Package Methods (0.14.0)

_get_iam_principal_email(credentials: google.auth.credentials.Credentials) -> str

PostgresChatMessageHistory( key: object, engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, history: langchain_google_cloud_sql_pg.async_chat_message_history.AsyncPostgresChatMessageHistory, )

aadd_message(message: langchain_core.messages.base.BaseMessage) -> None

aadd_messages( messages: typing.Sequence[langchain_core.messages.base.BaseMessage], ) -> None

aclear() -> None

add_message(message: langchain_core.messages.base.BaseMessage) -> None

add_messages( messages: typing.Sequence[langchain_core.messages.base.BaseMessage], ) -> None

clear() -> None

create( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, session_id: str, table_name: str, schema_name: str = "public", ) -> langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory

create_sync( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, session_id: str, table_name: str, schema_name: str = "public", ) -> langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory

__post_init__()

PostgresEngine( key: object, pool: sqlalchemy.ext.asyncio.engine.AsyncEngine, loop: typing.Optional[asyncio.events.AbstractEventLoop], thread: typing.Optional[threading.Thread], )

_ainit_chat_history_table(table_name: str, schema_name: str = "public") -> None

_ainit_checkpoint_table( table_name: str = "checkpoints", schema_name: str = "public" ) -> None

_ainit_vectorstore_table( table_name: str, vector_size: int, schema_name: str = "public", content_column: str = "content", embedding_column: str = "embedding", metadata_columns: list[langchain_google_cloud_sql_pg.engine.Column] = [], metadata_json_column: str = "langchain_metadata", id_column: typing.Union[ str, langchain_google_cloud_sql_pg.engine.Column ] = "langchain_id", overwrite_existing: bool = False, store_metadata: bool = True, ) -> None

_aload_table_schema( table_name: str, schema_name: str = "public" ) -> sqlalchemy.sql.schema.Table

_create( project_id: str, region: str, instance: str, database: str, ip_type: typing.Union[str, google.cloud.sql.connector.enums.IPTypes], user: typing.Optional[str] = None, password: typing.Optional[str] = None, loop: typing.Optional[asyncio.events.AbstractEventLoop] = None, thread: typing.Optional[threading.Thread] = None, quota_project: typing.Optional[str] = None, iam_account_email: typing.Optional[str] = None, engine_args: typing.Mapping = {}, ) -> langchain_google_cloud_sql_pg.engine.PostgresEngine

_run_as_async( coro: typing.Awaitable[langchain_google_cloud_sql_pg.engine.T], ) -> langchain_google_cloud_sql_pg.engine.T

_run_as_sync( coro: typing.Awaitable[langchain_google_cloud_sql_pg.engine.T], ) -> langchain_google_cloud_sql_pg.engine.T

afrom_instance( project_id: str, region: str, instance: str, database: str, user: typing.Optional[str] = None, password: typing.Optional[str] = None, ip_type: typing.Union[ str, google.cloud.sql.connector.enums.IPTypes ] = IPTypes.PUBLIC, quota_project: typing.Optional[str] = None, iam_account_email: typing.Optional[str] = None, engine_args: typing.Mapping = {}, ) -> langchain_google_cloud_sql_pg.engine.PostgresEngine

ainit_chat_history_table(table_name: str, schema_name: str = "public") -> None

ainit_checkpoint_table( table_name: str = "checkpoints", schema_name: str = "public" ) -> None

ainit_document_table( table_name: str, schema_name: str = "public", content_column: str = "page_content", metadata_columns: list[langchain_google_cloud_sql_pg.engine.Column] = [], metadata_json_column: str = "langchain_metadata", store_metadata: bool = True, ) -> None

ainit_vectorstore_table( table_name: str, vector_size: int, schema_name: str = "public", content_column: str = "content", embedding_column: str = "embedding", metadata_columns: list[langchain_google_cloud_sql_pg.engine.Column] = [], metadata_json_column: str = "langchain_metadata", id_column: typing.Union[ str, langchain_google_cloud_sql_pg.engine.Column ] = "langchain_id", overwrite_existing: bool = False, store_metadata: bool = True, ) -> None

close() -> None

from_engine( engine: sqlalchemy.ext.asyncio.engine.AsyncEngine, loop: typing.Optional[asyncio.events.AbstractEventLoop] = None, ) -> langchain_google_cloud_sql_pg.engine.PostgresEngine

from_engine_args( url: str | sqlalchemy.engine.url.URL, **kwargs: typing.Any ) -> langchain_google_cloud_sql_pg.engine.PostgresEngine

from_instance( project_id: str, region: str, instance: str, database: str, user: typing.Optional[str] = None, password: typing.Optional[str] = None, ip_type: typing.Union[ str, google.cloud.sql.connector.enums.IPTypes ] = IPTypes.PUBLIC, quota_project: typing.Optional[str] = None, iam_account_email: typing.Optional[str] = None, engine_args: typing.Mapping = {}, ) -> langchain_google_cloud_sql_pg.engine.PostgresEngine

init_chat_history_table(table_name: str, schema_name: str = "public") -> None

init_checkpoint_table( table_name: str = "checkpoints", schema_name: str = "public" ) -> None

init_document_table( table_name: str, schema_name: str = "public", content_column: str = "page_content", metadata_columns: list[langchain_google_cloud_sql_pg.engine.Column] = [], metadata_json_column: str = "langchain_metadata", store_metadata: bool = True, ) -> None

init_vectorstore_table( table_name: str, vector_size: int, schema_name: str = "public", content_column: str = "content", embedding_column: str = "embedding", metadata_columns: list[langchain_google_cloud_sql_pg.engine.Column] = [], metadata_json_column: str = "langchain_metadata", id_column: typing.Union[ str, langchain_google_cloud_sql_pg.engine.Column ] = "langchain_id", overwrite_existing: bool = False, store_metadata: bool = True, ) -> None

index_options() -> str

_generate_next_value_(start, count, last_values)

index_options() -> str

to_string()

index_options() -> str

to_string()

to_string() -> str