Toggle Menu
Prev Up Next

# `langchain_community.vectorstores.deeplake`.DeepLake

class langchain_community.vectorstores.deeplake.DeepLake(dataset_path: str = './deeplake/', token: Optional[str] = None, embedding: Optional[Embeddings] = None, embedding_function: Optional[Embeddings] = None, read_only: bool = False, ingestion_batch_size: int = 1024, num_workers: int = 0, verbose: bool = True, exec_option: Optional[str] = None, runtime: Optional[Dict] = None, index_params: Optional[Dict[str, Union[int, str]]] = None, **kwargs: Any)

  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

      from langchain_community.vectorstores import DeepLake
      from langchain_community.embeddings.openai import OpenAIEmbeddings

      embeddings = OpenAIEmbeddings()
      vectorstore = DeepLake("langchain_store", embeddings.embed_query)

  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",
      ...        runtime = {"tensor_db": True},
      ... )

  Parameters

    - 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.

  Attributes

  `embeddings` | Access the query embedding object if available.

  Methods

  `__init__`([dataset_path, token, embedding, ...]) | Creates an empty DeepLakeVectorStore or loads an existing one.
  `aadd_documents`(documents, **kwargs) | Run more documents through the embeddings and add to the vectorstore.
  `aadd_texts`(texts[, metadatas]) | Run more texts through the embeddings and add to the vectorstore.
  `add_documents`(documents, **kwargs) | Run more documents through the embeddings and add to the vectorstore.
  `add_texts`(texts[, metadatas, ids]) | Run more texts through the embeddings and add to the vectorstore.
  `adelete`([ids]) | Delete by vector ID or other criteria.
  `afrom_documents`(documents, embedding, **kwargs) | Return VectorStore initialized from documents and embeddings.
  `afrom_texts`(texts, embedding[, metadatas]) | Return VectorStore initialized from texts and embeddings.
  `amax_marginal_relevance_search`(query[, k, ...]) | Return docs selected using the maximal marginal relevance.
  `amax_marginal_relevance_search_by_vector`(...) | Return docs selected using the maximal marginal relevance.
  `as_retriever`(**kwargs) | Return VectorStoreRetriever initialized from this VectorStore.
  `asearch`(query, search_type, **kwargs) | Return docs most similar to query using specified search type.
  `asimilarity_search`(query[, k]) | Return docs most similar to query.
  `asimilarity_search_by_vector`(embedding[, k]) | Return docs most similar to embedding vector.
  `asimilarity_search_with_relevance_scores`(query) | Return docs and relevance scores in the range [0, 1], asynchronously.
  `asimilarity_search_with_score`(*args, **kwargs) | Run similarity search with distance asynchronously.
  `delete`([ids]) | Delete the entities in the dataset.
  `delete_dataset`() | Delete the collection.
  `ds`() |
  `force_delete_by_path`(path) | Force delete dataset by path.
  `from_documents`(documents, embedding, **kwargs) | Return VectorStore initialized from documents and embeddings.
  `from_texts`(texts[, embedding, metadatas, ...]) | Create a Deep Lake dataset from a raw documents.
  `max_marginal_relevance_search`(query[, k, ...]) | Return docs selected using maximal marginal relevance.
  `max_marginal_relevance_search_by_vector`(...) | Return docs selected using the maximal marginal relevance.
  `search`(query, search_type, **kwargs) | Return docs most similar to query using specified search type.
  `similarity_search`(query[, k]) | Return docs most similar to query.
  `similarity_search_by_vector`(embedding[, k]) | Return docs most similar to embedding vector.
  `similarity_search_with_relevance_scores`(query) | Return docs and relevance scores in the range [0, 1].
  `similarity_search_with_score`(query[, k]) | Run similarity search with Deep Lake with distance returned.

  __init__(dataset_path: str = './deeplake/', token: Optional[str] = None, embedding: Optional[Embeddings] = None, embedding_function: Optional[Embeddings] = None, read_only: bool = False, ingestion_batch_size: int = 1024, num_workers: int = 0, verbose: bool = True, exec_option: Optional[str] = None, runtime: Optional[Dict] = None, index_params: Optional[Dict[str, Union[int, str]]] = None, **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",
        ...        runtime = {"tensor_db": True},
        ... )

    Parameters

      - 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.

    Return type

      None

  async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]

    Run more documents through the embeddings and add to the vectorstore.

    Parameters

      - (List[Document] (documents) – Documents to add to the vectorstore.

      - documents (List[Document]) –

      - kwargs (Any) –

    Returns

      List of IDs of the added texts.

    Return type

      List[str]

  async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]

    Run more texts through the embeddings and add to the vectorstore.

    Parameters

      - texts (Iterable[str]) –

      - metadatas (Optional[List[dict]]) –

      - kwargs (Any) –

    Return type

      List[str]

  add_documents(documents: List[Document], **kwargs: Any) → List[str]

    Run more documents through the embeddings and add to the vectorstore.

    Parameters

      - (List[Document] (documents) – Documents to add to the vectorstore.

      - documents (List[Document]) –

      - kwargs (Any) –

    Returns

      List of IDs of the added texts.

    Return type

      List[str]

  add_texts(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>,
        ... )

    Parameters

      - 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 of IDs of the added texts.

    Return type

      List[str]

  async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]

    Delete by vector ID or other criteria.

    Parameters

      - ids (Optional[List[str]]) – List of ids to delete.

      - **kwargs (Any) – Other keyword arguments that subclasses might use.

    Returns

      True if deletion is successful, False otherwise, None if not implemented.

    Return type

      Optional[bool]

  async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

    Return VectorStore initialized from documents and embeddings.

    Parameters

      - documents (List[Document]) –

      - embedding (Embeddings) –

      - kwargs (Any) –

    Return type

      VST

  async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST

    Return VectorStore initialized from texts and embeddings.

    Parameters

      - texts (List[str]) –

      - embedding (Embeddings) –

      - metadatas (Optional[List[dict]]) –

      - kwargs (Any) –

    Return type

      VST

  async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

    Return docs selected using the maximal marginal relevance.

    Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

    Parameters

      - query (str) – Text to look up documents similar to.

      - k (int) – Number of Documents to return. Defaults to 4.

      - fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.

      - lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.

      - kwargs (Any) –

    Returns

      List of Documents selected by maximal marginal relevance.

    Return type

      List[Document]

  async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

    Return docs selected using the maximal marginal relevance.

    Parameters

      - embedding (List[float]) –

      - k (int) –

      - fetch_k (int) –

      - lambda_mult (float) –

      - kwargs (Any) –

    Return type

      List[Document]

  as_retriever(**kwargs: Any) → VectorStoreRetriever

    Return VectorStoreRetriever initialized from this VectorStore.

    Parameters

      - search_type (Optional[str]) – Defines the type of search that the Retriever should perform. Can be “similarity” (default), “mmr”, or “similarity_score_threshold”.

      - search_kwargs (Optional[Dict]) –

        Keyword arguments to pass to the search function. Can include things like:

        > k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold

        > > for similarity_score_threshold

        > fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR;

        > > 1 for minimum diversity and 0 for maximum. (Default: 0.5)

        > filter: Filter by document metadata

      - kwargs (Any) –

    Returns

      Retriever class for VectorStore.

    Return type

      VectorStoreRetriever

    Examples:

        # Retrieve more documents with higher diversity
        # Useful if your dataset has many similar documents
        docsearch.as_retriever(
            search_type="mmr",
            search_kwargs={'k': 6, 'lambda_mult': 0.25}
        )

        # Fetch more documents for the MMR algorithm to consider
        # But only return the top 5
        docsearch.as_retriever(
            search_type="mmr",
            search_kwargs={'k': 5, 'fetch_k': 50}
        )

        # Only retrieve documents that have a relevance score
        # Above a certain threshold
        docsearch.as_retriever(
            search_type="similarity_score_threshold",
            search_kwargs={'score_threshold': 0.8}
        )

        # Only get the single most similar document from the dataset
        docsearch.as_retriever(search_kwargs={'k': 1})

        # Use a filter to only retrieve documents from a specific paper
        docsearch.as_retriever(
            search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
        )

  async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]

    Return docs most similar to query using specified search type.

    Parameters

      - query (str) –

      - search_type (str) –

      - kwargs (Any) –

    Return type

      List[Document]

  async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]

    Return docs most similar to query.

    Parameters

      - query (str) –

      - k (int) –

      - kwargs (Any) –

    Return type

      List[Document]

  async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]

    Return docs most similar to embedding vector.

    Parameters

      - embedding (List[float]) –

      - k (int) –

      - kwargs (Any) –

    Return type

      List[Document]

  async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]

    Return docs and relevance scores in the range [0, 1], asynchronously.

    0 is dissimilar, 1 is most similar.

    Parameters

      - query (str) – input text

      - k (int) – Number of Documents to return. Defaults to 4.

      - **kwargs (Any) –

        kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to

        > filter the resulting set of retrieved docs

    Returns

      List of Tuples of (doc, similarity_score)

    Return type

      List[Tuple[Document, float]]

  async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]

    Run similarity search with distance asynchronously.

    Parameters

      - args (Any) –

      - kwargs (Any) –

    Return type

      List[Tuple[Document, float]]

  delete(ids: Optional[List[str]] = None, **kwargs: Any) → bool

    Delete the entities in the dataset.

    Parameters

      - 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

      Whether the delete operation was successful.

    Return type

      bool

  delete_dataset() → None

    Delete the collection.

    Return type

      None

  ds() → Any
    Return type

      Any

  classmethod force_delete_by_path(path: str) → None

    Force delete dataset by path.

    Parameters

      path (str) – path of the dataset to delete.

    Raises

      ValueError – if deeplake is not installed.

    Return type

      None

  classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

    Return VectorStore initialized from documents and embeddings.

    Parameters

      - documents (List[Document]) –

      - embedding (Embeddings) –

      - kwargs (Any) –

    Return type

      VST

  classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, dataset_path: str = './deeplake/', **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>, … )

    Parameters

      - 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

      Deep Lake dataset.

    Return type

      DeepLake

  max_marginal_relevance_search(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>, … )

    Parameters

      - query (str) – Text to look up documents similar to.

      - k (int) – Number of Documents to return. Defaults to 4.

      - fetch_k (int) – Number of Documents for MMR algorithm.

      - lambda_mult (float) – 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.

    Return type

      List[Document]

  max_marginal_relevance_search_by_vector(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>, … )

    Parameters

      - embedding (List[float]) – Embedding to look up documents similar to.

      - k (int) – Number of Documents to return. Defaults to 4.

      - fetch_k (int) – Number of Documents to fetch for MMR algorithm.

      - lambda_mult (float) – 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.

    Return type

      List[Document]

  search(query: str, search_type: str, **kwargs: Any) → List[Document]

    Return docs most similar to query using specified search type.

    Parameters

      - query (str) –

      - search_type (str) –

      - kwargs (Any) –

    Return type

      List[Document]

  similarity_search(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.similarity_search(
        ...     query=None,
        ...     tql="SELECT * WHERE id == <id>",
        ...     exec_option="compute_engine",
        ... )

    Parameters

      - 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 of Documents most similar to the query vector.

    Return type

      List[Document]

  similarity_search_by_vector(embedding: Union[List[float], 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>,
        ... )

    Parameters

      - 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 of Documents most similar to the query vector.

    Return type

      List[Document]

  similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]

    Return docs and relevance scores in the range [0, 1].

    0 is dissimilar, 1 is most similar.

    Parameters

      - query (str) – input text

      - k (int) – Number of Documents to return. Defaults to 4.

      - **kwargs (Any) –

        kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to

        > filter the resulting set of retrieved docs

    Returns

      List of Tuples of (doc, similarity_score)

    Return type

      List[Tuple[Document, float]]

  similarity_search_with_score(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>, … )

    Parameters

      - 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 of documents most similar to the query

        text with distance in float.

    Return type

      List[Tuple[Document, float]]

# Examples using DeepLake

- # activeloop token is needed if you are not signed in using CLI: `activeloop login -u <USERNAME> -p <PASSWORD>`

- Activeloop Deep Lake

- in case if some queries fail consider installing libdeeplake manually

- or shorter