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This rantir workflow showcases the use of Multimodal LLMs for parsing and extracting information from PDF documents within rantir.
In this scenario, we’re reviewing a candidate’s CV/resume through an AI that filters out unqualified applications. However, the candidate has included a hidden prompt intended to bypass our filter! By leveraging AI Vision, we can effectively address this issue. Read on to learn how!
How it works
- The candidate’s CV/resume, in PDF format, is downloaded from Google Drive for demonstration purposes.
- The PDF is converted to a PNG image using Stirling PDF. Since the hidden prompt is in white font, it remains invisible in the converted image.
- The image is processed by a Basic LLM node using a multimodal model, such as Google’s Gemini 1.5 Pro.
- Within the Basic LLM node, a "User Message" with Binary type is configured, allowing us to send the image file directly in the request.
- The LLM now successfully ignores the hidden prompt, yielding the expected response.
Requirements
- Google Gemini API Key. Alternatively, GPT-4 can also handle this use case.
- Stirling PDF or another service for converting PDFs to images. For privacy, consider self-hosting Stirling PDF to avoid using public APIs.
Customizing the workflow
- Replace the manual trigger with a webhook or other trigger to integrate into your existing services.
- This example validates qualifications; you can expand it to extract data points like years of experience, previous employers, etc.
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