Suya OCR API — vLLM-backed, OpenAI-compatible OCR service

FastAPI service wrapping the Surya-OCR-2 model (datalab-to) served through vLLM:
legacy /v1/api/ai/* endpoints, an OpenAI-compatible /v1/chat/completions endpoint,
a coalescing request batcher, a local OCR CLI, Docker packaging, multilingual
example outputs, and quantization/concurrency benchmarks.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

scripts/quant/capture.py

@@ -0,0 +1,77 @@
+"""Capture per-page OCR text, bboxes, and latency from the vLLM OCR endpoint."""
+from __future__ import annotations
+
+import argparse
+import base64
+import json
+import sys
+from pathlib import Path
+from typing import Any, Dict
+
+import requests
+
+
+def extract_capture(body: Dict[str, Any]) -> Dict[str, Any]:
+    data = body.get("data") or {}
+    if not isinstance(data, dict):
+        data = {}
+    blocks = ((data.get("ocr_text_json") or {}).get("blocks")) or []
+    boxes = [list(b["bbox"]) for b in blocks if isinstance(b, dict) and b.get("bbox")]
+    return {
+        "text": data.get("text_lines", "") or "",
+        "boxes": boxes,
+        "elapsed_seconds": data.get("elapsed_seconds"),
+    }
+
+
+def write_capture(cap: Dict[str, Any], out_dir: Path, stem: str) -> None:
+    out_dir.mkdir(parents=True, exist_ok=True)
+    (out_dir / f"{stem}.txt").write_text(cap["text"], encoding="utf-8")
+    (out_dir / f"{stem}.json").write_text(
+        json.dumps({"boxes": cap["boxes"], "elapsed_seconds": cap["elapsed_seconds"]}),
+        encoding="utf-8",
+    )
+
+
+def _payload(image_path: Path) -> Dict[str, Any]:
+    suffix = image_path.suffix.lstrip(".").lower() or "png"
+    return {
+        "file": base64.b64encode(image_path.read_bytes()).decode("utf-8"),
+        "type": "jpg" if suffix == "jpeg" else suffix,
+        "skip_text_detection": False,
+        "skip_table_detection": False,
+        "recognize_math": False,
+        "ocr_with_boxes": True,
+    }
+
+
+def capture_page(url: str, image_path: Path, out_dir: Path, timeout: float) -> Dict[str, Any]:
+    resp = requests.post(url, json=_payload(image_path), timeout=timeout)
+    cap = extract_capture(resp.json())
+    if cap["elapsed_seconds"] is None:
+        # Latency is a headline metric; a successful response with no timing means
+        # the response contract changed. Surface it loudly instead of silently
+        # dropping the page from the latency stats.
+        print(
+            f"WARNING: no elapsed_seconds in OCR response for {image_path.name}; "
+            "latency for this page will be missing",
+            file=sys.stderr,
+        )
+    write_capture(cap, out_dir, image_path.stem)
+    return cap
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Capture OCR text+boxes+latency per image.")
+    parser.add_argument("images", nargs="+", type=Path)
+    parser.add_argument("--url", required=True)
+    parser.add_argument("--out-dir", type=Path, required=True)
+    parser.add_argument("--timeout", type=float, default=900)
+    args = parser.parse_args()
+    for image in args.images:
+        cap = capture_page(args.url, image, args.out_dir, args.timeout)
+        print(f"captured {image.name}: {len(cap['boxes'])} boxes, {cap['elapsed_seconds']}s")
+
+
+if __name__ == "__main__":
+    main()