Method retrieve_online_documents_v2

sdk/python/feast/infra/provider.py:464–497

Searches for the top-k most similar documents in the online document store. Args: distance_metric: distance metric to use for the search. config: The config for the current feature store. table: The feature view whose embeddings should be searche

(
        self,
        config: RepoConfig,
        table: FeatureView,
        requested_features: List[str],
        query: Optional[List[float]],
        top_k: int,
        distance_metric: Optional[str] = None,
        query_string: Optional[str] = None,
        include_feature_view_version_metadata: bool = False,
    )

    @abstractmethod
    def retrieve_online_documents_v2(
        self,
        config: RepoConfig,
        table: FeatureView,
        requested_features: List[str],
        query: Optional[List[float]],
        top_k: int,
        distance_metric: Optional[str] = None,
        query_string: Optional[str] = None,
        include_feature_view_version_metadata: bool = False,
    ) -> List[
        Tuple[
            Optional[datetime],
            Optional[EntityKeyProto],
            Optional[Dict[str, ValueProto]],
        ]
    ]:
        """
        Searches for the top-k most similar documents in the online document store.

        Args:
            distance_metric: distance metric to use for the search.
            config: The config for the current feature store.
            table: The feature view whose embeddings should be searched.
            requested_features: the requested document feature names.
            query: The query embedding to search for (optional).
            top_k: The number of documents to return.
            query_string: The query string to search for using keyword search (bm25) (optional)

        Returns:
            A list of dictionaries, where each dictionary contains the datetime, entitykey, and a dictionary
            of feature key value pairs
        """
        pass

    @abstractmethod
    def validate_data_source(