Templates / Create a factual learning assistant with RAG, Gemini, Telegram & MongoDB

Create a factual learning assistant with RAG, Gemini, Telegram & MongoDB

Ready for 3.0

by Pawan ✓ · 607 views · created 2025-10-14 · Internal Wiki, AI RAG

Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.

What this workflow does

This AI workflow has 14 nodes and runs when someone submits an n8n form (“file upload”) (plus 3 other triggers). Along the way it connects Google Drive, MongoDB Atlas Vector Store, Google Gemini Chat Model and Telegram and 1 more.

How it runs

Trigger: When someone submits an n8n form (“file upload”) (plus 3 other triggers).

  1. MongoDB Atlas Vector Store: stores documents in the MongoDB Atlas vector store, with embeddings: Convert Documents to Embeddings; document loader: Default Data Loader
  2. RAG Agent (AI Agent): runs an AI agent, with model: Google Gemini Chat Model; memory: Simple Memory
  3. Download file (Google Drive): download
  4. Send Answer via Telegram (Telegram): uses Telegram
  5. MongoDB Atlas Vector Store: is searched by the agent in the MongoDB Atlas vector store
  6. Retrieve Documents from Embeddings (Embeddings Google Gemini): uses Embeddings Google Gemini

Services it uses

Google DriveMongoDB Atlas Vector StoreGoogle Gemini Chat ModelTelegramEmbeddings Google Gemini

Before you upgrade to n8n 3.0

Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.

Nodes used (12)

From the author

Who's it for? This template is perfect for educational institutions, coaching centers (like UPSC, GMAT, or specialized technical training), internal corporate knowledge bases, and SaaS companies that need to provide instant, accurate, and source-grounded…

Excerpt from Pawan's description on n8n.io.

View the original on n8n.io → Check your own copy

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