BigQueryVectorSearch#

class langchain_community.vectorstores.bigquery_vector_search.BigQueryVectorSearch(embedding: Embeddings, project_id: str, dataset_name: str, table_name: str, location: str = 'US', content_field: str = 'content', metadata_field: str = 'metadata', text_embedding_field: str = 'text_embedding', doc_id_field: str = 'doc_id', distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE, credentials: Any | None = None)[source]#

Deprecated since version 0.0.33: Use :class:`~langchain_google_community.BigQueryVectorSearch` instead.

Google Cloud BigQuery vector store.

To use, you need the following packages installed:: google-cloud-bigquery

Constructor for BigQueryVectorSearch.

Parameters:

embedding (Embeddings) – Text Embedding model to use.
project_id (str) – GCP project.
dataset_name (str) – BigQuery dataset to store documents and embeddings.
table_name (str) – BigQuery table name.
location (str, optional) – BigQuery region. Defaults to `US`(multi-region).
content_field (str) – Specifies the column to store the content. Defaults to content.
metadata_field (str) – Specifies the column to store the metadata. Defaults to metadata.
text_embedding_field (str) – Specifies the column to store the embeddings vector. Defaults to text_embedding.
doc_id_field (str) – Specifies the column to store the document id. Defaults to doc_id.
distance_strategy (DistanceStrategy, optional) –
Determines the strategy employed for calculating the distance between vectors in the embedding space. Defaults to EUCLIDEAN_DISTANCE. Available options are: - COSINE: Measures the similarity between two vectors of an inner

product space.
- EUCLIDEAN_DISTANCE: Computes the Euclidean distance between
  two vectors. This metric considers the geometric distance in the vector space, and might be more suitable for embeddings that rely on spatial relationships. This is the default behavior
credentials (Credentials, optional) – Custom Google Cloud credentials to use. Defaults to None.

Attributes

`embeddings`	Access the query embedding object if available.
`full_table_id`

Methods

`__init__`(embedding, project_id, ...[, ...])	Constructor for BigQueryVectorSearch.
`aadd_documents`(documents, **kwargs)	Async run more documents through the embeddings and add to the vectorstore.
`aadd_texts`(texts[, metadatas, ids])	Async run more texts through the embeddings and add to the vectorstore.
`add_documents`(documents, **kwargs)	Add or update documents in the vectorstore.
`add_texts`(texts[, metadatas])	Run more texts through the embeddings and add to the vectorstore.
`add_texts_with_embeddings`(texts, embs[, ...])	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)	Async return VectorStore initialized from documents and embeddings.
`afrom_texts`(texts, embedding[, metadatas, ids])	Async return VectorStore initialized from texts and embeddings.
`aget_by_ids`(ids, /)	Async get documents by their IDs.
`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)	Async return docs most similar to query using a specified search type.
`asimilarity_search`(query[, k])	Async return docs most similar to query.
`asimilarity_search_by_vector`(embedding[, k])	Async return docs most similar to embedding vector.
`asimilarity_search_with_relevance_scores`(query)	Async return docs and relevance scores in the range [0, 1].
`asimilarity_search_with_score`(args, *kwargs)	Async run similarity search with distance.
`delete`([ids])	Delete by vector ID or other criteria.
`explore_job_stats`(job_id)	Return the statistics for a single job execution.
`from_documents`(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
`from_texts`(texts, embedding[, metadatas])	Return VectorStore initialized from texts and embeddings.
`get_by_ids`(ids, /)	Get documents by their IDs.
`get_documents`([ids, filter])	Search documents by their ids or metadata values.
`max_marginal_relevance_search`(query[, k, ...])	Return docs selected using the 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 a specified search type.
`similarity_search`(query[, k, filter, ...])	Run similarity search.
`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 score.
`similarity_search_with_score_by_vector`(embedding)	Return docs most similar to embedding vector.

__init__(embedding: Embeddings, project_id: str, dataset_name: str, table_name: str, location: str = 'US', content_field: str = 'content', metadata_field: str = 'metadata', text_embedding_field: str = 'text_embedding', doc_id_field: str = 'doc_id', distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE, credentials: Any | None = None)[source]#

Constructor for BigQueryVectorSearch.

Parameters:

embedding (Embeddings) – Text Embedding model to use.
project_id (str) – GCP project.
dataset_name (str) – BigQuery dataset to store documents and embeddings.
table_name (str) – BigQuery table name.
location (str, optional) – BigQuery region. Defaults to `US`(multi-region).
content_field (str) – Specifies the column to store the content. Defaults to content.
metadata_field (str) – Specifies the column to store the metadata. Defaults to metadata.
text_embedding_field (str) – Specifies the column to store the embeddings vector. Defaults to text_embedding.
doc_id_field (str) – Specifies the column to store the document id. Defaults to doc_id.
distance_strategy (DistanceStrategy, optional) –
Determines the strategy employed for calculating the distance between vectors in the embedding space. Defaults to EUCLIDEAN_DISTANCE. Available options are: - COSINE: Measures the similarity between two vectors of an inner

product space.
- EUCLIDEAN_DISTANCE: Computes the Euclidean distance between
  two vectors. This metric considers the geometric distance in the vector space, and might be more suitable for embeddings that rely on spatial relationships. This is the default behavior
credentials (Credentials, optional) – Custom Google Cloud credentials to use. Defaults to None.

async aadd_documents(documents: list[Document], **kwargs: Any) → list[str]#

Async run more documents through the embeddings and add to the vectorstore.

Parameters:

documents (list[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of IDs does not match the number of documents.

Return type:

list[str]

async aadd_texts(texts: Iterable[str], metadatas: list[dict] | None = None, *, ids: list[str] | None = None, **kwargs: Any) → list[str]#

Async run more texts through the embeddings and add to the vectorstore.

Parameters:

texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (list[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
ids (list[str] | None) – Optional list
**kwargs (Any) – vectorstore specific parameters.

Returns:

List of ids from adding the texts into the vectorstore.

Raises:

ValueError – If the number of metadatas does not match the number of texts.
ValueError – If the number of ids does not match the number of texts.

Return type:

list[str]

add_documents(documents: list[Document], **kwargs: Any) → list[str]#

Add or update documents in the vectorstore.

Parameters:

documents (list[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of ids does not match the number of documents.

Return type:

list[str]

add_texts(texts: List[str], metadatas: List[dict] | None = None, **kwargs: Any) → List[str][source]#

Run more texts through the embeddings and add to the vectorstore.

Parameters:

texts (List[str]) – List of strings to add to the vectorstore.
metadatas (List[dict] | None) – Optional list of metadata associated with the texts.
kwargs (Any)

Returns:

List of ids from adding the texts into the vectorstore.

Return type:

List[str]

add_texts_with_embeddings(texts: List[str], embs: List[List[float]], metadatas: List[dict] | None = None, **kwargs: Any) → List[str][source]#

Run more texts through the embeddings and add to the vectorstore.

Parameters:

texts (List[str]) – List of strings to add to the vectorstore.
embs (List[List[float]]) – List of lists of floats with text embeddings for texts.
metadatas (List[dict] | None) – Optional list of metadata associated with the texts.
kwargs (Any)

Returns:

List of ids from adding the texts into the vectorstore.

Return type:

List[str]

async adelete(ids: List[str] | None = None, **kwargs: Any) → bool | None[source]#

Delete by vector ID or other criteria.

Parameters:

ids (List[str] | None) – List of ids to delete.
**kwargs (Any) – 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#

Async return VectorStore initialized from documents and embeddings.

Parameters:

documents (list[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from documents and embeddings.

Return type:

VectorStore

async classmethod afrom_texts(texts: list[str], embedding: Embeddings, metadatas: list[dict] | None = None, *, ids: list[str] | None = None, **kwargs: Any) → VST#

Async return VectorStore initialized from texts and embeddings.

Parameters:

texts (list[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (list[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
ids (list[str] | None) – Optional list of IDs associated with the texts.
kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from texts and embeddings.

Return type:

VectorStore

async aget_by_ids(ids: Sequence[str], /) → list[Document]#

Async get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:: ids (Sequence[str]) – List of ids to retrieve.
Returns:: List of Documents.
Return type:: list[Document]

Added in version 0.2.11.

async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Dict[str, Any] | None = None, brute_force: bool = False, fraction_lists_to_search: float | None = None, **kwargs: Any) → List[Document][source]#

Return docs selected using the maximal marginal relevance.

Parameters:

query (str)
k (int)
fetch_k (int)
lambda_mult (float)
filter (Dict[str, Any] | None)
brute_force (bool)
fraction_lists_to_search (float | None)
kwargs (Any)

Return type:

List[Document]

async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Dict[str, Any] | None = None, brute_force: bool = False, fraction_lists_to_search: float | None = None, **kwargs: Any) → List[Document][source]#

Return docs selected using the maximal marginal relevance.

Parameters:

embedding (List[float])
k (int)
fetch_k (int)
lambda_mult (float)
filter (Dict[str, Any] | None)
brute_force (bool)
fraction_lists_to_search (float | None)
kwargs (Any)

Return type:

List[Document]

as_retriever(**kwargs: Any) → VectorStoreRetriever#

Return VectorStoreRetriever initialized from this VectorStore.

Parameters:

**kwargs (Any) –

Keyword arguments to pass to the search function. Can include: search_type (Optional[str]): Defines the type of search that

the Retriever should perform. Can be “similarity” (default), “mmr”, or “similarity_score_threshold”.

search_kwargs (Optional[Dict]): Keyword arguments to pass to the

search function. Can include things like:

k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold

for similarity_score_threshold

fetch_k: Amount of documents to pass to MMR algorithm: (Default: 20)
lambda_mult: Diversity of results returned by MMR;: 1 for minimum diversity and 0 for maximum. (Default: 0.5)

filter: Filter by document metadata

Returns:

Retriever class for VectorStore.

Return type:

VectorStoreRetriever

Examples:

# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 6, 'lambda_mult': 0.25}
)

# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 5, 'fetch_k': 50}
)

# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={'score_threshold': 0.8}
)

# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})

# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
    search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)

async asearch(query: str, search_type: str, **kwargs: Any) → list[Document]#

Async return docs most similar to query using a specified search type.

Parameters:

query (str) – Input text.
search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents most similar to the query.

Raises:

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type:

list[Document]

async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → list[Document]#

Async return docs most similar to query.

Parameters:

query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents most similar to the query.

Return type:

list[Document]

async asimilarity_search_by_vector(embedding: list[float], k: int = 4, **kwargs: Any) → list[Document]#

Async return docs most similar to embedding vector.

Parameters:

embedding (list[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents most similar to the query vector.

Return type:

list[Document]

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → list[tuple[Document, float]]#

Async return docs and relevance scores in the range [0, 1].

0 is dissimilar, 1 is most similar.

Parameters:

query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) –
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)

Return type:

list[tuple[Document, float]]

async asimilarity_search_with_score(*args: Any, **kwargs: Any) → list[tuple[Document, float]]#

Async run similarity search with distance.

Parameters:

*args (Any) – Arguments to pass to the search method.
**kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Tuples of (doc, similarity_score).

Return type:

list[tuple[Document, float]]

delete(ids: List[str] | None = None, **kwargs: Any) → bool | None[source]#

Delete by vector ID or other criteria.

Parameters:

ids (List[str] | None) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.

Returns:

True if deletion is successful, False otherwise, None if not implemented.

Return type:

Optional[bool]

explore_job_stats(job_id: str) → Dict[source]#

Return the statistics for a single job execution.

Parameters:: job_id (str) – The BigQuery Job id.
Returns:: A dictionary of job statistics for a given job.
Return type:: Dict

classmethod from_documents(documents: list[Document], embedding: Embeddings, **kwargs: Any) → VST#

Return VectorStore initialized from documents and embeddings.

Parameters:

documents (list[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from documents and embeddings.

Return type:

VectorStore

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: List[dict] | None = None, **kwargs: Any) → BigQueryVectorSearch[source]#

Return VectorStore initialized from texts and embeddings.

Parameters:

texts (List[str])
embedding (Embeddings)
metadatas (List[dict] | None)
kwargs (Any)

Return type:

BigQueryVectorSearch

get_by_ids(ids: Sequence[str], /) → list[Document]#

Get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:: ids (Sequence[str]) – List of ids to retrieve.
Returns:: List of Documents.
Return type:: list[Document]

Added in version 0.2.11.

get_documents(ids: List[str] | None = None, filter: Dict[str, Any] | None = None) → List[Document][source]#

Search documents by their ids or metadata values.

Parameters:

ids (List[str] | None) – List of ids of documents to retrieve from the vectorstore.
filter (Dict[str, Any] | None) –
Filter on metadata properties, e.g. {

”str_property”: “foo”, “int_property”: 123

}

Returns:

List of ids from adding the texts into the vectorstore.

Return type:

List[Document]

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Dict[str, Any] | None = None, brute_force: bool = False, fraction_lists_to_search: float | None = 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 documents.

Parameters:

query (str) – search query text.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.
filter (Dict[str, Any] | None) –
Filter on metadata properties, e.g. {

”str_property”: “foo”, “int_property”: 123

}
brute_force (bool) – Whether to use brute force search. Defaults to False.
fraction_lists_to_search (float | None) – Optional percentage of lists to search, must be in range 0.0 and 1.0, exclusive. If Node, uses service’s default which is 0.05.
kwargs (Any)

Returns:

List of Documents selected by maximal marginal relevance.

Return type:

List[Document]

max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Dict[str, Any] | None = None, brute_force: bool = False, fraction_lists_to_search: float | None = 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 documents.

Parameters:

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.
filter (Dict[str, Any] | None) –
Filter on metadata properties, e.g. {

”str_property”: “foo”, “int_property”: 123

}
brute_force (bool) – Whether to use brute force search. Defaults to False.
fraction_lists_to_search (float | None) – Optional percentage of lists to search, must be in range 0.0 and 1.0, exclusive. If Node, uses service’s default which is 0.05.
kwargs (Any)

Returns:

List of Documents selected by maximal marginal relevance.

Return type:

List[Document]

search(query: str, search_type: str, **kwargs: Any) → list[Document]#

Return docs most similar to query using a specified search type.

Parameters:

query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents most similar to the query.

Raises:

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type:

list[Document]

similarity_search(query: str, k: int = 4, filter: Dict[str, Any] | None = None, brute_force: bool = False, fraction_lists_to_search: float | None = None, **kwargs: Any) → List[Document][source]#

Run similarity search.

Parameters:

query (str) – search query text.
k (int) – Number of Documents to return. Defaults to 4.
filter (Dict[str, Any] | None) –
Filter on metadata properties, e.g. {

”str_property”: “foo”, “int_property”: 123

}
brute_force (bool) – Whether to use brute force search. Defaults to False.
fraction_lists_to_search (float | None) – Optional percentage of lists to search, must be in range 0.0 and 1.0, exclusive. If Node, uses service’s default which is 0.05.
kwargs (Any)

Returns:

List of Documents most similar to the query vector.

Return type:

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Dict[str, Any] | None = None, brute_force: bool = False, fraction_lists_to_search: float | None = None, **kwargs: Any) → List[Document][source]#

Return docs most similar to embedding vector.

Parameters:

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Dict[str, Any] | None) –
Filter on metadata properties, e.g. {

”str_property”: “foo”, “int_property”: 123

}
brute_force (bool) – Whether to use brute force search. Defaults to False.
fraction_lists_to_search (float | None) – Optional percentage of lists to search, must be in range 0.0 and 1.0, exclusive. If Node, uses service’s default which is 0.05.
kwargs (Any)

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 (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) –
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).

Return type:

list[tuple[Document, float]]

similarity_search_with_score(query: str, k: int = 4, filter: Dict[str, Any] | None = None, brute_force: bool = False, fraction_lists_to_search: float | None = None, **kwargs: Any) → List[Tuple[Document, float]][source]#

Run similarity search with score.

Parameters:

query (str) – search query text.
k (int) – Number of Documents to return. Defaults to 4.
filter (Dict[str, Any] | None) –
Filter on metadata properties, e.g. {

”str_property”: “foo”, “int_property”: 123

}
brute_force (bool) – Whether to use brute force search. Defaults to False.
fraction_lists_to_search (float | None) – Optional percentage of lists to search, must be in range 0.0 and 1.0, exclusive. If Node, uses service’s default which is 0.05.
kwargs (Any)

Returns:

List of Documents most similar to the query vector, with similarity scores.

Return type:

List[Tuple[Document, float]]

similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Dict[str, Any] | None = None, brute_force: bool = False, fraction_lists_to_search: float | None = None, **kwargs: Any) → List[Tuple[Document, float]][source]#

Return docs most similar to embedding vector.

Parameters:

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Dict[str, Any] | None) –
Filter on metadata properties, e.g. {

”str_property”: “foo”, “int_property”: 123

}
brute_force (bool) – Whether to use brute force search. Defaults to False.
fraction_lists_to_search (float | None) – Optional percentage of lists to search, must be in range 0.0 and 1.0, exclusive. If Node, uses service’s default which is 0.05.
kwargs (Any)

Returns:

List of Documents most similar to the query vector with distance.

Return type:

List[Tuple[Document, float]]