feat(review): 新增确定性检查前置与结论结构化内核
- checks.py：确定性检查（find_zero_length / find_duplicate_entities 传 window + 纯 Python 派生空文字、未闭合多段线）。问题带 source=deterministic，handle 来自 工具/数据本身，不依赖 LLM 回忆 - findings.py：解析 LLM 的 JSON 结论 + handle 交叉校验（引用未知 handle 时保留问题、 剥掉 handle、verified=False）+ findings 装配 - config.py：新增 DETERMINISTIC_CHECKS / VALIDATE_HANDLES / CHECK_UNCLOSED_POLYLINES 开关与 ZERO_LENGTH_TOLERANCE / DUP_TOLERANCE / DUP_GROUP_LIMIT 实机验证：find_zero_length / find_duplicate_entities 的 window 参数在基座上生效 （schema 未声明，C++ 侧 parseCommonQuery 实际支持）。
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"""结构化结论：解析 LLM 的 JSON 输出、交叉校验 handle、装配 findings。
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设计要点：
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- `review_frame` 的结论是**受约束的 JSON**（`{summary, issues[]}`），不再是自由文本，
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因此不需要第二次 LLM 调用来「抽取」结构。
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- 解析失败时降级为 `summary=原文, issues=[]`：信息不丢，只是不进批注链路。
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- LLM 引用的 handle 若不在已采集证据中 → **保留问题、剥掉 handle**（verified=False），
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既不会丢发现，也绝不会批注到不存在的实体。
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"""
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import json
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import re
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# evidence 中带 handle 的数据源（items[].handle）
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_SOURCES = ("entities", "texts", "dimensions", "blocks", "polylines")
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_SEVERITIES = ("high", "medium", "low")
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_FENCE = re.compile(r"```(?:json)?\s*(.*?)```", re.S)
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_MISS = object() # 解析失败的哨兵（区别于合法的 JSON null）
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def _try_load(text):
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try:
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return json.loads(text)
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except (json.JSONDecodeError, TypeError):
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return _MISS
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def _iter_items(envelope):
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"""健壮迭代数据信封的 items。
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`result_json` 在解析失败时会返回**原始字符串**，因此必须做 dict/list 守卫。
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"""
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if not isinstance(envelope, dict):
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return []
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items = envelope.get("items")
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if not isinstance(items, list):
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return []
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return [i for i in items if isinstance(i, dict)]
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def _outermost_json(text):
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"""抓文本中第一个 `{`/`[` 起、括号配平的整段 JSON（正确处理字符串内的括号）。"""
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start = None
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for i, ch in enumerate(text):
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if ch in "{[":
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start, open_c = i, ch
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break
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if start is None:
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return None
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close_c = "}" if open_c == "{" else "]"
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depth = 0
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in_str = False
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esc = False
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for i in range(start, len(text)):
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ch = text[i]
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if in_str:
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if esc:
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esc = False
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elif ch == "\\":
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esc = True
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elif ch == '"':
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in_str = False
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continue
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if ch == '"':
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in_str = True
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elif ch == open_c:
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depth += 1
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elif ch == close_c:
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depth -= 1
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if depth == 0:
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cand = text[start : i + 1]
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return cand if _try_load(cand) is not _MISS else None
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return None
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def extract_json_block(text):
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"""从可能含围栏/前后缀的文本中抽出可解析的 JSON 字符串；抽不到返回 None。"""
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t = (text or "").strip()
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if not t:
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return None
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if _try_load(t) is not _MISS: # 整体就是 JSON
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return t
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m = _FENCE.search(t)
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if m:
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inner = m.group(1).strip()
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if _try_load(inner) is not _MISS:
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return inner
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t = inner
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return _outermost_json(t)
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def parse_conclusion(text, *, log=print):
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"""把 LLM 结论解析为 {"summary": str, "issues": [dict], "raw": str}。
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支持：`{summary, issues}` 对象、裸 issues 数组、围栏包裹、前后夹带解释文字。
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解析不到结构化内容时降级为 summary=原文、issues=[]。
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"""
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block = extract_json_block(text)
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if block is not None:
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data = json.loads(block)
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if isinstance(data, dict):
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raw_issues = data.get("issues")
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issues = (
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[i for i in raw_issues if isinstance(i, dict)]
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if isinstance(raw_issues, list)
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else []
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)
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summary = data.get("summary")
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return {
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"summary": str(summary).strip() if summary is not None else "",
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"issues": issues,
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"raw": text,
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}
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if isinstance(data, list):
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return {
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"summary": "",
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"issues": [i for i in data if isinstance(i, dict)],
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"raw": text,
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}
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log("[结论解析] 非结构化输出，降级为纯文本 summary")
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return {"summary": (text or "").strip(), "issues": [], "raw": text}
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def collect_known_handles(evidence, extra_handles=None):
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"""汇总证据里出现过的全部 handle（统一大写），外加 extra_handles。"""
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known = set()
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for key in _SOURCES:
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for item in _iter_items((evidence or {}).get(key)):
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handle = item.get("handle")
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if handle:
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known.add(str(handle).upper())
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for handle in extra_handles or ():
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if handle:
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known.add(str(handle).upper())
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return known
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def normalize_issue(issue, *, frame_index, source, known_handles, validate_handles):
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"""补 frame/source/verified 并做 handle 交叉校验，返回统一 issue 结构。"""
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issue = issue if isinstance(issue, dict) else {}
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handle = issue.get("handle")
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handle = str(handle) if handle else None
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verified = True
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# 确定性问题的 handle 来自工具/数据本身，恒可信；只校验 LLM 的引用
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if source != "deterministic" and handle and validate_handles:
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if handle.upper() not in known_handles:
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verified, handle = False, None
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severity = issue.get("severity")
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if severity not in _SEVERITIES:
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severity = "low"
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out = {
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"frame": frame_index,
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"category": str(issue.get("category") or "图面整体"),
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"severity": severity,
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"message": str(issue.get("message") or "").strip(),
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"handle": handle,
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"source": source,
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"verified": verified,
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}
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handles = issue.get("handles")
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if isinstance(handles, list) and handles:
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out["handles"] = [str(h) for h in handles if h]
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return out
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def build_finding(evidence, conclusion, checks_issues=None, *,
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validate_handles=True, log=print):
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"""把确定性检查与 LLM 结论合并、校验后装配成一个 findings 元素。"""
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evidence = evidence or {}
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index = evidence.get("index")
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checks = checks_issues or []
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known = collect_known_handles(
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evidence, extra_handles=[i.get("handle") for i in checks]
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)
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det = [
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normalize_issue(i, frame_index=index, source="deterministic",
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known_handles=known, validate_handles=validate_handles)
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for i in checks
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]
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llm = [
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normalize_issue(i, frame_index=index, source="llm",
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known_handles=known, validate_handles=validate_handles)
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for i in (conclusion or {}).get("issues", [])
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]
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issues = det + llm
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return {
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"frame": index,
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"sheet": (evidence.get("frame") or {}).get("sheet_size"),
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"summary": (conclusion or {}).get("summary", ""),
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"issues": issues,
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"counts": {
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"deterministic": len(det),
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"llm": len(llm),
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"unverified": sum(1 for i in issues if not i["verified"]),
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},
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}
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def flatten_issues(findings):
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"""拍平所有图框的 issues，供 annotator / save_report 使用。"""
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out = []
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for finding in findings or []:
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out.extend(finding.get("issues") or [])
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return out
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