fix: step3 过滤非法 source type + step1 重试质量门控 - Closes #57

- step3 _normalize_rule: 将 function_unit_description 等非法 source type 标准化为 text
- step1 覆盖反馈重试: 仅纳入实际提升覆盖率的 retry 结果，避免低质量输出稀释 ensemble
- 新增 UT: test_normalize_source_invalid_type

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

skills/ir_generation_skill/step1_semantic_index.py

@@ -880,11 +880,19 @@ def run_ensemble_semantic_index(doc: dict) -> dict:
             if v:
                 print(f"    {k}: {len(v)} 个问题")
 
-        # Feedback retry: re-run with coverage feedback (one retry)
+        # Feedback retry: re-run with coverage feedback (up to 2 retries, quality-gated)
+        retry_count = 0
+        while retry_count < 2:
             feedback = _build_coverage_feedback(gaps)
-        if feedback:
+            if not feedback:
-            print(f"\n  覆盖反馈重试 (feedback长度={len(feedback)}字符)...", flush=True)
+                break
+            retry_count += 1
+            print(f"\n  覆盖反馈重试 #{retry_count} (feedback长度={len(feedback)}字符)...", flush=True)
             try:
+                # record pre-retry coverage to gate quality
+                pre_warnings = len(gaps.get("coverage_warnings", []))
+                pre_missing_rows = len(gaps.get("missing_table_rows", []))
+
                 retry_prompt = build_prompt(doc, feedback, all_paths)
                 print(f"  重试 prompt 长度: {len(retry_prompt)} 字符", flush=True)
                 retry_result = call_llm(retry_prompt, max_retries=1, temperature=0.3)
@@ -892,27 +900,39 @@ def run_ensemble_semantic_index(doc: dict) -> dict:
                 n_retry_concepts = len(retry_result.get("concepts", []))
                 print(f"  重试返回: {n_retry_concepts} 概念, {n_retry_units} 功能单元", flush=True)
                 if n_retry_units > 0:
-                    # Check which new sections were covered
                     retry_sections = set()
                     for fu in retry_result.get("function_units", []):
                         for src in fu.get("sources", []):
                             if src.get("section"):
                                 retry_sections.add(src["section"])
                     print(f"  重试新增 sections: {sorted(retry_sections)}", flush=True)
-                    # Merge retry into results and re-validate
+                    # Quality gate: only include retry if it improves coverage
+                    trial_indices = semantic_indices + [retry_result]
+                    trial_merged = ensemble_merge(trial_indices)
+                    trial_passed, trial_gaps = _quick_validate(trial_merged, doc, all_paths)
+                    trial_warnings = len(trial_gaps.get("coverage_warnings", []))
+                    trial_missing = len(trial_gaps.get("missing_table_rows", []))
+                    if trial_warnings < pre_warnings or trial_missing < pre_missing_rows:
                         semantic_indices.append(retry_result)
-                    merged = ensemble_merge(semantic_indices)
+                        merged = trial_merged
-                    merged["ensemble_temperatures"] = list(temperatures) + ["feedback_retry"]
+                        passed, gaps = trial_passed, trial_gaps
-                    passed, gaps = _quick_validate(merged, doc, all_paths)
+                        merged["ensemble_temperatures"] = list(temperatures) + [f"feedback_retry_{retry_count}"]
                         merged["validation_passed"] = passed
                         merged["validation_gaps"] = {
                             k: v for k, v in gaps.items() if v
                         }
-                    print(f"  重试后验证: {'PASS' if passed else 'GAPS FOUND'}", flush=True)
+                        print(f"  重试后验证 (已采纳): {'PASS' if passed else 'GAPS FOUND'} "
+                              f"(warnings {pre_warnings}→{trial_warnings}, "
+                              f"missing_rows {pre_missing_rows}→{trial_missing})", flush=True)
+                    else:
+                        print(f"  重试结果未提升覆盖率，丢弃 "
+                              f"(warnings {pre_warnings}→{trial_warnings}, "
+                              f"missing_rows {pre_missing_rows}→{trial_missing})", flush=True)
             except Exception as e:
                 print(f"  覆盖反馈重试失败: {e}", flush=True)
                 import traceback
                 traceback.print_exc()
+                break
 
     return merged
 

skills/ir_generation_skill/step3_merge_and_audit.py

@@ -169,6 +169,34 @@ def _normalize_rule(rule: dict) -> dict:
             "value": "active"
         }]
 
+    # Ensure table/text sources have a section field (defensive against LLM omission)
+    # Also normalize invalid source types (LLM hallucinations like function_unit_description)
+    sources = rule.get("sources", [])
+    if sources:
+        valid_types = {"table", "text", "logic_tree"}
+
+        # try to infer a default section from sibling sources or the rule path
+        default_section = ""
+        for s in sources:
+            sec = s.get("section", "")
+            if sec and sec.strip():
+                default_section = sec.strip()
+                break
+        if not default_section:
+            path = rule.get("path", "")
+            if path:
+                default_section = path.split(" > ")[0] if " > " in path else path
+
+        for src in sources:
+            stype = src.get("type", "")
+            # Normalize invalid source types to "text"
+            if stype and stype not in valid_types:
+                src["type"] = "text"
+                stype = "text"
+            if stype in ("table", "text"):
+                if not src.get("section"):
+                    src["section"] = default_section
+
     return rule
 
 

skills/ir_generation_skill/tests/test_step3.py

@@ -465,3 +465,64 @@ class TestNormalizeRule:
         normalized = _normalize_rule(rule)
         assert normalized["trigger"]["operator"] == "AND"
         assert normalized["trigger"]["conditions"][0]["operator"] == ">="
+
+    def test_normalize_source_missing_section_from_sibling(self):
+        """Table/text sources without section get it from sibling sources."""
+        rule = {
+            "trigger": {"conditions": [{"signal": "x", "operator": "==", "value": "1"}]},
+            "sources": [
+                {"type": "table", "section": "3.1.1 系统限制", "row": 1},
+                {"type": "text", "text_snippet": "missing section"},
+            ],
+        }
+        normalized = _normalize_rule(rule)
+        assert normalized["sources"][1]["section"] == "3.1.1 系统限制"
+
+    def test_normalize_source_missing_section_from_path(self):
+        """Table/text sources without section and no sibling fall back to rule path."""
+        rule = {
+            "trigger": {"conditions": [{"signal": "x", "operator": "==", "value": "1"}]},
+            "path": "4.2 关闭流程 > decision_speed > action_disable",
+            "sources": [
+                {"type": "table", "row": 3, "text_snippet": "no section anywhere"},
+            ],
+        }
+        normalized = _normalize_rule(rule)
+        assert normalized["sources"][0]["section"] == "4.2 关闭流程"
+
+    def test_normalize_source_keeps_existing_section(self):
+        """Sources that already have section are not modified."""
+        rule = {
+            "trigger": {"conditions": [{"signal": "x", "operator": "==", "value": "1"}]},
+            "sources": [
+                {"type": "table", "section": "1.0 概述", "row": 1},
+            ],
+        }
+        normalized = _normalize_rule(rule)
+        assert normalized["sources"][0]["section"] == "1.0 概述"
+
+    def test_normalize_source_skips_logic_tree(self):
+        """Logic tree sources are not touched (don't need section)."""
+        rule = {
+            "trigger": {"conditions": [{"signal": "x", "operator": "==", "value": "1"}]},
+            "sources": [
+                {"type": "logic_tree", "image_id": "img1", "node_ids": ["n1"]},
+            ],
+        }
+        normalized = _normalize_rule(rule)
+        assert "section" not in normalized["sources"][0]
+
+    def test_normalize_source_invalid_type(self):
+        """Invalid source types (LLM hallucinations) are normalized to text."""
+        rule = {
+            "trigger": {"conditions": [{"signal": "x", "operator": "==", "value": "1"}]},
+            "sources": [
+                {"type": "function_unit_description", "text_snippet": "desc",
+                 "section": "3.1 功能"},
+                {"type": "unknown_type", "text_snippet": "also invalid"},
+            ],
+        }
+        normalized = _normalize_rule(rule)
+        assert normalized["sources"][0]["type"] == "text"
+        assert normalized["sources"][1]["type"] == "text"
+        assert normalized["sources"][0]["section"] == "3.1 功能"