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.