| 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' |
embedding_column (str) * | unknown | |
metadata_columns | Optional[list[Union[Column, ColumnDict]]] | Default: None |
metadata_json_column | str | Default: 'langchain_metadata' |
id_column (Union[str, Column, ColumnDict]) * | unknown | |
overwrite_existing | bool | Default: False |
store_metadata | bool | Default: True |
hybrid_search_config | HybridSearchConfig | Default: None |
Create a table for saving of vectors to be used with PGVectorStore.
Name of the column to store document content. Default: "page_content".
Name of the column to store vector embeddings. Default: "embedding".
A list of Columns to create for custom metadata. Default: None. Optional.
The column to store extra metadata in JSON format. Default: "langchain_metadata". Optional.
Column to store ids. Default: "langchain_id" column name with data type UUID. Optional.
Whether to drop existing table. Default: False.
Whether to store metadata in the table. Default: True.
Hybrid 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.
