Pinecone

Documentation Index

Fetch the complete documentation index at: https://docs.unstructured.io/llms.txt

Use this file to discover all available pages before exploring further.

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Requirements

• A Pinecone account. Get an account.
• A Pinecone API key in your Pinecone account. To create an API key, do the following:
  1. Sign in to your Pinecone account and open the API keys page.
  2. Click + API key.
  3. For API key name, enter some descriptive name for the API key.
  4. Click Create key.
  5. Copy the generated API key to some secure location. You will not be able to access this API key again after you close the dialog.
• A Pinecone serverless index in your Pinecone account. Creating a serverless index on your own is optional. An index is not required to exist in advance. When you set up the connector, at runtime, the index behavior is as follows: For the Unstructured Pipelines and Unstructured API:
  • Your workflow must contain an embedder node, and the embedder node must specify the embedding model that Unstructured will use to generate the embeedings.
  • If an existing index name is specified, and the number of dimensions that Unstructured generates does not match the number of dimensions that are specified in the existing index’s embedding settings, the run will fail. You must change the number of dimensions in your workflow’s embedder node or your existing index’s embedding settings to match, and try the run again.
  • If an index name is not specified, Unstructured creates a new index in your Pinecone account. The new index’s name will be u<short-workflow-id>-<short-embedding-model-name>-<number-of-dimensions>.
  For Unstructured Ingest:
  • If an existing index name is specified, and Unstructured generates embeddings, but the number of dimensions that are generated does not match the existing index’s embedding settings, the run will fail. You must change your Unstructured embedding settings or your existing index’s embedding settings to match, and try the run again.
  • If an index name is not specified, Unstructured creates a new index in your Pinecone account. The new index’s name will be unstructuredautocreated.
  If you create a new index or use an existing one, Unstructured recommends that all records in the target index have a field named record_id with a string data type. Unstructured can use this field to do intelligent document overwrites. Without this field, duplicate documents might be written to the index or, in some cases, the operation could fail altogether.
  To create a serverless index on your own, do the following:
  1. Sign in to your Pinecone account and open the Create a new index page.
  2. For Enter index name, enter some descriptive name for the index.
  3. For Configuration, select the check box labelled Custom settings, or click the tile labelled Manual configuration.
     Do not click any of the other tiles, such as text-embedding-3-large. Clicking any of these other tiles will cause Pinecone to generate embeddings instead of having Unstructured generate them. If Pinecone generates embeddings instead of Unstructured, this could cause any related Unstructured workflows to fail.
  4. For Vector type, select Dense.
  5. For Dimension, enter the number of dimensions for the embeddings that Unstructured will generate.
     The number of dimensions that you enter here must match the number of dimensions for the embedding model that you use in any related Unstructured workflows or ingestion pipelines. If these numbers do not match in both places, this could cause any related Unstructured workflows or ingestion pipelines to fail.
  6. For Metric, select cosine.
  7. Leave Capacity mode set to Serverless.
  8. You can leave Cloud provider and Region set to their default values, or you can select a cloud provider and region that is closest to you, if available.
  9. Click Create index.
• Within a Pinecone serverless index, custom namespaces are supported but are not required.

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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="pinecone",
                config={
                    "index_name": "<index-name>",
                    "namespace": "<namespace-name>",
                    "api_key": "<api-key>",
                    "batch_size": <batch-size>
                }
            )
        )
    )

    print(response.destination_connector_information)

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Configuration settings