Build a RAG Knowledge Chatbot with OpenAI, Google Drive, and Supabase

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🚀 Build Your Own Knowledge Chatbot Using Google Drive

Create a smart chatbot that answers questions using your Google Drive PDFs—perfect for support, internal docs, education, or research.

🛠️ Quick Setup Guide**

Step 1: Prerequisites

n8n instance (cloud or self-hosted)
Google Drive account (with PDFs)
Supabase account (vector database)
OpenAI API key
PostgreSQL database (for chat memory) else remove the node

Step 2: Supabase Setup

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 $$
#variable_conflict use_column
begin
  return query
  select
    id,
    content,
    metadata,
    1 - (documents.embedding &lt;=&gt; query_embedding) as similarity
  from documents
  where metadata @&gt; filter
  order by documents.embedding &lt;=&gt; query_embedding
  limit match_count;
end;
$$;

Step 3: Import & Configure n8n Workflow

Import this template into n8n
Add credentials:
- OpenAI API key
- Google Drive OAuth2
- Supabase URL & service key
- PostgreSQL connection
Set your Google Drive folder ID in triggers

Step 4: Test & Use

Add a PDF to your Drive folder → check Supabase for new entries
Start the workflow and chat → ask questions about your documents. "What can you help me with?"
Multi-turn chat → context is maintained per user