NgddlmZddlZddlmZmZmZmZmZm Z m Z m Z ddl Z ddlZddlmZddlmZddlmZdZn #e$rdZYnwxYwdd lmZdd lmZddlmZdd lmZejeZ Gd d eZ!dS)) annotationsN)AnyCallableDictIterableListOptionalTupleUnion) VectorStore)version_compare)SampleExtendErrorTFDocument) Embeddings)maximal_marginal_relevancec xeZdZUdZdZded<dgZedddddd d dddf dPd ZedQd!Z dRdSd)Z dTdUd,Z dVdWd<Z dXdYd=Z dXdZd?Z dXd[dAZ d\d]dEZ d\d^dFZedddefd_dGZdTd`dHZedadJZdbdKZdcdLZedMZedNZedOZdS)dDeepLakea`Activeloop Deep Lake` vector store. We integrated deeplake's similarity search and filtering for fast prototyping. Now, it supports Tensor Query Language (TQL) for production use cases over billion rows. Why Deep Lake? - Not only stores embeddings, but also the original data with version control. - Serverless, doesn't require another service and can be used with major cloud providers (S3, GCS, etc.) - More than just a multi-modal vector store. You can use the dataset to fine-tune your own LLM models. To use, you should have the ``deeplake`` python package installed. Example: .. code-block:: python from langchain_community.vectorstores import DeepLake from langchain_community.embeddings.openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings() vectorstore = DeepLake("langchain_store", embeddings.embed_query) z ./deeplake/str _LANGCHAIN_DEFAULT_DEEPLAKE_PATH lambda_multNFirT dataset_pathtoken Optional[str] embeddingOptional[Embeddings]embedding_function read_onlyboolingestion_batch_sizeint num_workersverbose exec_optionruntimeOptional[Dict] index_params$Optional[Dict[str, Union[int, str]]]kwargsrreturnNonec ||_||_||_tdurt d| ddikr;t t jddkrt dt jd||_|rt d td|j|p|||| || | d | |_ |p||_ d |j vrd nd |_d S)aCreates 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 = , ... ) >>> >>> # Create a vector store in the Deep Lake Managed Tensor Database >>> data = DeepLake( ... path = "hub://org_id/dataset_name", ... runtime = {"tensor_db": True}, ... ) Args: dataset_path (str): The full path for storing to the Deep Lake Vector Store. It can be: - a Deep Lake cloud path of the form ``hub://org_id/dataset_name``. Requires registration with Deep Lake. - an s3 path of the form ``s3://bucketname/path/to/dataset``. Credentials are required in either the environment or passed to the creds argument. - a local file system path of the form ``./path/to/dataset`` or ``~/path/to/dataset`` or ``path/to/dataset``. - a 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. 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 (Embeddings, optional): Function to convert either documents or query. Optional. embedding_function (Embeddings, optional): Function to convert either documents or query. Optional. Deprecated: keeping this parameter for backwards compatibility. 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 1024. 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, optional): Default method for search execution. It could be either ``"auto"``, ``"python"``, ``"compute_engine"`` or ``"tensor_db"``. Defaults to ``"auto"``. If None, it's set to "auto". - ``auto``- Selects the best execution method based on the storage location of the Vector Store. It is the default option. - ``python`` - Pure-python implementation that runs on the client and can be used for data stored anywhere. WARNING: using this option with big datasets is discouraged because it can lead to memory issues. - ``compute_engine`` - Performant C++ implementation of the Deep Lake Compute Engine that 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 and fully-hosted Managed Tensor Database that is responsible for storage and query execution. Only available for data stored in the Deep Lake Managed Database. Store datasets in this database by specifying runtime = {"tensor_db": True} during dataset creation. runtime (Dict, optional): Parameters for creating the Vector Store in Deep Lake's Managed Tensor Database. Not applicable when loading an existing Vector Store. To create a Vector Store in the Managed Tensor Database, set `runtime = {"tensor_db": True}`. index_params (Optional[Dict[str, Union[int, str]]], optional): Dictionary containing information about vector index that will be created. Defaults to None, which will utilize ``DEFAULT_VECTORSTORE_INDEX_PARAMS`` from ``deeplake.constants``. The specified key-values override the default ones. - threshold: The threshold for the dataset size above which an index will be created for the embedding tensor. When the threshold value is set to -1, index creation is turned off. Defaults to -1, which turns off the index. - distance_metric: This key specifies the method of calculating the distance between vectors when creating the vector database (VDB) index. It can either be a string that corresponds to a member of the DistanceType enumeration, or the string value itself. - If no value is provided, it defaults to "L2". - "L2" corresponds to DistanceType.L2_NORM. - "COS" corresponds to DistanceType.COSINE_SIMILARITY. - additional_params: Additional parameters for fine-tuning the index. **kwargs: Other optional keyword arguments. Raises: ValueError: If some condition is not met. FzdCould not import deeplake python package. Please install it with `pip install deeplake[enterprise]`. tensor_dbTz3.6.7zrTo use tensor_db option you need to update deeplake to `3.6.7` or higher. Currently installed deeplake version is z. zgUsing embedding function is deprecated and will be removed in the future. Please use embedding instead.)pathrrrr$r#r%r'idsidN)r r"r#_DEEPLAKE_INSTALLED ImportErrorr deeplake __version__rloggerwarningDeepLakeVectorStore vectorstore_embedding_functiontensors_id_tensor_name) selfrrrrrr r"r#r$r%r'r)s e/var/www/html/ai-engine/env/lib/python3.11/site-packages/langchain_community/vectorstores/deeplake.py__init__zDeepLake.__init__9sIV%9!& % ' 'M   T* * * 4g>>"DDT;C;OTTT  )   NN?    /  "1>Y#%      $6#B (-1A1I1I1K1K(K(KuuQUc|jSN)r;r>s r? embeddingszDeepLake.embeddingss ''rAtexts Iterable[str] metadatasOptional[List[dict]]r0Optional[List[str]] List[str]c *||di}|r|jdkr||d<n||d<| igtt|z}t |tst|}|t dt|dkrt d |jjd|||d|jj d d |S#t$r:}d t|vr"d }t |j dd z|z|d}~wwxYw)aqRun more texts through the embeddings and add to the vectorstore. Examples: >>> ids = deeplake_vectorstore.add_texts( ... texts = , ... metadatas = , ... 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. embedding_function (Optional[Embeddings], optional): Embedding function to use to convert the text into embeddings. **kwargs (Any): Any additional keyword arguments passed is not supported by this method. Returns: List[str]: List of IDs of the added texts. add_textsr0r1Nz$`texts` parameter shouldn't be None.rz%`texts` parameter shouldn't be empty.rT)textmetadataembedding_dataembedding_tensorr return_idsz2Failed to append a sample to the tensor 'metadata'zr**Hint: You might be using invalid type of argument in document loader (e.g. 'pathlib.PosixPath' instead of 'str')z r2) _validate_kwargsr=lenlist isinstance ValueErrorr:addr;embed_documentsrrargs)r>rFrHr0r)emsgs r?rMzDeepLake.add_textsse8 fk222  ##u,, #u "t  s4;;///I%&& KKE =CDD D ZZ1__DEE E '4#'"$!,#'#;#K  !   Cs1vvMMR!V!3c!9::: s+"C D5D  DtqlList[Document]c |j||}|d}|d}dt||D}|r9tt |}||durt d|d|S)aJFunction for performing tql_search. Args: tql (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: Tuple[List[Document], List[Tuple[Document, float]]] - A tuple of two lists. The first list contains Documents, and the second list contains tuples of Document and float score. Raises: ValueError: If return_score is True but some condition is not met. )queryr$rOrNc6g|]\}}t||S) page_contentrOr.0rNrOs r? z(DeepLake._search_tql..=C   h !!      rAFz specifying z" is not supported with tql search.)r:searchzipnextiterrW) r>r]r$r)resultrHrFdocsunsupported_arguments r? _search_tqlzDeepLake._search_tqls>!((#)  :& v  #&eY"7"7      #'V #5#5 *+588 5"6555  rAr`(Optional[Union[List[float], np.ndarray]]Optional[Callable]kdistance_metricuse_maximal_marginal_relevancefetch_k Optional[int]filterOptional[Union[Dict, Callable]] return_score deep_memory1Any[List[Document], List[Tuple[Document, float]]]c | dr2td| d| d<| dr#|| d| | |||||S|| d|r t |tr|j} n|} n|j r |j j} nd} | | td|r | |nd}t |tr@tj |tj}t|jd kr|d }|j||r|n|||| d d d |jg| }|d|d }|d |d |rn| dd}t)||t+|t|}fd|Dfd|Dfd|Ddt-D}| r2t tsgdt-|DS|S)a 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 (Optional[str], optional): `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. deep_memory (bool): Whether to use the Deep Memory model for improving search results. Defaults to False if deep_memory is not specified in the Vector Store initialization. If True, the distance metric is set to "deepmemory_distance", which represents the metric with which the model was trained. The search is performed using the Deep Memory model. If False, the distance metric is set to "COS" or whatever distance metric user specifies. 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. tql_queryz4`tql_query` is deprecated. Please use `tql` instead.r])r]r$r{rrrurvryrhNzAEither `embedding` or `embedding_function` needs to be specified.)dtyperrrOrN)rrtruryr$return_tensorsr|scorer?)rtrc g|] }| Sr2r2)reiscoress r?rfz$DeepLake._search..s111AfQi111rAc g|] }| Sr2r2)rerrFs r?rfz$DeepLake._search..s///!U1X///rAc g|] }| Sr2r2)rerrHs r?rfz$DeepLake._search..s777!1777rAc6g|]\}}t||Srbrrds r?rfz$DeepLake._search..rgrAcg|] \}}||f Sr2r2)redocrs r?rfz$DeepLake._search..s EEEZS%S%LEEErA)getr7r8poprorSrVr embed_queryr;rWrUnparrayfloat32rTshaper:rhr=rminri)r>r`rrrtrurvrwryr{r$r|r)r;rlrErindicesrmrHrrFs @@@r?_searchzDeepLake._searchOs| ::k " " 4 NNQ R R R"JJ{33F5M ::e   ##5M')##5 //M$    fh///  ',j99 9&8&D##&8##  % '"&":"F  "&   "* " 7<E++E222I i & & )"*===I9?##a''%aL !((7>ggQ+#'VT=QR#)  K( :& v ) 8 **]C88K0aU$$' G2111111F////w///E7777w777I  #&eY"7"7      Ffd++ " EE3tV3D3DEEE E rAc &|jd||ddd|S)a1 Return docs most similar to query. Examples: >>> # Search using an embedding >>> data = vector_store.similarity_search( ... query=, ... k=, ... exec_option=, ... ) >>> # Run tql search: >>> data = vector_store.similarity_search( ... query=None, ... tql="SELECT * WHERE 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. deep_memory (bool): Whether to use the Deep Memory model for improving search results. Defaults to False if deep_memory is not specified in the Vector Store initialization. If True, the distance metric is set to "deepmemory_distance", which represents the metric with which the model was trained. The search is performed using the Deep Memory model. If False, the distance metric is set to "COS" or whatever distance metric user specifies. Returns: List[Document]: List of Documents most similar to the query vector. F)r`rtrvr{r2rr>r`rtr)s r?similarity_searchzDeepLake.similarity_searchs;vt| +0       rAUnion[List[float], np.ndarray]c &|jd||ddd|S)a Return docs most similar to embedding vector. Examples: >>> # Search using an embedding >>> data = vector_store.similarity_search_by_vector( ... embedding=, ... k=, ... 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`. deep_memory (bool): Whether to use the Deep Memory model for improving search results. Defaults to False if deep_memory is not specified in the Vector Store initialization. If True, the distance metric is set to "deepmemory_distance", which represents the metric with which the model was trained. The search is performed using the Deep Memory model. If False, the distance metric is set to "COS" or whatever distance metric user specifies. Returns: List[Document]: List of Documents most similar to the query vector. F)rrtrvr{r2r)r>rrtr)s r?similarity_search_by_vectorz$DeepLake.similarity_search_by_vector%s;|t| +0       rAList[Tuple[Document, float]]c $|jd||dd|S)a] Run similarity search with Deep Lake with distance returned. Examples: >>> data = vector_store.similarity_search_with_score( ... query=, ... embedding= ... k=, ... 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. deep_memory (bool): Whether to use the Deep Memory model for improving search results. Defaults to False if deep_memory is not specified in the Vector Store initialization. If True, the distance metric is set to "deepmemory_distance", which represents the metric with which the model was trained. The search is performed using the Deep Memory model. If False, the distance metric is set to "COS" or whatever distance metric user specifies. Returns: List[Tuple[Document, float]]: List of documents most similar to the query text with distance in float.T)r`rtr{r2rrs r?similarity_search_with_scorez%DeepLake.similarity_search_with_scoreks8nt|       rAr List[float]floatc *|jd|||d||d|S)a| 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=, ... fetch_k=, ... k=, ... 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. deep_memory (bool): Whether to use the Deep Memory model for improving search results. Defaults to False if deep_memory is not specified in the Vector Store initialization. If True, the distance metric is set to "deepmemory_distance", which represents the metric with which the model was trained. The search is performed using the Deep Memory model. If False, the distance metric is set to "COS" or whatever distance metric user specifies. kwargs: Additional keyword arguments. Returns: List[Documents] - A list of documents. T)rrtrwrvrr$r2r)r>rrtrwrr$r)s r?'max_marginal_relevance_search_by_vectorz0DeepLake.max_marginal_relevance_search_by_vectorsApt| +/##      rAc |dp|j}|td|jd|||d|||d|S)a 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 = , ... embedding_function = , ... k = , ... 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. deep_memory (bool): Whether to use the Deep Memory model for improving search results. Defaults to False if deep_memory is not specified in the Vector Store initialization. If True, the distance metric is set to "deepmemory_distance", which represents the metric with which the model was trained. The search is performed using the Deep Memory model. If False, the distance metric is set to "COS" or whatever distance metric user specifies. 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. rNzXFor MMR search, you must specify an embedding function on `creation` or during add call.T)r`rtrwrvrr$rr2)rr;rWr)r>r`rtrwrr$r)rs r?max_marginal_relevance_searchz&DeepLake.max_marginal_relevance_searchs}x$ZZ 44P8P  %2 t|  +/##1      rAc L|d||d|}|||||S)a>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 = , ... embedding_function = , ... k = , ... 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. )rr)rFrHr0r2)rM)clsrFrrHr0rr)deeplake_datasets r? from_textszDeepLake.from_texts8sQd3XLIXXQWXX"" #    rAc |d}|d}|j|||dS)aDelete the entities in the dataset. Args: ids (Optional[List[str]], optional): The document_ids to delete. Defaults to None. **kwargs: Other keyword arguments that subclasses might use. - filter (Optional[Dict[str, str]], optional): The filter to delete by. - delete_all (Optional[bool], optional): Whether to drop the dataset. Returns: bool: Whether the delete operation was successful. ry delete_all)r0ryrT)rr:delete)r>r0r)ryrs r?rzDeepLake.deletersIH%%ZZ --  C:NNNtrAr/cr ddl}n#t$rtdwxYw|j|dddS)zForce delete dataset by path. Args: path (str): path of the dataset to delete. Raises: ValueError: if deeplake is not installed. rNzXCould not import deeplake python package. Please install it with `pip install deeplake`.T)large_okforce)r5r4r)rr/r5s r?force_delete_by_pathzDeepLake.force_delete_by_pathsf  OOOO   A   t4888888s!c2|ddS)zDelete the collection.T)rN)rrDs r?delete_datasetzDeepLake.delete_datasets t $$$$$rAcNtd|jjS)Nz^this method is deprecated and will be removed, better to use `db.vectorstore.dataset` instead.)r7r8r:datasetrDs r?dsz DeepLake.dss+ >   ''rAc|rC||}|||}|rtd|d|ddSdS)N`z` are not a valid argument to z method)_get_valid_args_get_unsupported_items TypeError)rr) method_name valid_itemsunsupported_itemss r?rSzDeepLake._validate_kwargss  --k::K # : :6; O O   8)88#.888     rAc |dkr|jSgS)Nrh)_valid_search_kwargs)rrs r?rzDeepLake._get_valid_argss ( " "+ +IrAcfd|D}d}|r4dt|}|S)Nc$i|] \}}|v || Sr2r2)rertvrs r? z3DeepLake._get_unsupported_items..s)JJJ41aQk5I5I!Q5I5I5IrAz`, `)itemsjoinsetkeys)r)rrs ` r?rzDeepLake._get_unsupported_itemss[JJJJ6<<>>JJJ   @ & C ,>,> ? ?   rA)rrrrrrrrrrr r!r"r!r#rr$rr%r&r'r(r)rr*r+)r*r)NN) rFrGrHrIr0rJr)rr*rKrC)r]rr$rr)rr*r^) NNNrpNFrqNFNF)r`rrrrrrsrtr!rurrvrrwrxryrzr{rr$rr|rr)rr*r})rp)r`rrtr!r)rr*r^)rrrtr!r)rr*r^)r`rrtr!r)rr*r)rprqrN)rrrtr!rwr!rrr$rr)rr*r^)r`rrtr!rwr!rrr$rr)rr*r^)rFrKrrrHrIr0rJrrr)rr*r)r0rJr)rr*r)r/rr*r+)r*r+)r*r)__name__ __module__ __qualname____doc__r__annotations__rr@propertyrErMrorrrrrr classmethodrrrrrrSr staticmethodrr2rAr?rrs4-:$9999)?=#*.37$(%)"&=ATVTVTVTVTVl(((X( +/#' BBBBBN&*66666t $>B15)-/4!#26"%)!QQQQQlA A A A A LD D D D D R< < < < < B %) @ @ @ @ @ J %) K K K K K Z+/*.#'< 7 7 7 7 [7 r(999[9&%%%%((((  [ [ !!\!!!rAr)" __future__rloggingtypingrrrrrr r r numpyrr5r r9deeplake.core.fast_forwardingr deeplake.util.exceptionsrr3r4langchain_core.documentsrlangchain_core.embeddingsrlangchain_core.vectorstores&langchain_community.vectorstores.utilsr getLoggerrr7rr2rAr?rsv""""""NNNNNNNNNNNNNNNNNNNN OOO;;;;;;======::::::    .-----000000333333MMMMMM  8 $ $c!c!c!c!c!{c!c!c!c!c!s=AA