This n8n workflow shows how using multimodal LLMs with AI vision can tackle tricky image validation tasks, which are near impossible to achieve with code and often impractical to be done by humans at scale.

You may need image validation when users submit photos or images that are required to meet certain criteria before being accepted. For example, a wine review website may require users to only submit photos of wine with labels, or a bank may require account holders to submit scanned documents for verification.

In this demonstration, our scenario will be to analyze a set of portraits to verify if they meet the criteria for valid passport photos according to the UK government website.

How it works:

Our set of portraits are jpg files downloaded from our Google Drive using the Google Drive node. Each image is resized using the Edit Image node to ensure a balance between resolution and processing speed.

Using the Basic LLM node, we’ll define a “user message” option with the type of binary (data). This will allow us to pass our portrait to the LLM as an input. With our prompt containing the criteria pulled off the passport photo requirements webpage, the LLM is able to validate whether the photo does or doesn’t meet its criteria.

A structured output parser is used to structure the LLM’s response to a JSON object, which has the “is_valid” boolean property. This can be useful to further extend the workflow.

Not using Gemini? n8n’s LLM node works with any compatible multimodal LLM, so feel free to swap Gemini out for OpenAI’s GPT-4 or Anthropic’s Claude Sonnet.

Don’t need to validate portraits? Try other use cases such as document classification, security footage analysis, people tagging in photos, and more.