SkillOpt v0.1.0: initial release

- Skill optimization framework with training loop analogy
- 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen)
- WebUI for browser-based training control
- Pluggable architecture for extending benchmarks and backends

skillopt/envs/docvqa/dataloader.py

@@ -0,0 +1,61 @@
+from __future__ import annotations
+
+import ast
+import csv
+from pathlib import Path
+
+from skillopt.datasets.base import SplitDataLoader
+
+
+def _parse_answers(raw: str) -> list[str]:
+    text = str(raw or "").strip()
+    if not text:
+        return []
+    try:
+        parsed = ast.literal_eval(text)
+    except Exception:
+        return [text]
+    if isinstance(parsed, list):
+        return [str(item).strip() for item in parsed if str(item).strip()]
+    return [str(parsed).strip()]
+
+
+def _extract_document_path(question: str) -> tuple[str, str]:
+    marker = "document_path:"
+    if marker not in question:
+        return question.strip(), ""
+    main, tail = question.split(marker, 1)
+    return main.strip(), tail.strip()
+
+
+def _normalize_row(row: dict[str, str]) -> dict:
+    question_text, document_path = _extract_document_path(str(row.get("question") or ""))
+    answers = _parse_answers(row.get("answer") or row.get("ground_truth") or "")
+    image_path = str(row.get("image_path") or document_path or "").strip()
+    task_type = str(row.get("topic") or row.get("category") or "docvqa").strip() or "docvqa"
+    return {
+        "id": str(row.get("questionId") or row.get("id") or "").strip(),
+        "question": question_text,
+        "answer": answers[0] if answers else "",
+        "answers": answers,
+        "task_type": task_type,
+        "subtask": task_type,
+        "image_paths": [image_path] if image_path else [],
+        "image_path": image_path,
+        "questionId": str(row.get("questionId") or "").strip(),
+        "docId": str(row.get("docId") or "").strip(),
+        "ucsf_document_id": str(row.get("ucsf_document_id") or "").strip(),
+        "ucsf_document_page_no": str(row.get("ucsf_document_page_no") or "").strip(),
+        "source_split": str(row.get("source_split") or "").strip(),
+    }
+
+
+class DocVQADataLoader(SplitDataLoader):
+    def load_split_items(self, split_path: str) -> list[dict]:
+        path = Path(split_path)
+        csv_files = sorted(path.glob("*.csv"))
+        if not csv_files:
+            raise FileNotFoundError(f"No .csv file found in {split_path}")
+        with csv_files[0].open(encoding="utf-8", newline="") as f:
+            reader = csv.DictReader(f)
+            return [_normalize_row(row) for row in reader]