Pinecone connector

Use this as an implementation starting point: adapt the worked example to the requested dataset and the source documentation. See Custom API for the shared SDK pattern and document-upload workflow.

What this connector is forCopy Anchor Link

Managed vector search, indexes, embeddings, and retrieval operations.

Use this guide to choose the resources and row meanings for a Vortexify source integration. Connection ownership belongs to a connector; each dataset gets its own sync, configuration, and extraction behavior.

Data and tables you can pullCopy Anchor Link

The following table families are a starting point for source selection. They describe possible datasets to implement using the vendor reference, not a guarantee that the example implements every table. Actual availability depends on the product edition, deployment, account permissions, and API/schema version.

Relationships and extraction considerationsCopy Anchor Link

These are proposed dataset names, not a universal schema guaranteed by the vendor. Preserve source object IDs and relationship references, and split nested child collections into separate tables when appropriate. Choose the concrete endpoint/report and row meaning from the vendor reference before implementing a sync.

Connecting to PineconeCopy Anchor Link

Use an API key and the data-plane index host. Listing IDs plus fetch can support enumeration for supported index types; similarity queries alone are not exhaustive exports.

The example's config keys show the connection fields it uses. Store credentials in platform configuration marked as secret; host/project/database identifiers are ordinary connection settings. Environment and network access must match the selected source.

requirements.inCopy Anchor Link

Declare these packages in the resource's requirements.in. Use the published vortexify package for the SDK and the source-specific driver/client shown below. Choose compatible dependency versions for your runtime; the connector and sync reference explains where each file belongs.

vortexify==0.1.1
pandas>=2,<3
pinecone

Use an API key and the data-plane index host. Listing IDs plus fetch can support enumeration for supported index types; similarity queries alone are not exhaustive exports.

Worked example: listed record vectorsCopy Anchor Link

This source-read pattern shows how to shape the selected resource into DataFrames. Use it inside the dataset sync, retaining shared connection ownership in the connector. Its scope is the named resource and filters; adapt the projection, batching, and preview to the requested table.

Connection factoryCopy Anchor Link

Use this client in the owning connector's client() method and release it in close().

from pinecone import Pinecone


def make_index(config: dict):
    client = Pinecone(api_key=config["api_key"])
    return client.Index(host=config["index_host"])

Source readCopy Anchor Link

import pandas as pd


def read_batches(index, config):
    namespace = config.get("namespace", "")
    for ids in index.list(namespace=namespace):
        response = index.fetch(ids=ids, namespace=namespace)
        rows = [
            {"id": key, "values": value.values, "metadata": value.metadata}
            for key, value in response.vectors.items()
        ]
        if rows:
            yield pd.json_normalize(rows)

What to adaptCopy Anchor Link

Use a supported index type for ID listing and explicitly choose the namespace; this does not enumerate every namespace automatically. Listing then fetching enumerates records, whereas similarity query does not. A preview should request a bounded ID page then fetch those IDs.

Use the custom API SDK starting point to connect the read operation to Connector and Sync. For a source with its own driver, retain that client in Connector.client() and replace the HTTP request with the driver read. An extract(*, avg_row_size_bytes=None) implementation yields bounded DataFrames. An independent preview() uses a fresh connector and a small read; it must not call extraction or advance real state.

Choose explicit output columns and stable IDs for the requested table. The examples use json_normalize where useful to illustrate row shaping; nested arrays still need deliberate child tables and a stable declared output schema. A first-page sample is useful for development, while a full sync must follow all relevant continuation/parent-resource paths. Design incremental watermarks, update/deletion behavior, and rate-limit handling for the actual source before enabling incremental mode.

The purpose is to give a coding agent a concrete pattern to adapt, including for similar sources. These snippets are examples rather than a claim of complete vendor coverage. Check the source operation reference and representative responses before running with a customer's account; live vendor execution has not been verified here.

Sources and further readingCopy Anchor Link

• Vendor connection and resource reference — The interface/access prerequisites underlying this connection path; follow the selected resource's documentation for fields, queries, and pagination.
• Vortexify connectors and syncs — Connector ownership, schemas, preview isolation, extraction, and checkpoint contracts.
• Vortexify CLI workflow — Registering resources, committing source, and providing credential configuration.