Class SpannerVectorStore (0.3.0)

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.