up Qdrant Cloud:
  • For Qdrant local, the path to the local Qdrant installation, for example: /qdrant/local
  • For Qdrant client-server, the Qdrant server URL, for example: http://localhost:6333
  • For Qdrant Cloud:
    • A Qdrant account.
    • A Qdrant cluster.
    • The cluster’s URL. To get this URL, do the following:
      1. Sign in to your Qdrant Cloud account.
      2. On the sidebar, under Dashboard, click Clusters.
      3. Click the cluster’s name.
      4. Note the value of the Endpoint field, for example: https://<random-guid>.<region-id>.<cloud-provider>.cloud.qdrant.io.
    • A Qdrant API key.
  • The name of the target collection on the Qdrant local installation, Qdrant server, or Qdrant Cloud cluster. Qdrant requires the target collection to exist before Unstructured can write to the collection. The following example code demonstrates the use of the Python Qdrant Client to create a collection on a Qdrant Cloud cluster, configuring the collection for vectors with 3072 dimensions:
    Python
    from qdrant_client import QdrantClient, models
import os

client = QdrantClient(
    url=os.getenv("QDRANT_URL"),
    api_key=os.getenv("QDRANT_API_KEY")
)

client.create_collection(
    collection_name=os.getenv("QDRANT_COLLECTION"),
    vectors_config=models.VectorParams(
        size=3072,
        distance=models.Distance.COSINE
    )
)

collection = client.get_collection(
                 collection_name=os.getenv("QDRANT_COLLECTION")
             )

print(f"The collection named '{os.getenv("QDRANT_COLLECTION")}' exists and " +
      f"has a status of '{collection.status}'.")
To create a Qdrant destination connector, see the following examples. 
import os

from unstructured_client import UnstructuredClient
from unstructured_client.models.operations import CreateDestinationRequest
from unstructured_client.models.shared import (
    CreateDestinationConnector,
    DestinationConnectorType,
    QdrantCloudDestinationConnectorConfigInput
)

with UnstructuredClient(api_key_auth=os.getenv("UNSTRUCTURED_API_KEY")) as client:
    response = client.destinations.create_destination(
        request=CreateDestinationRequest(
            create_destination_connector=CreateDestinationConnector(
                name="<name>",
                type=DestinationConnectorType.QDRANT_CLOUD,
                config=QdrantCloudDestinationConnectorConfigInput(
                    url="<url>",
                    collection_name="<collection-name>",
                    batch_size=<batch-size>,
                    api_key="<api-key>"
                )
            )
        )
    )

    print(response.destination_connector_information)
Replace the preceding placeholders as follows:
  • <name> (required) - A unique name for this connector.
  • <url> (required) - The Qdrant cluster’s URL.
  • <collection-name> (required) - The name of the target collection on the Qdrant cluster.
  • <batch-size> - The maximum number of records to transmit at a time. The default is 50 if not otherwise specified.
  • <api-key> (required) - The Qdrant API key.