📖 Build a RAG knowledge chatbot with OpenAI, Google Drive, and Supabase
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Description
🚀 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 $$
#variable_conflict use_column
begin
return query
select
id,
content,
metadata,
1 - (documents.embedding <=> query_embedding) as similarity
from documents
where metadata @> filter
order by documents.embedding <=> query_embedding
limit match_count;
end;
$$;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
⚡ Features
- Auto-syncs new/updated PDFs from Google Drive
- Extracts, chunks, and vectorizes text
- Finds relevant info and answers questions
- Maintains chat history per user
📝 Troubleshooting
- Check folder permissions & IDs if no docs found
- Verify API keys & Supabase setup for errors
- Ensure PostgreSQL is connected for chat memory
Tags: RAG, Chatbot, Google Drive, Supabase, OpenAI, n8n Setup Time: ~20 minutes
🔗 Nodes Used
Google Drive, Google Drive Trigger, Supabase, AI Agent, Embeddings OpenAI, OpenAI Chat Model
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File