Source code for airflow.providers.weaviate.operators.weaviate

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from __future__ import annotations

from functools import cached_property
from typing import TYPE_CHECKING, Any, Sequence

from airflow.models import BaseOperator
from airflow.providers.weaviate.hooks.weaviate import WeaviateHook

if TYPE_CHECKING:
    from airflow.utils.context import Context


[docs]class WeaviateIngestOperator(BaseOperator):
    """
    Operator that store vector in the Weaviate class.

    .. seealso::
        For more information on how to use this operator, take a look at the guide:
        :ref:`howto/operator:WeaviateIngestOperator`

    Operator that accepts input json to generate embeddings on or accepting provided custom vectors
    and store them in the Weaviate class.

    :param conn_id: The Weaviate connection.
    :param class_name: The Weaviate class to be used for storing the data objects into.
    :param input_json: The JSON representing Weaviate data objects to generate embeddings on (or provides
        custom vectors) and store them in the Weaviate class. Either input_json or input_callable should be
        provided.
    """

[docs]    template_fields: Sequence[str] = ("input_json",)

    def __init__(
        self,
        conn_id: str,
        class_name: str,
        input_json: list[dict[str, Any]],
        **kwargs: Any,
    ) -> None:
        self.batch_params = kwargs.pop("batch_params", {})
        self.hook_params = kwargs.pop("hook_params", {})
        super().__init__(**kwargs)
        self.class_name = class_name
        self.conn_id = conn_id
        self.input_json = input_json

    @cached_property
[docs]    def hook(self) -> WeaviateHook:
        """Return an instance of the WeaviateHook."""
        return WeaviateHook(conn_id=self.conn_id, **self.hook_params)

[docs]    def execute(self, context: Context) -> None:
        self.log.debug("Input json: %s", self.input_json)
        self.hook.batch_data(self.class_name, self.input_json, **self.batch_params)