SupabaseVectorStore

class llama_index.vector_stores.SupabaseVectorStore(postgres_connection_string: str, collection_name: str, dimension: int = 1536, **kwargs: Any)

Bases: VectorStore

Supbabase Vector.

In this vector store, embeddings are stored in Postgres table using pgvector.

During query time, the index uses pgvector/Supabase to query for the top k most similar nodes.

Parameters

• postgres_connection_string (str) – postgres connection string

• collection_name (str) – name of the collection to store the embeddings in

Attributes Summary

client	Get client.
flat_metadata
stores_text

Methods Summary

add(nodes, **add_kwargs)	Add nodes to index.
delete(ref_doc_id, **delete_kwargs)	Delete doc.
get_by_id(doc_id, **kwargs)	Get row ids by doc id.
query(query, **kwargs)	Query index for top k most similar nodes.

Attributes Documentation

client

Get client.

flat_metadata = False

stores_text: bool = True

Methods Documentation

add(nodes: List[BaseNode], **add_kwargs: Any) → List[str]

Add nodes to index.

Parameters

nodes – List[BaseNode]: list of nodes with embeddings

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete doc.

:param : param ref_doc_id (str): document id

get_by_id(doc_id: str, **kwargs: Any) → list

Get row ids by doc id.

Parameters

doc_id (str) – document id

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Query index for top k most similar nodes.

Parameters

query (List[float]) – query embedding