class_docstr: Ingest vector embeddings into Pinecone.
  
  .. seealso::
      For more information on how to use this operator, take a look at the guide:
      :ref:`howto/operator:PineconeIngestOperator`
  
  :param conn_id: The connection id to use when connecting to Pinecone.
  :param index_name: Name of the Pinecone index.
  :param input_vectors: Data to be ingested, in the form of a list of tuples where each tuple
      contains (id, vector_embedding, metadata).
  :param namespace: The namespace to write to. If not specified, the default namespace is used.
  :param batch_size: The number of vectors to upsert in each batch.
  :param upsert_kwargs: .. seealso:: https://docs.pinecone.io/reference/upsert
docstr: Ingest data into Pinecone using the PineconeHook.
code: def execute(self, context: Context) -> None:
      """Ingest data into Pinecone using the PineconeHook."""
      self.hook.upsert(
          index_name=self.index_name,
          vectors=self.input_vectors,
          namespace=self.namespace,
          batch_size=self.batch_size,
          **self.upsert_kwargs,
      )
  
      self.log.info(
          "Successfully ingested data into Pinecone index %s.",
          self.index_name,
      )
  
file_path: airflow/airflow/providers/pinecone/operators/pinecone.py
imports: from __future__ import annotations
  from functools import cached_property
  from typing import TYPE_CHECKING, Any, Sequence
  from airflow.models import BaseOperator
  from airflow.providers.pinecone.hooks.pinecone import PineconeHook
label_desc: Use image node_id 3 for calling the PineconeIngestOperator obj's underlying member method code with example usage: obj.execute(context) without return types