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Drupal AI: Focal Point Operations & Streaming

· 3 min read
VictorStackAI
VictorStackAI
VictorStackAI

The Drupal AI module is getting granular with a new 'Focal Point' operation type and fixing critical streaming consistency issues in the API Explorer.

The Hook​

Managing images at scale is painful when you rely on CSS center center cropping—you inevitably chop off heads in hero banners. The Drupal AI module now supports a dedicated FocalPoint operation type, allowing us to offload that visual intelligence to models like GPT-4o or Llava, while recent fixes to the API Explorer finally make streaming responses reliable for debugging.

Why I Built It​

I've been experimenting with automated image tagging, but tagging is only half the battle. Knowing where the subject is in a photo is crucial for responsive design.

Until now, hooking up a Vision Language Model (VLM) to the Focal Point module required custom glue code. The new issue #3571939 introduces a standardized Operation Type for this.

Simultaneously, while building these workflows, I noticed the AI Explorer in the Drupal admin UI was failing to stream responses correctly for certain providers, making it impossible to debug long chains of reasoning without staring at a blank screen for 30 seconds.

The Solution​

Focal Point Operation​

The new operation type abstracts the coordinate mapping. Instead of parsing a raw JSON response from a VLM and manually updating a field, the FocalPoint operation does the heavy lifting.

Here is how the flow looks conceptually:

Fixing the Stream​

The streaming issue (#3571925) was a classic case of PHP output buffering fighting with the AI module's chunked response handling. The patch unifies how the stream parameter is handled across the API Explorer and the underlying provider plugins.

// Output would hang until the entire response was generated
foreach ($stream as $chunk) {
  echo $chunk;
  // No flush, web server buffers content
}

The Code​

I've set up a demo repository that configures the AI Automator with the new Focal Point operation patches applied.

View Code

What I Learned​

  • Vision Models are Cheap enough for this: With GPT-4o-mini and others, running a pass on every uploaded hero image to set a focal point costs fractions of a cent and saves hours of manual content editing.
  • Streaming is brittle: Never assume standard PHP output handling works the same across local dev (DDEV/Lando) and production. The buffering settings often differ.
  • Automators are the killer feature: The ability to chain these small operations (Tagging -> Focal Point -> Alt Text) turns Drupal into a truly "intelligent" CMS rather than just a bucket for text.

References​