Create a RAG Telegram bot using Google Drive, PostgreSQL, and local Ollama

Who's it for This template is for developers, teams, and automation enthusiasts who want a private, PIN-protected Telegram chatbot that answers questions from their own documents — without relying on external AI APIs. Ideal for internal knowledge bases, private document search, or anyone running a local LLM stack with Ollama.

How it works / What it does The workflow has two flows running in parallel:

Document Ingestion: Monitors a Google Drive folder for new files. When a file is added, it is downloaded, split into 500-character chunks (with 50-character overlap), embedded using Ollama's nomic-embed-text model, and stored in a PostgreSQL database with pgvector. Telegram Bot: Accepts messages from users. New users are registered and prompted for a PIN code. Once verified, users can ask any question in plain text. The question is embedded, the top 5 most similar document chunks are retrieved via cosine similarity, and qwen2.5:7b generates a context-aware answer which is sent back to the user. All queries are logged.

How to set up

Enable the pgvector extension in PostgreSQL and create the 4 required tables — full SQL is included in the workflow's sticky notes Install Ollama and pull the required models: ollama pull nomic-embed-text and ollama pull qwen2.5:7b Add your credentials in n8n: Telegram Bot token (from @BotFather), PostgreSQL connection, and Google Drive OAuth2 Open the Google Drive Trigger node and select the folder you want to monitor In the Register New User node, replace YOURPINCODE with your chosen access PIN Activate the workflow and send a message to your Telegram bot

Requirements

Ollama running locally (accessible at http://host.docker.internal:11434 if using Docker) PostgreSQL with pgvector extension (Supabase free tier works) Telegram Bot token from @BotFather Google Drive account

How to customize the workflow