Cv resume PDF parsing with multimodal vision AI

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2/3/2026

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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 to a Basic LLM node to process using our multimodal model - in this example, we'll use Google's Gemini 1.5 Pro.
In the Basic LLM node, we'll need to set a User Message with the type of Binary. This allows us to directly send the image file in our request.
The LLM is now immune to the hidden prompt and its response is has expected.

The example CV/Resume with hidden prompt can be found here: https://drive.google.com/file/d/1MORAdeev6cMcTJBV2EYALAwll8gCDRav/view?usp=sharing

Requirements

Google Gemini API Key. Alternatively, GPT4 will also work for this use-case.
Stirling PDF or another service which can convert PDFs into images. Note for data privacy, this example uses a public API and it is recommended that you self-host and use a private instance of Stirling PDF instead.

Customising the workflow

Swap out the manual trigger for another trigger such as a webhook to integrate into your existing services.
This example demonstrates a validation use-case ie. "does the candidate look qualified?". You can try additionally extracting data points instead such as years of experiences, previous companies etc.

n8n Workflow: Multimodal Vision AI for Resume/CV PDF Parsing

This n8n workflow leverages multimodal vision AI to parse and extract structured information from PDF resumes or CVs. It automates the process of taking a PDF document, converting it into an image, and then using a large language model (LLM) with vision capabilities to extract key details.

What it does

This workflow performs the following key steps:

Manual Trigger: Initiates the workflow upon a manual execution.
Google Drive File Selection: Allows the user to select a PDF resume/CV file from Google Drive.
Image Conversion: Converts the selected PDF file into an image format using the Edit Image node. This is crucial for multimodal vision AI models that process images.
Conditional Check (Placeholder): Includes an If node, which currently acts as a placeholder for potential future logic, such as checking file types or other conditions before proceeding.
Multimodal AI Processing:
- Basic LLM Chain: Sets up a LangChain LLM chain, which orchestrates the interaction with the AI model.
- Google Gemini Chat Model: Utilizes the Google Gemini Chat Model, a powerful multimodal AI, to analyze the image of the resume/CV.
- Structured Output Parser: Processes the AI's response to extract information in a structured format (e.g., JSON), making it easy to use in subsequent steps.

Prerequisites/Requirements

To use this workflow, you will need:

n8n Instance: A running instance of n8n.
Google Drive Account: To access and select PDF files.
Google Gemini API Key: For the Google Gemini Chat Model node to function. This requires access to Google's AI services.
LangChain Integration: Ensure the @n8n/n8n-nodes-langchain package is installed in your n8n instance.

Setup/Usage

Import the Workflow: Download the provided JSON and import it into your n8n instance.
Configure Credentials:
- Google Drive: Set up your Google Drive OAuth2 credentials in n8n.
- Google Gemini Chat Model: Configure your Google Gemini API key as a credential in n8n for the Google Gemini Chat Model node.
Activate the Workflow: Ensure the workflow is active.
Execute Manually: Click "Execute Workflow" in the When clicking ‘Execute workflow’ node.
Select PDF: In the Google Drive node, select the PDF resume/CV you wish to parse.
Review Output: The Structured Output Parser node will output the extracted, structured information from the resume. You can then connect further nodes to process this data (e.g., save to a database, send to an HR system).

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