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feat: batch LLM OCR — multiple images in one API call + downscaling

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- Add run_ocr_llm_batch() — sends N images in a single vision API call
  with structured JSON prompt, up to 20 images per batch
- Add _resize_for_llm() — downscales images to 1600px max dimension
  before sending to LLM, reducing per-image token cost
- Update bulk_process() to pre-read all files and batch-OCR in one call
- Graceful fallback: if batch JSON parsing fails, retries individually
- Frontend shows llm_batch engine badge

Without batch: N photos = N API calls (each with full prompt overhead)
With batch: N photos = ceil(N/20) API calls + image downscaling savings
This commit is contained in:
Shawn
2026-05-25 17:26:10 -04:00
parent 3a67070063
commit 70d8374ca6
3 changed files with 155 additions and 12 deletions
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static/index.html
+2 -2
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@@ -487,8 +487,8 @@ async function uploadSelected() {
// OCR
const ocr = data.ocr;
const engineCls = ocr.engine === 'llm' ? 'llm' : 'tesseract';
html += '<div style="margin-top:10px;font-weight:600;">🔤 OCR <span class="engine-badge ' + engineCls + '">' + esc(ocr.engine || 'tesseract') + (ocr.llm_model ? ' ' + ocr.llm_model : '') + '</span></div>';
const engineCls = ocr.engine === 'llm' || ocr.engine === 'llm_batch' ? 'llm' : 'tesseract';
html += '<div style="margin-top:10px;font-weight:600;">🔤 OCR <span class="engine-badge ' + engineCls + '">' + esc(ocr.engine || 'tesseract') + (ocr.llm_model ? ' ' + esc(ocr.llm_model) : '') + '</span></div>';
if (ocr.raw_text) {
html += '<div class="ocr-text">' + esc(ocr.raw_text) + '</div>';
}