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CV Resume PDF Parsing with Multimodal Vision AI

n8n template #2416
This n8n workflow demonstrates how we can use Multimodal LLMs to parse and extract from PDF documents in n8n. In this particular scenario, we're passing a candidate's CV/resume to an AI which filters out unqualified applications. However, this sneaky candidate has added in hidden prompt to bypass our bot! Whatever will we do? No fret, using AI Vision is one approach to solve this problem... read on! How it works Our candidate's CV/Resume is a PDF downloaded via Google Drive for this demonstration. The PDF is then converted into an image PNG using a tool called Stirling PDF. Since the hidden prompt has a white font color, it is is invisible in the converted image. The image is then forwarded

How to set it up

  1. Open template #2416 on n8n.io and click Use for free, or copy the template JSON and paste it onto an empty canvas in n8n.
  2. Create 2 credentials under Credentials → Add credential: Google Drive, Google Gemini Chat Model. Then open each of those nodes and pick the credential.
  3. Check the model in the Google Gemini Chat Model node. The template may point at a model your account can't use; swap it for one you have access to.
  4. Click Execute workflow and step through each node's output to confirm the data looks right.
  5. Toggle the workflow to Active so its trigger runs on its own.

Hand off to your agent

Prompt

Help me set up the n8n workflow "CV Resume PDF Parsing with Multimodal Vision AI" (https://n8n.io/workflows/2416). It uses: Edit Image, HTTP Request, Google Drive, Basic LLM Chain, Structured Output Parser, Google Gemini Chat Model. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.

Paste into Claude Code and it will do the rest.

Apps and nodes

Questions

Yes. Jimleuk published it free on n8n.io as a verified creator, and it has 16K views. It runs on n8n Cloud or a self-hosted n8n instance.

Google Drive, Google Gemini Chat Model. The other 4 nodes are built into n8n.

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