Create a table for saving of vectors to be used with PGVectorStore.
ainit_vectorstore_table(
self,
table_name: str,
vector_size: int,
*,
schema_name: str = 'public',
content_column: str = 'content',
embedding_column: str = 'embedding',
metadata_columns: Optional[list[Union[Column, ColumnDict]]] = None,
metadata_json_column: str = 'langchain_metadata',
id_column: Union[str, Column, ColumnDict] = 'langchain_id',
overwrite_existing: bool = False,
store_metadata: bool = True,
hybrid_search_config: Optional[HybridSearchConfig] = None
) -> None| Name | Type | Description |
|---|---|---|
table_name* | str | The database table name. |
vector_size* | int | Vector size for the embedding model to be used. |
schema_name | str | Default: 'public'The schema name. Default: "public". |
content_column | str | Default: 'content'Name of the column to store document content. Default: "page_content". |
embedding_column (str) * | unknown | Name of the column to store vector embeddings. Default: "embedding". |
metadata_columns | Optional[list[Union[Column, ColumnDict]]] | Default: NoneA list of Columns to create for custom metadata. Default: None. Optional. |
metadata_json_column | str | Default: 'langchain_metadata'The column to store extra metadata in JSON format. Default: "langchain_metadata". Optional. |
id_column (Union[str, Column, ColumnDict]) * | unknown | Column to store ids. Default: "langchain_id" column name with data type UUID. Optional. |
overwrite_existing | bool | Default: FalseWhether to drop existing table. Default: False. |
store_metadata | bool | Default: TrueWhether to store metadata in the table. Default: True. |
hybrid_search_config | HybridSearchConfig | Default: NoneHybrid search configuration. Note that queries might be slow if the hybrid search column does not exist. For best hybrid search performance, consider creating a TSV column and adding GIN index. Default: None. |