fix(r4-28): 检索相关性下限,无关问题空 sources

_select_graph_balanced 引入相关性下限 0.25(score_gate.py 具名常量,
env QA_MIN_SOURCE_SCORE 可覆盖):低于下限的候选不入选,全部低于下限
返回空;graph 配额语义不变,audit 增 min_source_score/below_min_source_score。
Qdrant 查询(direct_flow_retriever/v16_retriever 两处)加同源 score_threshold
双保险(collection distance=Cosine,0.25 与垃圾实测带 0.19-0.20 留余量、
远低于正常命中 >0.7)。pc_retriever 为禁用后端,零改动。
新增 test_score_gate.py:30 低->0、5 高+25 低->5、graph 配额、env 覆盖。

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

agent/qa/src/rag/qa_v1_1_73/direct_flow_retriever.py

@@ -16,6 +16,7 @@ from src.rag.normalized_release import (
 )
 
 from .schemas import EvidenceItem, RetrievalHypothesis
+from .score_gate import min_source_score
 
 ALLOWED_QDRANT_COLLECTIONS = {
     "plant_srt_direct_v1_bge_m3_20260702",
@@ -203,11 +204,14 @@ class DirectFlowRetriever:
     def qdrant_search(self, query: str, limit: int) -> List[EvidenceItem]:
         try:
             vector = self.embed(query)
+            # 缺陷 #28（2026-09-11）：相关性下限双保险（与 pipeline
+            # _select_graph_balanced 同源同值，见 score_gate.py）。
             response = self.qdrant.query_points(
                 collection_name=self.collection,
                 query=vector,
                 limit=limit,
                 with_payload=True,
+                score_threshold=min_source_score(),
             )
         except Exception:
             return []

agent/qa/src/rag/qa_v1_1_73/pipeline.py

@@ -43,6 +43,7 @@ from .question_decomposition import QuestionDecompositionPlanner
 from .query_builder import QueryBuilder
 from .reranker import BGEReranker
 from .schemas import EvidenceItem, HypothesisEntity, RetrievalHypothesis
+from .score_gate import min_source_score
 from .soft_gate_policy import (
     LEGACY,
     doi_from_paper_id,
@@ -3317,6 +3318,14 @@ class QAV1Pipeline:
         top_k_for_llm: int,
         graph_min_evidence: int,
     ) -> tuple[List[EvidenceItem], Dict[str, Any]]:
+        # 缺陷 #28（2026-09-11）：相关性下限。低于下限的候选不入选；
+        # 全部低于下限 -> 返回空（前端呈现未命中，与 Qdrant
+        # score_threshold 双保险）。阈值见 score_gate.py（env 可覆盖）。
+        _min_score = min_source_score()
+        _below_min = len(ranked) - sum(
+            1 for item in ranked if item.final_score >= _min_score
+        )  # 被下限过滤的候选数（审计用，见 return 处）
+        ranked = [item for item in ranked if item.final_score >= _min_score]
         graph_items = [item for item in ranked if cls._is_graph_item(item)]
         other_items = [item for item in ranked if not cls._is_graph_item(item)]
         selected: List[EvidenceItem] = []
@@ -3365,6 +3374,8 @@ class QAV1Pipeline:
             "graph_selected": graph_selected,
             "non_graph_selected": len(selected) - graph_selected,
             "graph_quota_satisfied": graph_selected >= graph_min_evidence,
+            "min_source_score": _min_score,
+            "below_min_source_score": _below_min,
             "note": "Graph quota is enforced after BGE rerank. If fewer graph candidates exist, all available graph candidates are retained and the deficit is reported.",
         }
 

agent/qa/src/rag/qa_v1_1_73/score_gate.py

@@ -0,0 +1,53 @@
+"""score_gate.py —— 检索相关性下限（缺陷 #28，2026-09-11）。
+
+语义：候选证据的最终得分（EvidenceItem.final_score）低于下限即视为
+「与问题无关」，不得进入 evidence_top30 / sources。
+
+证据与阈值依据（2026-09-11 实测）：
+- 无关问题「景甜是谁」实测 30 条 final_score 全部 0.19-0.20（纯植物学
+  无关文献）；正常命中（DISCOVERY.md §4.1 金标样本）vector_score=0.714、
+  rerank_score=0.989、final_score=0.982。
+- BGE rerank 路径 final_score = 0.8*bge(sigmoid) + 0.2*rrf/max_rrf；
+  bge 是 sigmoid 概率，0.20 垃圾分对应 raw logit ≈ -1.4，0.25 对应 -1.1，
+  0.30 对应 -0.85，正常命中的 sigmoid 通常 >0.9。
+- Qdrant collection distance=Cosine（score ∈ [-1,1]；bge-m3 文本嵌入对
+  随机中文问题的 top-k 得分通常 <0.2）。
+
+默认 0.25：与垃圾实测带（0.19-0.20）留 ~25% 余量，又显著低于正常命中
+（>0.7），同时为 0.2~0.3 边缘问题保留再排序机会（阈值过滤器只是预筛，
+被过滤者已低于下限，保留也无意义）。
+env `QA_MIN_SOURCE_SCORE` 可覆盖（0-1，非法值回落默认），供复测按
+acceptance-r4 §8.3 授权临时调参。
+
+本模块无第三方依赖、无 IO，可被 v16_retriever / direct_flow_retriever /
+pipeline 直接 import（编译期与运行期均安全）。
+"""
+from __future__ import annotations
+
+import os
+
+# 相关性下限（与 Qdrant score_threshold 同源同值，双保险）
+DEFAULT_MIN_SOURCE_SCORE = 0.25
+_ENV_KEY = "QA_MIN_SOURCE_SCORE"
+
+
+def _env_override() -> float:
+    """env 覆盖：QA_MIN_SOURCE_SCORE ∈ [0,1] 才生效，否则回落默认。"""
+    raw = os.environ.get(_ENV_KEY, "").strip()
+    if not raw:
+        return DEFAULT_MIN_SOURCE_SCORE
+    try:
+        value = float(raw)
+    except ValueError:
+        return DEFAULT_MIN_SOURCE_SCORE
+    if not (0.0 <= value <= 1.0):
+        return DEFAULT_MIN_SOURCE_SCORE
+    return value
+
+
+def min_source_score() -> float:
+    """当前生效的相关性下限（模块导入期缓存 env 值，与部署环境一致）。"""
+    return min_source_score.value
+
+
+min_source_score.value = _env_override()  # type: ignore[attr-defined]

agent/qa/src/rag/qa_v1_1_73/test_score_gate.py

@@ -0,0 +1,98 @@
+"""test_score_gate.py —— 相关性下限选择单测（缺陷 #28，acceptance-r4 §2.4）。
+
+运行（venv 内，源码树）：
+    cd agent/qa/src/rag/qa_v1_1_73
+    python -m pytest test_score_gate.py -v
+（编译期 compile_qa.sh 会把全部 test_*.py 一并编进 .so 树，部署时不冲突；
+正式单测都在源码树跑。）
+
+覆盖：
+- 30 条全部低于下限 -> 选入 0（acceptance 桩）
+- 5 高 + 25 低 -> 仅选入 5（遵循现有配额逻辑）
+- graph 配额不受影响；低于下限的 graph 候选不入选
+- env 覆盖 QA_MIN_SOURCE_SCORE 生效与非法值回落
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+# 本文件位于 agent/qa/src/rag/qa_v1_1_73/，parents[2] = agent/qa/src
+sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
+
+from rag.qa_v1_1_73 import score_gate  # noqa: E402
+from rag.qa_v1_1_73.schemas import EvidenceItem  # noqa: E402
+from rag.qa_v1_1_73.pipeline import QAV1Pipeline  # noqa: E402
+
+
+def _item(idx: int, *, final: float, graph: bool = False, key: str = "") -> EvidenceItem:
+    return EvidenceItem(
+        evidence_id=f"E{idx}",
+        kb="V16",
+        source=f"src{idx}",
+        relation="has",
+        target=f"tgt{idx}",
+        evidence=f"evidence body {idx}",
+        relation_id=(key or f"rid-{idx}"),
+        retrieval_methods=(["neo4j_bounded_expand"] if graph else ["qdrant_vector"]),
+        final_score=final,
+    )
+
+
+def _select(items, *, top_k=30, graph_min=0):
+    selected, audit = QAV1Pipeline._select_graph_balanced(
+        items, top_k_for_llm=top_k, graph_min_evidence=graph_min
+    )
+    return selected, audit
+
+
+def test_all_below_threshold_selects_zero():
+    """acceptance §2.4：30 条全部低于下限 -> 选入 0。"""
+    items = [_item(i, final=0.20) for i in range(30)]
+    selected, audit = _select(items)
+    assert selected == []
+    assert audit["policy"].startswith("graph_balanced_top30")
+    assert audit["below_min_source_score"] == 30
+    assert audit["min_source_score"] == score_gate.min_source_score()
+
+
+def test_five_high_twentyfive_low_selects_only_five():
+    """acceptance §2.4：5 高 + 25 低 -> 仅选入 5（沿用现有配额与 rerank 序）。"""
+    items = [_item(i, final=0.90 - i * 0.01) for i in range(5)]
+    items += [_item(5 + i, final=0.20) for i in range(25)]
+    selected, audit = _select(items)
+    assert len(selected) == 5
+    assert {item.evidence_id for item in selected} == {f"E{i}" for i in range(5)}
+    # 顺序保持 rerank 序
+    assert [item.evidence_id for item in selected] == [f"E{i}" for i in range(5)]
+    assert audit["below_min_source_score"] == 25
+
+
+def test_below_threshold_graph_candidates_excluded():
+    """低分 graph 候选不占用 graph 配额（配额逻辑语义不变）。"""
+    items = [
+        _item(1, final=0.30, graph=True),
+        _item(2, final=0.21, graph=True),
+        _item(3, final=0.95, graph=False),
+        _item(4, final=0.19, graph=False),
+    ]
+    selected, audit = _select(items, top_k=30, graph_min=1)
+    assert [item.evidence_id for item in selected] == ["E1", "E3"]
+    assert audit["graph_selected"] == 1
+    assert audit["below_min_source_score"] == 2
+
+
+def test_env_override_and_invalid_fallback(monkeypatch):
+    """env 解析函数行为：合法值生效；非法值/越界回落默认。"""
+    monkeypatch.setenv("QA_MIN_SOURCE_SCORE", "0.35")
+    assert score_gate._env_override() == 0.35
+    monkeypatch.setenv("QA_MIN_SOURCE_SCORE", "abc")
+    assert score_gate._env_override() == score_gate.DEFAULT_MIN_SOURCE_SCORE
+    monkeypatch.setenv("QA_MIN_SOURCE_SCORE", "1.5")
+    assert score_gate._env_override() == score_gate.DEFAULT_MIN_SOURCE_SCORE
+    monkeypatch.delenv("QA_MIN_SOURCE_SCORE", raising=False)
+    # 生效值（value 属性）驱动 _select_graph_balanced 的过滤
+    monkeypatch.setattr(score_gate.min_source_score, "value", 0.35)
+    items = [_item(i, final=0.30) for i in range(5)]
+    selected, _ = _select(items)
+    assert selected == []

agent/qa/src/rag/qa_v1_1_73/v16_retriever.py

@@ -9,6 +9,7 @@ from typing import Any, Dict, List
 import requests
 from neo4j import GraphDatabase
 from qdrant_client import QdrantClient
+from .score_gate import min_source_score
 
 
 TOKEN_RE = re.compile(r"[A-Za-z][A-Za-z0-9_.-]{2,}|[\u4e00-\u9fff]{2,}")
@@ -100,11 +101,15 @@ class V16IndependentRetriever:
         self, question: str, limit: int, include_candidate: bool
     ) -> List[Dict[str, Any]]:
         try:
+            # 缺陷 #28（2026-09-11）：相关性下限双保险（与 pipeline
+            # _select_graph_balanced 同源同值）。collection distance=Cosine，
+            # 阈值语义 = 余弦相似度下限；低于下限的点直接不返回。
             response = self.qdrant.query_points(
                 collection_name=self.collection,
                 query=self.embed(question),
                 limit=limit * 2,
                 with_payload=True,
+                score_threshold=min_source_score(),
             )
         except Exception:
             return []