feat: add steps_json-backed RunLog wrapper

app/harness/baseline_run_log.py

@@ -0,0 +1,71 @@
+"""RunLog 包装器：traces 表空时从 predictions.steps_json 重建轨迹。
+
+用于对 infer_adhoc 这类 traces 未落表、轨迹在 steps_json 的历史 run 跑离线诊断。
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from app.harness.steps_json_traces import steps_json_to_trace_rows
+
+
+class StepsJsonRunLog:
+    """委托内层 RunLog；get_traces 空表时回退 steps_json。
+
+    实现 core/evolution/protocols.py 的 RunLog Protocol（duck-typing）。
+    对 traces 已落表的正常 run 完全透传；仅当底层 traces 为空时，
+    才从 predictions.steps_json 经 steps_json_to_trace_rows 重建轨迹行。
+    """
+
+    def __init__(self, inner: Any) -> None:
+        """构造包装器。
+
+        参数:
+            inner: 内层 RunLog 实现（如 app/harness/log.py::RunLogImpl），
+                需提供 get_predictions / get_traces 两个 async 方法。
+        """
+        self._inner = inner
+
+    async def get_predictions(
+        self, run_id: str, *, question_ids: list[str] | None = None
+    ) -> list[dict[str, Any]]:
+        """透传内层预测查询。
+
+        参数:
+            run_id: 运行标识。
+            question_ids: 可选的题目 ID 过滤列表。
+
+        返回:
+            内层返回的预测记录字典列表，原样透传。
+        """
+        return await self._inner.get_predictions(run_id, question_ids=question_ids)
+
+    async def get_traces(
+        self, run_id: str, *, question_ids: list[str] | None = None
+    ) -> list[dict[str, Any]]:
+        """查询轨迹；底层 traces 表空时从 steps_json 回退重建。
+
+        参数:
+            run_id: 运行标识。
+            question_ids: 可选的题目 ID 过滤列表。
+
+        返回:
+            轨迹行字典列表。
+
+        关键实现细节:
+            - 内层 traces 非空 → 原样返回，不触发回退（正常 run 路径）。
+            - 内层 traces 为空 → 拉取同一过滤条件下的 predictions，
+              逐题经 steps_json_to_trace_rows 展开为轨迹行并拼接。
+            - steps_json 缺失时以空串传入，由下游确定性返回 []。
+        """
+        inner_rows = await self._inner.get_traces(run_id, question_ids=question_ids)
+        if inner_rows:
+            return inner_rows
+        preds = await self._inner.get_predictions(run_id, question_ids=question_ids)
+        rows: list[dict[str, Any]] = []
+        for p in preds:
+            rows.extend(
+                steps_json_to_trace_rows(p["video_id"], p["question_id"], p.get("steps_json") or "")
+            )
+        return rows

tests/unit/test_baseline_run_log.py

@@ -0,0 +1,33 @@
+import json
+
+import pytest
+
+from app.harness.baseline_run_log import StepsJsonRunLog
+
+
+class _FakeInner:
+    def __init__(self, preds, traces):
+        self._preds, self._traces = preds, traces
+
+    async def get_predictions(self, run_id, *, question_ids=None):
+        return [p for p in self._preds if not question_ids or p["question_id"] in question_ids]
+
+    async def get_traces(self, run_id, *, question_ids=None):
+        return list(self._traces)
+
+
+@pytest.mark.asyncio
+async def test_get_traces_falls_back_to_steps_json_when_table_empty():
+    steps = [{"thought": "t", "tool_call": {"tool": "view_node", "args": {}}, "tool_output": "o"}]
+    preds = [{"video_id": "v1", "question_id": "q1", "steps_json": json.dumps(steps)}]
+    log = StepsJsonRunLog(_FakeInner(preds, traces=[]))
+    rows = await log.get_traces("r", question_ids=["q1"])
+    assert rows[0]["tool_name"] == "view_node" and rows[0]["question_id"] == "q1"
+
+
+@pytest.mark.asyncio
+async def test_get_traces_prefers_nonempty_inner_table():
+    inner_traces = [{"video_id": "v1", "question_id": "q1", "step": 0, "tool_name": "x"}]
+    log = StepsJsonRunLog(_FakeInner([], inner_traces))
+    rows = await log.get_traces("r")
+    assert rows == inner_traces