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Class BigtableVectorStore (0.8.0)

BigtableVectorStore(instance_id: str, table_id: str, embedding_service: langchain_core.embeddings.embeddings.Embeddings, collection: str, content_column: langchain_google_bigtable.async_vector_store.ColumnConfig = ColumnConfig(column_qualifier='content', column_family='langchain', encoding=<Encoding.UTF8: 'utf-8'>), embedding_column: langchain_google_bigtable.async_vector_store.ColumnConfig = ColumnConfig(column_qualifier='embedding', column_family='langchain', encoding=<Encoding.UTF8: 'utf-8'>), project_id: typing.Optional[str] = None, metadata_mappings: typing.Optional[typing.List[langchain_google_bigtable.async_vector_store.VectorMetadataMapping]] = None, metadata_as_json_column: typing.Optional[langchain_google_bigtable.async_vector_store.ColumnConfig] = None, engine: typing.Optional[langchain_google_bigtable.engine.BigtableEngine] = None, app_profile_id: typing.Optional[str] = None, **kwargs: typing.Any)

A vector store implementation using Google Cloud Bigtable.

This class provides the main user-facing interface, conforming to the langchain_core.vectorstores.VectorStore standard, and handles both synchronous and asynchronous operations by wrapping an async core.

Properties

embeddings

Access the query embedding object.

Returns
Type	Description
`(Embeddings)`	The embedding service object.

Methods

BigtableVectorStore

BigtableVectorStore(instance_id: str, table_id: str, embedding_service: langchain_core.embeddings.embeddings.Embeddings, collection: str, content_column: langchain_google_bigtable.async_vector_store.ColumnConfig = ColumnConfig(column_qualifier='content', column_family='langchain', encoding=<Encoding.UTF8: 'utf-8'>), embedding_column: langchain_google_bigtable.async_vector_store.ColumnConfig = ColumnConfig(column_qualifier='embedding', column_family='langchain', encoding=<Encoding.UTF8: 'utf-8'>), project_id: typing.Optional[str] = None, metadata_mappings: typing.Optional[typing.List[langchain_google_bigtable.async_vector_store.VectorMetadataMapping]] = None, metadata_as_json_column: typing.Optional[langchain_google_bigtable.async_vector_store.ColumnConfig] = None, engine: typing.Optional[langchain_google_bigtable.engine.BigtableEngine] = None, app_profile_id: typing.Optional[str] = None, **kwargs: typing.Any)

Initializes the BigtableVectorStore.

Parameters
Name	Description
`instance_id`	`str` Your Bigtable instance ID.
`table_id`	`str` The ID of the table to use for the vector store.
`embedding_service`	`Embeddings` The embedding service to use.
`collection`	`str` A name for the collection of vectors for this store. Internally, this is used as the row key prefix.
`content_column`	`ColumnConfig` Configuration for the document content column.
`embedding_column`	`ColumnConfig` Configuration for the vector embedding column.
`project_id`	`Optional[str]` Your Google Cloud project ID.
`metadata_mappings`	`Optional[List[VectorMetadataMapping]]` Mappings for storing metadata in separate columns.
`metadata_as_json_column`	`Optional[ColumnConfig]` Configuration for storing all metadata in a single JSON column.
`engine`	`Optional[BigtableEngine]` The BigtableEngine to use for connecting to Bigtable.
`app_profile_id`	`Optional[str]` The Bigtable app profile ID to use for requests.
`\\kwargs`	`Any` Additional arguments for engine creation if an engine is not provided.

_get_async_store

_get_async_store() -> (
    langchain_google_bigtable.async_vector_store.AsyncBigtableVectorStore
)

Lazily initializes and returns the underlying async store.

Returns
Type	Description
`(AsyncBigtableVectorStore)`	The initialized asynchronous vector store.

aadd_documents

aadd_documents(
    documents: typing.List[langchain_core.documents.base.Document],
    ids: typing.Optional[list] = None,
    **kwargs: typing.Any
) -> typing.List[str]

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

Parameters
Name	Description
`documents`	`List[Document]` A list of documents to add.
`ids`	`Optional[list]` list of IDs for the documents.
`\\kwargs`	`Any` Additional arguments.

Returns
Type	Description
`(List[str])`	A list of the row keys of the added documents.

aadd_texts

aadd_texts(
    texts: typing.Iterable[str],
    metadatas: typing.Optional[typing.List[dict]] = None,
    ids: typing.Optional[list] = None,
    **kwargs: typing.Any
) -> typing.List[str]

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

Parameters
Name	Description
`texts`	`Iterable[str]` An iterable of texts to add.
`metadatas`	`Optional[List[dict]]` Optional list of metadatas.
`ids`	`Optional[list]` list of IDs for the texts.
`\\kwargs`	`Any` Additional arguments.

Returns
Type	Description
`(List[str])`	A list of the row keys of the added texts.

add_documents

add_documents(
    documents: typing.List[langchain_core.documents.base.Document],
    ids: typing.Optional[list] = None,
    **kwargs: typing.Any
) -> typing.List[str]

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

Parameters
Name	Description
`documents`	`List[Document]` A list of documents to add.
`ids`	`Optional[list]` list of IDs for the documents.
`\\kwargs`	`Any` Additional arguments.

Returns
Type	Description
`(List[str])`	A list of the row keys of the added documents.

add_texts

add_texts(
    texts: typing.Iterable[str],
    metadatas: typing.Optional[typing.List[typing.Dict]] = None,
    ids: typing.Optional[list] = None,
    **kwargs: typing.Any
) -> typing.List[str]

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

Parameters
Name	Description
`texts`	`Iterable[str]` An iterable of texts to add.
`metadatas`	`Optional[List[Dict]]` Optional list of metadatas.
`ids`	`Optional[list]` list of IDs for the texts.
`\\kwargs`	`Any` Additional arguments.

Returns
Type	Description
`(List[str])`	A list of the row keys of the added texts.

adelete

adelete(
    ids: typing.Optional[typing.List[str]] = None, **kwargs: typing.Any
) -> typing.Optional[bool]

Delete by vector ID.

Parameters
Name	Description
`ids`	`Optional[List[str]]` A list of document IDs to delete.
`\\kwargs`	`Any` Additional arguments.

Returns
Type	Description
`(Optional[bool])`	True if the deletion was successful.

afrom_documents

afrom_documents(
    documents: typing.List[langchain_core.documents.base.Document],
    embedding: langchain_core.embeddings.embeddings.Embeddings,
    ids: typing.Optional[list] = None,
    **kwargs: typing.Any
) -> langchain_google_bigtable.vector_store.BigtableVectorStore

Return VectorStore initialized from documents and embeddings. This is an asynchronous method that creates the store and adds documents.

Parameters
Name	Description
`documents`	`List[Document]` List of documents to add.
`embedding`	`Embeddings` The embedding service to use. ids (Optional[list]): list of IDs for the documents.
`\\kwargs`	`Any` Keyword arguments to pass to the `create` method.

Returns
Type	Description
`(BigtableVectorStore)`	An instance of the vector store.

afrom_texts

afrom_texts(
    texts: typing.List[str],
    embedding: langchain_core.embeddings.embeddings.Embeddings,
    metadatas: typing.Optional[typing.List[dict]] = None,
    ids: typing.Optional[list] = None,
    **kwargs: typing.Any
) -> langchain_google_bigtable.vector_store.BigtableVectorStore

Return VectorStore initialized from texts and embeddings.

Parameters
Name	Description
`texts`	`List[str]` List of text strings to add.
`embedding`	`Embeddings` The embedding service to use.
`metadatas`	`Optional[List[dict]]` Optional list of metadata for each text.
`ids`	`Optional[list]` list of IDs for the texts.
`\\kwargs`	`Any` Keyword arguments to pass to the `acreate` method.

Returns
Type	Description
`(BigtableVectorStore)`	An instance of the vector store.

aget_by_ids

aget_by_ids(
    ids: typing.Sequence[str],
) -> typing.List[langchain_core.documents.base.Document]

Return documents by their IDs.

Parameter
Name	Description
`ids`	`List[str]` A list of document IDs to retrieve.

Returns
Type	Description
`(List[Document])`	A list of the retrieved documents.

amax_marginal_relevance_search

amax_marginal_relevance_search(
    query: str,
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    query_parameters: typing.Optional[
        langchain_google_bigtable.async_vector_store.QueryParameters
    ] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

Return docs selected using the maximal marginal relevance.

Parameters
Name	Description
`query`	`str` The text to search for.
`k`	`int` The number of results to return.
`fetch_k`	`int` The number of documents to fetch for MMR.
`lambda_mult`	`float` The lambda multiplier for MMR.
`query_parameters`	`Optional[QueryParameters]` Custom query parameters for this search.
`\\kwargs`	`Any` Additional keyword arguments (e.g., filter).

Returns
Type	Description
`(List[Document])`	A list of documents selected by MMR.

amax_marginal_relevance_search_by_vector

amax_marginal_relevance_search_by_vector(
    embedding: typing.List[float],
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    query_parameters: typing.Optional[
        langchain_google_bigtable.async_vector_store.QueryParameters
    ] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

Return docs selected using the maximal marginal relevance.

Parameters
Name	Description
`embedding`	`List[float]` The embedding vector to search for.
`k`	`int` The number of results to return.
`fetch_k`	`int` The number of documents to fetch for MMR.
`lambda_mult`	`float` The lambda multiplier for MMR.
`query_parameters`	`Optional[QueryParameters]` Custom query parameters for this search.
`\\kwargs`	`Any` Additional keyword arguments (e.g., filter).

Returns
Type	Description
`(List[Document])`	A list of documents selected by MMR.

as_retriever

as_retriever(
    **kwargs: typing.Any,
) -> langchain_core.vectorstores.base.VectorStoreRetriever

Return VectorStoreRetriever initialized from this VectorStore.

Parameter
Name	Description
`\\kwargs`	`Any` Keyword arguments to pass to the retriever.

Returns
Type	Description
`(VectorStoreRetriever)`	The initialized retriever.

asimilarity_search

asimilarity_search(
    query: str,
    k: int = 4,
    query_parameters: typing.Optional[
        langchain_google_bigtable.async_vector_store.QueryParameters
    ] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

Return docs most similar to query.

Parameters
Name	Description
`query`	`str` The text to search for.
`k`	`int` The number of results to return.
`query_parameters`	`Optional[QueryParameters]` Custom query parameters for this search.
`\\kwargs`	`Any` Additional keyword arguments (e.g., filter).

Returns
Type	Description
`(List[Document])`	A list of documents most similar to the query.

asimilarity_search_by_vector

asimilarity_search_by_vector(
    embedding: typing.List[float],
    k: int = 4,
    query_parameters: typing.Optional[
        langchain_google_bigtable.async_vector_store.QueryParameters
    ] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

Return docs most similar to embedding vector.

Parameters
Name	Description
`embedding`	`List[float]` The embedding vector to search for.
`k`	`int` The number of results to return.
`query_parameters`	`Optional[QueryParameters]` Custom query parameters for this search.
`\\kwargs`	`Any` Additional keyword arguments (e.g., filter).

Returns
Type	Description
`(List[Document])`	A list of documents most similar to the embedding.

asimilarity_search_with_relevance_scores

asimilarity_search_with_relevance_scores(
    query: str,
    k: int = 4,
    query_parameters: typing.Optional[
        langchain_google_bigtable.async_vector_store.QueryParameters
    ] = None,
    **kwargs: typing.Any
) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

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

Parameters
Name	Description
`query`	`str` The text to search for.
`k`	`int` The number of results to return.
`query_parameters`	`Optional[QueryParameters]` Custom query parameters for this search.
`\\kwargs`	`Any` Additional keyword arguments (e.g., filter).

Returns
Type	Description
`(List[Tuple[Document, float]])`	A list of tuples containing the document and its relevance score.

asimilarity_search_with_score

asimilarity_search_with_score(
    query: str,
    k: int = 4,
    query_parameters: typing.Optional[
        langchain_google_bigtable.async_vector_store.QueryParameters
    ] = None,
    **kwargs: typing.Any
) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

Run similarity search with distance.

Parameters
Name	Description
`query`	`str` The text to search for.
`k`	`int` The number of results to return.
`query_parameters`	`Optional[QueryParameters]` Custom query parameters for this search.
`\\kwargs`	`Any` Additional keyword arguments (e.g., filter).

Returns
Type	Description
`(List[Tuple[Document, float]])`	A list of tuples containing the document and its distance score.

asimilarity_search_with_score_by_vector

asimilarity_search_with_score_by_vector(
    embedding: typing.List[float],
    k: int = 4,
    query_parameters: typing.Optional[
        langchain_google_bigtable.async_vector_store.QueryParameters
    ] = None,
    **kwargs: typing.Any
) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

Run similarity search with distance by vector.

Parameters
Name	Description
`embedding`	`List[float]` The embedding vector to search for.
`k`	`int` The number of results to return.
`query_parameters`	`Optional[QueryParameters]` Custom query parameters for this search.
`\\kwargs`	`Any` Additional keyword arguments (e.g., filter).

Returns
Type	Description
`(List[Tuple[Document, float]])`	A list of tuples containing the document and its distance score.

close

close() -> None

Close the engine connection.

create

create(instance_id: str, table_id: str, embedding_service: langchain_core.embeddings.embeddings.Embeddings, collection: str, engine: typing.Optional[langchain_google_bigtable.engine.BigtableEngine] = None, content_column: langchain_google_bigtable.async_vector_store.ColumnConfig = ColumnConfig(column_qualifier='content', column_family='langchain', encoding=<Encoding.UTF8: 'utf-8'>), embedding_column: langchain_google_bigtable.async_vector_store.ColumnConfig = ColumnConfig(column_qualifier='embedding', column_family='langchain', encoding=<Encoding.UTF8: 'utf-8'>), project_id: typing.Optional[str] = None, metadata_mappings: typing.Optional[typing.List[langchain_google_bigtable.async_vector_store.VectorMetadataMapping]] = None, metadata_as_json_column: typing.Optional[langchain_google_bigtable.async_vector_store.ColumnConfig] = None, app_profile_id: typing.Optional[str] = None, credentials: typing.Optional[google.auth.credentials.Credentials] = None, client_options: typing.Optional[typing.Dict[str, typing.Any]] = None, **kwargs: typing.Any) -> langchain_google_bigtable.vector_store.BigtableVectorStore

Asynchronously initializes the engine and creates an instance of the vector store.

Parameters
Name	Description
`instance_id`	`str` Your Bigtable instance ID.
`table_id`	`str` The ID of the table to use for the vector store.
`embedding_service`	`Embeddings` The embedding service to use.
`collection`	`str` A name for the collection of vectors for this store. Internally, this is used as the row key prefix.
`engine`	`Optional[BigtableEngine]` An optional, existing BigtableEngine.
`content_column`	`ColumnConfig` Configuration for the document content column.
`embedding_column`	`ColumnConfig` Configuration for the vector embedding column.
`project_id`	`Optional[str]` Your Google Cloud project ID.
`query_parameters`	`Optional[QueryParameters]` Default QueryParameters for searches.
`metadata_mappings`	`Optional[List[VectorMetadataMapping]]` Mappings for metadata columns.
`metadata_as_json_column`	`Optional[ColumnConfig]` Configuration for a single JSON metadata column.
`app_profile_id`	`Optional[str]` The Bigtable app profile ID to use.
`credentials`	`Optional[google.auth.credentials.Credentials]` Custom credentials to use.
`client_options`	`Optional[Dict[str, Any]]` Client options for the Bigtable client.
`\\kwargs`	`Any` Additional keyword arguments for engine initialization.

Returns
Type	Description
`(BigtableVectorStore)`	An instance of the vector store.

create_sync

create_sync(instance_id: str, table_id: str, embedding_service: langchain_core.embeddings.embeddings.Embeddings, collection: str, engine: typing.Optional[langchain_google_bigtable.engine.BigtableEngine] = None, content_column: langchain_google_bigtable.async_vector_store.ColumnConfig = ColumnConfig(column_qualifier='content', column_family='langchain', encoding=<Encoding.UTF8: 'utf-8'>), embedding_column: langchain_google_bigtable.async_vector_store.ColumnConfig = ColumnConfig(column_qualifier='embedding', column_family='langchain', encoding=<Encoding.UTF8: 'utf-8'>), project_id: typing.Optional[str] = None, metadata_mappings: typing.Optional[typing.List[langchain_google_bigtable.async_vector_store.VectorMetadataMapping]] = None, metadata_as_json_column: typing.Optional[langchain_google_bigtable.async_vector_store.ColumnConfig] = None, app_profile_id: typing.Optional[str] = None, credentials: typing.Optional[google.auth.credentials.Credentials] = None, client_options: typing.Optional[typing.Dict[str, typing.Any]] = None, **kwargs: typing.Any) -> langchain_google_bigtable.vector_store.BigtableVectorStore

Synchronously initializes the engine and creates an instance of the vector store.

Parameters
Name	Description
`instance_id`	`str` Your Bigtable instance ID.
`table_id`	`str` The ID of the table to use for the vector store.
`embedding_service`	`Embeddings` The embedding service to use.
`collection`	`str` A name for the collection of vectors for this store. Internally, this is used as the row key prefix.
`engine`	`Optional[BigtableEngine]` An optional, existing BigtableEngine.
`content_column`	`ColumnConfig` Configuration for the document content column.
`embedding_column`	`ColumnConfig` Configuration for the vector embedding column.
`project_id`	`Optional[str]` Your Google Cloud project ID.
`metadata_mappings`	`Optional[List[VectorMetadataMapping]]` Mappings for metadata columns.
`metadata_as_json_column`	`Optional[ColumnConfig]` Configuration for a single JSON metadata column.
`app_profile_id`	`Optional[str]` The Bigtable app profile ID to use.
`credentials`	`Optional[google.auth.credentials.Credentials]` Custom credentials to use.
`client_options`	`Optional[Dict[str, Any]]` Client options for the Bigtable client.
`\\kwargs`	`Any` Additional keyword arguments for engine initialization.

Returns
Type	Description
`(BigtableVectorStore)`	An instance of the vector store.

delete

delete(
    ids: typing.Optional[typing.List[str]] = None, **kwargs: typing.Any
) -> typing.Optional[bool]

Delete by vector ID.

Parameters
Name	Description
`ids`	`Optional[List[str]]` A list of document IDs to delete.
`\\kwargs`	`Any` Additional arguments.

Returns
Type	Description
`(Optional[bool])`	True if the deletion was successful.

from_documents

from_documents(
    documents: typing.List[langchain_core.documents.base.Document],
    embedding: langchain_core.embeddings.embeddings.Embeddings,
    ids: typing.Optional[list] = None,
    **kwargs: typing.Any
) -> langchain_google_bigtable.vector_store.BigtableVectorStore

Return VectorStore initialized from documents and embeddings. This is a synchronous method that creates the store and adds documents.

Parameters
Name	Description
`documents`	`List[Document]` List of documents to add.
`embedding`	`Embeddings` The embedding service to use.
`ids`	`Optional[list]` list of IDs for the texts.
`\\kwargs`	`Any` Keyword arguments to pass to the `create_sync` method.

Returns
Type	Description
`(BigtableVectorStore)`	An instance of the vector store.

from_texts

from_texts(
    texts: typing.List[str],
    embedding: langchain_core.embeddings.embeddings.Embeddings,
    metadatas: typing.Optional[typing.List[dict]] = None,
    ids: typing.Optional[list] = None,
    **kwargs: typing.Any
) -> langchain_google_bigtable.vector_store.BigtableVectorStore

Return VectorStore initialized from texts and embeddings.

Parameters
Name	Description
`texts`	`List[str]` List of text strings to add.
`embedding`	`Embeddings` The embedding service to use.
`metadatas`	`Optional[List[dict]]` Optional list of metadata for each text.
`ids`	`Optional[list]` list of IDs for the texts.
`\\kwargs`	`Any` Keyword arguments to pass to the `create_sync` factory method.

Returns
Type	Description
`(BigtableVectorStore)`	An instance of the vector store.

get_by_ids

get_by_ids(
    ids: typing.Sequence[str],
) -> typing.List[langchain_core.documents.base.Document]

Return documents by their IDs.

Parameter
Name	Description
`ids`	`List[str]` A list of document IDs to retrieve.

Returns
Type	Description
`(List[Document])`	A list of the retrieved documents.

get_engine

get_engine() -> typing.Optional[langchain_google_bigtable.engine.BigtableEngine]

Get the BigtableEngine instance.

Returns
Type	Description
`(Optional[BigtableEngine])`	The engine instance if it exists.

max_marginal_relevance_search

max_marginal_relevance_search(
    query: str,
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    query_parameters: typing.Optional[
        langchain_google_bigtable.async_vector_store.QueryParameters
    ] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

Return docs selected using the maximal marginal relevance.

Parameters
Name	Description
`query`	`str` The text to search for.
`k`	`int` The number of results to return.
`fetch_k`	`int` The number of documents to fetch for MMR.
`lambda_mult`	`float` The lambda multiplier for MMR.
`query_parameters`	`Optional[QueryParameters]` Custom query parameters for this search.
`\\kwargs`	`Any` Additional keyword arguments (e.g., filter).

Returns
Type	Description
`(List[Document])`	A list of documents selected by MMR.

max_marginal_relevance_search_by_vector

max_marginal_relevance_search_by_vector(
    embedding: typing.List[float],
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    query_parameters: typing.Optional[
        langchain_google_bigtable.async_vector_store.QueryParameters
    ] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

Return docs selected using the maximal marginal relevance.

Parameters
Name	Description
`embedding`	`List[float]` The embedding vector to search for.
`k`	`int` The number of results to return.
`fetch_k`	`int` The number of documents to fetch for MMR.
`lambda_mult`	`float` The lambda multiplier for MMR.
`query_parameters`	`Optional[QueryParameters]` Custom query parameters for this search.
`\\kwargs`	`Any` Additional keyword arguments (e.g., filter).

Returns
Type	Description
`(List[Document])`	A list of documents selected by MMR.

similarity_search

similarity_search(
    query: str,
    k: int = 4,
    query_parameters: typing.Optional[
        langchain_google_bigtable.async_vector_store.QueryParameters
    ] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

Return docs most similar to query.

Parameters
Name	Description
`query`	`str` The text to search for.
`k`	`int` The number of results to return.
`query_parameters`	`Optional[QueryParameters]` Custom query parameters for this search.
`\\kwargs`	`Any` Additional keyword arguments (e.g., filter).

Returns
Type	Description
`(List[Document])`	A list of documents most similar to the query.

similarity_search_by_vector

similarity_search_by_vector(
    embedding: typing.List[float],
    k: int = 4,
    query_parameters: typing.Optional[
        langchain_google_bigtable.async_vector_store.QueryParameters
    ] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

Return docs most similar to embedding vector.

Parameters
Name	Description
`embedding`	`List[float]` The embedding vector to search for.
`k`	`int` The number of results to return.
`query_parameters`	`Optional[QueryParameters]` Custom query parameters for this search.
`\\kwargs`	`Any` Additional keyword arguments (e.g., filter).

Returns
Type	Description
`(List[Document])`	A list of documents most similar to the embedding.

similarity_search_with_relevance_scores

similarity_search_with_relevance_scores(
    query: str,
    k: int = 4,
    query_parameters: typing.Optional[
        langchain_google_bigtable.async_vector_store.QueryParameters
    ] = None,
    **kwargs: typing.Any
) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

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

Parameters
Name	Description
`query`	`str` The text to search for.
`k`	`int` The number of results to return.
`query_parameters`	`Optional[QueryParameters]` Custom query parameters for this search.
`\\kwargs`	`Any` Additional keyword arguments (e.g., filter).

Returns
Type	Description
`(List[Tuple[Document, float]])`	A list of tuples containing the document and its relevance score.

similarity_search_with_score

similarity_search_with_score(
    query: str,
    k: int = 4,
    query_parameters: typing.Optional[
        langchain_google_bigtable.async_vector_store.QueryParameters
    ] = None,
    **kwargs: typing.Any
) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

Run similarity search with distance.

Parameters
Name	Description
`query`	`str` The text to search for.
`k`	`int` The number of results to return.
`query_parameters`	`Optional[QueryParameters]` Custom query parameters for this search.
`\\kwargs`	`Any` Additional keyword arguments (e.g., filter).

Returns
Type	Description
`(List[Tuple[Document, float]])`	A list of tuples containing the document and its distance score.

similarity_search_with_score_by_vector

similarity_search_with_score_by_vector(
    embedding: typing.List[float],
    k: int = 4,
    query_parameters: typing.Optional[
        langchain_google_bigtable.async_vector_store.QueryParameters
    ] = None,
    **kwargs: typing.Any
) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

Run similarity search with distance by vector.

Parameters
Name	Description
`embedding`	`List[float]` The embedding vector to search for.
`k`	`int` The number of results to return.
`query_parameters`	`Optional[QueryParameters]` Custom query parameters for this search.
`\\kwargs`	`Any` Additional keyword arguments (e.g., filter).

Returns
Type	Description
`(List[Tuple[Document, float]])`	A list of tuples containing the document and its distance score.