Class AlloyDBVectorStore (0.7.0) 

AlloyDBVectorStore(
    key: object,
    engine: langchain_google_alloydb_pg.engine.AlloyDBEngine,
    vs: langchain_google_alloydb_pg.async_vectorstore.AsyncAlloyDBVectorStore,
)

Google AlloyDB Vector Store class

Properties

embeddings

Access the query embedding object if available.

Methods

AlloyDBVectorStore

AlloyDBVectorStore(
    key: object,
    engine: langchain_google_alloydb_pg.engine.AlloyDBEngine,
    vs: langchain_google_alloydb_pg.async_vectorstore.AsyncAlloyDBVectorStore,
)

AlloyDBVectorStore constructor.

Parameters
Name Description
key object

Prevent direct constructor usage.
engine AlloyDBEngine

Connection pool engine for managing connections to Postgres database.
vs AsyncAlloyDBVectorstore

The async only VectorStore implementation
Exceptions
Type Description
Exception If called directly by user.

_select_relevance_score_fn

_select_relevance_score_fn() -> typing.Callable[[float], float]

Select a relevance function based on distance strategy.

aadd_documents

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

Embed documents and add to the table.

Exceptions
Type Description
InvalidTextRepresentationErro

aadd_embeddings

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

Add data along with embeddings to the table.

aadd_images

aadd_images(
    uris: typing.List[str],
    metadatas: typing.Optional[typing.List[dict]] = None,
    ids: typing.Optional[typing.List[str]] = None,
    **kwargs: typing.Any
) -> typing.List[str]

Embed images and add to the table.

aadd_texts

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

Embed texts and add to the table.

Exceptions
Type Description
InvalidTextRepresentationErro

aapply_vector_index

aapply_vector_index(
    index: langchain_google_alloydb_pg.indexes.BaseIndex,
    name: typing.Optional[str] = None,
    concurrently: bool = False,
) -> None

Create an index on the vector store table.

add_documents

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

Embed documents and add to the table.

Exceptions
Type Description
InvalidTextRepresentationErro

add_embeddings

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

Add data along with embeddings to the table.

add_images

add_images(
    uris: typing.List[str],
    metadatas: typing.Optional[typing.List[dict]] = None,
    ids: typing.Optional[typing.List[str]] = None,
    **kwargs: typing.Any
) -> typing.List[str]

Embed images and add to the table.

add_texts

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

Embed texts and add to the table.

Exceptions
Type Description
InvalidTextRepresentationErro

adelete

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

Delete records from the table.

Exceptions
Type Description
InvalidTextRepresentationErro

adrop_vector_index

adrop_vector_index(index_name: typing.Optional[str] = None) -> None

Drop the vector index.

afrom_documents

afrom_documents(
    documents: typing.List[langchain_core.documents.base.Document],
    embedding: langchain_core.embeddings.embeddings.Embeddings,
    engine: langchain_google_alloydb_pg.engine.AlloyDBEngine,
    table_name: str,
    schema_name: str = "public",
    ids: typing.Optional[typing.List] = None,
    content_column: str = "content",
    embedding_column: str = "embedding",
    metadata_columns: typing.List[str] = [],
    ignore_metadata_columns: typing.Optional[typing.List[str]] = None,
    id_column: str = "langchain_id",
    metadata_json_column: str = "langchain_metadata",
    distance_strategy: langchain_google_alloydb_pg.indexes.DistanceStrategy = DistanceStrategy.COSINE_DISTANCE,
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    index_query_options: typing.Optional[
        langchain_google_alloydb_pg.indexes.QueryOptions
    ] = None,
    **kwargs: typing.Any
) -> langchain_google_alloydb_pg.vectorstore.AlloyDBVectorStore

Create an AlloyDBVectorStore instance from documents.

Parameters
Name Description
documents List[Document]

Documents to add to the vector store.
embedding Embeddings

Text embedding model to use.
engine AlloyDBEngine

Connection pool engine for managing connections to AlloyDB database.
table_name str

Name of an existing table.
schema_name str, optional

Name of the database schema. Defaults to "public".
content_column str, optional

Column that represent a Document’s page_content. Defaults to "content".
embedding_column str, optional

Column for embedding vectors. The embedding is generated from the document value. Defaults to "embedding".
metadata_columns List[str], optional

Column(s) that represent a document's metadata. Defaults to an empty list.
ignore_metadata_columns Optional[List[str]], optional

Column(s) to ignore in pre-existing tables for a document's metadata. Can not be used with metadata_columns. Defaults to None.
id_column str, optional

Column that represents the Document's id. Defaults to "langchain_id".
metadata_json_column str, optional

Column to store metadata as JSON. Defaults to "langchain_metadata".
distance_strategy DistanceStrategy

Distance strategy to use for vector similarity search. Defaults to COSINE_DISTANCE.
k int

Number of Documents to return from search. 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.
index_query_options QueryOptions

Index query option.
Exceptions
Type Description
InvalidTextRepresentationErro

afrom_texts

afrom_texts(
    texts: typing.List[str],
    embedding: langchain_core.embeddings.embeddings.Embeddings,
    engine: langchain_google_alloydb_pg.engine.AlloyDBEngine,
    table_name: str,
    schema_name: str = "public",
    metadatas: typing.Optional[typing.List[dict]] = None,
    ids: typing.Optional[typing.List] = None,
    content_column: str = "content",
    embedding_column: str = "embedding",
    metadata_columns: typing.List[str] = [],
    ignore_metadata_columns: typing.Optional[typing.List[str]] = None,
    id_column: str = "langchain_id",
    metadata_json_column: str = "langchain_metadata",
    distance_strategy: langchain_google_alloydb_pg.indexes.DistanceStrategy = DistanceStrategy.COSINE_DISTANCE,
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    index_query_options: typing.Optional[
        langchain_google_alloydb_pg.indexes.QueryOptions
    ] = None,
    **kwargs: typing.Any
) -> langchain_google_alloydb_pg.vectorstore.AlloyDBVectorStore

Create an AlloyDBVectorStore instance from texts.

Parameters
Name Description
texts List[str]

Texts to add to the vector store.
embedding Embeddings

Text embedding model to use.
engine AlloyDBEngine

Connection pool engine for managing connections to AlloyDB database.
table_name str

Name of an existing table.
schema_name str, optional

Name of the database schema. Defaults to "public".
metadatas Optional[List[dict]], optional

List of metadatas to add to table records. Defaults to None.
content_column str, optional

Column that represent a Document’s page_content. Defaults to "content".
embedding_column str, optional

Column for embedding vectors. The embedding is generated from the document value. Defaults to "embedding".
metadata_columns List[str], optional

Column(s) that represent a document's metadata. Defaults to an empty list.
ignore_metadata_columns Optional[List[str]], optional

Column(s) to ignore in pre-existing tables for a document's metadata. Can not be used with metadata_columns. Defaults to None.
id_column str, optional

Column that represents the Document's id. Defaults to "langchain_id".
metadata_json_column str, optional

Column to store metadata as JSON. Defaults to "langchain_metadata".
distance_strategy DistanceStrategy

Distance strategy to use for vector similarity search. Defaults to COSINE_DISTANCE.
k int

Number of Documents to return from search. 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.
index_query_options QueryOptions

Index query option.
Exceptions
Type Description
InvalidTextRepresentationErro

ais_valid_index

ais_valid_index(index_name: typing.Optional[str] = None) -> bool

Check if index exists in the table.

amax_marginal_relevance_search(
    query: str,
    k: typing.Optional[int] = None,
    fetch_k: typing.Optional[int] = None,
    lambda_mult: typing.Optional[float] = None,
    filter: typing.Optional[str] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

Return docs selected using the maximal marginal relevance.

amax_marginal_relevance_search_by_vector

amax_marginal_relevance_search_by_vector(
    embedding: typing.List[float],
    k: typing.Optional[int] = None,
    fetch_k: typing.Optional[int] = None,
    lambda_mult: typing.Optional[float] = None,
    filter: typing.Optional[str] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

Return docs selected using the maximal marginal relevance.

amax_marginal_relevance_search_with_score_by_vector

amax_marginal_relevance_search_with_score_by_vector(
    embedding: typing.List[float],
    k: typing.Optional[int] = None,
    fetch_k: typing.Optional[int] = None,
    lambda_mult: typing.Optional[float] = None,
    filter: typing.Optional[str] = None,
    **kwargs: typing.Any
) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

Return docs and distance scores selected using the maximal marginal relevance.

apply_vector_index

apply_vector_index(
    index: langchain_google_alloydb_pg.indexes.BaseIndex,
    name: typing.Optional[str] = None,
    concurrently: bool = False,
) -> None

Create an index on the vector store table.

areindex

areindex(index_name: typing.Optional[str] = None) -> None

Re-index the vector store table.

aset_maintenance_work_mem

aset_maintenance_work_mem(num_leaves: int, vector_size: int) -> None

Set database maintenance work memory (for ScaNN index creation).

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

Return docs selected by similarity search on query.

asimilarity_search_by_vector

asimilarity_search_by_vector(
    embedding: typing.List[float],
    k: typing.Optional[int] = None,
    filter: typing.Optional[str] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

Return docs selected by vector similarity search.

asimilarity_search_image

asimilarity_search_image(
    image_uri: str,
    k: typing.Optional[int] = None,
    filter: typing.Optional[str] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

Return docs selected by similarity search on query.

asimilarity_search_with_score

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

Return docs and distance scores selected by similarity search on query.

asimilarity_search_with_score_by_vector

asimilarity_search_with_score_by_vector(
    embedding: typing.List[float],
    k: typing.Optional[int] = None,
    filter: typing.Optional[str] = None,
    **kwargs: typing.Any
) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

Return docs and distance scores selected by vector similarity search.

create

create(
    engine: langchain_google_alloydb_pg.engine.AlloyDBEngine,
    embedding_service: langchain_core.embeddings.embeddings.Embeddings,
    table_name: str,
    schema_name: str = "public",
    content_column: str = "content",
    embedding_column: str = "embedding",
    metadata_columns: typing.List[str] = [],
    ignore_metadata_columns: typing.Optional[typing.List[str]] = None,
    id_column: str = "langchain_id",
    metadata_json_column: typing.Optional[str] = "langchain_metadata",
    distance_strategy: langchain_google_alloydb_pg.indexes.DistanceStrategy = DistanceStrategy.COSINE_DISTANCE,
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    index_query_options: typing.Optional[
        langchain_google_alloydb_pg.indexes.QueryOptions
    ] = None,
) -> langchain_google_alloydb_pg.vectorstore.AlloyDBVectorStore

Create an AlloyDBVectorStore instance.

Parameters
Name Description
engine AlloyDBEngine

Connection pool engine for managing connections to AlloyDB database.
embedding_service Embeddings

Text embedding model to use.
table_name str

Name of an existing table.
schema_name str, optional

Name of the database schema. Defaults to "public".
content_column str

Column that represent a Document’s page_content. Defaults to "content".
embedding_column str

Column for embedding vectors. The embedding is generated from the document value. Defaults to "embedding".
metadata_columns List[str]

Column(s) that represent a document's metadata.
ignore_metadata_columns List[str]

Column(s) to ignore in pre-existing tables for a document's metadata. Can not be used with metadata_columns. Defaults to None.
id_column str

Column that represents the Document's id. Defaults to "langchain_id".
metadata_json_column str

Column to store metadata as JSON. Defaults to "langchain_metadata".
distance_strategy DistanceStrategy

Distance strategy to use for vector similarity search. Defaults to COSINE_DISTANCE.
k int

Number of Documents to return from search. 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.
index_query_options QueryOptions

Index query option.

create_sync

create_sync(
    engine: langchain_google_alloydb_pg.engine.AlloyDBEngine,
    embedding_service: langchain_core.embeddings.embeddings.Embeddings,
    table_name: str,
    schema_name: str = "public",
    content_column: str = "content",
    embedding_column: str = "embedding",
    metadata_columns: typing.List[str] = [],
    ignore_metadata_columns: typing.Optional[typing.List[str]] = None,
    id_column: str = "langchain_id",
    metadata_json_column: str = "langchain_metadata",
    distance_strategy: langchain_google_alloydb_pg.indexes.DistanceStrategy = DistanceStrategy.COSINE_DISTANCE,
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    index_query_options: typing.Optional[
        langchain_google_alloydb_pg.indexes.QueryOptions
    ] = None,
) -> langchain_google_alloydb_pg.vectorstore.AlloyDBVectorStore

Create an AlloyDBVectorStore instance.

Parameters
Name Description
key object

Prevent direct constructor usage.
engine AlloyDBEngine

Connection pool engine for managing connections to AlloyDB database.
embedding_service Embeddings

Text embedding model to use.
table_name str

Name of an existing table.
schema_name str, optional

Name of the database schema. Defaults to "public".
content_column str, optional

Column that represent a Document’s page_content. Defaults to "content".
embedding_column str, optional

Column for embedding vectors. The embedding is generated from the document value. Defaults to "embedding".
metadata_columns List[str]

Column(s) that represent a document's metadata. Defaults to an empty list.
ignore_metadata_columns Optional[List[str]]

Column(s) to ignore in pre-existing tables for a document's metadata. Can not be used with metadata_columns. Defaults to None.
id_column str, optional

Column that represents the Document's id. Defaults to "langchain_id".
metadata_json_column str, optional

Column to store metadata as JSON. Defaults to "langchain_metadata".
distance_strategy DistanceStrategy, optional

Distance strategy to use for vector similarity search. Defaults to COSINE_DISTANCE.
k int, optional

Number of Documents to return from search. Defaults to 4.
fetch_k int, optional

Number of Documents to fetch to pass to MMR algorithm. Defaults to 20.
lambda_mult float, optional

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.
index_query_options Optional[QueryOptions], optional

Index query option. Defaults to None.

delete

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

Delete records from the table.

Exceptions
Type Description
InvalidTextRepresentationErro

drop_vector_index

drop_vector_index(index_name: typing.Optional[str] = None) -> None

Drop the vector index.

from_documents

from_documents(
    documents: typing.List[langchain_core.documents.base.Document],
    embedding: langchain_core.embeddings.embeddings.Embeddings,
    engine: langchain_google_alloydb_pg.engine.AlloyDBEngine,
    table_name: str,
    schema_name: str = "public",
    ids: typing.Optional[typing.List] = None,
    content_column: str = "content",
    embedding_column: str = "embedding",
    metadata_columns: typing.List[str] = [],
    ignore_metadata_columns: typing.Optional[typing.List[str]] = None,
    id_column: str = "langchain_id",
    metadata_json_column: str = "langchain_metadata",
    distance_strategy: langchain_google_alloydb_pg.indexes.DistanceStrategy = DistanceStrategy.COSINE_DISTANCE,
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    index_query_options: typing.Optional[
        langchain_google_alloydb_pg.indexes.QueryOptions
    ] = None,
    **kwargs: typing.Any
) -> langchain_google_alloydb_pg.vectorstore.AlloyDBVectorStore

Create an AlloyDBVectorStore instance from documents.

Parameters
Name Description
documents List[Document]

Documents to add to the vector store.
embedding Embeddings

Text embedding model to use.
engine AlloyDBEngine

Connection pool engine for managing connections to AlloyDB database.
table_name str

Name of an existing table.
schema_name str, optional

Name of the database schema. Defaults to "public".
content_column str, optional

Column that represent a Document’s page_content. Defaults to "content".
embedding_column str, optional

Column for embedding vectors. The embedding is generated from the document value. Defaults to "embedding".
metadata_columns List[str], optional

Column(s) that represent a document's metadata. Defaults to an empty list.
ignore_metadata_columns Optional[List[str]], optional

Column(s) to ignore in pre-existing tables for a document's metadata. Can not be used with metadata_columns. Defaults to None.
id_column str, optional

Column that represents the Document's id. Defaults to "langchain_id".
metadata_json_column str, optional

Column to store metadata as JSON. Defaults to "langchain_metadata".
distance_strategy DistanceStrategy

Distance strategy to use for vector similarity search. Defaults to COSINE_DISTANCE.
k int

Number of Documents to return from search. 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.
index_query_options QueryOptions

Index query option.
Exceptions
Type Description
InvalidTextRepresentationErro

from_texts

from_texts(
    texts: typing.List[str],
    embedding: langchain_core.embeddings.embeddings.Embeddings,
    engine: langchain_google_alloydb_pg.engine.AlloyDBEngine,
    table_name: str,
    schema_name: str = "public",
    metadatas: typing.Optional[typing.List[dict]] = None,
    ids: typing.Optional[typing.List] = None,
    content_column: str = "content",
    embedding_column: str = "embedding",
    metadata_columns: typing.List[str] = [],
    ignore_metadata_columns: typing.Optional[typing.List[str]] = None,
    id_column: str = "langchain_id",
    metadata_json_column: str = "langchain_metadata",
    distance_strategy: langchain_google_alloydb_pg.indexes.DistanceStrategy = DistanceStrategy.COSINE_DISTANCE,
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    index_query_options: typing.Optional[
        langchain_google_alloydb_pg.indexes.QueryOptions
    ] = None,
    **kwargs: typing.Any
) -> langchain_google_alloydb_pg.vectorstore.AlloyDBVectorStore

Create an AlloyDBVectorStore instance from texts.

Parameters
Name Description
texts List[str]

Texts to add to the vector store.
embedding Embeddings

Text embedding model to use.
engine AlloyDBEngine

Connection pool engine for managing connections to AlloyDB database.
table_name str

Name of an existing table.
schema_name str, optional

Name of the database schema. Defaults to "public".
metadatas Optional[List[dict]], optional

List of metadatas to add to table records. Defaults to None.
content_column str, optional

Column that represent a Document’s page_content. Defaults to "content".
embedding_column str, optional

Column for embedding vectors. The embedding is generated from the document value. Defaults to "embedding".
metadata_columns List[str], optional

Column(s) that represent a document's metadata. Defaults to empty list.
ignore_metadata_columns Optional[List[str]], optional

Column(s) to ignore in pre-existing tables for a document's metadata. Can not be used with metadata_columns. Defaults to None.
id_column str, optional

Column that represents the Document's id. Defaults to "langchain_id".
metadata_json_column str, optional

Column to store metadata as JSON. Defaults to "langchain_metadata".
distance_strategy DistanceStrategy

Distance strategy to use for vector similarity search. Defaults to COSINE_DISTANCE.
k int

Number of Documents to return from search. 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.
index_query_options QueryOptions

Index query option.
Exceptions
Type Description
InvalidTextRepresentationErro

is_valid_index

is_valid_index(index_name: typing.Optional[str] = None) -> bool

Check if index exists in the table.

max_marginal_relevance_search(
    query: str,
    k: typing.Optional[int] = None,
    fetch_k: typing.Optional[int] = None,
    lambda_mult: typing.Optional[float] = None,
    filter: typing.Optional[str] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

Return docs selected using the maximal marginal relevance.

max_marginal_relevance_search_by_vector

max_marginal_relevance_search_by_vector(
    embedding: typing.List[float],
    k: typing.Optional[int] = None,
    fetch_k: typing.Optional[int] = None,
    lambda_mult: typing.Optional[float] = None,
    filter: typing.Optional[str] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

Return docs selected using the maximal marginal relevance.

max_marginal_relevance_search_with_score_by_vector

max_marginal_relevance_search_with_score_by_vector(
    embedding: typing.List[float],
    k: typing.Optional[int] = None,
    fetch_k: typing.Optional[int] = None,
    lambda_mult: typing.Optional[float] = None,
    filter: typing.Optional[str] = None,
    **kwargs: typing.Any
) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

Return docs and distance scores selected using the maximal marginal relevance.

reindex

reindex(index_name: typing.Optional[str] = None) -> None

Re-index the vector store table.

set_maintenance_work_mem

set_maintenance_work_mem(num_leaves: int, vector_size: int) -> None

Set database maintenance work memory (for ScaNN index creation).

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

Return docs selected by similarity search on query.

similarity_search_by_vector

similarity_search_by_vector(
    embedding: typing.List[float],
    k: typing.Optional[int] = None,
    filter: typing.Optional[str] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

Return docs selected by vector similarity search.

similarity_search_image

similarity_search_image(
    image_uri: str,
    k: typing.Optional[int] = None,
    filter: typing.Optional[str] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

Return docs selected by similarity search on image.

similarity_search_with_score

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

Return docs and distance scores selected by similarity search on query.

similarity_search_with_score_by_vector

similarity_search_with_score_by_vector(
    embedding: typing.List[float],
    k: typing.Optional[int] = None,
    filter: typing.Optional[str] = None,
    **kwargs: typing.Any
) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

Return docs and distance scores selected by similarity search on vector.