166 lines
4.7 KiB
Python
166 lines
4.7 KiB
Python
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"""叙事落库前的确定性安检：防止 prompt 分区标记或摘录块泄漏进正文。"""
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from __future__ import annotations
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# 与 app.agents.memoir.prompts.format_narrative_user_content 保持一致
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ORAL_SECTION_MARKER = "【本段用户口述】"
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EVIDENCE_SECTION_MARKER = "【仅供参考的相关记忆摘录"
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# 摘录引导语中的固定短语（用于粗检）
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EVIDENCE_SECTION_TAIL = "不得把其中具体事实写成本轮亲历经历"
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def body_contains_prompt_artifact(markdown_body: str) -> bool:
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s = (markdown_body or "").strip()
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if not s:
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return False
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if ORAL_SECTION_MARKER in s:
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return True
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if EVIDENCE_SECTION_MARKER in s:
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return True
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if EVIDENCE_SECTION_TAIL in s:
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return True
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return False
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def longest_common_substring_len(a: str, b: str, min_len: int = 14) -> int:
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"""O(n*m) DP；仅用于短 evidence / body，防止过大。"""
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a = a or ""
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b = b or ""
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if len(a) > 8000 or len(b) > 8000:
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return 0
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if not a or not b:
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return 0
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best = 0
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prev = [0] * (len(b) + 1)
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for i in range(1, len(a) + 1):
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cur = [0] * (len(b) + 1)
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for j in range(1, len(b) + 1):
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if a[i - 1] == b[j - 1]:
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cur[j] = prev[j - 1] + 1
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if cur[j] > best:
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best = cur[j]
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else:
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cur[j] = 0
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prev = cur
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return best if best >= min_len else 0
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def evidence_substring_leak_score(
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body: str, evidence_plain: str, min_len: int = 14
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) -> int:
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"""
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若正文与 evidence 存在较长公共子串，且该子串不在 oral/existing 中，
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则视为摘录渗漏风险（返回子串长度），否则 0。
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"""
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body = (body or "").strip()
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ev = (evidence_plain or "").strip()
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if not body or not ev or len(ev) < min_len:
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return 0
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return longest_common_substring_len(body, ev, min_len=min_len)
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def longest_common_substring(a: str, b: str) -> str:
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"""返回 a、b 的最长公共子串（长度上限防 DP 爆内存）。"""
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a = a or ""
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b = b or ""
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if len(a) > 8000 or len(b) > 8000:
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return ""
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best_i, best_len = 0, 0
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prev = [0] * (len(b) + 1)
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for i in range(1, len(a) + 1):
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cur = [0] * (len(b) + 1)
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for j in range(1, len(b) + 1):
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if a[i - 1] == b[j - 1]:
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cur[j] = prev[j - 1] + 1
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if cur[j] > best_len:
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best_len = cur[j]
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best_i = i
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else:
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cur[j] = 0
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prev = cur
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if best_len <= 0:
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return ""
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start = best_i - best_len
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return a[start:best_i]
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# 具体场合描写：易由「相关摘录」渗入正文但长 LCS 抓不住（词短）。
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EVIDENCE_SCENE_ANCHOR_TOKENS: tuple[str, ...] = (
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"聚餐",
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"酒席",
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"酒桌",
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"宴会",
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"宴席",
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"当晚",
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"那晚",
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"昨夜",
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"前一晚",
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"前一天晚上",
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)
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def evidence_scene_anchor_leak(
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body: str,
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evidence_plain: str,
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oral: str,
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existing: str,
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) -> bool:
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"""
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True：正文出现了与「摘录」共享的具体场合锚点词，且口述与旧正文均未出现，
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视为摘录场景渗漏（短词不走 LCS 阈值）。
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"""
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body = (body or "").strip()
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ev = (evidence_plain or "").strip()
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o = (oral or "").strip()
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ex = (existing or "").strip()
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if not body or not ev:
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return False
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base = f"{o}\n{ex}"
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for tok in EVIDENCE_SCENE_ANCHOR_TOKENS:
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if tok not in body:
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continue
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if tok in base:
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continue
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if tok in ev:
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return True
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return False
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def evidence_leakage_heuristic(
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body: str,
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evidence_plain: str,
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oral: str,
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existing: str,
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min_len: int,
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) -> bool:
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"""
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True：正文与 evidence 的最长公共子串足够长，且该子串未出现在口述或已有正文中，
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视为摘录渗漏，应回退安全正文。
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"""
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body = (body or "").strip()
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ev = (evidence_plain or "").strip()
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if not body or not ev:
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return False
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lcs = longest_common_substring(body, ev)
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if len(lcs) < min_len:
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return False
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o = oral or ""
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ex = existing or ""
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if lcs in o or lcs in ex:
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return False
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return True
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def strip_evidence_for_overlap_check(evidence_text: str) -> str:
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"""去掉 chunk 标记行等噪声，仅保留内容用于 overlap。"""
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lines: list[str] = []
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for line in (evidence_text or "").splitlines():
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t = line.strip()
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if t.startswith("[chunk_id="):
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continue
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if t.startswith("[摘要:"):
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continue
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lines.append(line)
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return "\n".join(lines).strip()
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