Rank resumes by job description similarity using Gemini embeddings

by Atha Ahsan Xavier Haris ✓ · 28 views · created 2026-06-30 · HR, AI Summarization

What this workflow does

This workflow has 7 nodes and runs when someone submits an n8n form (“Sort Resumes by Job Description Similarity”). It uses only n8n's built-in nodes. It also calls generativelanguage.googleapis.com over HTTP.

How it runs

Trigger: When someone submits an n8n form (“Sort Resumes by Job Description Similarity”).

1. Split Resume PDFs (Code): runs custom JavaScript code
2. Create Job Description Embedding (HTTP Request): calls generativelanguage.googleapis.com (POST)
3. Convert PDF to Base (Extract from File): extracts data from a file (binary to propery)
4. Create Resume Embedding (HTTP Request): calls generativelanguage.googleapis.com (POST)
5. Rank Resumes by Similarity (Code): runs custom JavaScript code
6. Show Results (n8n Form): shows the next form page

HTTP calls to: generativelanguage.googleapis.com

Before you upgrade to n8n 3.0

Nodes used (5)

• n8n Form Triggern8n-nodes-base.formTrigger
• Coden8n-nodes-base.code
• n8n Formn8n-nodes-base.form
• Extract from Filen8n-nodes-base.extractFromFile
• HTTP Requestn8n-nodes-base.httpRequest

From the author

Quick overview This workflow collects resume PDFs and a job description via an n8n form, generates embeddings with Google Gemini, calculates cosine similarity between each resume and the job description, and returns a CSV-style ranked list of filenames. How…

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