Weaviate

If you’re new to Unstructured, read this note first.Before you can create a destination connector, you must first sign in to your Unstructured account:

If you do not already have an Unstructured account, sign up for free. After you sign up, you are automatically signed in to your new Unstructured Let’s Go account, at https://platform.unstructured.io. To sign up for a Business account instead, contact Unstructured Sales, or learn more.
If you already have an Unstructured Let’s Go, Pay-As-You-Go, or Business SaaS account and are not already signed in, sign in to your account at https://platform.unstructured.io. For other types of Business accounts, see your Unstructured account administrator for sign-in instructions, or email Unstructured Support at support@unstructured.io.

After you sign in, the Unstructured user interface (UI) appears, which you use to get your Unstructured API key.

After you sign in to your Unstructured Let’s Go, Pay-As-You-Go, or Business account, click API Keys on the sidebar.
For a Business account, before you click API Keys, make sure you have selected the organizational workspace you want to create an API key for. Each API key works with one and only one organizational workspace. Learn more.
Click Generate New Key.
Follow the on-screen instructions to finish generating the key.
The generated key is displayed. Copy this key to a secure location, as you will not be able to access it again after you close the dialog. If you lose this key, you must generate a new one.

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 the notebook Dropbox-To-Pinecone Connector API Quickstart for Unstructured, 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, email Unstructured Support at support@unstructured.io.You are now ready to start creating a destination connector! Keep reading to learn how.

Send processed data from Unstructured to Weaviate. The requirements are as follows.

For the Unstructured UI or the Unstructured API: only Weaviate Cloud clusters are supported.
For Unstructured Ingest: Weaviate Cloud clusters, Weaviate installed locally, and Embedded Weaviate are supported.
For Weaviate installed locally, you will need the name of the target collection on the local instance.
For Embedded Weaviate, you will need the instance’s connection URL and the name of the target collection on the instance.
For Weaviate Cloud, you will need:
- A Weaviate database instance. The following information assumes that you have a Weaviate Cloud (WCD) account with a Weaviate database cluster in that account. Create a WCD account. Create a database cluster. For other database options, learn more.
- The URL and API key for the database cluster. Get the URL and API key.
- The name of the target collection in the database. Create a collection. An existing collection is not required. At runtime, the collection behavior is as follows: For the Unstructured UI or the Unstructured API:
  - If an existing collection name is specified, and Unstructured generates embeddings, but the number of dimensions that are generated does not match the existing collection’s embedding settings, the run will fail. You must change your Unstructured embedding settings or your existing collection’s embedding settings to match, and try the run again.
  - If a collection name is not specified, Unstructured creates a new collection in your Weaviate cluster. If Unstructured generates embeddings, the new collection’s name will be U<short-workflow-id>_<short-embedding-model-name>_<number-of-dimensions>. If Unstructured does not generate embeddings, the new collection’s name will be U<short-workflow-id.
  For Unstructured Ingest:
  - If an existing collection name is specified, and Unstructured generates embeddings, but the number of dimensions that are generated does not match the existing collection’s embedding settings, the run will fail. You must change your Unstructured embedding settings or your existing collection’s embedding settings to match, and try the run again.
  - If a collection name is not specified, Unstructured creates a new collection in your Weaviate cluster. The new collection’s name will be Unstructuredautocreated.
  If Unstructured creates a new collection and generates embeddings, you will not see an embeddings property in tools such as the Weaviate Cloud Collections user interface. To view the generated embeddings, you can run a Weaviate GraphQL query such as the following. In this query, replace <collection-name> with the name of the new collection, and replace <property-name> with the name of each additional available property that you want to return results for, such as text, type, element_id, record_id, and so on. The embeddings will be returned in the vector property.
  { Get { <collection-name> { _additional { vector } <property-name> <property-name> } } }

If auto-schema is enabled in Weaviate (which it is by default), Weaviate can infer missing properties and add them to the collection definition at run time. However, it is a Weaviate best practice to manually define as much of the data schema in advance as possible, since manual definition gives you the most control. The minimum viable schema for Unstructured includes only the element_id and record_id properties. The text and type properties should also be included, but they are technically optional. If you are using Unstructured to generate embeddings, you must The following code example shows how to use the weaviate-client Python package to create a collection in a Weaviate Cloud database cluster with this minimum viable schema, and to specify that Unstructured will generate the embeddings for this collection. To connect to a locally hosted Weaviate instance instead, call weaviate.connect_to_local. To connect to Embedded Weaviate instead, call weaviate.connect_to_embedded.

import os
import weaviate
from weaviate.classes.init import Auth
import weaviate.classes.config as wvc

client = weaviate.connect_to_weaviate_cloud(
    cluster_url=os.getenv("WEAVIATE_URL"),
    auth_credentials=Auth.api_key(api_key=os.getenv("WEAVIATE_API_KEY")),
)

collection = client.collections.create(
    name="MyCollection",
    properties=[
        wvc.Property(name="element_id", data_type=wvc.DataType.UUID),
        wvc.Property(name="record_id", data_type=wvc.DataType.TEXT),
        wvc.Property(name="text", data_type=wvc.DataType.TEXT),
        wvc.Property(name="type", data_type=wvc.DataType.TEXT),
    ],
    vectorizer_config=None, # Unstructured will generate the embeddings instead of Weaviate.
)

client.close()

For objects in the metadata field that Unstructured produces and that you want to store in a Weaviate collection, be sure to follow Unstructured’s metadata field naming convention. For example, if Unstructured produces a metadata field with the following child objects:

"metadata": {
  "is_extracted": "true",
  "coordinates": {
    "points": [
      [
        134.20055555555555,
        241.36027777777795
      ],
      [
        134.20055555555555,
        420.0269444444447
      ],
      [
        529.7005555555555,
        420.0269444444447
      ],
      [
        529.7005555555555,
        241.36027777777795
      ]
    ],
    "system": "PixelSpace",
    "layout_width": 1654,
    "layout_height": 2339
  },
  "filetype": "application/pdf",
  "languages": [
    "eng"
  ],
  "page_number": 1,
  "image_mime_type": "image/jpeg",
  "filename": "realestate.pdf",
  "data_source": {
    "url": "file:///home/etl/node/downloads/00000000-0000-0000-0000-000000000001/7458635f-realestate.pdf",
    "record_locator": {
      "protocol": "file",
      "remote_file_path": "file:///home/etl/node/downloads/00000000-0000-0000-0000-000000000001/7458635f-realestate.pdf"
    }
  },
  "entities": {
    "items": [
      {
        "entity": "HOME FOR FUTURE",
        "type": "ORGANIZATION"
      },
      {
        "entity": "221 Queen Street, Melbourne VIC 3000",
        "type": "LOCATION"
      }
    ],
    "relationships": [
      {
        "from": "HOME FOR FUTURE",
        "relationship": "based_in",
        "to": "221 Queen Street, Melbourne VIC 3000"
      }
    ]
  }
}

You could create corresponding properties in your collection’s schema by using the following property names and data types:

import os
import weaviate
from weaviate.classes.init import Auth
import weaviate.classes.config as wvc

client = weaviate.connect_to_weaviate_cloud(
    cluster_url=os.getenv("WEAVIATE_URL"),
    auth_credentials=Auth.api_key(api_key=os.getenv("WEAVIATE_API_KEY")),
)

collection = client.collections.create(
    name="MyCollection",
    properties=[
        wvc.Property(name="element_id", data_type=wvc.DataType.UUID),
        wvc.Property(name="record_id", data_type=wvc.DataType.TEXT),
        wvc.Property(name="text", data_type=wvc.DataType.TEXT),
        wvc.Property(name="type", data_type=wvc.DataType.TEXT),
        wvc.Property(
            name="metadata",
            data_type=wvc.DataType.OBJECT,
            nested_properties=[
                wvc.Property(name="is_extracted", data_type=wvc.DataType.TEXT),
                wvc.Property(
                    name="coordinates",
                    data_type=wvc.DataType.OBJECT,
                    nested_properties=[
                        wvc.Property(name="points", data_type=wvc.DataType.TEXT),
                        wvc.Property(name="system", data_type=wvc.DataType.TEXT),
                        wvc.Property(name="layout_width", data_type=wvc.DataType.NUMBER),
                        wvc.Property(name="layout_height", data_type=wvc.DataType.NUMBER),
                    ],
                ),
                wvc.Property(name="filetype", data_type=wvc.DataType.TEXT),
                wvc.Property(name="languages", data_type=wvc.DataType.TEXT_ARRAY),
                wvc.Property(name="page_number", data_type=wvc.DataType.TEXT),
                wvc.Property(name="image_mime_type", data_type=wvc.DataType.TEXT),
                wvc.Property(name="filename", data_type=wvc.DataType.TEXT),
                wvc.Property(
                    name="data_source",
                    data_type=wvc.DataType.OBJECT,
                    nested_properties=[
                        wvc.Property(name="url", data_type=wvc.DataType.TEXT),
                        wvc.Property(name="record_locator", data_type=wvc.DataType.TEXT),
                    ],
                ),
                wvc.Property(
                    name="entities", 
                    data_type=wvc.DataType.OBJECT,
                    nested_properties=[
                        wvc.Property(
                            name="items", 
                            data_type=wvc.DataType.OBJECT_ARRAY,
                            nested_properties=[
                                wvc.Property(name="entity", data_type=wvc.DataType.TEXT),
                                wvc.Property(name="type", data_type=wvc.DataType.TEXT),
                            ],
                        ),
                        wvc.Property(
                            name="relationships", 
                            data_type=wvc.DataType.OBJECT_ARRAY,
                            nested_properties=[
                                wvc.Property(name="to", data_type=wvc.DataType.TEXT),
                                wvc.Property(name="from", data_type=wvc.DataType.TEXT),
                                wvc.Property(name="relationship", data_type=wvc.DataType.TEXT),
                            ],
                        ),
                    ],
                ),
            ],
        ),
    ],
    vectorizer_config=None, # Unstructured will generate the embeddings instead of Weaviate.
)

client.close()

Unstructured cannot provide a schema that is guaranteed to work in all circumstances. This is because these schemas will vary based on your source files’ types; how you want Unstructured to partition, chunk, and generate embeddings; any custom post-processing code that you run; and other factors. See also:

To create a Weaviate 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

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="weaviate-cloud",
                config={
                    "cluster_url": "<host-url>",
                    "collection": "<class-name>",
                    "api_key": "<api-key>"
                }
            )
        )
    )

    print(response.destination_connector_information)

Replace the preceding placeholders as follows:

<name> (required) - A unique name for this connector.
<host-url> (required) - The URL of the Weaviate database cluster.
<class-name> - The name of the target collection within the cluster. If no value is provided, see the beginning of this article for the behavior at run time.
<api-key> (required) - The API key provided by Weaviate to access the cluster.

Unstructured API

Workflow Operations

Legacy APIs

Troubleshooting