Class SpannerVectorStore (0.5.0)

SpannerVectorStore(instance_id: str, database_id: str, table_name: str, embedding_service: langchain_core.embeddings.embeddings.Embeddings, id_column: str = 'langchain_id', content_column: str = 'content', embedding_column: str = 'embedding', client: typing.Optional[google.cloud.spanner_v1.client.Client] = None, metadata_columns: typing.Optional[typing.List[str]] = None, ignore_metadata_columns: typing.Optional[typing.List[str]] = None, metadata_json_column: typing.Optional[str] = None, query_parameters: langchain_google_spanner.vector_store.QueryParameters = <langchain_google_spanner.vector_store.QueryParameters object>)

Initialize the SpannerVectorStore.

Parameters:

instance_id (str): The ID of the Spanner instance.
database_id (str): The ID of the Spanner database.
table_name (str): The name of the table.
embedding_service (Embeddings): The embedding service.
id_column (str): The name of the row ID column. Defaults to ID_COLUMN_NAME.
content_column (str): The name of the content column. Defaults to CONTENT_COLUMN_NAME.
embedding_column (str): The name of the embedding column. Defaults to EMBEDDING_COLUMN_NAME.
client (Client): The Spanner client. Defaults to Client().
metadata_columns (Optional[List[str]]): List of metadata columns. Defaults to None.
ignore_metadata_columns (Optional[List[str]]): List of metadata columns to ignore. Defaults to None.
metadata_json_column (Optional[str]): The generic metadata column. Defaults to None.
query_parameters (QueryParameters): The query parameters. Defaults to QueryParameters().

Methods

_generate_sql

_generate_sql(
    dialect,
    table_name,
    id_column,
    content_column,
    embedding_column,
    column_configs,
    primary_key,
    secondary_indexes: typing.Optional[
        typing.List[langchain_google_spanner.vector_store.SecondaryIndex]
    ] = None,
)

Generate SQL for creating the vector store table.

Parameters:

dialect: The database dialect.
table_name: The name of the table.
id_column: The name of the row ID column.
content_column: The name of the content column.
embedding_column: The name of the embedding column.
column_names: List of tuples containing metadata column information.

Returns:

str: The generated SQL.

_select_relevance_score_fn

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

The 'correct' relevance function may differ depending on a few things, including:

the distance / similarity metric used by the VectorStore
the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
embedding dimensionality
etc.

Vectorstores should define their own selection-based method of relevance.

add_documents

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

Add documents to the vector store.

Parameters
Name	Description
`documents`	`List[Document]` Documents to add to the vector store.
`ids`	`Optional[List[str]]` Optional list of IDs for the documents.

Returns
Type	Description
`List[str]`	List of IDs of the added texts.

add_texts

add_texts(
    texts: typing.Iterable[str],
    metadatas: typing.Optional[typing.List[dict]] = None,
    ids: typing.Optional[typing.List[str]] = None,
    batch_size: int = 5000,
    **kwargs: typing.Any
) -> typing.List[str]

Add texts to the vector store index.

Parameters
Name	Description
`texts`	`Iterable[str]` Iterable of strings to add to the vector store.
`metadatas`	`Optional[List[dict]]` Optional list of metadatas associated with the texts.
`ids`	`Optional[List[str]]` Optional list of IDs for the texts.
`batch_size`	`int` The batch size for inserting data. Defaults to 5000.

Returns
Type	Description
`List[str]`	List of IDs of the added texts.

delete

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

Delete records from the vector store.

Parameters
Name	Description
`ids`	`Optional[List[str]]` List of IDs to delete.
`documents`	`Optional[List[Document]]` List of documents to delete.

Returns
Type	Description
`Optional[bool]`	True if deletion is successful, False otherwise, None if not implemented.

from_documents

from_documents(documents: typing.List[langchain_core.documents.base.Document], embedding: langchain_core.embeddings.embeddings.Embeddings, instance_id: str, database_id: str, table_name: str, id_column: str = 'langchain_id', content_column: str = 'content', embedding_column: str = 'embedding', ids: typing.Optional[typing.List[str]] = None, client: typing.Optional[google.cloud.spanner_v1.client.Client] = None, metadata_columns: typing.Optional[typing.List[str]] = None, ignore_metadata_columns: typing.Optional[typing.List[str]] = None, metadata_json_column: typing.Optional[str] = None, query_parameter: langchain_google_spanner.vector_store.QueryParameters = <langchain_google_spanner.vector_store.QueryParameters object>, **kwargs: typing.Any) -> langchain_google_spanner.vector_store.SpannerVectorStore

Initialize SpannerVectorStore from a list of documents.

Parameters
Name	Description
`documents`	`List[Document]` List of documents.
`embedding`	`Embeddings` The embedding service.
`id_column`	`str` The name of the row ID column. Defaults to ID_COLUMN_NAME.
`content_column`	`str` The name of the content column. Defaults to CONTENT_COLUMN_NAME.
`embedding_column`	`str` The name of the embedding column. Defaults to EMBEDDING_COLUMN_NAME.
`ids`	`Optional[List[str]]` Optional list of IDs for the documents. Defaults to None.
`client`	`Client` The Spanner client. Defaults to Client().
`metadata_columns`	`Optional[List[str]]` List of metadata columns. Defaults to None.
`ignore_metadata_columns`	`Optional[List[str]]` List of metadata columns to ignore. Defaults to None.
`metadata_json_column`	`Optional[str]` The generic metadata column. Defaults to None.
`query_parameter`	`QueryParameters` The query parameters. Defaults to QueryParameters().

Returns
Type	Description
`SpannerVectorStore`	Initialized SpannerVectorStore instance.

from_texts

from_texts(texts: typing.List[str], embedding: langchain_core.embeddings.embeddings.Embeddings, instance_id: str, database_id: str, table_name: str, metadatas: typing.Optional[typing.List[dict]] = None, id_column: str = 'langchain_id', content_column: str = 'content', embedding_column: str = 'embedding', ids: typing.Optional[typing.List[str]] = None, client: typing.Optional[google.cloud.spanner_v1.client.Client] = None, metadata_columns: typing.Optional[typing.List[str]] = None, ignore_metadata_columns: typing.Optional[typing.List[str]] = None, metadata_json_column: typing.Optional[str] = None, query_parameter: langchain_google_spanner.vector_store.QueryParameters = <langchain_google_spanner.vector_store.QueryParameters object>, **kwargs: typing.Any) -> langchain_google_spanner.vector_store.SpannerVectorStore

Initialize SpannerVectorStore from a list of texts.

Parameters
Name	Description
`texts`	`List[str]` List of texts.
`embedding`	`Embeddings` The embedding service.
`metadatas`	`Optional[List[dict]]` Optional list of metadatas associated with the texts. Defaults to None.
`id_column`	`str` The name of the row ID column. Defaults to ID_COLUMN_NAME.
`content_column`	`str` The name of the content column. Defaults to CONTENT_COLUMN_NAME.
`embedding_column`	`str` The name of the embedding column. Defaults to EMBEDDING_COLUMN_NAME.
`ids`	`Optional[List[str]]` Optional list of IDs for the texts. Defaults to None.
`client`	`Client` The Spanner client. Defaults to Client().
`metadata_columns`	`Optional[List[str]]` List of metadata columns. Defaults to None.
`ignore_metadata_columns`	`Optional[List[str]]` List of metadata columns to ignore. Defaults to None.
`metadata_json_column`	`Optional[str]` The generic metadata column. Defaults to None.
`query_parameter`	`QueryParameters` The query parameters. Defaults to QueryParameters().

Returns
Type	Description
`SpannerVectorStore`	Initialized SpannerVectorStore instance.

init_vector_store_table

init_vector_store_table(
    instance_id: str,
    database_id: str,
    table_name: str,
    client: typing.Optional[google.cloud.spanner_v1.client.Client] = None,
    id_column: typing.Union[
        str, langchain_google_spanner.vector_store.TableColumn
    ] = "langchain_id",
    content_column: str = "content",
    embedding_column: str = "embedding",
    metadata_columns: typing.Optional[
        typing.List[langchain_google_spanner.vector_store.TableColumn]
    ] = None,
    primary_key: typing.Optional[str] = None,
    vector_size: typing.Optional[int] = None,
    secondary_indexes: typing.Optional[
        typing.List[langchain_google_spanner.vector_store.SecondaryIndex]
    ] = None,
) -> bool

Initialize the vector store new table in Google Cloud Spanner.

Parameters:

instance_id (str): The ID of the Spanner instance.
database_id (str): The ID of the Spanner database.
table_name (str): The name of the table to initialize.
client (Client): The Spanner client. Defaults to Client(project="span-cloud-testing").
id_column (str): The name of the row ID column. Defaults to ID_COLUMN_NAME.
content_column (str): The name of the content column. Defaults to CONTENT_COLUMN_NAME.
embedding_column (str): The name of the embedding column. Defaults to EMBEDDING_COLUMN_NAME.
metadata_columns (Optional[List[Tuple]]): List of tuples containing metadata column information. Defaults to None.
vector_size (Optional[int]): The size of the vector. Defaults to None.

max_marginal_relevance_search

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

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

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

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

max_marginal_relevance_search_with_score_by_vector

max_marginal_relevance_search_with_score_by_vector(
    embedding: typing.List[float],
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    pre_filter: typing.Optional[str] = None,
) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

Return docs and their similarity scores selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

similarity_search

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

Perform similarity search for a given query.

Parameters
Name	Description
`query`	`str` The query string.
`k`	`int` The number of nearest neighbors to retrieve. Defaults to 4.
`pre_filter`	`Optional[str]` Pre-filter condition for the query. Defaults to None.

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

similarity_search_by_vector

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

Perform similarity search by vector.

Parameters
Name	Description
`embedding`	`List[float]` The embedding vector.
`k`	`int` The number of nearest neighbors to retrieve. Defaults to 4.
`pre_filter`	`Optional[str]` Pre-filter condition for the query. Defaults to None.

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

similarity_search_with_score

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

Perform similarity search for a given query with scores.

Parameters
Name	Description
`query`	`str` The query string.
`k`	`int` The number of nearest neighbors to retrieve. Defaults to 4.
`pre_filter`	`Optional[str]` Pre-filter condition for the query. Defaults to None.

Returns
Type	Description
`List[Tuple[Document, float]]`	List of tuples containing Document and similarity score.

similarity_search_with_score_by_vector

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

Perform similarity search for a given query.

Parameters
Name	Description
`query`	`str` The query string.
`k`	`int` The number of nearest neighbors to retrieve. Defaults to 4.
`pre_filter`	`Optional[str]` Pre-filter condition for the query. Defaults to None.

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