feat: add soak corpus loaders and scenario generators

tests/unit/test_soak_corpus.py

@@ -0,0 +1,131 @@
+"""soak harness 纯函数层测试(M2 设计 §8;T10/T11)。
+
+语料还原器与不变量断言函数是纯函数,用真实 schema 的二次构造微型库测试
+(真实场景优先: schema 与 data/soak 实库逐字一致)。
+"""
+
+import json
+import sqlite3
+
+import pytest
+
+from tools.soak.corpus import (
+    assemble_frame_messages,
+    chs_image_messages,
+    load_replay_payloads,
+    load_trace_chains,
+)
+from tools.soak.scenarios import weighted_mix
+
+
+def _mini_harness_db(path):
+    """predictions 表 schema 与 data/soak/harness.db 逐字一致的微型库。"""
+    conn = sqlite3.connect(path)
+    conn.execute(
+        "CREATE TABLE predictions (run_id TEXT, timestamp TEXT, video_id TEXT,"
+        " question_id TEXT, task_type TEXT, prediction TEXT, answer TEXT, evidence TEXT,"
+        " reasoning TEXT, steps_used INTEGER, prompt_tokens INTEGER,"
+        " completion_tokens INTEGER, stop_reason TEXT, steps_json TEXT)"
+    )
+    steps = [
+        {"thought": f"思考{i}", "tool_call": {"tool": "view_node", "args": {"i": i}},
+         "tool_output": f"输出{i}" * 50}
+        for i in range(3)
+    ]
+    conn.execute(
+        "INSERT INTO predictions VALUES ('r1','t','vid1','q1','synopsis','A','A','','',3,1,1,'stop',?)",
+        (json.dumps(steps, ensure_ascii=False),),
+    )
+    conn.execute(
+        "INSERT INTO predictions VALUES ('r1','t','vid2','q2','synopsis','B','B','','',0,1,1,'stop','[]')"
+    )
+    conn.commit()
+    conn.close()
+
+
+def _mini_telemetry_db(path):
+    conn = sqlite3.connect(path)
+    conn.execute(
+        "CREATE TABLE llm_calls (call_id TEXT PRIMARY KEY, parent_call_id TEXT,"
+        " session_id TEXT, model_name TEXT, provider TEXT, messages TEXT, response TEXT,"
+        " thinking TEXT, prompt_tokens INTEGER, completion_tokens INTEGER, latency_ms INTEGER,"
+        " ttft_ms REAL, max_inter_token_ms REAL, cache_hit INTEGER, error TEXT, created_at TEXT)"
+    )
+    msgs = json.dumps([{"role": "user", "content": "回放负载"}])
+    conn.execute(
+        "INSERT INTO llm_calls VALUES ('c1',NULL,'s1','m','p',?,'r','',1,1,1,NULL,NULL,0,NULL,'t')",
+        (msgs,),
+    )
+    conn.execute(
+        "INSERT INTO llm_calls VALUES ('c2',NULL,'s1','m','p','not-json','r','',1,1,1,NULL,NULL,0,NULL,'t')"
+    )
+    conn.commit()
+    conn.close()
+
+
+class TestTraceChains:
+    def test_cumulative_chain_shapes(self, tmp_path):
+        db = tmp_path / "h.db"
+        _mini_harness_db(db)
+        chains = load_trace_chains(db)
+        assert len(chains) == 1  # steps_used=0 的行剔除
+        chain = chains[0]
+        assert len(chain) == 3  # 每步一个累积快照
+        # 快照单调增长: system+user 起步,每步追加 assistant+user
+        assert [len(snap) for snap in chain] == [4, 6, 8]
+        first = chain[0]
+        assert first[0]["role"] == "system" and first[1]["role"] == "user"
+        assert first[2]["role"] == "assistant" and "思考0" in first[2]["content"]
+        assert first[3]["role"] == "user" and "输出0" in first[3]["content"]
+        # 后续快照是前缀扩展(累积语义)
+        assert chain[1][:4] == chain[0]
+
+    def test_max_steps_cap(self, tmp_path):
+        db = tmp_path / "h.db"
+        _mini_harness_db(db)
+        assert [len(s) for s in load_trace_chains(db, max_steps=2)[0]] == [4, 6]
+
+
+class TestReplayPayloads:
+    def test_loads_and_skips_unparseable(self, tmp_path):
+        db = tmp_path / "t.db"
+        _mini_telemetry_db(db)
+        payloads = load_replay_payloads(db)
+        assert len(payloads) == 1  # 坏 JSON 行剔除
+        assert payloads[0][0]["content"] == "回放负载"
+
+
+class TestImageAssembly:
+    def test_frame_messages_two_tiers(self, tmp_path):
+        vid = tmp_path / "vid1" / "frames"
+        vid.mkdir(parents=True)
+        for i in range(6):
+            (vid / f"f{i}.jpg").write_bytes(b"\xff\xd8fakejpeg" + bytes([i]))
+        msgs = assemble_frame_messages(tmp_path, n_frames=4, rng=lambda: 0.0)
+        parts = msgs[0]["content"]
+        images = [p for p in parts if p["type"] == "image_url"]
+        assert len(images) == 4
+        assert images[0]["image_url"]["url"].startswith("data:image/jpeg;base64,")
+
+    def test_chs_single_image_with_instruction(self, tmp_path):
+        (tmp_path / "chs_0001.jpg").write_bytes(b"\xff\xd8fake")
+        msgs = chs_image_messages(tmp_path / "chs_0001.jpg")
+        parts = msgs[0]["content"]
+        assert any(p["type"] == "image_url" for p in parts)
+        text = next(p["text"] for p in parts if p["type"] == "text")
+        assert "JSON" in text  # 固定结构化指令
+
+
+class TestWeightedMix:
+    def test_ratio_approximation(self):
+        import random
+
+        rng = random.Random(42).random
+        weights = {"P1": 0.1, "P2": 0.2, "P3": 0.5, "P5": 0.2}
+        picks = [weighted_mix(weights, rng) for _ in range(10_000)]
+        for key, w in weights.items():
+            assert picks.count(key) / 10_000 == pytest.approx(w, abs=0.05)
+
+    def test_invalid_weights_rejected(self):
+        with pytest.raises(ValueError):
+            weighted_mix({"P1": 0.0}, lambda: 0.5)