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>

tests/test_quant_capture.py

@@ -0,0 +1,41 @@
+import json
+
+from scripts.quant.capture import extract_capture, write_capture
+
+
+def test_extract_capture_pulls_text_boxes_and_latency():
+    body = {
+        "data": {
+            "text_lines": "line one\nline two",
+            "elapsed_seconds": 5.4,
+            "ocr_text_json": {
+                "blocks": [
+                    {"bbox": [1, 2, 3, 4], "html": "line one"},
+                    {"bbox": [5, 6, 7, 8], "html": "line two"},
+                ]
+            },
+        },
+        "message": "success",
+        "code": 200,
+    }
+
+    result = extract_capture(body)
+
+    assert result["text"] == "line one\nline two"
+    assert result["boxes"] == [[1, 2, 3, 4], [5, 6, 7, 8]]
+    assert result["elapsed_seconds"] == 5.4
+
+
+def test_extract_capture_tolerates_missing_fields():
+    result = extract_capture({"data": {}})
+    assert result == {"text": "", "boxes": [], "elapsed_seconds": None}
+
+
+def test_write_capture_writes_txt_and_json(tmp_path):
+    cap = {"text": "hello", "boxes": [[0, 0, 1, 1]], "elapsed_seconds": 2.0}
+
+    write_capture(cap, tmp_path, "page1")
+
+    assert (tmp_path / "page1.txt").read_text(encoding="utf-8") == "hello"
+    loaded = json.loads((tmp_path / "page1.json").read_text(encoding="utf-8"))
+    assert loaded == {"boxes": [[0, 0, 1, 1]], "elapsed_seconds": 2.0}