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8 Commits
| Author | SHA1 | Date | |
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| 4a8032665f | |||
| 6536c7fa9d | |||
| 2cd02453ec | |||
| 140e49342c | |||
| 93bbfe6029 | |||
| 6b1424b1c4 | |||
| efb5ed481e | |||
| e54a221f34 |
@@ -86,7 +86,8 @@ COVERAGE_TARGET = float(os.environ.get("IR_COVERAGE_TARGET", "0.95"))
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ENSEMBLE_TEMPERATURES = [
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ENSEMBLE_TEMPERATURES = [
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float(os.environ.get("IR_ENSEMBLE_T1", "0.0")),
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float(os.environ.get("IR_ENSEMBLE_T1", "0.0")),
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float(os.environ.get("IR_ENSEMBLE_T2", "0.3")),
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float(os.environ.get("IR_ENSEMBLE_T2", "0.3")),
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float(os.environ.get("IR_ENSEMBLE_T3", "0.7")),
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float(os.environ.get("IR_ENSEMBLE_T3", "0.5")),
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float(os.environ.get("IR_ENSEMBLE_T4", "0.7")),
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]
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]
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@@ -880,9 +880,9 @@ def run_ensemble_semantic_index(doc: dict) -> dict:
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if v:
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if v:
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print(f" {k}: {len(v)} 个问题")
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print(f" {k}: {len(v)} 个问题")
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# Feedback retry: re-run with coverage feedback (up to 2 retries, quality-gated)
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# Feedback retry: re-run with coverage feedback (up to 3 retries, quality-gated)
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retry_count = 0
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retry_count = 0
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while retry_count < 2:
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while retry_count < 3:
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feedback = _build_coverage_feedback(gaps)
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feedback = _build_coverage_feedback(gaps)
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if not feedback:
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if not feedback:
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break
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break
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@@ -906,13 +906,16 @@ def run_ensemble_semantic_index(doc: dict) -> dict:
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if src.get("section"):
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if src.get("section"):
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retry_sections.add(src["section"])
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retry_sections.add(src["section"])
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print(f" 重试新增 sections: {sorted(retry_sections)}", flush=True)
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print(f" 重试新增 sections: {sorted(retry_sections)}", flush=True)
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# Quality gate: only include retry if it improves coverage
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# Quality gate: include retry if it adds new sections or doesn't regress coverage
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trial_indices = semantic_indices + [retry_result]
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trial_indices = semantic_indices + [retry_result]
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trial_merged = ensemble_merge(trial_indices)
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trial_merged = ensemble_merge(trial_indices)
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trial_passed, trial_gaps = _quick_validate(trial_merged, doc, all_paths)
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trial_passed, trial_gaps = _quick_validate(trial_merged, doc, all_paths)
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trial_warnings = len(trial_gaps.get("coverage_warnings", []))
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trial_warnings = len(trial_gaps.get("coverage_warnings", []))
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trial_missing = len(trial_gaps.get("missing_table_rows", []))
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trial_missing = len(trial_gaps.get("missing_table_rows", []))
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if trial_warnings < pre_warnings or trial_missing < pre_missing_rows:
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improved = trial_warnings < pre_warnings or trial_missing < pre_missing_rows
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no_regression = trial_warnings <= pre_warnings and trial_missing <= pre_missing_rows
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has_new_sections = len(retry_sections) > 0
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if improved or (no_regression and has_new_sections):
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semantic_indices.append(retry_result)
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semantic_indices.append(retry_result)
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merged = trial_merged
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merged = trial_merged
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passed, gaps = trial_passed, trial_gaps
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passed, gaps = trial_passed, trial_gaps
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@@ -174,11 +174,25 @@ def _normalize_rule(rule: dict) -> dict:
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sources = rule.get("sources", [])
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sources = rule.get("sources", [])
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valid_types = {"table", "text", "logic_tree"}
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valid_types = {"table", "text", "logic_tree"}
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def _clean_section(val):
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"""Normalize section value: list→first element, ensure string."""
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if isinstance(val, list):
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return str(val[0]).strip() if val else ""
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if isinstance(val, str):
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return val.strip()
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return str(val).strip() if val else ""
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# Normalize section fields that might be lists (LLM format instability)
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for s in sources:
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sec = s.get("section")
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if sec is not None:
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s["section"] = _clean_section(sec)
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# try to infer a default section from the rule path
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# try to infer a default section from the rule path
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default_section = ""
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default_section = ""
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for s in sources:
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for s in sources:
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sec = s.get("section", "")
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sec = s.get("section", "")
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if sec and sec.strip():
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if sec and isinstance(sec, str) and sec.strip():
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default_section = sec.strip()
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default_section = sec.strip()
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break
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break
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if not default_section:
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if not default_section:
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@@ -192,7 +206,12 @@ def _normalize_rule(rule: dict) -> dict:
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if stype and stype not in valid_types:
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if stype and stype not in valid_types:
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src["type"] = "text"
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src["type"] = "text"
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stype = "text"
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stype = "text"
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if stype in ("table", "text"):
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if stype == "table":
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if not src.get("section"):
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src["section"] = default_section
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if src.get("row") is None:
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src["row"] = 0
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elif stype == "text":
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if not src.get("section"):
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if not src.get("section"):
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src["section"] = default_section
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src["section"] = default_section
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else:
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else:
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@@ -512,6 +512,18 @@ class TestNormalizeRule:
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normalized = _normalize_rule(rule)
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normalized = _normalize_rule(rule)
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assert "section" not in normalized["sources"][0]
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assert "section" not in normalized["sources"][0]
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def test_normalize_table_source_null_row(self):
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"""Table source with null row gets row=0 (defensive)."""
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rule = {
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"trigger": {"conditions": [{"signal": "x", "operator": "==", "value": "1"}]},
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"sources": [
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{"type": "table", "section": "3.1 功能", "row": None},
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],
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}
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normalized = _normalize_rule(rule)
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assert normalized["sources"][0]["row"] == 0
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def test_normalize_source_invalid_type(self):
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def test_normalize_source_invalid_type(self):
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"""Invalid source types (LLM hallucinations) are normalized to text."""
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"""Invalid source types (LLM hallucinations) are normalized to text."""
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rule = {
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rule = {
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@@ -538,3 +550,28 @@ class TestNormalizeRule:
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assert len(normalized["sources"]) == 1
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assert len(normalized["sources"]) == 1
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assert normalized["sources"][0]["type"] == "text"
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assert normalized["sources"][0]["type"] == "text"
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assert normalized["sources"][0]["section"] == "3.1 策略"
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assert normalized["sources"][0]["section"] == "3.1 策略"
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def test_normalize_section_is_list(self):
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"""Section field that is a list (LLM format bug) is normalized to string."""
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rule = {
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"trigger": {"conditions": [{"signal": "x", "operator": "==", "value": "1"}]},
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"sources": [
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{"type": "table", "section": ["状态", "系统设置"], "row": 1},
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{"type": "text", "section": ["后台限制"], "text_snippet": "x"},
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],
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}
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normalized = _normalize_rule(rule)
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assert normalized["sources"][0]["section"] == "状态"
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assert normalized["sources"][1]["section"] == "后台限制"
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def test_normalize_section_is_empty_list(self):
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"""Empty list section falls back to rule path."""
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rule = {
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"trigger": {"conditions": [{"signal": "x", "operator": "==", "value": "1"}]},
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"path": "4.2 关闭流程 > decision",
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"sources": [
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{"type": "table", "section": [], "row": 1},
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],
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}
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normalized = _normalize_rule(rule)
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assert normalized["sources"][0]["section"] == "4.2 关闭流程"
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@@ -83,8 +83,8 @@ def test_output_dir_structure():
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def test_ensemble_temperatures_count():
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def test_ensemble_temperatures_count():
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"""Should have exactly 3 ensemble temperatures."""
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"""Should have exactly 4 ensemble temperatures."""
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assert len(config.ENSEMBLE_TEMPERATURES) == 3
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assert len(config.ENSEMBLE_TEMPERATURES) == 4
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def test_max_tokens_is_int():
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def test_max_tokens_is_int():
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