Class PostgresEngine (0.2.2)

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_store_table

_ainit_chat_store_table(
    table_name: str, schema_name: str = "public", overwrite_existing: bool = False
) -> None

Create a table to save chat store.

Parameters
Name	Description
`table_name`	`str` The table name to store chat history.
`schema_name`	`str` The schema name to store the chat store table. Default: "public".
`overwrite_existing`	`bool Returns: None` Whether to drop existing table. Default: False.

_ainit_doc_store_table

_ainit_doc_store_table(
    table_name: str, schema_name: str = "public", overwrite_existing: bool = False
) -> None

Create an table for the DocumentStore.

Parameters
Name	Description
`table_name`	`str` The table name to store documents.
`schema_name`	`str` The schema name to store the documents table. Default: "public".
`overwrite_existing`	`bool` Whether to drop existing table. Default: False.

_ainit_index_store_table

_ainit_index_store_table(
    table_name: str, schema_name: str = "public", overwrite_existing: bool = False
) -> None

Create a table to save Index metadata.

Parameters
Name	Description
`table_name`	`str` The table name to store index metadata.
`schema_name`	`str` The schema name to store the index metadata table. Default: "public".
`overwrite_existing`	`bool` Whether to drop existing table. Default: False.

_ainit_vector_store_table

_ainit_vector_store_table(
    table_name: str,
    vector_size: int,
    schema_name: str = "public",
    id_column: typing.Union[str, llama_index_cloud_sql_pg.engine.Column] = "node_id",
    text_column: str = "text",
    embedding_column: str = "embedding",
    metadata_json_column: str = "li_metadata",
    metadata_columns: list[llama_index_cloud_sql_pg.engine.Column] = [],
    ref_doc_id_column: str = "ref_doc_id",
    node_column: str = "node_data",
    stores_text: bool = True,
    overwrite_existing: bool = False,
) -> None

Create a table for the VectorStore.

Parameters
Name	Description
`table_name`	`str` The table name to store nodes with embedding vectors.
`vector_size`	`int` Vector size for the embedding model to be used.
`schema_name`	`str` The schema name to store the vector store table. Default: "public".
`id_column`	`str` Column that represents if of a Node. Defaults to "node_id".
`text_column`	`str` Column that represent text content of a Node. Defaults to "text".
`embedding_column`	`str` Column for embedding vectors. The embedding is generated from the content of Node. Defaults to "embedding".
`metadata_json_column`	`str` Column to store metadata as JSON. Defaults to "li_metadata".
`metadata_columns`	`list[str]` Column(s) that represent extracted metadata keys in their own columns.
`ref_doc_id_column`	`str` Column that represents id of a node's parent document. Defaults to "ref_doc_id".
`node_column`	`str` Column that represents the whole JSON node. Defaults to "node_data".
`stores_text`	`bool` Whether the table stores text. Defaults to "True".
`overwrite_existing`	`bool` Whether to drop existing table. Default: False.

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 an existing table in a PgSQL database, potentially from a specific database schema.

Returns
Type	Description
`(sqlalchemy.Table)`	The loaded table, including its table schema information.

_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,
) -> llama_index_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.

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

_run_as_async

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

Run an async coroutine asynchronously

_run_as_sync

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

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

ainit_chat_store_table

ainit_chat_store_table(
    table_name: str, schema_name: str = "public", overwrite_existing: bool = False
) -> None

Create a table to save chat store.

Parameters
Name	Description
`table_name`	`str` The table name to store chat store.
`schema_name`	`str` The schema name to store the chat store table. Default: "public".
`overwrite_existing`	`bool Returns: None` Whether to drop existing table. Default: False.

ainit_doc_store_table

ainit_doc_store_table(
    table_name: str, schema_name: str = "public", overwrite_existing: bool = False
) -> None

Create a table for the DocumentStore.

Parameters
Name	Description
`table_name`	`str` The table name to store documents.
`schema_name`	`str` The schema name to store the documents table. Default: "public".
`overwrite_existing`	`bool` Whether to drop existing table. Default: False.

ainit_index_store_table

ainit_index_store_table(
    table_name: str, schema_name: str = "public", overwrite_existing: bool = False
) -> None

Create a table to save Index metadata.

Parameters
Name	Description
`table_name`	`str` The table name to store index metadata.
`schema_name`	`str` The schema name to store the index metadata table. Default: "public".
`overwrite_existing`	`bool` Whether to drop existing table. Default: False.

ainit_vector_store_table

ainit_vector_store_table(
    table_name: str,
    vector_size: int,
    schema_name: str = "public",
    id_column: typing.Union[str, llama_index_cloud_sql_pg.engine.Column] = "node_id",
    text_column: str = "text",
    embedding_column: str = "embedding",
    metadata_json_column: str = "li_metadata",
    metadata_columns: list[llama_index_cloud_sql_pg.engine.Column] = [],
    ref_doc_id_column: str = "ref_doc_id",
    node_column: str = "node_data",
    stores_text: bool = True,
    overwrite_existing: bool = False,
) -> None

Create a table for the VectorStore.

Parameters
Name	Description
`table_name`	`str` The table name to store nodes with embedding vectors.
`vector_size`	`int` Vector size for the embedding model to be used.
`schema_name`	`str` The schema name to store the vector store table. Default: "public".
`id_column`	`str` Column that represents if of a Node. Defaults to "node_id".
`text_column`	`str` Column that represent text content of a Node. Defaults to "text".
`embedding_column`	`str` Column for embedding vectors. The embedding is generated from the content of Node. Defaults to "embedding".
`metadata_json_column`	`str` Column to store metadata as JSON. Defaults to "li_metadata".
`metadata_columns`	`list[str]` Column(s) that represent extracted metadata keys in their own columns.
`ref_doc_id_column`	`str` Column that represents id of a node's parent document. Defaults to "ref_doc_id".
`node_column`	`str` Column that represents the whole JSON node. Defaults to "node_data".
`stores_text`	`bool` Whether the table stores text. Defaults to "True".
`overwrite_existing`	`bool` Whether to drop existing table. Default: False.

close

close() -> None

Dispose of connection pool

from_engine

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

Create an PostgresEngine instance from an AsyncEngine.

from_engine_args

from_engine_args(
    url: typing.Union[str, sqlalchemy.engine.url.URL], **kwargs: typing.Any
) -> llama_index_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,
) -> llama_index_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.

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

init_chat_store_table

init_chat_store_table(
    table_name: str, schema_name: str = "public", overwrite_existing: bool = False
) -> None

Create a table to save chat store.

Parameters
Name	Description
`table_name`	`str` The table name to store chat store.
`schema_name`	`str` The schema name to store the chat store table. Default: "public".
`overwrite_existing`	`bool Returns: None` Whether to drop existing table. Default: False.

init_doc_store_table

init_doc_store_table(
    table_name: str, schema_name: str = "public", overwrite_existing: bool = False
) -> None

Create a table for the DocumentStore.

Parameters
Name	Description
`table_name`	`str` The table name to store documents.
`schema_name`	`str` The schema name to store the documents table. Default: "public".
`overwrite_existing`	`bool` Whether to drop existing table. Default: False.

init_index_store_table

init_index_store_table(
    table_name: str, schema_name: str = "public", overwrite_existing: bool = False
) -> None

Create a table to save Index metadata.

Parameters
Name	Description
`table_name`	`str` The table name to store index metadata.
`schema_name`	`str` The schema name to store the index metadata table. Default: "public".
`overwrite_existing`	`bool` Whether to drop existing table. Default: False.

init_vector_store_table

init_vector_store_table(
    table_name: str,
    vector_size: int,
    schema_name: str = "public",
    id_column: typing.Union[str, llama_index_cloud_sql_pg.engine.Column] = "node_id",
    text_column: str = "text",
    embedding_column: str = "embedding",
    metadata_json_column: str = "li_metadata",
    metadata_columns: list[llama_index_cloud_sql_pg.engine.Column] = [],
    ref_doc_id_column: str = "ref_doc_id",
    node_column: str = "node_data",
    stores_text: bool = True,
    overwrite_existing: bool = False,
) -> None

Create a table for the VectorStore.

Parameters
Name	Description
`table_name`	`str` The table name to store nodes with embedding vectors.
`vector_size`	`int` Vector size for the embedding model to be used.
`schema_name`	`str` The schema name to store the vector store table. Default: "public".
`id_column`	`str` Column that represents if of a Node. Defaults to "node_id".
`text_column`	`str` Column that represent text content of a Node. Defaults to "text".
`embedding_column`	`str` Column for embedding vectors. The embedding is generated from the content of Node. Defaults to "embedding".
`metadata_json_column`	`str` Column to store metadata as JSON. Defaults to "li_metadata".
`metadata_columns`	`list[str]` Column(s) that represent extracted metadata keys in their own columns.
`ref_doc_id_column`	`str` Column that represents id of a node's parent document. Defaults to "ref_doc_id".
`node_column`	`str` Column that represents the whole JSON node. Defaults to "node_data".
`stores_text`	`bool` Whether the table stores text. Defaults to "True".
`overwrite_existing`	`bool` Whether to drop existing table. Default: False.

Class PostgresEngine (0.2.2) Stay organized with collections Save and categorize content based on your preferences.

Methods

PostgresEngine

_ainit_chat_store_table

_ainit_doc_store_table

_ainit_index_store_table

_ainit_vector_store_table

_aload_table_schema

_create

_run_as_async

_run_as_sync

afrom_instance

ainit_chat_store_table

ainit_doc_store_table

ainit_index_store_table

ainit_vector_store_table

close

from_engine

from_engine_args

from_instance

init_chat_store_table

init_doc_store_table

init_index_store_table

init_vector_store_table

Class PostgresEngine (0.2.2)