mpgreg commented on code in PR #36177:
URL: https://github.com/apache/airflow/pull/36177#discussion_r1424283026
##########
airflow/providers/weaviate/hooks/weaviate.py:
##########
@@ -606,3 +627,220 @@ def object_exists(self, uuid: str | UUID, **kwargs) ->
bool:
"""
client = self.conn
return client.data_object.exists(uuid, **kwargs)
+
+ def _generate_uuids(
+ self,
+ df: pd.DataFrame,
+ class_name: str,
+ unique_columns: list[str],
+ vector_column: str | None = None,
+ uuid_column: str | None = None,
+ ) -> tuple[pd.DataFrame, str]:
+ """
+ Adds UUIDs to a DataFrame, useful for replace operations where UUIDs
must be known before ingestion.
+
+ By default, UUIDs are generated using a custom function if
'uuid_column' is not specified.
+ The function can potentially ingest the same data multiple times with
different UUIDs.
+
+ :param df: A dataframe with data to generate a UUID from.
+ :param class_name: The name of the class use as part of the uuid
namespace.
+ :param uuid_column: Name of the column to create. Default is 'id'.
+ :param unique_columns: A list of columns to use for UUID generation.
By default, all columns except
+ vector_column will be used.
+ :param vector_column: Name of the column containing the vector data.
If specified the vector will be
+ removed prior to generating the uuid.
+ """
+ column_names = df.columns.to_list()
+
+ difference_columns =
set(unique_columns).difference(set(df.columns.to_list()))
+ if difference_columns:
+ raise ValueError(f"Columns {', '.join(difference_columns)} don't
exist in dataframe")
+
+ if uuid_column is None:
+ self.log.info("No uuid_column provided. Generating UUIDs as column
name `id`.")
+ if "id" in column_names:
+ raise ValueError(
+ "Property 'id' already in dataset. Consider renaming or
specify 'uuid_column'."
+ )
+ else:
+ uuid_column = "id"
+
+ if uuid_column in column_names:
+ raise ValueError(
+ f"Property {uuid_column} already in dataset. Consider renaming
or specify a different"
+ f" 'uuid_column'."
+ )
+
+ df[uuid_column] = (
+ df[unique_columns]
+ .drop(columns=[vector_column], inplace=False, errors="ignore")
+ .apply(lambda row: generate_uuid5(identifier=row.to_dict(),
namespace=class_name), axis=1)
+ )
+
+ return df, uuid_column
+
+ def _check_existing_objects(self, data: pd.DataFrame, uuid_column: str,
class_name: str, existing: str):
Review Comment:
Even worse consistency errors occur if all of the chunks change. For
instance, if the change adds a word right at a chunk boundary causing the
splitter to now break the chunks completely differently.
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