71fbd39e32
Route all memory ingest/retrieve/enrichment/compaction through async MemoryService. Remove legacy sync memory implementations (ingest/retrieve/compaction); Celery and memoir Phase2 call asyncio.run into MemoryService-backed helpers. Memoir Phase1 batch ingest uses MemoryService.ingest_transcripts_batch; drop chapters. evidence_bundle_json mirror (Alembic 0015). Evaluation uses snapshot/link-only bundles; raise EvidenceClosureMissing instead of partial/fallback lineage tiers. Split memoir state into NarrativeCoverageState and InterviewControlState; delete the _interview_meta_store adapter layer. Remove rolling-query and recent-fact fallback settings from config and evidence assembly. Update judges, docs, tests, and PlaygroundPage alignment. Made-with: Cursor
437 lines
15 KiB
Python
437 lines
15 KiB
Python
"""Interview quality helpers: known facts, persona threads, and anti-repeat guard."""
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from __future__ import annotations
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import re
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from collections.abc import Iterable
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from langchain_core.messages import AIMessage, BaseMessage
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from app.agents.stage_constants import STAGE_DISPLAY_ZH, STAGE_SLOT_KEYS
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from app.agents.state_schema import (
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KnownFact,
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MemoirStateSchema,
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PersonaThread,
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narrative_coverage_state,
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)
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# 与 `apply_duplicate_question_guard` 中整段替换句一致；用于判定是否需触发二次生成。
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DUPLICATE_QUESTION_GUARD_FALLBACK_ZH = "这一段我记住了。"
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_QUESTION_SPLIT_RE = re.compile(r"[?？]+")
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_SENTENCE_SPLIT_RE = re.compile(r"(?<=[。！？!?])")
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_PUNCT_RE = re.compile(r"[\s，。！？；：、“”‘’（）()《》【】\\[\\],.!?:;\"'`~·…-]+")
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# 「我演罗密欧」等扮演亲历，但排除「我演示…」类口癖
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_AUTOBIO_IYAN_NOT_DEMO_RE = re.compile(r"我演(?!示)")
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_TRAIT_HINTS: tuple[tuple[str, tuple[str, ...]], ...] = (
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("执着坚持", ("坚持", "执着", "咬牙", "熬过", "顶住", "训练", "反复")),
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("规划目标感", ("计划", "规划", "目标", "打算", "一步步", "安排", "准备")),
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("求真较真", ("弄明白", "搞清楚", "想通", "为什么", "较真", "求证")),
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("行动力", ("决定", "创业", "开始做", "尝试", "报名", "跑去", "去做")),
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(
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"家庭责任感",
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("家里", "父母", "妈妈", "爸爸", "妻子", "丈夫", "孩子", "照顾", "支持"),
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),
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("即时反馈驱动", ("反馈", "看到结果", "成就感", "立刻", "马上见效")),
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("自由天性", ("自由", "无拘无束", "满世界跑", "疯玩", "野", "不管")),
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("动手创造", ("自己动手", "搭", "做", "造", "修", "拆", "烤", "生火", "种")),
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("重感情念旧", ("想起来", "怀念", "舍不得", "还记得", "那时候", "小时候")),
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("好胜争先", ("比赛", "赢", "比", "第一", "不服输", "较劲")),
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)
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_SCENE_CUE_WORDS: tuple[tuple[str, str], ...] = (
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("田野", "田野的泥土和青草气息"),
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("河里", "河水的凉意"),
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("海边", "海风和咸咸的空气"),
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("溜冰", "冰面上咔嚓咔嚓的声响"),
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("游泳", "一头扎进水里的畅快"),
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("烤红薯", "红薯外焦里糯、掰开冒热气的香味"),
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("烤", "火堆噼啪响、烟气里混着食物焦香"),
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("打水漂", "石片在水面跳跃、一圈一圈涟漪散开"),
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("捉", "追着跑、手心攥紧怕跑掉的紧张"),
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("雪", "雪花落在脸上化成水珠的凉"),
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("风", "风灌进领子里的感觉"),
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("下雨", "雨点打在屋顶上的声音"),
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("自行车", "骑车下坡风呼呼吹过耳朵"),
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("火车", "绿皮车厢里混着泡面和橘子皮的味道"),
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("学校", "教室里粉笔灰飘在阳光里的样子"),
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("考试", "翻卷子时纸张沙沙响"),
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("工厂", "机器轰鸣、油污和铁锈的气味"),
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("做饭", "锅铲碰锅底的声响、油花溅起来的滋滋声"),
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)
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def extract_scene_cues(user_message: str) -> list[str]:
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msg = (user_message or "").strip()
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if not msg:
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return []
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cues: list[str] = []
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for keyword, description in _SCENE_CUE_WORDS:
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if keyword in msg:
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cues.append(description)
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return cues[:3]
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_SLOT_REPEAT_PATTERNS: dict[str, tuple[str, ...]] = {
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"place": ("哪里长大", "家乡", "老家", "在哪长大", "什么地方长大"),
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"people": ("谁对你影响", "家里都有谁", "小时候和谁", "身边有什么人"),
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"daily_life": ("平时怎么过", "日常都做什么", "小时候都玩什么"),
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"emotion": ("那时候什么感觉", "当时什么感受", "小时候开心吗"),
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"turning_event": ("印象最深的事", "难忘的事", "转折"),
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"school": ("什么学校", "在哪上学", "读的什么学校", "上什么学校"),
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"city": ("在哪个城市", "去了哪里读书", "在哪读书"),
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"motivation": ("为什么想学", "为什么选这个", "动力是什么"),
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"challenge": ("遇到什么困难", "最大的难处", "辛苦吗"),
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"change": ("后来有什么变化", "这件事怎么改变你", "之后有什么不同"),
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"job": ("做什么工作", "具体做什么", "工作内容是什么"),
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"environment": ("工作环境", "在哪工作", "什么单位"),
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"decision": ("为什么做这个决定", "怎么决定的"),
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"pressure": ("压力大吗", "最难的时候", "最大的压力"),
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"growth": ("学到了什么", "成长在哪里", "后来有什么提升"),
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"relationship": ("和家人关系怎么样", "和伴侣关系怎么样"),
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"conflict": ("有什么矛盾", "怎么吵起来的", "冲突"),
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"support": ("谁支持你", "谁帮过你", "怎么支持你的"),
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"responsibility": ("承担什么责任", "家里靠谁", "你主要负责什么"),
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"value": ("你最看重什么", "信念是什么", "原则是什么"),
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"regret": ("最大的遗憾", "后悔过吗"),
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"pride": ("最骄傲的事", "最自豪的事"),
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"lesson": ("学到了什么道理", "最大的感悟", "给你什么启发"),
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}
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def _normalize_text(text: str) -> str:
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return _PUNCT_RE.sub("", (text or "").strip().lower())
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def _dedupe_keep_last(items: Iterable[str], *, limit: int) -> list[str]:
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out: list[str] = []
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seen: set[str] = set()
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for raw in reversed([str(x).strip() for x in items if str(x).strip()]):
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key = _normalize_text(raw)
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if not key or key in seen:
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continue
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seen.add(key)
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out.append(raw)
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if len(out) >= limit:
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break
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out.reverse()
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return out
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def _merge_known_facts(
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existing: Iterable[KnownFact],
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additions: Iterable[KnownFact],
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*,
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limit: int = 24,
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) -> list[KnownFact]:
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merged: dict[tuple[str, str, str], KnownFact] = {}
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for item in list(existing) + list(additions):
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key = (
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(item.stage or "").strip(),
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(item.slot_name or "").strip(),
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_normalize_text(f"{item.label}:{item.value}"),
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)
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if not key[2]:
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continue
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merged[key] = item
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values = list(merged.values())[-limit:]
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return values
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def _merge_persona_threads(
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existing: Iterable[PersonaThread],
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additions: Iterable[PersonaThread],
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*,
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limit: int = 12,
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) -> list[PersonaThread]:
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merged: dict[tuple[str, str], PersonaThread] = {}
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for item in list(existing) + list(additions):
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key = (_normalize_text(item.trait), _normalize_text(item.evidence))
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if not key[0]:
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continue
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merged[key] = item
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values = list(merged.values())[-limit:]
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return values
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def _trim_sentence(text: str, *, limit: int = 80) -> str:
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s = re.sub(r"\s+", " ", (text or "").strip())
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if len(s) <= limit:
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return s
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return s[: limit - 1].rstrip() + "…"
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def build_runtime_interview_state(
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state: MemoirStateSchema,
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*,
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user_message: str,
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active_stage: str,
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birth_year: int | None = None,
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birth_place: str = "",
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grew_up_place: str = "",
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occupation: str = "",
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) -> MemoirStateSchema:
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"""Merge current-turn hints into a prompt-only state view."""
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additions: list[KnownFact] = []
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if birth_year:
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additions.append(
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KnownFact(
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label="出生年份",
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value=f"{birth_year}年",
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source="profile",
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)
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)
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if birth_place:
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additions.append(
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KnownFact(
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label="出生地",
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value=birth_place.strip(),
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source="profile",
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stage="childhood",
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slot_name="place",
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)
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)
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if grew_up_place:
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additions.append(
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KnownFact(
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label="成长地",
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value=grew_up_place.strip(),
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source="profile",
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stage="childhood",
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slot_name="place",
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)
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)
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if occupation:
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additions.append(
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KnownFact(
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label="职业背景",
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value=occupation.strip(),
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source="profile",
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stage="career",
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slot_name="job",
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)
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)
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msg = _trim_sentence(user_message, limit=120)
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if msg:
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additions.append(
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KnownFact(
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label="本轮新信息",
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value=msg,
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source="current_turn",
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stage=active_stage,
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)
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)
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persona_additions: list[PersonaThread] = []
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narrative_state = narrative_coverage_state(state)
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haystack = " ".join(
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[msg]
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+ [fact.value for fact in state.known_facts[-8:]]
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+ list(narrative_state.filled_slots_for_stage(active_stage).values())[:4]
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)
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for trait, markers in _TRAIT_HINTS:
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for marker in markers:
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if marker and marker in haystack:
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persona_additions.append(
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PersonaThread(
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trait=trait,
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evidence=_trim_sentence(
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marker if marker in msg else haystack, limit=70
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),
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source="heuristic",
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stage=active_stage,
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)
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)
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break
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return state.model_copy(
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update={
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"known_facts": _merge_known_facts(state.known_facts, additions),
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"persona_threads": _merge_persona_threads(
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state.persona_threads, persona_additions
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),
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}
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)
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def extract_recent_questions(
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messages: Iterable[BaseMessage], *, limit: int = 4
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) -> list[str]:
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questions: list[str] = []
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for msg in messages:
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if not isinstance(msg, AIMessage):
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continue
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text = str(getattr(msg, "content", "") or "").strip()
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if not text:
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continue
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for part in _QUESTION_SPLIT_RE.split(text):
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part = part.strip()
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if not part:
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continue
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if any(w in text for w in ("？", "?")):
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questions.append(_trim_sentence(part + "？", limit=50))
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return _dedupe_keep_last(questions, limit=limit)
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def update_recent_questions(
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existing: Iterable[str],
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generated_segments: Iterable[str],
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*,
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limit: int = 4,
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) -> list[str]:
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fresh: list[str] = list(existing)
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for seg in generated_segments:
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text = str(seg or "").strip()
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if not text or ("？" not in text and "?" not in text):
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continue
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parts = [p.strip() for p in _QUESTION_SPLIT_RE.split(text) if p.strip()]
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if not parts:
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continue
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fresh.append(_trim_sentence(parts[-1] + "？", limit=50))
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return _dedupe_keep_last(fresh, limit=limit)
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def apply_duplicate_question_guard(
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segments: Iterable[str],
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*,
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state: MemoirStateSchema,
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recent_questions: Iterable[str],
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) -> tuple[list[str], bool]:
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"""Downgrade obvious repeated-fact questions into acknowledgment-only text."""
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recent_norms = {_normalize_text(q) for q in recent_questions if _normalize_text(q)}
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known_patterns: list[str] = []
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for fact in state.known_facts:
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slot_patterns = _SLOT_REPEAT_PATTERNS.get(fact.slot_name or "", ())
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known_patterns.extend(slot_patterns)
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if fact.label == "本轮新信息":
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known_patterns.append(fact.value)
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cleaned: list[str] = []
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touched = False
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for seg in segments:
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text = str(seg or "").strip()
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if not text:
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continue
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text_norm = _normalize_text(text)
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repeated = False
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if ("？" in text or "?" in text) and text_norm:
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if any(q and (q in text_norm or text_norm in q) for q in recent_norms):
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repeated = True
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if not repeated:
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for pattern in known_patterns:
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pat_norm = _normalize_text(pattern)
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if pat_norm and pat_norm in text_norm:
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repeated = True
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break
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if repeated:
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sentences = [s.strip() for s in _SENTENCE_SPLIT_RE.split(text) if s.strip()]
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kept = [s for s in sentences if "？" not in s and "?" not in s]
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replacement = kept[0] if kept else DUPLICATE_QUESTION_GUARD_FALLBACK_ZH
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if not replacement.endswith(("。", "！", "…")):
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replacement += "。"
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cleaned.append(replacement)
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touched = True
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else:
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cleaned.append(text)
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if not cleaned:
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cleaned = [DUPLICATE_QUESTION_GUARD_FALLBACK_ZH]
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return cleaned, touched
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def segments_are_only_duplicate_guard_fallback(segments: Iterable[str]) -> bool:
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"""是否为「仅兜底_ack、无实质承接」——适合再打一枪模型。"""
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parts = [str(s or "").strip() for s in segments if str(s or "").strip()]
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return len(parts) == 1 and parts[0] == DUPLICATE_QUESTION_GUARD_FALLBACK_ZH
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# 助手可见回复中，明显声称「我本人有过某种人生经历」的高置信子串（偏保守、宁可漏网不误伤泛化共情）。
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_AUTOBIOGRAPHICAL_MARKERS_ZH: tuple[str, ...] = (
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"我小时候",
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"我小学",
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"我中学",
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"我初中",
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"我高中",
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"我大学",
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"我上学那",
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"我念书",
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"我读书那",
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"我暗恋",
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"我当时暗恋",
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"我爸妈",
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"我父亲",
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"我母亲",
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"我爹",
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"我妈",
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"我爷爷",
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"我奶奶",
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"我外公",
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"我外婆",
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"我前任",
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"我老公",
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"我老婆",
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"我丈夫",
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"我妻子",
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"我男友",
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"我女友",
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"我对象",
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"我儿子",
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"我女儿",
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"我孩子",
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"我以前也",
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"我当时也",
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"我那时候也",
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"我也经历过",
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"我也有过",
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"我也演过",
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"我也上台",
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"我演过",
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"我饰演",
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"我演出",
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"我演的是",
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"我演的",
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"我扮演",
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"感觉我熟",
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"这我熟",
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)
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AUTOBIOGRAPHICAL_BOUNDARY_FALLBACK_ZH = (
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"你刚说的这段很有画面，我想多听你讲讲那时候你心里是什么感觉。"
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)
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def _segment_has_autobiographical_claim_zh(text: str) -> bool:
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s = (text or "").strip()
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if not s:
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return False
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if _AUTOBIO_IYAN_NOT_DEMO_RE.search(s):
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return True
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return any(m and m in s for m in _AUTOBIOGRAPHICAL_MARKERS_ZH)
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def apply_autobiographical_boundary_guard(
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segments: Iterable[str],
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) -> tuple[list[str], bool]:
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"""将明显带有「助手自传式经历」的段落替换为中性承接，避免身份越界。"""
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cleaned: list[str] = []
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touched = False
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for seg in segments:
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text = str(seg or "").strip()
|
||
if not text:
|
||
continue
|
||
if _segment_has_autobiographical_claim_zh(text):
|
||
cleaned.append(AUTOBIOGRAPHICAL_BOUNDARY_FALLBACK_ZH)
|
||
touched = True
|
||
else:
|
||
cleaned.append(text)
|
||
if not cleaned:
|
||
cleaned = [AUTOBIOGRAPHICAL_BOUNDARY_FALLBACK_ZH]
|
||
touched = True
|
||
return cleaned, touched
|
||
|
||
|
||
def stage_slot_hint_lines(stage: str) -> list[str]:
|
||
keys = STAGE_SLOT_KEYS.get(stage, ())
|
||
stage_zh = STAGE_DISPLAY_ZH.get(stage, stage)
|
||
return [f"{stage_zh}:{key}" for key in keys]
|