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WIP: memory system improvements (in progress)

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Interview/chat prompt layers, reply planner, style profiles, memory
injection, interview meta store, and related tests. Work not finished.

Made-with: Cursor
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Kevin
2026-04-22 16:56:28 +08:00
parent e848f26354
commit 3121d1384d
28 changed files with 2790 additions and 452 deletions
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423
api/app/agents/chat/interview_turn_plan.py
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@@ -1,6 +1,11 @@
"""
访谈轮次编排（方案 A）：由服务端显式给出 turn_mode / 主槽 / 挂钩摘录，
减少仅靠长 prompt 软约束时模型「随便问、不往回忆录引」的漂移。
访谈轮次编排（Option B 定位：InterviewTurnPlan 是**本轮行为模式的唯一决策源**）。
约束说明：
- 主 prompt（prompt_layers）只提供跨轮通用的承接-深挖-串联节奏与身份守则；
- 本轮是否「情绪优先」「跟话头」「回忆推进」完全由 `plan_interview_turn()` 决定；
- 对 LLM 的硬指令由 `InterviewTurnPlan.render_system_directive()` 输出，主 prompt **不得**再
针对具体模式另立软约束。
"""
from __future__ import annotations
@@ -11,17 +16,173 @@ from typing import Literal
from app.agents.chat.prompts_conversation import SLOT_NAME_MAP
from app.agents.stage_constants import STAGE_SLOT_KEYS
InterviewTurnMode = Literal["emotion_first", "memoir_push", "follow_user_only"]
InterviewTurnMode = Literal[
"emotion_first",
"clarify_first",
"memoir_push",
"follow_user_only",
]
SubjectOwner = Literal["user_only"]
MemoryUsage = Literal["none", "allowed_with_attribution"]
ReplyShape = Literal["flexible", "ack_only", "ack_then_question"]
AnchorSourceKind = Literal["user_message", "memory", "none"]
# 本轮承接焦点（规则基线或 focus planner LLM 输出；供 directive 与日志）
FocusPrimary = Literal[
"emotion",
"relationship",
"identity",
"scene",
"memoir_gap",
"follow_user",
]
FocusSource = Literal["rule", "llm", "fallback"]
@dataclass(frozen=True)
class InterviewTurnPlan:
"""单轮访谈的硬目标（供注入 system prompt 顶部，优先级高于一般性建议）。"""
"""单轮访谈的硬目标（优先级高于主 prompt 的一般性建议）。
作为唯一决策源，对 LLM 的落地由 `render_system_directive()` 输出；其它调用方应通过
`is_emotion_first` / `is_clarify_first` / `is_follow_user_only` / `is_memoir_push` /
`requires_explicit_question` 等语义属性读取决策，避免在主 prompt 里重复立法。
扩展字段承载「主语归属 / 记忆引用策略 / 回复形状」，供防上下文污染与双阶段 planner 合并。
"""
mode: InterviewTurnMode
anchor_slot_key: str | None
anchor_slot_readable: str
anchor_snippet: str
anchor_source_kind: AnchorSourceKind = "none"
assistant_identity_question: bool = False
subject_owner: SubjectOwner = "user_only"
memory_usage: MemoryUsage = "none"
memory_reference_style: str = "你之前提过"
forbid_first_person_experience: bool = True
reply_shape: ReplyShape = "flexible"
# 本轮承接重点（单一事实源的一部分；与 mode 配合，由 directive 落地）
primary_focus: FocusPrimary = "memoir_gap"
secondary_focus: FocusPrimary | None = None
focus_summary: str = ""
focus_source: FocusSource = "rule"
# ---- 语义属性：供 prompt_layers / interview_agent 等调用方消费，禁止重复立法 ----
@property
def is_emotion_first(self) -> bool:
return self.mode == "emotion_first"
@property
def is_clarify_first(self) -> bool:
return self.mode == "clarify_first"
@property
def is_follow_user_only(self) -> bool:
return self.mode == "follow_user_only"
@property
def is_memoir_push(self) -> bool:
return self.mode == "memoir_push"
@property
def requires_explicit_question(self) -> bool:
"""memoir_push 要求恰好一个开放式追问；其它模式允许不问或尽量不问。"""
return self.mode == "memoir_push"
@property
def allows_zero_question(self) -> bool:
"""emotion_first / clarify_first 整轮允许不问；follow_user_only 仅允许最多一个轻问。"""
return self.mode in ("emotion_first", "clarify_first", "follow_user_only")
def render_system_directive(self) -> str:
"""对 LLM 的硬指令（供注入 system prompt 顶部）。"""
return format_interview_turn_directive_block(self)
def _focus_primary_for_mode(mode: InterviewTurnMode) -> FocusPrimary:
if mode == "emotion_first":
return "emotion"
if mode == "clarify_first":
return "follow_user"
if mode == "follow_user_only":
return "follow_user"
return "memoir_gap"
def apply_safe_mode_override(
baseline: InterviewTurnMode,
override: str | None,
*,
primary_focus: str | None,
) -> InterviewTurnMode | None:
"""若 planner 建议覆盖 mode，在此做安全收敛；返回 None 表示保持 baseline。"""
if not override or override == baseline:
return None
if override not in (
"emotion_first",
"clarify_first",
"memoir_push",
"follow_user_only",
):
return None
# 情绪优先一旦成立，不因 planner 改回强推素材
if baseline == "emotion_first" and override != "emotion_first":
return None
# 跟话头 / 模糊先澄清：不允许改回问卷式 memoir_push
if baseline in ("follow_user_only", "clarify_first") and override == "memoir_push":
return None
# memoir_push -> emotion_first：仅当焦点偏情绪/关系/身份（或空，由模型判断）
if baseline == "memoir_push" and override == "emotion_first":
pf = (primary_focus or "").strip()
if pf in ("", "emotion", "relationship", "identity"):
return "emotion_first"
return None
if baseline == "memoir_push" and override == "follow_user_only":
return "follow_user_only"
if baseline == "follow_user_only" and override == "emotion_first":
return "emotion_first"
if baseline == "follow_user_only" and override == "clarify_first":
return "clarify_first"
if baseline == "memoir_push" and override == "clarify_first":
pf = (primary_focus or "").strip()
if pf in ("", "emotion", "relationship", "identity", "follow_user"):
return "clarify_first"
return None
if baseline == "clarify_first" and override == "emotion_first":
return "emotion_first"
if baseline == "clarify_first" and override == "follow_user_only":
return "follow_user_only"
return None
def _focus_directive_lines(plan: InterviewTurnPlan) -> str:
"""本轮承接重点：focus_summary 仅作追问角度，不支配正文措辞。"""
if (plan.focus_summary or "").strip():
summary = (plan.focus_summary or "").strip()
if len(summary) > 200:
summary = summary[:199].rstrip() + "…"
return (
"- **本轮追问/承接角度（非正文提纲；主回复仍须从用户本轮原话长出来）**："
+ summary
+ "\n"
)
labels: dict[FocusPrimary, str] = {
"emotion": "情绪与感受",
"relationship": "关系与他人（谁在场、谁在看、和谁较劲）",
"identity": "身份与面子（自我形象、怕丢脸、要强）",
"scene": "现场与感官（画面、光线、身体感受）",
"memoir_gap": "叙述槽与成稿素材（当前主追问方向）",
"follow_user": "顺着用户话头自然展开",
}
pri = labels.get(plan.primary_focus, labels["memoir_gap"])
if plan.secondary_focus and plan.secondary_focus != plan.primary_focus:
sec = labels.get(plan.secondary_focus, "")
if sec:
return (
f"- **本轮承接重点**：先锚定「{pri}」，必要时再轻点「{sec}」作辅助。\n"
)
return f"- **本轮承接重点**：优先锚定「{pri}」。\n"
def primary_empty_slot(stage: str, empty_slots: list[str]) -> str | None:
@@ -48,16 +209,51 @@ def extract_anchor_snippet(
user_message: str,
max_chars: int = 180,
) -> str:
"""优先记忆摘录，其次用户原话（用于追问挂钩，非事实断言）。"""
mem = _strip_scene_hint(memory_evidence_text)
if mem and len(mem) >= 4:
return mem[:max_chars].strip()
"""优先用户本轮原话，其次极短记忆线索（用于追问挂钩，非事实断言）。
旧记忆不得压过用户当前话头；仅当本轮原话过短时再借 `memory_anchor_source` 挂钩。
`memory_evidence_text` 此处仅为**一条短线索**（非整段 `[M…]` 摘录块）。
"""
um = (user_message or "").strip()
if len(um) >= 10:
return um[:max_chars].strip()
mem = _strip_scene_hint(memory_evidence_text)
if mem and len(mem) >= 4:
for line in mem.splitlines():
s = line.strip()
if s.startswith("[M") and "]" in s:
rest = s.split("]", 1)[1].strip()
if rest and len(rest) >= 4:
return rest[:max_chars].strip()
return mem[:max_chars].strip()
if len(um) >= 4:
return um[:max_chars].strip()
return ""
def determine_anchor_source_kind(
*,
memory_evidence_text: str,
user_message: str,
) -> AnchorSourceKind:
"""标记挂钩线索来源，避免把用户本轮原话误标成旧记忆。"""
um = (user_message or "").strip()
if len(um) >= 10:
return "user_message"
mem = _strip_scene_hint(memory_evidence_text)
if mem and len(mem) >= 4:
for line in mem.splitlines():
s = line.strip()
if s.startswith("[M") and "]" in s:
rest = s.split("]", 1)[1].strip()
if rest and len(rest) >= 4:
return "memory"
return "memory"
if len(um) >= 4:
return "user_message"
return "none"
_EMOTION_MARKERS: tuple[str, ...] = (
"哭",
"难受",
@@ -79,6 +275,52 @@ _EMOTION_MARKERS: tuple[str, ...] = (
"扛不住",
"放不下",
"意难平",
"难堪",
"丢脸",
"怕丢人",
"臊得慌",
)
# 说不清 / 暧昧放慢：不触发 memoir_push 式强推槽位
_AMBIGUITY_MARKERS: tuple[str, ...] = (
"说不清",
"说不明白",
"说不上来",
"说不上",
"说不好",
"不知道怎么说",
"不知道咋说",
"好像",
"也不是",
"不完全是",
"不太确定",
"不确定",
"有点模糊",
"很模糊",
"模模糊糊",
"难说",
"有一点，但我不确定",
)
_SOFT_SLOW_MARKERS: tuple[str, ...] = (
"害羞",
"羞涩",
"不好意思",
"脸红",
"暧昧",
)
_ASSISTANT_IDENTITY_QUESTION_MARKERS: tuple[str, ...] = (
"你是哪里人",
"你哪的人",
"你是哪里长大的",
"你在哪长大",
"你的童年",
"你小时候",
"你家里",
"你爸妈",
"你父母",
"你也有过",
"你也是",
)
@@ -97,6 +339,33 @@ def _is_emotion_heavy(text: str) -> bool:
return False
def _is_ambiguous_or_needs_slow_pace(text: str) -> bool:
"""模糊表达、暧昧/羞涩等：先澄清或放慢，不强推叙述槽。"""
t = (text or "").strip()
if not t:
return True
if any(m in t for m in _AMBIGUITY_MARKERS):
return True
if any(m in t for m in _SOFT_SLOW_MARKERS):
return True
return False
def _is_too_vague_for_memoir_push(text: str) -> bool:
"""过短或仍含糊时，不进入 memoir_push。"""
t = (text or "").strip()
if len(t) < 12:
return True
return _is_ambiguous_or_needs_slow_pace(t)
def _is_asking_assistant_identity_or_life(text: str) -> bool:
t = (text or "").strip()
if not t:
return False
return any(marker in t for marker in _ASSISTANT_IDENTITY_QUESTION_MARKERS)
def plan_interview_turn(
*,
current_stage: str,
@@ -108,14 +377,31 @@ def plan_interview_turn(
"""
粗规则（可迭代）：
- 情绪浓：先共情，不强推叙述槽搜集问。
- 说不清/暧昧羞涩等：模糊先澄清，禁止替用户下结论或强推槽位。
- 刚切换人生阶段：跟着用户节奏，不做「新阶段问卷首开」。
- 当前阶段无空槽：深度跟进，不重启盘点。
- 默认：memoir_push，锁一个主槽 + 挂钩摘录。
- 默认：memoir_push（仅当本轮话头足够具体、非含糊短答时）。
"""
snippet = extract_anchor_snippet(
memory_evidence_text=memory_evidence_text,
user_message=normalized_user_message,
)
anchor_source_kind = determine_anchor_source_kind(
memory_evidence_text=memory_evidence_text,
user_message=normalized_user_message,
)
mem_trim = _strip_scene_hint(memory_evidence_text).strip()
mem_use: MemoryUsage = (
"allowed_with_attribution" if mem_trim else "none"
)
um = (normalized_user_message or "").strip()
asks_assistant_identity = _is_asking_assistant_identity_or_life(um)
reply_shape: ReplyShape = "flexible"
if any(
k in um
for k in ("讲讲", "说说", "她的故事", "他的故事", "后来呢", "然后呢")
):
reply_shape = "ack_then_question"
if _is_emotion_heavy(normalized_user_message):
slot = primary_empty_slot(current_stage, empty_slots)
@@ -129,6 +415,12 @@ def plan_interview_turn(
anchor_slot_key=slot,
anchor_slot_readable=readable,
anchor_snippet=snippet,
anchor_source_kind=anchor_source_kind,
assistant_identity_question=asks_assistant_identity,
memory_usage=mem_use,
reply_shape=reply_shape,
primary_focus=_focus_primary_for_mode("emotion_first"),
focus_source="rule",
)
if stage_switched_this_turn:
@@ -137,6 +429,12 @@ def plan_interview_turn(
anchor_slot_key=None,
anchor_slot_readable="（刚自然谈到本阶段，先顺着对方语势，勿问卷式首开）",
anchor_snippet=snippet,
anchor_source_kind=anchor_source_kind,
assistant_identity_question=asks_assistant_identity,
memory_usage=mem_use,
reply_shape=reply_shape,
primary_focus=_focus_primary_for_mode("follow_user_only"),
focus_source="rule",
)
if not empty_slots:
@@ -145,6 +443,32 @@ def plan_interview_turn(
anchor_slot_key=None,
anchor_slot_readable="（本阶段主要叙述槽已有素材）请 depth-first：接续画面或情绪线，别重启童年在哪长大式盘点",
anchor_snippet=snippet,
anchor_source_kind=anchor_source_kind,
assistant_identity_question=asks_assistant_identity,
memory_usage=mem_use,
reply_shape=reply_shape,
primary_focus=_focus_primary_for_mode("follow_user_only"),
focus_source="rule",
)
if _is_too_vague_for_memoir_push(um):
slot = primary_empty_slot(current_stage, empty_slots)
readable = (
SLOT_NAME_MAP.get(slot, slot or "")
if slot
else "（先澄清感受时可暂不强绑某一槽位）"
)
return InterviewTurnPlan(
mode="clarify_first",
anchor_slot_key=slot,
anchor_slot_readable=readable,
anchor_snippet=snippet,
anchor_source_kind=anchor_source_kind,
assistant_identity_question=asks_assistant_identity,
memory_usage=mem_use,
reply_shape=reply_shape,
primary_focus=_focus_primary_for_mode("clarify_first"),
focus_source="rule",
)
slot = primary_empty_slot(current_stage, empty_slots)
@@ -154,6 +478,12 @@ def plan_interview_turn(
anchor_slot_key=slot,
anchor_slot_readable=SLOT_NAME_MAP.get(slot, slot),
anchor_snippet=snippet,
anchor_source_kind=anchor_source_kind,
assistant_identity_question=asks_assistant_identity,
memory_usage=mem_use,
reply_shape=reply_shape,
primary_focus=_focus_primary_for_mode("memoir_push"),
focus_source="rule",
)
@@ -165,6 +495,45 @@ def format_interview_turn_directive_block(plan: InterviewTurnPlan) -> str:
else "（无可用摘录时，必须从用户本轮原话里抽词作挂钩，禁止编造）"
)
if plan.memory_usage == "allowed_with_attribution":
mem_block = (
"- **过往记忆线索**：主 Context 里若有一条极短线索，**只**用于帮你选追问角度；"
"**不是**正文提纲，**不是**长素材。\n"
"- **禁止**用「"
+ plan.memory_reference_style
+ "…」开场或整段复述旧记忆；若轻勾连，**最多半句**，且须让本轮原话占主位。\n"
"- **禁止**把线索写成助手亲历；**禁止**把线索当「咱俩共同回忆」。\n"
)
else:
mem_block = (
"- **过往记忆线索**：本轮无可用线索（或已策略性不注入）；"
"**不要**编造「你说过…」式假归因。\n"
)
perspective_block = (
"- **主语与视角**：用户可见回复主线必须是**用户**的经历与感受；"
"助手**没有**真实人生传记。\n"
)
if plan.forbid_first_person_experience:
perspective_block += (
"- **禁止助手自传式措辞**：不得使用「我小时候」「我演过/我扮演」「我当时暗恋」"
"「我爸妈」等自称亲历表述。\n"
)
if plan.assistant_identity_question:
perspective_block += (
"- **本轮用户在问助手本人**：必须明确守住边界，不能假装自己有籍贯、童年、家庭或恋爱经历；"
"若要借记忆继续聊，只能明确归因到用户，例如「你刚提到上海」「你前面说过……」。\n"
)
if plan.reply_shape == "ack_only":
shape_block = "- **本轮回复形状**：以短承接为主；若无必要可整轮不问。\n"
elif plan.reply_shape == "ack_then_question":
shape_block = (
"- **本轮回复形状**：先短承接，再尽量带**一条**贴用户话头的开放式问（仍守全篇最多一个问句）。\n"
)
else:
shape_block = ""
if plan.mode == "emotion_first":
mode_rules = (
"- **情绪优先**：本轮以承接、并肩与安全感为主，**不要**为推进「叙述槽大纲」而追加信息搜集型追问。\n"
@@ -173,6 +542,16 @@ def format_interview_turn_directive_block(plan: InterviewTurnPlan) -> str:
+ plan.anchor_slot_readable
+ "」仅供你心里知道后续方向，**不要**在本轮用问卷口吻硬推该槽。"
)
elif plan.mode == "clarify_first":
mode_rules = (
"- **模糊先澄清**：用户正在表达不确定、说不清，或暧昧/羞涩等未命名感受；"
"以承接、并列为先，**禁止**把模糊感受改写成确定关系、命运、动机或人生结论。\n"
"- 若带问句，最多**一个**，且须邀请对方**用自己的词**继续摸索或澄清（例如更接近哪一种、还是也不完全是），"
"禁止封闭式逼认、禁止替用户命名关系或事件结局。\n"
"- 允许整轮**只承接不问**；参考主槽「"
+ plan.anchor_slot_readable
+ "」仅供你心里知道后续方向，**不要**问卷式硬推该槽。"
)
elif plan.mode == "follow_user_only":
mode_rules = (
"- **跟话头**：本轮禁止问卷式首开、禁止重启式盘点；顺着用户刚展开的画面、人物或情绪自然往下。\n"
@@ -181,20 +560,38 @@ def format_interview_turn_directive_block(plan: InterviewTurnPlan) -> str:
else:
mode_rules = (
"- **回忆推进（memoir_push）**：对用户可见回复中须有**恰好一个**开放式回忆追问，\n"
" 且意图明显在补足下面「主追问方向」；问句必须挂住**挂钩摘录**或**用户本轮原词**（二者至少其一）。\n"
"- 禁止用日常寒暄替代该追问；仍遵守全篇「最多一个问句」「禁止晚会硬切」。"
" 且意图明显在补足下面「主追问方向」；问句必须挂住**用户本轮原词**或**挂钩摘录**（二者至少其一，且**优先原词**）。\n"
"- **禁止**把用户仍含糊的表达改写成确定事实或关系；禁止用日常寒暄替代该追问；"
"仍遵守全篇「最多一个问句」「禁止晚会硬切」。"
)
focus_block = _focus_directive_lines(plan)
if plan.anchor_source_kind == "user_message":
anchor_label = "（仅作追问挂钩，来自用户本轮原话摘录；优先把这句里的原词接住）"
elif plan.anchor_source_kind == "memory":
anchor_label = "（仅作追问挂钩，来自检索到的用户过往口述/摘要；不是用户本轮新说的内容）"
else:
anchor_label = "（无可用挂钩时，必须从用户本轮原话里抽词承接，禁止编造）"
return f"""## 本轮编排指令（硬规则，优先于后文一般性建议）
{mode_rules}
- **主追问方向（叙述槽）**：{plan.anchor_slot_readable}
- **挂钩摘录**（仅作衔接线索，**不是**用户本轮新说的内容；禁止写成就等于用户刚讲的原话）：{snippet_line}
{focus_block}{mem_block}{perspective_block}{shape_block}- **主追问方向（叙述槽）**：{plan.anchor_slot_readable}
- **挂钩线索**{anchor_label}：{snippet_line}
"""
__all__ = [
"FocusPrimary",
"FocusSource",
"InterviewTurnMode",
"InterviewTurnPlan",
"MemoryUsage",
"ReplyShape",
"SubjectOwner",
"AnchorSourceKind",
"apply_safe_mode_override",
"determine_anchor_source_kind",
"extract_anchor_snippet",
"format_interview_turn_directive_block",
"plan_interview_turn",