Repository: topoteretes/cognee
File: cognee/infrastructure/databases/vector/vector_db_interface.py (Python)

Code before:
            - limit (int): The maximum number of results to return from the search.
            - with_vector (bool): Whether to return the vector representations with search
              results. (default False)
        """
        raise NotImplementedError

    @abstractmethod
    async def batch_search(
        self, collection_name: str, query_texts: List[str], limit: int, with_vectors: bool = False
    ):
        """
        Perform a batch search using multiple text queries against a collection.

        Parameters:
        -----------

            - collection_name (str): The name of the collection to conduct the batch search in.
            - query_texts (List[str]): A list of text queries to use for the search.
            - limit (int): The maximum number of results to return for each query.
            - with_vectors (bool): Whether to include vector representations with search
              results. (default False)
        """
        raise NotImplementedError

    @abstractmethod
    async def delete_data_points(self, collection_name: str, data_point_ids: Union[List[str], list[str]]):
        """
        Delete specified data points from a collection.

        Parameters:
        -----------

            - collection_name (str): The name of the collection from which to delete data
              points.
            - data_point_ids (Union[List[str], list[str]]): A list of IDs of the data points to delete.
        """
        raise NotImplementedError

    @abstractmethod
    async def prune(self):
        """
        Remove obsolete or unnecessary data from the database.
        """
        raise NotImplementedError

    @abstractmethod
    async def embed_data(self, data: List[str]) -> List[List[float]]:
        """
        Embed textual data into vector representations.

        Parameters:

Review comment (suggestion):
```suggestion
    async def delete_data_points(self, collection_name: str, data_point_ids: list[str]):
```

Code after:
            - limit (int): The maximum number of results to return from the search.
            - with_vector (bool): Whether to return the vector representations with search
              results. (default False)
        """
        raise NotImplementedError

    @abstractmethod
    async def batch_search(
        self, collection_name: str, query_texts: List[str], limit: int, with_vectors: bool = False
    ):
        """
        Perform a batch search using multiple text queries against a collection.

        Parameters:
        -----------

            - collection_name (str): The name of the collection to conduct the batch search in.
            - query_texts (List[str]): A list of text queries to use for the search.
            - limit (int): The maximum number of results to return for each query.
            - with_vectors (bool): Whether to include vector representations with search
              results. (default False)
        """
        raise NotImplementedError

    @abstractmethod
    async def delete_data_points(
        self, collection_name: str, data_point_ids: Union[List[str], list[str]]
    ):
        """
        Delete specified data points from a collection.

        Parameters:
        -----------

            - collection_name (str): The name of the collection from which to delete data
              points.
            - data_point_ids (Union[List[str], list[str]]): A list of IDs of the data points to delete.
        """
        raise NotImplementedError

    @abstractmethod
    async def prune(self):
        """
        Remove obsolete or unnecessary data from the database.
        """
        raise NotImplementedError

    @abstractmethod
    async def embed_data(self, data: List[str]) -> List[List[float]]:
        """
        Embed textual data into vector representations.