Source code for langchain.vectorstores.deeplake
"""Wrapper around Activeloop Deep Lake."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
try:
    import deeplake
    from deeplake.core.fast_forwarding import version_compare
    from deeplake.core.vectorstore import DeepLakeVectorStore
    _DEEPLAKE_INSTALLED = True
except ImportError:
    _DEEPLAKE_INSTALLED = False
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
logger = logging.getLogger(__name__)
[docs]class DeepLake(VectorStore):
    """Wrapper around Deep Lake, a data lake for deep learning applications.
    We integrated deeplake's similarity search and filtering for fast prototyping,
    Now, it supports Tensor Query Language (TQL) for production use cases
    over billion rows.
    Why Deep Lake?
    - Not only stores embeddings, but also the original data with version control.
    - Serverless, doesn't require another service and can be used with major
        cloud providers (S3, GCS, etc.)
    - More than just a multi-modal vector store. You can use the dataset
        to fine-tune your own LLM models.
    To use, you should have the ``deeplake`` python package installed.
    Example:
        .. code-block:: python
                from langchain.vectorstores import DeepLake
                from langchain.embeddings.openai import OpenAIEmbeddings
                embeddings = OpenAIEmbeddings()
                vectorstore = DeepLake("langchain_store", embeddings.embed_query)
    """
    _LANGCHAIN_DEFAULT_DEEPLAKE_PATH = "./deeplake/"
    def __init__(
        self,
        dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
        token: Optional[str] = None,
        embedding_function: Optional[Embeddings] = None,
        read_only: bool = False,
        ingestion_batch_size: int = 1000,
        num_workers: int = 0,
        verbose: bool = True,
        exec_option: str = "python",
        **kwargs: Any,
    ) -> None:
        """Creates an empty DeepLakeVectorStore or loads an existing one.
        The DeepLakeVectorStore is located at the specified ``path``.
        Examples:
            >>> # Create a vector store with default tensors
            >>> deeplake_vectorstore = DeepLake(
            ...        path = <path_for_storing_Data>,
            ... )
            >>>
            >>> # Create a vector store in the Deep Lake Managed Tensor Database
            >>> data = DeepLake(
            ...        path = "hub://org_id/dataset_name",
            ...        exec_option = "tensor_db",
            ... )
        Args:
            dataset_path (str): Path to existing dataset or where to create
                a new one. Defaults to _LANGCHAIN_DEFAULT_DEEPLAKE_PATH.
            token (str, optional):  Activeloop token, for fetching credentials
                to the dataset at path if it is a Deep Lake dataset.
                Tokens are normally autogenerated. Optional.
            embedding_function (str, optional): Function to convert
                either documents or query. Optional.
            read_only (bool): Open dataset in read-only mode. Default is False.
            ingestion_batch_size (int): During data ingestion, data is divided
                into batches. Batch size is the size of each batch.
                Default is 1000.
            num_workers (int): Number of workers to use during data ingestion.
                Default is 0.
            verbose (bool): Print dataset summary after each operation.
                Default is True.
            exec_option (str): DeepLakeVectorStore supports 3 ways to perform
                searching - "python", "compute_engine", "tensor_db".
                Default is "python".
                - ``python`` - Pure-python implementation that runs on the client.
                WARNING: using this with big datasets can lead to memory
                issues. Data can be stored anywhere.
                - ``compute_engine`` - C++ implementation of the Deep Lake Compute
                Engine that runs on the client. Can be used for any data stored in
                or connected to Deep Lake. Not for in-memory or local datasets.
                - ``tensor_db`` - Hosted Managed Tensor Database that is
                responsible for storage and query execution. Only for data stored in
                the Deep Lake Managed Database. Use runtime = {"db_engine": True} during
                dataset creation.
            **kwargs: Other optional keyword arguments.
        Raises:
            ValueError: If some condition is not met.
        """
        self.ingestion_batch_size = ingestion_batch_size
        self.num_workers = num_workers
        self.verbose = verbose
        if _DEEPLAKE_INSTALLED is False:
            raise ValueError(
                "Could not import deeplake python package. "
                "Please install it with `pip install deeplake`."
            )
        if version_compare(deeplake.__version__, "3.6.2") == -1:
            raise ValueError(
                "deeplake version should be >= 3.6.3, but you've installed"
                f" {deeplake.__version__}. Consider upgrading deeplake version \
                    pip install --upgrade deeplake."
            )
        self.dataset_path = dataset_path
        self.vectorstore = DeepLakeVectorStore(
            path=self.dataset_path,
            embedding_function=embedding_function,
            read_only=read_only,
            token=token,
            exec_option=exec_option,
            verbose=verbose,
            **kwargs,
        )
        self._embedding_function = embedding_function
        self._id_tensor_name = "ids" if "ids" in self.vectorstore.tensors() else "id"
[docs]    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore.
        Examples:
            >>> ids = deeplake_vectorstore.add_texts(
            ...     texts = <list_of_texts>,
            ...     metadatas = <list_of_metadata_jsons>,
            ...     ids = <list_of_ids>,
            ... )
        Args:
            texts (Iterable[str]): Texts to add to the vectorstore.
            metadatas (Optional[List[dict]], optional): Optional list of metadatas.
            ids (Optional[List[str]], optional): Optional list of IDs.
            **kwargs: other optional keyword arguments.
        Returns:
            List[str]: List of IDs of the added texts.
        """
        kwargs = {}
        if ids:
            if self._id_tensor_name == "ids":  # for backwards compatibility
                kwargs["ids"] = ids
            else:
                kwargs["id"] = ids
        if metadatas is None:
            metadatas = [{}] * len(list(texts))
        return self.vectorstore.add(
            text=texts,
            metadata=metadatas,
            embedding_data=texts,
            embedding_tensor="embedding",
            embedding_function=kwargs.get("embedding_function")
            or self._embedding_function.embed_documents,  # type: ignore
            return_ids=True,
            **kwargs,
        )
    def _search_tql(
        self,
        tql_query: Optional[str],
        exec_option: Optional[str] = None,
        return_score: bool = False,
    ) -> Any[List[Document], List[Tuple[Document, float]]]:
        """Function for performing tql_search.
        Args:
            tql_query (str): TQL Query string for direct evaluation.
                Available only for `compute_engine` and `tensor_db`.
            exec_option (str, optional): Supports 3 ways to search.
                Could be "python", "compute_engine" or "tensor_db". Default is "python".
                - ``python`` - Pure-python implementation for the client.
                    WARNING: not recommended for big datasets due to potential memory
                    issues.
                - ``compute_engine`` - C++ implementation of Deep Lake Compute
                    Engine for the client. Not for in-memory or local datasets.
                - ``tensor_db`` - Hosted Managed Tensor Database for storage
                    and query execution. Only for data in Deep Lake Managed Database.
                        Use runtime = {"db_engine": True} during dataset creation.
            return_score (bool): Return score with document. Default is False.
        Returns:
            List[Document] - A list of documents
        Raises:
            ValueError: If return_score is True but some condition is not met.
        """
        result = self.vectorstore.search(
            query=tql_query,
            exec_option=exec_option,
        )
        metadatas = result["metadata"]
        texts = result["text"]
        docs = [
            Document(
                page_content=text,
                metadata=metadata,
            )
            for text, metadata in zip(texts, metadatas)
        ]
        if return_score:
            raise ValueError("scores can't be returned with tql search")
        return docs
    def _search(
        self,
        query: Optional[str] = None,
        embedding: Optional[Union[List[float], np.ndarray]] = None,
        embedding_function: Optional[Callable] = None,
        k: int = 4,
        distance_metric: str = "L2",
        use_maximal_marginal_relevance: bool = False,
        fetch_k: Optional[int] = 20,
        filter: Optional[Union[Dict, Callable]] = None,
        return_score: bool = False,
        exec_option: Optional[str] = None,
        **kwargs: Any,
    ) -> Any[List[Document], List[Tuple[Document, float]]]:
        """
        Return docs similar to query.
        Args:
            query (str, optional): Text to look up similar docs.
            embedding (Union[List[float], np.ndarray], optional): Query's embedding.
            embedding_function (Callable, optional): Function to convert `query`
                into embedding.
            k (int): Number of Documents to return.
            distance_metric (str): `L2` for Euclidean, `L1` for Nuclear, `max`
                for L-infinity distance, `cos` for cosine similarity, 'dot' for dot
                product.
            filter (Union[Dict, Callable], optional): Additional filter prior
                to the embedding search.
                - ``Dict`` - Key-value search on tensors of htype json, on an
                    AND basis (a sample must satisfy all key-value filters to be True)
                    Dict = {"tensor_name_1": {"key": value},
                            "tensor_name_2": {"key": value}}
                - ``Function`` - Any function compatible with `deeplake.filter`.
            use_maximal_marginal_relevance (bool): Use maximal marginal relevance.
            fetch_k (int): Number of Documents for MMR algorithm.
            return_score (bool): Return the score.
            exec_option (str, optional): Supports 3 ways to perform searching.
                Could be "python", "compute_engine" or "tensor_db".
                - ``python`` - Pure-python implementation for the client.
                    WARNING: not recommended for big datasets.
                - ``compute_engine`` - C++ implementation of Deep Lake Compute
                    Engine for the client. Not for in-memory or local datasets.
                - ``tensor_db`` - Hosted Managed Tensor Database for storage
                    and query execution. Only for data in Deep Lake Managed Database.
                    Use runtime = {"db_engine": True} during dataset creation.
            **kwargs: Additional keyword arguments.
        Returns:
            List of Documents by the specified distance metric,
            if return_score True, return a tuple of (Document, score)
        Raises:
            ValueError: if both `embedding` and `embedding_function` are not specified.
        """
        if kwargs.get("tql_query"):
            return self._search_tql(
                tql_query=kwargs["tql_query"],
                exec_option=exec_option,
                return_score=return_score,
            )
        if embedding_function:
            if isinstance(embedding_function, Embeddings):
                _embedding_function = embedding_function.embed_query
            else:
                _embedding_function = embedding_function
        elif self._embedding_function:
            _embedding_function = self._embedding_function.embed_query
        else:
            _embedding_function = None
        if embedding is None:
            if _embedding_function is None:
                raise ValueError(
                    "Either `embedding` or `embedding_function` needs to be"
                    " specified."
                )
            embedding = _embedding_function(query) if query else None
        if isinstance(embedding, list):
            embedding = np.array(embedding, dtype=np.float32)
            if len(embedding.shape) > 1:
                embedding = embedding[0]
        result = self.vectorstore.search(
            embedding=embedding,
            k=fetch_k if use_maximal_marginal_relevance else k,
            distance_metric=distance_metric,
            filter=filter,
            exec_option=exec_option,
            return_tensors=["embedding", "metadata", "text"],
        )
        scores = result["score"]
        embeddings = result["embedding"]
        metadatas = result["metadata"]
        texts = result["text"]
        if use_maximal_marginal_relevance:
            lambda_mult = kwargs.get("lambda_mult", 0.5)
            indices = maximal_marginal_relevance(  # type: ignore
                embedding,  # type: ignore
                embeddings,
                k=min(k, len(texts)),
                lambda_mult=lambda_mult,
            )
            scores = [scores[i] for i in indices]
            texts = [texts[i] for i in indices]
            metadatas = [metadatas[i] for i in indices]
        docs = [
            Document(
                page_content=text,
                metadata=metadata,
            )
            for text, metadata in zip(texts, metadatas)
        ]
        if return_score:
            return [(doc, score) for doc, score in zip(docs, scores)]
        return docs
[docs]    def similarity_search(
        self,
        query: str,
        k: int = 4,
        **kwargs: Any,
    ) -> List[Document]:
        """
        Return docs most similar to query.
        Examples:
            >>> # Search using an embedding
            >>> data = vector_store.similarity_search(
            ...     query=<your_query>,
            ...     k=<num_items>,
            ...     exec_option=<preferred_exec_option>,
            ... )
            >>> # Run tql search:
            >>> data = vector_store.tql_search(
            ...     tql_query="SELECT * WHERE id == <id>",
            ...     exec_option="compute_engine",
            ... )
        Args:
            k (int): Number of Documents to return. Defaults to 4.
            query (str): Text to look up similar documents.
            **kwargs: Additional keyword arguments include:
                embedding (Callable): Embedding function to use. Defaults to None.
                distance_metric (str): 'L2' for Euclidean, 'L1' for Nuclear, 'max'
                    for L-infinity, 'cos' for cosine, 'dot' for dot product.
                    Defaults to 'L2'.
                filter (Union[Dict, Callable], optional): Additional filter
                    before embedding search.
                    - Dict: Key-value search on tensors of htype json,
                        (sample must satisfy all key-value filters)
                        Dict = {"tensor_1": {"key": value}, "tensor_2": {"key": value}}
                    - Function: Compatible with `deeplake.filter`.
                    Defaults to None.
                exec_option (str): Supports 3 ways to perform searching.
                    'python', 'compute_engine', or 'tensor_db'. Defaults to 'python'.
                    - 'python': Pure-python implementation for the client.
                        WARNING: not recommended for big datasets.
                    - 'compute_engine': C++ implementation of the Compute Engine for
                        the client. Not for in-memory or local datasets.
                    - 'tensor_db': Managed Tensor Database for storage and query.
                        Only for data in Deep Lake Managed Database.
                        Use `runtime = {"db_engine": True}` during dataset creation.
        Returns:
            List[Document]: List of Documents most similar to the query vector.
        """
        return self._search(
            query=query,
            k=k,
            use_maximal_marginal_relevance=False,
            return_score=False,
            **kwargs,
        )
[docs]    def similarity_search_by_vector(
        self,
        embedding: Union[List[float], np.ndarray],
        k: int = 4,
        **kwargs: Any,
    ) -> List[Document]:
        """
        Return docs most similar to embedding vector.
        Examples:
            >>> # Search using an embedding
            >>> data = vector_store.similarity_search_by_vector(
            ...    embedding=<your_embedding>,
            ...    k=<num_items_to_return>,
            ...    exec_option=<preferred_exec_option>,
            ... )
        Args:
            embedding (Union[List[float], np.ndarray]):
                Embedding to find similar docs.
            k (int): Number of Documents to return. Defaults to 4.
            **kwargs: Additional keyword arguments including:
                filter (Union[Dict, Callable], optional):
                    Additional filter before embedding search.
                    - ``Dict`` - Key-value search on tensors of htype json. True
                        if all key-value filters are satisfied.
                        Dict = {"tensor_name_1": {"key": value},
                                "tensor_name_2": {"key": value}}
                    - ``Function`` - Any function compatible with
                        `deeplake.filter`.
                    Defaults to None.
                exec_option (str): Options for search execution include
                    "python", "compute_engine", or "tensor_db". Defaults to
                    "python".
                    - "python" - Pure-python implementation running on the client.
                        Can be used for data stored anywhere. WARNING: using this
                        option with big datasets is discouraged due to potential
                        memory issues.
                    - "compute_engine" - Performant C++ implementation of the Deep
                        Lake Compute Engine. Runs on the client and can be used for
                        any data stored in or connected to Deep Lake. It cannot be
                        used with in-memory or local datasets.
                    - "tensor_db" - Performant, fully-hosted Managed Tensor Database.
                        Responsible for storage and query execution. Only available
                        for data stored in the Deep Lake Managed Database.
                        To store datasets in this database, specify
                        `runtime = {"db_engine": True}` during dataset creation.
                distance_metric (str): `L2` for Euclidean, `L1` for Nuclear,
                    `max` for L-infinity distance, `cos` for cosine similarity,
                    'dot' for dot product. Defaults to `L2`.
        Returns:
            List[Document]: List of Documents most similar to the query vector.
        """
        return self._search(
            embedding=embedding,
            k=k,
            use_maximal_marginal_relevance=False,
            return_score=False,
            **kwargs,
        )
[docs]    def similarity_search_with_score(
        self,
        query: str,
        k: int = 4,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """
        Run similarity search with Deep Lake with distance returned.
        Examples:
        >>> data = vector_store.similarity_search_with_score(
        ...     query=<your_query>,
        ...     embedding=<your_embedding_function>
        ...     k=<number_of_items_to_return>,
        ...     exec_option=<preferred_exec_option>,
        ... )
        Args:
            query (str): Query text to search for.
            k (int): Number of results to return. Defaults to 4.
            **kwargs: Additional keyword arguments. Some of these arguments are:
                distance_metric: `L2` for Euclidean, `L1` for Nuclear, `max` L-infinity
                    distance, `cos` for cosine similarity, 'dot' for dot product.
                    Defaults to `L2`.
                filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
                    embedding_function (Callable): Embedding function to use. Defaults
                    to None.
                exec_option (str): DeepLakeVectorStore supports 3 ways to perform
                    searching. It could be either "python", "compute_engine" or
                    "tensor_db". Defaults to "python".
                    - "python" - Pure-python implementation running on the client.
                        Can be used for data stored anywhere. WARNING: using this
                        option with big datasets is discouraged due to potential
                        memory issues.
                    - "compute_engine" - Performant C++ implementation of the Deep
                        Lake Compute Engine. Runs on the client and can be used for
                        any data stored in or connected to Deep Lake. It cannot be used
                        with in-memory or local datasets.
                    - "tensor_db" - Performant, fully-hosted Managed Tensor Database.
                        Responsible for storage and query execution. Only available for
                        data stored in the Deep Lake Managed Database. To store datasets
                        in this database, specify `runtime = {"db_engine": True}`
                        during dataset creation.
        Returns:
            List[Tuple[Document, float]]: List of documents most similar to the query
                text with distance in float."""
        return self._search(
            query=query,
            k=k,
            return_score=True,
            **kwargs,
        )
[docs]    def max_marginal_relevance_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        exec_option: Optional[str] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """
        Return docs selected using the maximal marginal relevance. Maximal marginal
        relevance optimizes for similarity to query AND diversity among selected docs.
        Examples:
        >>> data = vector_store.max_marginal_relevance_search_by_vector(
        ...        embedding=<your_embedding>,
        ...        fetch_k=<elements_to_fetch_before_mmr_search>,
        ...        k=<number_of_items_to_return>,
        ...        exec_option=<preferred_exec_option>,
        ... )
        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            fetch_k: Number of Documents to fetch for MMR algorithm.
            lambda_mult: Number between 0 and 1 determining the degree of diversity.
                0 corresponds to max diversity and 1 to min diversity. Defaults to 0.5.
            exec_option (str): DeepLakeVectorStore supports 3 ways for searching.
                Could be "python", "compute_engine" or "tensor_db". Defaults to
                "python".
                - "python" - Pure-python implementation running on the client.
                    Can be used for data stored anywhere. WARNING: using this
                    option with big datasets is discouraged due to potential
                    memory issues.
                - "compute_engine" - Performant C++ implementation of the Deep
                    Lake Compute Engine. Runs on the client and can be used for
                    any data stored in or connected to Deep Lake. It cannot be used
                    with in-memory or local datasets.
                - "tensor_db" - Performant, fully-hosted Managed Tensor Database.
                    Responsible for storage and query execution. Only available for
                    data stored in the Deep Lake Managed Database. To store datasets
                    in this database, specify `runtime = {"db_engine": True}`
                    during dataset creation.
            **kwargs: Additional keyword arguments.
        Returns:
            List[Documents] - A list of documents.
        """
        return self._search(
            embedding=embedding,
            k=k,
            fetch_k=fetch_k,
            use_maximal_marginal_relevance=True,
            lambda_mult=lambda_mult,
            exec_option=exec_option,
            **kwargs,
        )
[docs]    def max_marginal_relevance_search(
        self,
        query: str,
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        exec_option: Optional[str] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs selected using maximal marginal relevance.
        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.
        Examples:
        >>> # Search using an embedding
        >>> data = vector_store.max_marginal_relevance_search(
        ...        query = <query_to_search>,
        ...        embedding_function = <embedding_function_for_query>,
        ...        k = <number_of_items_to_return>,
        ...        exec_option = <preferred_exec_option>,
        ... )
        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            fetch_k: Number of Documents for MMR algorithm.
            lambda_mult: Value between 0 and 1. 0 corresponds
                        to maximum diversity and 1 to minimum.
                        Defaults to 0.5.
            exec_option (str): Supports 3 ways to perform searching.
                - "python" - Pure-python implementation running on the client.
                        Can be used for data stored anywhere. WARNING: using this
                        option with big datasets is discouraged due to potential
                        memory issues.
                    - "compute_engine" - Performant C++ implementation of the Deep
                        Lake Compute Engine. Runs on the client and can be used for
                        any data stored in or connected to Deep Lake. It cannot be
                        used with in-memory or local datasets.
                    - "tensor_db" - Performant, fully-hosted Managed Tensor Database.
                        Responsible for storage and query execution. Only available
                        for data stored in the Deep Lake Managed Database. To store
                        datasets in this database, specify
                        `runtime = {"db_engine": True}` during dataset creation.
            **kwargs: Additional keyword arguments
        Returns:
            List of Documents selected by maximal marginal relevance.
        Raises:
            ValueError: when MRR search is on but embedding function is
                not specified.
        """
        embedding_function = kwargs.get("embedding") or self._embedding_function
        if embedding_function is None:
            raise ValueError(
                "For MMR search, you must specify an embedding function on"
                " `creation` or during add call."
            )
        return self._search(
            query=query,
            k=k,
            fetch_k=fetch_k,
            use_maximal_marginal_relevance=True,
            lambda_mult=lambda_mult,
            exec_option=exec_option,
            embedding_function=embedding_function,  # type: ignore
            **kwargs,
        )
[docs]    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Optional[Embeddings] = None,
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
        **kwargs: Any,
    ) -> DeepLake:
        """Create a Deep Lake dataset from a raw documents.
        If a dataset_path is specified, the dataset will be persisted in that location,
        otherwise by default at `./deeplake`
        Examples:
        >>> # Search using an embedding
        >>> vector_store = DeepLake.from_texts(
        ...        texts = <the_texts_that_you_want_to_embed>,
        ...        embedding_function = <embedding_function_for_query>,
        ...        k = <number_of_items_to_return>,
        ...        exec_option = <preferred_exec_option>,
        ... )
        Args:
            dataset_path (str): - The full path to the dataset. Can be:
                - Deep Lake cloud path of the form ``hub://username/dataset_name``.
                    To write to Deep Lake cloud datasets,
                    ensure that you are logged in to Deep Lake
                    (use 'activeloop login' from command line)
                - AWS S3 path of the form ``s3://bucketname/path/to/dataset``.
                    Credentials are required in either the environment
                - Google Cloud Storage path of the form
                    ``gcs://bucketname/path/to/dataset`` Credentials are required
                    in either the environment
                - Local file system path of the form ``./path/to/dataset`` or
                    ``~/path/to/dataset`` or ``path/to/dataset``.
                - In-memory path of the form ``mem://path/to/dataset`` which doesn't
                    save the dataset, but keeps it in memory instead.
                    Should be used only for testing as it does not persist.
            texts (List[Document]): List of documents to add.
            embedding (Optional[Embeddings]): Embedding function. Defaults to None.
                Note, in other places, it is called embedding_function.
            metadatas (Optional[List[dict]]): List of metadatas. Defaults to None.
            ids (Optional[List[str]]): List of document IDs. Defaults to None.
            **kwargs: Additional keyword arguments.
        Returns:
            DeepLake: Deep Lake dataset.
        Raises:
            ValueError: If 'embedding' is provided in kwargs. This is deprecated,
                please use `embedding_function` instead.
        """
        if kwargs.get("embedding"):
            raise ValueError(
                "using embedding as embedidng_functions is deprecated. "
                "Please use `embedding_function` instead."
            )
        deeplake_dataset = cls(
            dataset_path=dataset_path, embedding_function=embedding, **kwargs
        )
        deeplake_dataset.add_texts(
            texts=texts,
            metadatas=metadatas,
            ids=ids,
            embedding_function=embedding.embed_documents,  # type: ignore
        )
        return deeplake_dataset
[docs]    def delete(
        self,
        ids: Any[List[str], None] = None,
        filter: Any[Dict[str, str], None] = None,
        delete_all: Any[bool, None] = None,
    ) -> bool:
        """Delete the entities in the dataset.
        Args:
            ids (Optional[List[str]], optional): The document_ids to delete.
                Defaults to None.
            filter (Optional[Dict[str, str]], optional): The filter to delete by.
                Defaults to None.
            delete_all (Optional[bool], optional): Whether to drop the dataset.
                Defaults to None.
        Returns:
            bool: Whether the delete operation was successful.
        """
        self.vectorstore.delete(
            ids=ids,
            filter=filter,
            delete_all=delete_all,
        )
        return True
[docs]    @classmethod
    def force_delete_by_path(cls, path: str) -> None:
        """Force delete dataset by path.
        Args:
            path (str): path of the dataset to delete.
        Raises:
            ValueError: if deeplake is not installed.
        """
        try:
            import deeplake
        except ImportError:
            raise ValueError(
                "Could not import deeplake python package. "
                "Please install it with `pip install deeplake`."
            )
        deeplake.delete(path, large_ok=True, force=True)
[docs]    def delete_dataset(self) -> None:
        """Delete the collection."""
        self.delete(delete_all=True)