langchain.vectorstores.singlestoredb.SingleStoreDB¶
class langchain.vectorstores.singlestoredb.SingleStoreDB(embedding: Embeddings, *, distance_strategy: DistanceStrategy = DistanceStrategy.DOT_PRODUCT, table_name: str = 'embeddings', content_field: str = 'content', metadata_field: str = 'metadata', vector_field: str = 'vector', pool_size: int = 5, max_overflow: int = 10, timeout: float = 30, **kwargs: Any)[source]¶
SingleStore DB vector store.
The prerequisite for using this class is the installation of the singlestoredb
Python package.
The SingleStoreDB vectorstore can be created by providing an embedding function and
the relevant parameters for the database connection, connection pool, and
optionally, the names of the table and the fields to use.
Initialize with necessary components.
Parameters
embedding (Embeddings) – A text embedding model.
distance_strategy (DistanceStrategy, optional) – Determines the strategy employed for calculating
the distance between vectors in the embedding space.
Defaults to DOT_PRODUCT.
Available options are:
- DOT_PRODUCT: Computes the scalar product of two vectors.
This is the default behavior
EUCLIDEAN_DISTANCE: Computes the Euclidean distance betweentwo vectors. This metric considers the geometric distance in
the vector space, and might be more suitable for embeddings
that rely on spatial relationships.
table_name (str, optional) – Specifies the name of the table in use.
Defaults to “embeddings”.
content_field (str, optional) – Specifies the field to store the content.
Defaults to “content”.
metadata_field (str, optional) – Specifies the field to store metadata.
Defaults to “metadata”.
vector_field (str, optional) – Specifies the field to store the vector.
Defaults to “vector”.