Class PostgresEngine (0.14.0)

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

A class for managing connections to a Cloud SQL for Postgres database.

Methods

PostgresEngine

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

PostgresEngine constructor.

Parameters
Name	Description
`key`	`object` Prevent direct constructor usage.
`pool`	`AsyncEngine` Async engine connection pool.
`loop`	`Optional[asyncio.AbstractEventLoop]` Async event loop used to create the engine.
`thread`	`Optional[Thread]` Thread used to create the engine async.

Exceptions
Type	Description
`Exception`	If the constructor is called directly by the user.

_ainit_chat_history_table

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

Create a Cloud SQL table to store chat history.

Parameters
Name	Description
`table_name`	`str` Table name to store chat history.
`schema_name`	`str` Schema name to store chat history table. Default: "public".

_ainit_checkpoint_table

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

Create PgSQL tables to save checkpoints.

Parameters
Name	Description
`schema_name`	`str` The schema name to store the checkpoint tables. Default: "public".
`table_name`	`str` The PgSQL database table name. Default: "checkpoints".

_ainit_vectorstore_table

_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

Create a table for saving of vectors to be used with PostgresVectorStore.

Parameters
Name	Description
`table_name`	`str` The Postgres database table name.
`vector_size`	`int` Vector size for the embedding model to be used.
`schema_name`	`str` The schema name to store Postgres database table. Default: "public".
`content_column`	`str` Name of the column to store document content. Default: "page_content".
`embedding_column`	`str` Name of the column to store vector embeddings. Default: "embedding".
`metadata_columns`	`list[Column]` A list of Columns to create for custom metadata. Default: []. Optional.
`metadata_json_column`	`str` The column to store extra metadata in JSON format. Default: "langchain_metadata". Optional.
`id_column`	`Union[str, Column]` Column to store ids. Default: "langchain_id" column name with data type UUID. Optional.
`overwrite_existing`	`bool` Whether to drop existing table. Default: False.
`store_metadata`	`bool` Whether to store metadata in the table. Default: True.

Exceptions
Type	Description
`DuplicateTableErro`
`UndefinedObjectErro`

_aload_table_schema

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

Load table schema from existing table in PgSQL database.

Returns
Type	Description
`(sqlalchemy.Table)`	The loaded table.

_create

_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

Create a PostgresEngine instance.

Parameters
Name	Description
`project_id`	`str` GCP project ID.
`region`	`str` Postgres instance region.
`instance`	`str` Postgres instance name.
`database`	`str` Database name.
`ip_type`	`Union[str, IPTypes]` IP address type. Defaults to IPTypes.PUBLIC.
`user`	`Optional[str]` Postgres user name. Defaults to None.
`password`	`Optional[str]` Postgres user password. Defaults to None.
`loop`	`Optional[asyncio.AbstractEventLoop]` Async event loop used to create the engine.
`thread`	`Optional[Thread]` Thread used to create the engine async.
`quota_project`	`Optional[str]` Project that provides quota for API calls.
`iam_account_email`	`Optional[str]` IAM service account email. Defaults to None.
`engine_args`	`Mapping` Additional arguments that are passed directly to `sqlalchemy.ext.asyncio.mymodule.MyClass.create_async_engine`. This can be used to specify additional parameters to the underlying pool during it's creation.

Exceptions
Type	Description
`ValueError`	If only one of `user` and `password` is specified.

_run_as_async

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

Run an async coroutine asynchronously

_run_as_sync

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

Run an async coroutine synchronously

afrom_instance

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

Create a PostgresEngine from a Postgres instance.

Parameters
Name	Description
`project_id`	`str` GCP project ID.
`region`	`str` Postgres instance region.
`instance`	`str` Postgres instance name.
`database`	`str` Database name.
`user`	`Optional[str], optional` Postgres user name. Defaults to None.
`password`	`Optional[str], optional` Postgres user password. Defaults to None.
`ip_type`	`Union[str, IPTypes], optional` IP address type. Defaults to IPTypes.PUBLIC.
`quota_project`	`Optional[str]` Project that provides quota for API calls.
`iam_account_email`	`Optional[str], optional` IAM service account email. Defaults to None.
`engine_args`	`Mapping` Additional arguments that are passed directly to `sqlalchemy.ext.asyncio.mymodule.MyClass.create_async_engine`. This can be used to specify additional parameters to the underlying pool during it's creation.

Returns
Type	Description
`PostgresEngine`	A newly created PostgresEngine instance.

ainit_chat_history_table

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

Create a Cloud SQL table to store chat history.

Parameter
Name	Description
`table_name`	`str` Table name to store chat history.

ainit_checkpoint_table

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

Create an PgSQL table to save checkpoint messages.

Parameters
Name	Description
`schema_name`	`str` The schema name to store checkpoint tables. Default: "public".
`table_name`	`str` The PgSQL database table name. Default: "checkpoints".

ainit_document_table

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

Create a table for saving of langchain documents.

Parameters
Name	Description
`table_name`	`str` The PgSQL database table name.
`schema_name`	`str` The schema name to store PgSQL database table. Default: "public".
`content_column`	`str` Name of the column to store document content. Default: "page_content".
`metadata_columns`	`list[sqlalchemy.Column]` A list of SQLAlchemy Columns to create for custom metadata. Optional.
`metadata_json_column`	`str` The column to store extra metadata in JSON format. Default: "langchain_metadata". Optional.
`store_metadata`	`bool` Whether to store extra metadata in a metadata column if not described in 'metadata' field list (Default: True).

Exceptions
Type	Description
`DuplicateTableErro`

ainit_vectorstore_table

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

Create a table for saving of vectors to be used with PostgresVectorStore.

Parameters
Name	Description
`table_name`	`str` The Postgres database table name.
`vector_size`	`int` Vector size for the embedding model to be used.
`schema_name`	`str` The schema name to store Postgres database table. Default: "public".
`content_column`	`str` Name of the column to store document content. Default: "page_content".
`embedding_column`	`str` Name of the column to store vector embeddings. Default: "embedding".
`metadata_columns`	`list[Column]` A list of Columns to create for custom metadata. Default: []. Optional.
`metadata_json_column`	`str` The column to store extra metadata in JSON format. Default: "langchain_metadata". Optional.
`id_column`	`Union[str, Column]` Column to store ids. Default: "langchain_id" column name with data type UUID. Optional.
`overwrite_existing`	`bool` Whether to drop existing table. Default: False.
`store_metadata`	`bool` Whether to store metadata in the table. Default: True.

close

close() -> None

Dispose of connection pool

from_engine

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

Create an PostgresEngine instance from an AsyncEngine.

from_engine_args

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

Create an PostgresEngine instance from arguments. These parameters are pass directly into sqlalchemy's create_async_engine function.

Parameters
Name	Description
`url`	`Union[str URL]` the URL used to connect to a database
`\\kwargs`	`Any, optional` sqlalchemy `create_async_engine` arguments

Exceptions
Type	Description
`ValueError`	If `postgresql+asyncpg` is not specified as the PG driver

from_instance

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

Create a PostgresEngine from a Postgres instance.

Parameters
Name	Description
`project_id`	`str` GCP project ID.
`region`	`str` Postgres instance region.
`instance`	`str` Postgres instance name.
`database`	`str` Database name.
`user`	`Optional[str], optional` Postgres user name. Defaults to None.
`password`	`Optional[str], optional` Postgres user password. Defaults to None.
`ip_type`	`Union[str, IPTypes], optional` IP address type. Defaults to IPTypes.PUBLIC.
`quota_project`	`Optional[str]` Project that provides quota for API calls.
`iam_account_email`	`Optional[str], optional` IAM service account email. Defaults to None.
`engine_args`	`Mapping` Additional arguments that are passed directly to `sqlalchemy.ext.asyncio.mymodule.MyClass.create_async_engine`. This can be used to specify additional parameters to the underlying pool during it's creation.

Returns
Type	Description
`PostgresEngine`	A newly created PostgresEngine instance.

init_chat_history_table

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

Create a Cloud SQL table to store chat history.

Parameters
Name	Description
`table_name`	`str` Table name to store chat history.
`schema_name`	`str` Schema name to store chat history table. Default: "public".

init_checkpoint_table

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

Create Cloud SQL tables to store checkpoints.

Parameters
Name	Description
`schema_name`	`str` The schema name to store checkpoint tables. Default: "public".
`table_name`	`str` The PgSQL database table name. Default: "checkpoints".

init_document_table

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

Create a table for saving of langchain documents.

Parameters
Name	Description
`table_name`	`str` The PgSQL database table name.
`schema_name`	`str` The schema name to store PgSQL database table. Default: "public".
`content_column`	`str` Name of the column to store document content. Default: "page_content".
`metadata_columns`	`list[sqlalchemy.Column]` A list of SQLAlchemy Columns to create for custom metadata. Optional.
`metadata_json_column`	`str` The column to store extra metadata in JSON format. Default: "langchain_metadata". Optional.
`store_metadata`	`bool` Whether to store extra metadata in a metadata column if not described in 'metadata' field list (Default: True).

Exceptions
Type	Description
`DuplicateTableErro`

init_vectorstore_table

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

Create a table for saving of vectors to be used with PostgresVectorStore.

Parameters
Name	Description
`table_name`	`str` The Postgres database table name.
`vector_size`	`int` Vector size for the embedding model to be used.
`schema_name`	`str` The schema name to store Postgres database table. Default: "public".
`content_column`	`str` Name of the column to store document content. Default: "page_content".
`embedding_column`	`str` Name of the column to store vector embeddings. Default: "embedding".
`metadata_columns`	`list[Column]` A list of Columns to create for custom metadata. Default: []. Optional.
`metadata_json_column`	`str` The column to store extra metadata in JSON format. Default: "langchain_metadata". Optional.
`id_column`	`Union[str, Column]` Column to store ids. Default: "langchain_id" column name with data type UUID. Optional.
`overwrite_existing`	`bool` Whether to drop existing table. Default: False.
`store_metadata`	`bool` Whether to store metadata in the table. Default: True.

Exceptions
Type	Description
`UndefinedObjectErro`

Class PostgresEngine (0.14.0) Stay organized with collections Save and categorize content based on your preferences.

Methods

PostgresEngine

_ainit_chat_history_table

_ainit_checkpoint_table

_ainit_vectorstore_table

_aload_table_schema

_create

_run_as_async

_run_as_sync

afrom_instance

ainit_chat_history_table

ainit_checkpoint_table

ainit_document_table

ainit_vectorstore_table

close

from_engine

from_engine_args

from_instance

init_chat_history_table

init_checkpoint_table

init_document_table

init_vectorstore_table

Class PostgresEngine (0.14.0)