Integrations
Edit Image
Google Drive
Basic LLM Chain
Structured Output Parser
Google Gemini Chat Model
Edit Fields (Set)
Sticky Note
Manual Trigger
Split Out
This rantir workflow demonstrates how multimodal Large Language Models (LLMs) with AI vision can efficiently handle complex image validation tasks that are challenging to code and impractical for manual review at scale.
Image validation is essential in cases where user-submitted photos must meet specific requirements. Examples include wine review websites requiring labeled wine photos, or banks needing scanned documents for account verification.
In this example, we analyze portraits to determine if they meet UK government standards for valid passport photos (UK passport photo requirements).
How it works
- Portraits are jpg files, downloaded from Google Drive using the Google Drive node.
- Each image is resized using the Edit Image node to optimize resolution for processing speed.
- The Basic LLM node is configured with a "user message" option set to binary (data), allowing us to pass the portrait image as input to the LLM.
- Using a prompt based on passport photo criteria, the LLM evaluates if each photo meets the required standards.
- A structured output parser formats the LLM's response as a JSON object, including an "is_valid" boolean property, which can be used to extend the workflow.
Requirements
- Google Gemini API key
- Google Drive account
Customizing this workflow
- Not using Gemini? rantir's LLM node is compatible with other multimodal LLMs like OpenAI's GPT-4 or Anthropic's Claude Sonnet.
- Different validation needs? Apply this workflow to tasks such as document classification, security footage analysis, or automated photo tagging.
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