feat(question_gen): is_duplicate + generate_one — 去重判定与单题生成编排

- is_duplicate: 余弦相似度去重，空池短路
- generate_one: 异步重试循环，不含去重（由调用方汇总点原子执行）

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

tests/unit/test_synthesizer.py

@@ -1,11 +1,13 @@
-"""synthesizer 模块单元测试 — AnchorContext + 题型映射常量 + sample_anchor + prompt 构造 + VLM 解析。"""
+"""synthesizer 模块单元测试 — AnchorContext + 题型映射常量 + sample_anchor + prompt 构造 + VLM 解析 + 去重 + 单题生成。"""
 
 from __future__ import annotations
 
 import dataclasses
 import random
 from pathlib import Path
+from unittest.mock import AsyncMock, MagicMock
 
+import numpy as np
 import pytest
 
 from app.question_gen.synthesizer import (
@@ -13,6 +15,8 @@ from app.question_gen.synthesizer import (
     AnchorContext,
     TaskTypeSpec,
     build_generation_prompt,
+    generate_one,
+    is_duplicate,
     parse_vlm_response,
     sample_anchor,
 )
@@ -379,3 +383,119 @@ class TestParseVlmResponse:
         raw = '{"question": "Q?", "options": ["A. 1", "B. 2", "C. 3", "D. 4"], "answer": "C"}'
         result = parse_vlm_response(raw, "video_abc", "Action Reasoning", 42)
         assert result["question_id"] == "gen-video_abc-042"
+
+
+# ---------------------------------------------------------------------------
+# is_duplicate 测试
+# ---------------------------------------------------------------------------
+
+
+class TestIsDuplicate:
+    """is_duplicate embedding 去重判定测试。"""
+
+    @staticmethod
+    def _fake_embed(texts: str | list[str]) -> np.ndarray:
+        """确定性 + L2 归一化的 fake embedding。"""
+        if isinstance(texts, str):
+            texts = [texts]
+        vecs = []
+        for t in texts:
+            rs = np.random.RandomState(hash(t) % 2**31)
+            v = rs.randn(4).astype(np.float32)
+            v /= np.linalg.norm(v)
+            vecs.append(v)
+        return np.array(vecs, dtype=np.float32)
+
+    def test_empty_pool_never_duplicate(self) -> None:
+        """空池始终返回 False。"""
+        pool = np.zeros((0, 4), dtype=np.float32)
+        assert is_duplicate("anything", pool, self._fake_embed, 0.85) is False
+
+    def test_identical_text_is_duplicate(self) -> None:
+        """相同文本的 embedding 与自身余弦相似度为 1，必定判重。"""
+        text = "What is happening in the video?"
+        emb = self._fake_embed(text)
+        pool = emb.copy()
+        assert is_duplicate(text, pool, self._fake_embed, 0.85) is True
+
+    def test_different_text_not_duplicate(self) -> None:
+        """极高阈值下，不同文本不判重。"""
+        pool_texts = ["aaa", "bbb", "ccc", "ddd", "eee"]
+        pool = self._fake_embed(pool_texts)
+        assert is_duplicate("completely unique text xyz", pool, self._fake_embed, 0.99) is False
+
+
+# ---------------------------------------------------------------------------
+# generate_one 测试
+# ---------------------------------------------------------------------------
+
+
+class TestGenerateOne:
+    """generate_one 单题异步生成测试。"""
+
+    @staticmethod
+    def _load_test_tree() -> tuple[TreeIndex, str]:
+        """加载真实测试树。"""
+        videos_dir = Path("store/videos")
+        first_vid = sorted(videos_dir.iterdir())[0]
+        return TreeIndex.load_json(str(first_vid / "tree.json")), first_vid.name
+
+    @pytest.mark.asyncio
+    async def test_success_path(self) -> None:
+        """mock VLM 返回合法 JSON，应成功生成 GeneratedQuestion。"""
+        vlm = AsyncMock()
+        vlm.chat_with_images.return_value = MagicMock(
+            content='{"question":"Q?","options":["A. 1","B. 2","C. 3","D. 4"],"answer":"A"}',
+        )
+
+        def embed_fn(t: str | list[str]) -> np.ndarray:
+            shape = (1, 4) if isinstance(t, str) else (len(t), 4)
+            return np.zeros(shape, dtype=np.float32)
+
+        tree, vid = self._load_test_tree()
+        result = await generate_one(
+            vlm=vlm,
+            embed_fn=embed_fn,
+            tree=tree,
+            video_id=vid,
+            task_type="Object Recognition",
+            seq=1,
+            exemplars=[],
+            used_node_ids=set(),
+            max_retries=3,
+            similarity_threshold=0.85,
+            rng=random.Random(42),
+            session_id="test",
+        )
+        assert result is not None
+        assert result.question_id == f"gen-{vid}-001"
+        assert result.task_type == "Object Recognition"
+        assert result.source_nodes  # non-empty
+        assert result.difficulty == "medium"
+
+    @pytest.mark.asyncio
+    async def test_all_retries_exhausted_returns_none(self) -> None:
+        """VLM 始终返回无效 JSON，耗尽重试后返回 None。"""
+        vlm = AsyncMock()
+        vlm.chat_with_images.return_value = MagicMock(content="invalid")
+
+        def embed_fn(t: str | list[str]) -> np.ndarray:
+            return np.zeros((1, 4), dtype=np.float32)
+
+        tree, vid = self._load_test_tree()
+        result = await generate_one(
+            vlm=vlm,
+            embed_fn=embed_fn,
+            tree=tree,
+            video_id=vid,
+            task_type="Object Recognition",
+            seq=1,
+            exemplars=[],
+            used_node_ids=set(),
+            max_retries=2,
+            similarity_threshold=0.85,
+            rng=random.Random(42),
+            session_id="test",
+        )
+        assert result is None
+        assert vlm.chat_with_images.call_count == 2