- 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 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.
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},