Product Diagram showing how OpenAI’s embedding model converts user prompts into vector outputs, illustrating the transformation of text into numerical representations using text-embedding-ada-002.

Search by Meaning, Not Metadata: Semantic Image Filtering with 4D.Vector

Your users don’t think in filenames or folder hierarchies. They think in ideas.

  • “A robot painted in watercolor.”
  • “A sunny beach filled with color.”
  • “Something that feels like Mona Lisa… but from the future.”

It doesn’t matter if that idea comes from an image, a customer order, an email, or a 4D Write Pro document — the challenge is the same: how do you deliver results that match intent, not just keywords?

With 4D.Vector and 4D AI Kit, your application can finally make sense of meaning. In this post, we’ll illustrate it with semantic image similarity search. And here’s the key: we’re not really working with raw images at all — we’re working with their descriptions. The very same approach works for any kind of text data in your application.

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