text: extension vector;       -- Create a table to store your documents       create table documents (       id bigserial primary key,       content text, -- corresponds to Document.pageContent       metadata jsonb, -- corresponds to Document.metadata       embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed       );       CREATE FUNCTION match_documents(query_embedding vector(1536), match_count int)           RETURNS TABLE(               id uuid,               content text,               metadata jsonb,               -- we return matched vectors to enable maximal marginal relevance searches               embedding vector(1536),               similarity float)           LANGUAGE plpgsql           AS $$           # variable_conflict use_column       BEGIN           RETURN query           SELECT               id,               content,               metadata,               embedding,               1