n8n-Dokumentations-Experten-Bot mit Gemini RAG und Supabase

-- Enable the pgvector extension to work with embedding vectors (REMOVE THIS LINE IF ALREADY ENABLED) create 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(768) -- 768 works for Gemini embeddings, CHANGE IF OPENAI EMBEDDINGS ); -- Create a function to search for documents create function match_documents ( query_embedding vector(1536), match_count int default null, filter jsonb DEFAULT '{}' ) returns table ( id bigint, content text, metadata jsonb, similarity float ) language plpgsql as $$ #variable_conflict use_column begin return query select id, content, metadata, 1 - (documents.embedding query_embedding) as similarity from documents where metadata @> filter order by documents.embedding query_embedding limit match_count; end; $$;