Package Classes (0.4.1)

Chat message history that stores history in Spanner.

Column(name: str, data_type: str, nullable: bool = True)

Save docs to Google Cloud Spanner.

Loads data from Google Cloud Spanner.

Abstract base class for dialect semantics.

Enum for distance calculation strategies.

Implementation of dialect semantics for Google SQL.

Implementation of dialect semantics for PostgreSQL.

Class representing query parameters for nearest neighbors search.

Enum for nearest neighbors search algorithms.

SecondaryIndex(index_name: 'str', columns: 'list[str]', storing_columns: 'Optional[list[str]]' = None)

Initialize the SpannerVectorStore.

Parameters:

• instance_id (str): The ID of the Spanner instance.
• database_id (str): The ID of the Spanner database.
• table_name (str): The name of the table.
• embedding_service (Embeddings): The embedding service.
• id_column (str): The name of the row ID column. Defaults to ID_COLUMN_NAME.
• content_column (str): The name of the content column. Defaults to CONTENT_COLUMN_NAME.
• embedding_column (str): The name of the embedding column. Defaults to EMBEDDING_COLUMN_NAME.
• client (Client): The Spanner client. Defaults to Client().
• metadata_columns (Optional[List[str]]): List of metadata columns. Defaults to None.
• ignore_metadata_columns (Optional[List[str]]): List of metadata columns to ignore. Defaults to None.
• metadata_json_column (Optional[str]): The generic metadata column. Defaults to None.
• query_parameters (QueryParameters): The query parameters. Defaults to QueryParameters().

Represents column configuration, to be used as part of create DDL statement for table creation.

Cloud Spanner-based chat message history

API documentation for loader module.

API documentation for vector_store module.