Summary of entries of Methods for langchain-google-spanner.
langchain_google_spanner.loader._load_doc_to_row
_load_doc_to_row( table_fields: typing.List[str], doc: langchain_core.documents.base.Document, content_column: str, metadata_json_column: str, parse_json: bool = True, ) -> tupleLoad document to row.
langchain_google_spanner.chat_message_history.SpannerChatMessageHistory._verify_schema
_verify_schema() -> NoneVerify table exists with required schema for SpannerChatMessageHistory class.
See more: langchain_google_spanner.chat_message_history.SpannerChatMessageHistory._verify_schema
langchain_google_spanner.chat_message_history.SpannerChatMessageHistory.add_message
add_message(message: langchain_core.messages.base.BaseMessage) -> NoneAppend the message to the record in Cloud Spanner.
See more: langchain_google_spanner.chat_message_history.SpannerChatMessageHistory.add_message
langchain_google_spanner.chat_message_history.SpannerChatMessageHistory.clear
clear() -> NoneClear session memory from Cloud Spanner.
See more: langchain_google_spanner.chat_message_history.SpannerChatMessageHistory.clear
langchain_google_spanner.chat_message_history.SpannerChatMessageHistory.create_chat_history_table
create_chat_history_table( instance_id: str, database_id: str, table_name: str, client: typing.Optional[google.cloud.spanner_v1.client.Client] = None, ) -> NoneCreate a chat history table in a Cloud Spanner database.
See more: langchain_google_spanner.chat_message_history.SpannerChatMessageHistory.create_chat_history_table
langchain_google_spanner.loader.SpannerDocumentSaver
SpannerDocumentSaver( instance_id: str, database_id: str, table_name: str, content_column: str = "page_content", metadata_columns: typing.List[str] = [], metadata_json_column: str = "langchain_metadata", primary_key: typing.Optional[str] = None, client: typing.Optional[google.cloud.spanner_v1.client.Client] = None, )Initialize Spanner document saver.
See more: langchain_google_spanner.loader.SpannerDocumentSaver
langchain_google_spanner.loader.SpannerDocumentSaver.add_documents
add_documents(documents: typing.List[langchain_core.documents.base.Document])Add documents to the Spanner table.
See more: langchain_google_spanner.loader.SpannerDocumentSaver.add_documents
langchain_google_spanner.loader.SpannerDocumentSaver.create_table
create_table( client: google.cloud.spanner_v1.client.Client, instance_id: str, database_id: str, table_name: str, primary_key: str, metadata_json_column: str, content_column: str, metadata_columns: typing.List[langchain_google_spanner.loader.Column], )Create a new table in Spanner database.
See more: langchain_google_spanner.loader.SpannerDocumentSaver.create_table
langchain_google_spanner.loader.SpannerDocumentSaver.delete
delete(documents: typing.List[langchain_core.documents.base.Document])Delete documents from the table.
See more: langchain_google_spanner.loader.SpannerDocumentSaver.delete
langchain_google_spanner.loader.SpannerDocumentSaver.init_document_table
init_document_table( instance_id: str, database_id: str, table_name: str, content_column: str = "page_content", metadata_columns: typing.List[langchain_google_spanner.loader.Column] = [], primary_key: str = "", store_metadata: bool = True, metadata_json_column: str = "langchain_metadata", )Create a new table to store docs with a custom schema.
See more: langchain_google_spanner.loader.SpannerDocumentSaver.init_document_table
langchain_google_spanner.loader.SpannerLoader
SpannerLoader( instance_id: str, database_id: str, query: str, content_columns: typing.List[str] = [], metadata_columns: typing.List[str] = [], format: str = "text", databoost: bool = False, metadata_json_column: str = "langchain_metadata", staleness: typing.Union[float, datetime.datetime] = 0.0, client: typing.Optional[google.cloud.spanner_v1.client.Client] = None, )Initialize Spanner document loader.
langchain_google_spanner.loader.SpannerLoader.lazy_load
lazy_load() -> typing.Iterator[langchain_core.documents.base.Document]A lazy loader for langchain documents from a Spanner database.
See more: langchain_google_spanner.loader.SpannerLoader.lazy_load
langchain_google_spanner.loader.SpannerLoader.load
load() -> typing.List[langchain_core.documents.base.Document]Load langchain documents from a Spanner database.
See more: langchain_google_spanner.loader.SpannerLoader.load
langchain_google_spanner.vector_store.DialectSemantics.getDistanceFunction
getDistanceFunction(distance_strategy=DistanceStrategy.EUCLIDEAN) -> strAbstract method to get the distance function based on the provided distance strategy.
See more: langchain_google_spanner.vector_store.DialectSemantics.getDistanceFunction
langchain_google_spanner.vector_store.GoogleSqlSemantics.getDistanceFunction
getDistanceFunction(distance_strategy=DistanceStrategy.EUCLIDEAN) -> strAbstract method to get the distance function based on the provided distance strategy.
See more: langchain_google_spanner.vector_store.GoogleSqlSemantics.getDistanceFunction
langchain_google_spanner.vector_store.PGSqlSemantics.getDistanceFunction
getDistanceFunction(distance_strategy=DistanceStrategy.EUCLIDEAN) -> strAbstract method to get the distance function based on the provided distance strategy.
See more: langchain_google_spanner.vector_store.PGSqlSemantics.getDistanceFunction
langchain_google_spanner.vector_store.QueryParameters
QueryParameters( algorithm=NearestNeighborsAlgorithm.EXACT_NEAREST_NEIGHBOR, distance_strategy=DistanceStrategy.EUCLIDEAN, read_timestamp: typing.Optional[datetime.datetime] = None, min_read_timestamp: typing.Optional[datetime.datetime] = None, max_staleness: typing.Optional[datetime.timedelta] = None, exact_staleness: typing.Optional[datetime.timedelta] = None, )Initialize query parameters.
See more: langchain_google_spanner.vector_store.QueryParameters
langchain_google_spanner.vector_store.SpannerVectorStore._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 | langchain_google_spanner.vector_store.VectorSearchIndex ] ] = None, vector_size: typing.Optional[int] = None, ) -> typing.List[str]Generate SQL for creating the vector store table.
See more: langchain_google_spanner.vector_store.SpannerVectorStore._generate_sql
langchain_google_spanner.vector_store.SpannerVectorStore._select_relevance_score_fn
_select_relevance_score_fn() -> typing.Callable[[float], float]The 'correct' relevance function.
See more: langchain_google_spanner.vector_store.SpannerVectorStore._select_relevance_score_fn
langchain_google_spanner.vector_store.SpannerVectorStore.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.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.add_documents
langchain_google_spanner.vector_store.SpannerVectorStore.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.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.add_texts
langchain_google_spanner.vector_store.SpannerVectorStore.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.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.delete
langchain_google_spanner.vector_store.SpannerVectorStore.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 = Initialize SpannerVectorStore from a list of documents.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.from_documents
langchain_google_spanner.vector_store.SpannerVectorStore.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 = Initialize SpannerVectorStore from a list of texts.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.from_texts
langchain_google_spanner.vector_store.SpannerVectorStore.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 | langchain_google_spanner.vector_store.VectorSearchIndex ] ] = None, ) -> boolInitialize the vector store new table in Google Cloud Spanner.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.init_vector_store_table
langchain_google_spanner.vector_store.SpannerVectorStore.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.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.max_marginal_relevance_search
langchain_google_spanner.vector_store.SpannerVectorStore.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.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.max_marginal_relevance_search_by_vector
langchain_google_spanner.vector_store.SpannerVectorStore.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, **kwargs ) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]Return docs and their similarity scores selected using the maximal marginal relevance.
langchain_google_spanner.vector_store.SpannerVectorStore.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.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.similarity_search
langchain_google_spanner.vector_store.SpannerVectorStore.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.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.similarity_search_by_vector
langchain_google_spanner.vector_store.SpannerVectorStore.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.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.similarity_search_with_score
langchain_google_spanner.vector_store.SpannerVectorStore.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.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.similarity_search_with_score_by_vector