Create a smart chatbot that answers questions using your Google Drive PDFs—perfect for support, internal docs, education, or research.
Create supabase account (its free)
Create a project
Copy the sql and paste it in supabase sql editor
-- Enable the pgvector extension to work with embedding vectors 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(1536) -- 1536 works for OpenAI embeddings, change if needed );
-- 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 $$ #variable_conflict use_column begin return query select id, content, metadata, 1 - (documents.embedding <=> query_embedding) as similarity from documents where metadata @> filter order by documents.embedding <=> query_embedding limit match_count; end; $$;
Tags: RAG, Chatbot, Google Drive, Supabase, OpenAI, n8n
Setup Time: ~20 minutes