viruPVS/langchain-docs huggingface dataset:
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.
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.
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 = 1000, 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[source]¶
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) – 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 (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.
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, optional) – DeepLakeVectorStore supports 3 ways to perform
searching - “python”, “compute_engine”, “tensor_db” and auto.
Default is None.
- 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.WARNING: using this with big datasets can lead to memory
issues. Data can be stored anywhere.
compute_engine - C++ implementation of the Deep Lake ComputeEngine 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 isresponsible for storage and query execution. Only for data stored in
the Deep Lake Managed Database. Use runtime = {“db_engine”: 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.
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 thedistance 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.
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.
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.
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.
Returns
List of IDs of the added texts.
Return type
List[str]
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][source]¶
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 – List of ids to delete.
**kwargs – 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.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
**kwargs – 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)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → bool[source]¶
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[source]¶
Delete the collection.
ds() → Any[source]¶
classmethod force_delete_by_path(path: str) → None[source]¶
Force delete dataset by path.
Parameters
path (str) – path of the dataset to delete.
Raises
ValueError – if deeplake is not installed.
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
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[source]¶
Create a Deep Lake dataset from a raw documents.
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 formgcs://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’tsave 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
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][source]¶
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 – 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 DeepLake 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
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.
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][source]¶
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 – 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.
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 DeepLake 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.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
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 filterbefore 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.
WARNING: not recommended for big datasets.
’compute_engine’: C++ implementation of the Compute Engine forthe 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 improvingsearch 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][source]¶
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}}
”tensor_name_2”: {“key”: value}}
Function - Any function compatible withdeeplake.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 DeepLake 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 improvingsearch 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 – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – 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)
similarity_search_with_score(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]][source]¶
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 performsearching. 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