Package Methods (0.7.0)

_aget_messages( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, session_id: str, table_name: str, ) -> typing.List[langchain_core.messages.base.BaseMessage]

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

_parse_doc_from_row(content_columns: typing.Iterable[str], metadata_columns: typing.Iterable[str], row: dict, metadata_json_column: typing.Optional[str] = 'langchain_metadata', formatter: typing.Callable = 

_parse_row_from_doc( doc: langchain_core.documents.base.Document, column_names: typing.Iterable[str], content_column: str = "page_content", metadata_json_column: typing.Optional[str] = "langchain_metadata", ) -> typing.Dict

csv_formatter(row, content_columns) -> str

json_formatter(row, content_columns) -> str

text_formatter(row, content_columns) -> str

yaml_formatter(row, content_columns) -> str

cosine_similarity( X: typing.Union[ typing.List[typing.List[float]], typing.List[numpy.ndarray], numpy.ndarray ], Y: typing.Union[ typing.List[typing.List[float]], typing.List[numpy.ndarray], numpy.ndarray ], ) -> numpy.ndarray

maximal_marginal_relevance( query_embedding: numpy.ndarray, embedding_list: list, lambda_mult: float = 0.5, k: int = 4, ) -> typing.List[int]

PostgresChatMessageHistory( key, engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, session_id: str, table_name: str, messages: typing.List[langchain_core.messages.base.BaseMessage], )

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

async_messages() -> None

clear() -> None

create( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, session_id: str, table_name: str, )

create_sync( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, session_id: str, table_name: str, )

sync_messages() -> None

__post_init__()

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

_aexecute(query: str, params: typing.Optional[dict] = None)

_aexecute_outside_tx(query: str)

_afetch(query: str, params: typing.Optional[dict] = None)

_aload_table_schema(table_name: str) -> 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, ) -> langchain_google_cloud_sql_pg.engine.PostgresEngine

_execute(query: str, params: typing.Optional[dict] = None)

_fetch(query: str, params: typing.Optional[dict] = None)

_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, ) -> langchain_google_cloud_sql_pg.engine.PostgresEngine

ainit_chat_history_table(table_name) -> None

ainit_document_table( table_name: str, content_column: str = "page_content", metadata_columns: typing.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, content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[langchain_google_cloud_sql_pg.engine.Column] = [], metadata_json_column: str = "langchain_metadata", id_column: str = "langchain_id", overwrite_existing: bool = False, store_metadata: bool = True, ) -> None

from_engine( engine: sqlalchemy.ext.asyncio.engine.AsyncEngine, ) -> 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, ) -> langchain_google_cloud_sql_pg.engine.PostgresEngine

init_chat_history_table(table_name) -> None

init_document_table( table_name: str, content_column: str = "page_content", metadata_columns: typing.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, content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[langchain_google_cloud_sql_pg.engine.Column] = [], metadata_json_column: str = "langchain_metadata", id_column: str = "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

PostgresDocumentSaver( key, engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, table_name: str, content_column: str, metadata_columns: typing.List[str] = [], metadata_json_column: typing.Optional[str] = None, )

_aload_table_schema() -> sqlalchemy.sql.schema.Table

aadd_documents(docs: typing.List[langchain_core.documents.base.Document]) -> None

add_documents(docs: typing.List[langchain_core.documents.base.Document]) -> None

adelete(docs: typing.List[langchain_core.documents.base.Document]) -> None

create( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, table_name: str, content_column: str = "page_content", metadata_columns: typing.List[str] = [], metadata_json_column: typing.Optional[str] = "langchain_metadata", )

create_sync( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, table_name: str, content_column: str = "page_content", metadata_columns: typing.List[str] = [], metadata_json_column: str = "langchain_metadata", )

delete(docs: typing.List[langchain_core.documents.base.Document]) -> None

PostgresLoader( key, engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, query: str, content_columns: typing.List[str], metadata_columns: typing.List[str], formatter: typing.Callable, metadata_json_column: typing.Optional[str] = None, )

_collect_async_items(docs_generator)

alazy_load() -> typing.AsyncIterator[langchain_core.documents.base.Document]

aload() -> typing.List[langchain_core.documents.base.Document]

create( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, query: typing.Optional[str] = None, table_name: typing.Optional[str] = None, content_columns: typing.Optional[typing.List[str]] = None, metadata_columns: typing.Optional[typing.List[str]] = None, metadata_json_column: typing.Optional[str] = None, format: typing.Optional[str] = None, formatter: typing.Optional[typing.Callable] = None, )

create_sync( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, query: typing.Optional[str] = None, table_name: typing.Optional[str] = None, content_columns: typing.Optional[typing.List[str]] = None, metadata_columns: typing.Optional[typing.List[str]] = None, metadata_json_column: typing.Optional[str] = None, format: typing.Optional[str] = None, formatter: typing.Optional[typing.Callable] = None, )

lazy_load() -> typing.Iterator[langchain_core.documents.base.Document]

load() -> typing.List[langchain_core.documents.base.Document]

PostgresVectorStore( key, engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, embedding_service: langchain_core.embeddings.embeddings.Embeddings, table_name: str, content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[str] = [], id_column: str = "langchain_id", metadata_json_column: typing.Optional[str] = "langchain_metadata", distance_strategy: langchain_google_cloud_sql_pg.indexes.DistanceStrategy = DistanceStrategy.COSINE_DISTANCE, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, index_query_options: typing.Optional[ langchain_google_cloud_sql_pg.indexes.QueryOptions ] = None, )

__query_collection( embedding: typing.List[float], k: typing.Optional[int] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[typing.Any]

_aadd_embeddings( texts: typing.Iterable[str], embeddings: typing.List[typing.List[float]], metadatas: typing.Optional[typing.List[dict]] = None, ids: typing.Optional[typing.List[str]] = None, **kwargs: typing.Any ) -> typing.List[str]

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

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

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

aapply_vector_index( index: langchain_google_cloud_sql_pg.indexes.BaseIndex, name: typing.Optional[str] = None, concurrently: bool = False, ) -> None

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

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

adelete( ids: typing.Optional[typing.List[str]] = None, **kwargs: typing.Any ) -> typing.Optional[bool]

adrop_vector_index(index_name: str = "langchainvectorindex") -> None

afrom_documents( documents: typing.List[langchain_core.documents.base.Document], embedding: langchain_core.embeddings.embeddings.Embeddings, engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, table_name: str, ids: typing.Optional[typing.List[str]] = None, content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[str] = [], ignore_metadata_columns: typing.Optional[typing.List[str]] = None, id_column: str = "langchain_id", metadata_json_column: str = "langchain_metadata", **kwargs: typing.Any ) -> langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore

afrom_texts( texts: typing.List[str], embedding: langchain_core.embeddings.embeddings.Embeddings, engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, table_name: str, metadatas: typing.Optional[typing.List[dict]] = None, ids: typing.Optional[typing.List[str]] = None, content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[str] = [], ignore_metadata_columns: typing.Optional[typing.List[str]] = None, id_column: str = "langchain_id", metadata_json_column: str = "langchain_metadata", **kwargs: typing.Any ) -> langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore

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

amax_marginal_relevance_search_by_vector( embedding: typing.List[float], k: typing.Optional[int] = None, fetch_k: typing.Optional[int] = None, lambda_mult: typing.Optional[float] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[langchain_core.documents.base.Document]

amax_marginal_relevance_search_with_score_by_vector( embedding: typing.List[float], k: typing.Optional[int] = None, fetch_k: typing.Optional[int] = None, lambda_mult: typing.Optional[float] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

areindex(index_name: str = "langchainvectorindex") -> None

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

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

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

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

create( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, embedding_service: langchain_core.embeddings.embeddings.Embeddings, table_name: str, content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[str] = [], ignore_metadata_columns: typing.Optional[typing.List[str]] = None, id_column: str = "langchain_id", metadata_json_column: typing.Optional[str] = "langchain_metadata", distance_strategy: langchain_google_cloud_sql_pg.indexes.DistanceStrategy = DistanceStrategy.COSINE_DISTANCE, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, index_query_options: typing.Optional[ langchain_google_cloud_sql_pg.indexes.QueryOptions ] = None, )

create_sync( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, embedding_service: langchain_core.embeddings.embeddings.Embeddings, table_name: str, content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[str] = [], ignore_metadata_columns: typing.Optional[typing.List[str]] = None, id_column: str = "langchain_id", metadata_json_column: str = "langchain_metadata", distance_strategy: langchain_google_cloud_sql_pg.indexes.DistanceStrategy = DistanceStrategy.COSINE_DISTANCE, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, index_query_options: typing.Optional[ langchain_google_cloud_sql_pg.indexes.QueryOptions ] = None, )

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

from_documents( documents: typing.List[langchain_core.documents.base.Document], embedding: langchain_core.embeddings.embeddings.Embeddings, engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, table_name: str, ids: typing.Optional[typing.List[str]] = None, content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[str] = [], ignore_metadata_columns: typing.Optional[typing.List[str]] = None, id_column: str = "langchain_id", metadata_json_column: str = "langchain_metadata", **kwargs: typing.Any ) -> langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore

from_texts( texts: typing.List[str], embedding: langchain_core.embeddings.embeddings.Embeddings, engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, table_name: str, metadatas: typing.Optional[typing.List[dict]] = None, ids: typing.Optional[typing.List[str]] = None, content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[str] = [], ignore_metadata_columns: typing.Optional[typing.List[str]] = None, id_column: str = "langchain_id", metadata_json_column: str = "langchain_metadata", **kwargs: typing.Any )

is_valid_index(index_name: str = "langchainvectorindex") -> bool

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

max_marginal_relevance_search_by_vector( embedding: typing.List[float], k: typing.Optional[int] = None, fetch_k: typing.Optional[int] = None, lambda_mult: typing.Optional[float] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[langchain_core.documents.base.Document]

max_marginal_relevance_search_with_score_by_vector( embedding: typing.List[float], k: typing.Optional[int] = None, fetch_k: typing.Optional[int] = None, lambda_mult: typing.Optional[float] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

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

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

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

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