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}.
**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.
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)