Package Methods (0.3.0)
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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.EUCLIDEIAN) -> 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.GoogleSqlSemnatics.getDistanceFunction
getDistanceFunction(distance_strategy=DistanceStrategy.EUCLIDEIAN) -> strAbstract method to get the distance function based on the provided distance strategy.
See more: langchain_google_spanner.vector_store.GoogleSqlSemnatics.getDistanceFunction
langchain_google_spanner.vector_store.PGSqlSemnatics.getDistanceFunction
getDistanceFunction(distance_strategy=DistanceStrategy.EUCLIDEIAN) -> strAbstract method to get the distance function based on the provided distance strategy.
See more: langchain_google_spanner.vector_store.PGSqlSemnatics.getDistanceFunction
langchain_google_spanner.vector_store.QueryParameters
QueryParameters(
algorithm=NearestNeighborsAlgorithm.EXACT_NEAREST_NEIGHBOR,
distance_strategy=DistanceStrategy.EUCLIDEIAN,
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]
] = None,
)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 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.
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]
] = 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,
) -> 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