Templates / Build a Google Drive document similarity database with Ollama and Postgres

Build a Google Drive document similarity database with Ollama and Postgres

Ready for 3.0

by Siddharth Gupta ✓ · 3 views · created 2026-09-06 · Market Research, 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 56 nodes and runs when a chat message arrives. Along the way it connects Google Drive, Postgres, Postgres PGVector Store and Embeddings Ollama.

How it runs

Trigger: When a chat message arrives.

  1. Prompt Main Action (Chat): send and wait for a reply
  2. Route Operations Mode (Switch): routes items into 2 branches
  3. Prompt Operational Rules (Chat): send and wait for a reply
  4. Confirm Pipeline Launch (Chat): send and wait for a reply
  5. Verify Rules Selection (If): checks a condition and branches into true / false
  6. Validate Launch Parameters (Code): runs custom JavaScript code
  7. Clear Vector Table (Postgres): delete table
  8. Broadcast Error Alert (Chat): uses Chat
  9. Evaluate Validation Results (If): checks a condition and branches into true / false
  10. Clear Scraped Pages Table (Postgres): delete table

…plus 39 more steps. See the full node list below.

Services it uses

Google DrivePostgresPostgres PGVector StoreEmbeddings Ollama

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 (17)

From the author

Quick overview Builds a searchable semantic similarity database from Google Drive documents using PostgreSQL, PGVector, and Ollama embeddings, then analyzes document similarity and provides interactive internal linking recommendations through chunk-level and…

Excerpt from Siddharth Gupta's description on n8n.io.

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

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