Common utilities and models for LangChain integration.
LangChain VectorStore backed by Azure Database for PostgreSQL (sync).
The store validates or creates the backing table on initialization, and optionally discovers an existing vector index configuration. It supports inserting, deleting, fetching by id, similarity search, and MMR search.
Fields such as schema_name, table_name, and column names control the
schema layout. embedding_type, embedding_dimension, and
embedding_index describe the vector column and its index behavior.
Metadata can be stored in a single JSONB column by passing a string (default
"metadata"), in multiple typed columns via a list of strings/tuples, or
disabled by setting metadata_columns=None.
:param embedding: The embedding model to use for embedding vector generation. :type embedding: Embeddings | None :param connection: The database connection or connection pool to use. :type connection: Connection | ConnectionPool :param schema_name: The name of the database schema to use. :type schema_name: str :param table_name: The name of the database table to use. :type table_name: str :param id_column: The name of the column containing document IDs (UUIDs). :type id_column: str :param content_column: The name of the column containing document content. :type content_column: str :param embedding_column: The name of the column containing document embeddings. :type embedding_column: str :param embedding_type: The type of the embedding vectors. :type embedding_type: VectorType | None :param embedding_dimension: The dimensionality of the embedding vectors. :type embedding_dimension: PositiveInt | None :param embedding_index: The algorithm used for indexing the embedding vectors. :type embedding_index: Algorithm | None :param _embedding_index_name: (internal) The name of the discovered or created index. :type _embedding_index_name: str | None :param metadata_columns: The columns to use for storing metadata. :type metadata_columns: list[str] | list[tuple[str, str]] | str | None
LangChain VectorStore backed by Azure Database for PostgreSQL (async).
The store validates or creates the backing table on initialization, and optionally discovers an existing vector index configuration. It supports inserting, deleting, fetching by id, similarity search, and MMR search.
Fields such as schema_name, table_name, and column names control the
schema layout. embedding_type, embedding_dimension, and
embedding_index describe the vector column and its index behavior.
Metadata can be stored in a single JSONB column by passing a string (default
"metadata"), in multiple typed columns via a list of strings/tuples, or
disabled by setting metadata_columns=None.
:param embedding: The embedding model to use for embedding vector generation. :type embedding: Embeddings | None :param connection: The database connection or connection pool to use. :type connection: AsyncConnection | AsyncConnectionPool :param schema_name: The name of the database schema to use. :type schema_name: str :param table_name: The name of the database table to use. :type table_name: str :param id_column: The name of the column containing document IDs (UUIDs). :type id_column: str :param content_column: The name of the column containing document content. :type content_column: str :param embedding_column: The name of the column containing document embeddings. :type embedding_column: str :param embedding_type: The type of the embedding vectors. :type embedding_type: VectorType | None :param embedding_dimension: The dimensionality of the embedding vectors. :type embedding_dimension: PositiveInt | None :param embedding_index: The algorithm used for indexing the embedding vectors. :type embedding_index: Algorithm | None :param _embedding_index_name: (internal) The name of the discovered or created index. :type _embedding_index_name: str | None :param metadata_columns: The columns to use for storing metadata. :type metadata_columns: list[str] | list[tuple[str, str]] | str | None
Common utilities and models for asynchronous LangChain integration.
