**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
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