Merge commit e95582a: PR #20 proactive chat, topic chips, low-info turn plan

- Merge staging workflow parent and resolve conflicts with English/i18n and WS pool
- Re-greeting: language-aware fallbacks and prompts; router passes user_language
- RealtimeSession: topic suggestion callbacks + TTS sync path preserved

Co-authored-by: Cursor <cursoragent@cursor.com>

api/app/agents/chat/interview_agent.py

@@ -24,6 +24,7 @@ from app.agents.chat.prompts_conversation import (
     SLOT_NAME_MAP,
     SLOT_NAME_MAP_EN,
     get_opening_prompt,
+    get_re_greeting_prompt,
 )
 from app.agents.chat.reply_limits import (
     nonempty_segments_or_fallback,
@@ -69,6 +70,16 @@ def _opening_fallback_for(language: str) -> str:
     return _OPENING_FALLBACK_EN if language == "en" else _OPENING_FALLBACK_ZH
 
 
+_RE_GREETING_FALLBACK_ZH = "上次聊到的事我还记着，今天想继续往下讲讲吗？"
+_RE_GREETING_FALLBACK_EN = (
+    "I still remember what we touched on last time — want to keep going today?"
+)
+
+
+def _re_greeting_fallback_for(language: str) -> str:
+    return _RE_GREETING_FALLBACK_EN if language == "en" else _RE_GREETING_FALLBACK_ZH
+
+
 # 仅在「重复问句守卫」把正文削成单句兜底时追加二次 system，只多调一次模型。
 _DUPLICATE_GUARD_LLM_RETRY_SYSTEM_APPENDIX = """## 二次生成（纠偏）
 上一版模型输出因包含与「最近已问过的问题」或「已确认事实」重复的问句，已被系统弃用。请**重新写一整条回复**：
@@ -548,3 +559,123 @@ class InterviewAgent:
         except Exception as e:
             logger.error("生成开场白失败: {}", e, exc_info=True)
             return [_opening_fallback_for(language)]
+
+    async def generate_re_greeting_message(
+        self,
+        conversation_id: str,
+        memoir_state: MemoirStateSchema,
+        idle_hours: float,
+        user_profile_context: str = "",
+        background_voice: str = "default",
+        occupation: str = "",
+        profile_birth_year: Optional[int] = None,
+        profile_era_place: str = "",
+        language: str = "zh",
+    ) -> List[str]:
+        """老对话回访问候：用户带着已有历史回到对话时，AI 主动做承接式开场。
+
+        与 generate_opening_message 的差异：prompt 明确告知有历史 + 距上次的时间感受，
+        要求轻轻引用历史里的具体细节，不能用首次见面式硬开场。
+        """
+        if not self.llm:
+            return [_re_greeting_fallback_for(language)]
+        try:
+            narrative_state = narrative_coverage_state(memoir_state)
+            control_state = interview_control_state(memoir_state)
+            empty_slots = control_state.prompt_empty_slots_for_stage(
+                narrative_state, memoir_state.current_stage
+            )
+            slot_table = SLOT_NAME_MAP_EN if language == "en" else SLOT_NAME_MAP
+            empty_slots_readable = [slot_table.get(s, s) for s in empty_slots]
+            persona = normalize_interview_persona(settings.chat_interview_persona)
+            prompt = get_re_greeting_prompt(
+                current_stage=memoir_state.current_stage,
+                empty_slots_readable=empty_slots_readable,
+                user_profile_context=user_profile_context,
+                persona=persona,
+                background_voice=background_voice,
+                occupation=occupation,
+                profile_birth_year=profile_birth_year,
+                profile_era_place=profile_era_place,
+                idle_hours=idle_hours,
+                language=language,
+            )
+            hw = await get_history_with_window(
+                conversation_id,
+                max_pairs=settings.chat_history_max_pairs,
+                max_chars=settings.chat_history_max_chars,
+            )
+            messages: List[Any] = [SystemMessage(content=prompt)]
+            messages.extend(hw.window)
+            re_greet_tail = (
+                "（用户回到这个已有历史的对话，还没说话。"
+                "请基于上文做温和的承接式回访问候。）"
+                if language != "en"
+                else (
+                    "(The user returned to this conversation with prior history and has not spoken yet. "
+                    "Give a gentle, grounded re-greeting based on the conversation above.)"
+                )
+            )
+            messages.append(HumanMessage(content=re_greet_tail))
+            log_agent_payload(
+                logger,
+                "InterviewAgent.re_greeting.prompt",
+                format_history_string(
+                    messages,
+                    omit_system_body=settings.agent_log_omit_system_message_body,
+                ),
+            )
+            re_greet_llm = self.llm.bind(
+                max_tokens=settings.chat_opening_max_tokens,
+                temperature=float(settings.chat_interview_temperature),
+            )
+            llm_t0 = time.perf_counter()
+            with agent_span(
+                logger,
+                "InterviewAgent.re_greeting.llm",
+                conversation_id=conversation_id,
+            ):
+                logger.info(
+                    "event=chat_prompt_built agent=InterviewAgent.generate_re_greeting_message "
+                    "prompt_chars={} history_pairs_total={} history_pairs_windowed={} idle_hours={:.2f}",
+                    _message_contents_char_count(messages),
+                    hw.turn_total,
+                    len(hw.window) // 2,
+                    idle_hours,
+                )
+                response = await re_greet_llm.ainvoke(messages)
+            logger.info(
+                "event=chat_llm_done agent=InterviewAgent.generate_re_greeting_message "
+                "response_latency_ms={:.2f}",
+                (time.perf_counter() - llm_t0) * 1000,
+            )
+            response_text = (
+                response.content if hasattr(response, "content") else str(response)
+            )
+            log_agent_payload(
+                logger, "InterviewAgent.re_greeting.raw_response", response_text
+            )
+            raw_list = segments_from_llm_response(response_text, max_segments=2)
+            if not raw_list:
+                raw_list = [response_text.strip()]
+            max_chars = int(settings.chat_interview_max_chars_per_segment)
+            out = truncate_chat_segments(
+                raw_list,
+                max_segments=2,
+                max_chars_per_segment=max_chars,
+            )
+            log_agent_summary(
+                logger,
+                "InterviewAgent.re_greeting segments={} conversation_id={} idle_hours={:.2f}",
+                len(out),
+                conversation_id,
+                idle_hours,
+            )
+            segments = out if out else [response_text.strip()[:max_chars]]
+            return nonempty_segments_or_fallback(
+                segments,
+                fallback=_re_greeting_fallback_for(language),
+            )
+        except Exception as e:
+            logger.error("生成回访问候失败: {}", e, exc_info=True)
+            return [_re_greeting_fallback_for(language)]

api/app/agents/chat/interview_turn_plan.py

@@ -14,7 +14,7 @@ from dataclasses import dataclass
 from typing import Literal
 
 from app.agents.chat.prompts_conversation import SLOT_NAME_MAP
-from app.agents.stage_constants import STAGE_SLOT_KEYS
+from app.agents.stage_constants import STAGE_KEYWORD_WEIGHTS, STAGE_SLOT_KEYS
 
 InterviewTurnMode = Literal[
     "emotion_first",
@@ -66,6 +66,7 @@ class InterviewTurnPlan:
     secondary_focus: FocusPrimary | None = None
     focus_summary: str = ""
     focus_source: FocusSource = "rule"
+    low_information_reply: bool = False
 
     # ---- 语义属性：供 prompt_layers / interview_agent 等调用方消费，禁止重复立法 ----
 
@@ -323,6 +324,17 @@ _ASSISTANT_IDENTITY_QUESTION_MARKERS: tuple[str, ...] = (
     "你也是",
 )
 
+_LOW_INFORMATION_REPLY_MAX_CHARS = 8
+_LOW_INFORMATION_REPLY_CHARS: frozenset[str] = frozenset(
+    # 这不是短语白名单，而是一组低信息的应声/确认/语气字符。
+    # 只要短回复里出现不在此集合中的字，就会被视为有潜在叙事信号。
+    "嗯唔呃哦噢喔啊呀呢嘛吧哈"
+    "对是的了好行可允许以"
+    "没错确实不否无有还太很挺就"
+    "什么这样那样当当然"
+)
+_ACK_STRIP_CHARS = " \t\r\n，。！？!?、,.；;：:~～…"
+
 
 def _is_emotion_heavy(text: str) -> bool:
     t = (text or "").strip()
@@ -351,6 +363,32 @@ def _is_ambiguous_or_needs_slow_pace(text: str) -> bool:
     return False
 
 
+def _normalized_short_reply(text: str) -> str:
+    return "".join(ch for ch in (text or "").strip() if ch not in _ACK_STRIP_CHARS)
+
+
+def _has_substantive_short_reply_signal(compact: str) -> bool:
+    """短回复里的叙事实质信号：地点/人事词、阶段关键词、年份数字等。"""
+    if any(ch.isdigit() for ch in compact):
+        return True
+    if any(("a" <= ch.lower() <= "z") for ch in compact):
+        return True
+    for weighted_keywords in STAGE_KEYWORD_WEIGHTS.values():
+        if any(keyword and keyword in compact for keyword, _ in weighted_keywords):
+            return True
+    return any(ch not in _LOW_INFORMATION_REPLY_CHARS for ch in compact)
+
+
+def _is_low_information_reply(text: str) -> bool:
+    """识别短、低信息、没有新增叙事素材的确认/应声回复。"""
+    compact = _normalized_short_reply(text)
+    if not compact:
+        return False
+    if len(compact) > _LOW_INFORMATION_REPLY_MAX_CHARS:
+        return False
+    return not _has_substantive_short_reply_signal(compact)
+
+
 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()
@@ -396,6 +434,7 @@ def plan_interview_turn(
     )
     um = (normalized_user_message or "").strip()
     asks_assistant_identity = _is_asking_assistant_identity_or_life(um)
+    low_information_reply = _is_low_information_reply(um)
     reply_shape: ReplyShape = "flexible"
     if any(
         k in um
@@ -403,6 +442,45 @@ def plan_interview_turn(
     ):
         reply_shape = "ack_then_question"
 
+    if low_information_reply:
+        slot = primary_empty_slot(current_stage, empty_slots)
+        if slot:
+            readable = SLOT_NAME_MAP.get(slot, slot)
+            return InterviewTurnPlan(
+                mode="memoir_push",
+                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="ack_then_question",
+                primary_focus=_focus_primary_for_mode("memoir_push"),
+                focus_summary=(
+                    f"用户只做了简短确认；短接一句后，不澄清“{um}”，"
+                    f"主动从「{readable}」打开一个具体、好回答的新回忆话题"
+                ),
+                focus_source="rule",
+                low_information_reply=True,
+            )
+        return InterviewTurnPlan(
+            mode="follow_user_only",
+            anchor_slot_key=None,
+            anchor_slot_readable="（本阶段主要叙述槽已有素材）请回到上文最近的人/事/地方或情绪线，主动打开一个纵深问题",
+            anchor_snippet=snippet,
+            anchor_source_kind=anchor_source_kind,
+            assistant_identity_question=asks_assistant_identity,
+            memory_usage=mem_use,
+            reply_shape="ack_then_question",
+            primary_focus=_focus_primary_for_mode("follow_user_only"),
+            focus_summary=(
+                f"用户只做了简短确认；短接一句后，不澄清“{um}”，"
+                "回到上文最近的具体线索，主动递一个新的回忆追问"
+            ),
+            focus_source="rule",
+            low_information_reply=True,
+        )
+
     if _is_emotion_heavy(normalized_user_message):
         slot = primary_empty_slot(current_stage, empty_slots)
         readable = (
@@ -534,6 +612,17 @@ def format_interview_turn_directive_block(plan: InterviewTurnPlan) -> str:
     else:
         shape_block = ""
 
+    ack_block = ""
+    if plan.low_information_reply:
+        ack_block = (
+            "- **低信息短回复处理**：用户本轮只是简短确认、应声或泛泛回应，没有新增叙事素材。"
+            "不要把这个短答本身当成需要澄清的内容，不要反复追问「你是说……吗」，"
+            "也不要停在原地等用户继续补充。\n"
+            "- 先用半句自然接住，再主动从「主追问方向」、上文最近的具体人/事/地方，"
+            "或过往记忆线索里挑一个**具体、好回答**的新回忆话题；若挂钩线索为空，"
+            "允许直接借当前阶段未聊方向起问，但禁止编造用户没说过的细节。\n"
+        )
+
     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"
@@ -557,13 +646,29 @@ def format_interview_turn_directive_block(plan: InterviewTurnPlan) -> str:
             "- **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"
             "- 若带问句，最多**一个**，且必须**从用户原词或下面摘录**长出来，禁止空泛「还有吗」。"
         )
+        if plan.low_information_reply:
+            mode_rules = (
+                "- **跟话头 + 主动续话**：用户本轮只是简短确认，没有新素材；"
+                "不要围着短答本身澄清，也不要重复上一问等对方补充。\n"
+                "- 若带问句，最多**一个**，优先从上文最近的具体人/事/地方或下面摘录长出来；"
+                "若无可用摘录，就从当前阶段已聊内容的纵深处打开一个新回忆话题。"
+            )
     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**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」。"
-        )
+        if plan.low_information_reply:
+            mode_rules = (
+                "- **回忆推进（memoir_push）**：用户本轮只是简短确认，"
+                "对用户可见回复中须有**恰好一个**开放式回忆追问，主动把话头往下递。\n"
+                "  追问可挂住**上文最近具体细节**、**主追问方向**或**挂钩摘录**；"
+                "不要要求从低信息短答里抽词。\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」。"
+            )
+        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**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)
 
@@ -576,7 +681,7 @@ def format_interview_turn_directive_block(plan: InterviewTurnPlan) -> str:
 
     return f"""## 本轮编排指令（硬规则，优先于后文一般性建议）
 {mode_rules}
-{focus_block}{mem_block}{perspective_block}{shape_block}- **主追问方向（叙述槽）**：{plan.anchor_slot_readable}
+{focus_block}{ack_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}
 """
 

api/app/agents/chat/orchestrator.py

@@ -508,3 +508,28 @@ class ChatOrchestrator:
             profile_era_place=profile_era_place,
             language=language,
         )
+
+    async def generate_re_greeting_message(
+        self,
+        conversation_id: str,
+        memoir_state: MemoirStateSchema,
+        idle_hours: float,
+        user_profile_context: str = "",
+        background_voice: str = "default",
+        occupation: str = "",
+        profile_birth_year: Optional[int] = None,
+        profile_era_place: str = "",
+        language: str = "zh",
+    ) -> List[str]:
+        """委托 InterviewAgent 生成老对话回访问候（持久化由调用方负责）。"""
+        return await self.interview_agent.generate_re_greeting_message(
+            conversation_id=conversation_id,
+            memoir_state=memoir_state,
+            idle_hours=idle_hours,
+            user_profile_context=user_profile_context,
+            background_voice=background_voice,
+            occupation=occupation,
+            profile_birth_year=profile_birth_year,
+            profile_era_place=profile_era_place,
+            language=language,
+        )

api/app/agents/chat/prompts_conversation.py

@@ -505,10 +505,259 @@ def get_guided_conversation_prompt(
 # 旧的超大 system prompt 已拆入 BehaviorPolicy / Context / StyleProfile 三层，此处不再保留快照。
 
 
+def _get_re_greeting_prompt_en(
+    current_stage: str,
+    empty_slots_readable: List[str],
+    user_profile_context: str = "",
+    background_voice: str = "default",
+    occupation: str = "",
+    idle_hours: float = 6.0,
+) -> str:
+    """English re-greeting; mirrors Chinese structure with lighter persona nuance."""
+    stage_name = stage_display_name(current_stage, language="en")
+    bv = normalize_background_voice(background_voice)
+
+    if idle_hours >= 168:
+        idle_phrase = "it's been quite a while"
+    elif idle_hours >= 48:
+        idle_phrase = "it's been several days"
+    elif idle_hours >= 20:
+        idle_phrase = "about a day has passed"
+    else:
+        idle_phrase = "only a little time has passed"
+
+    if empty_slots_readable:
+        topics_str = ", ".join(empty_slots_readable[:4])
+        topic_hint = (
+            f"## You can still explore ({stage_name})\n"
+            f"If picking up last time feels hard, gently land on one of these: {topics_str}."
+        )
+    else:
+        topic_hint = (
+            f"## Current focus ({stage_name})\n"
+            "Most beats here are covered; prefer returning to a concrete person, place, or moment from before."
+        )
+
+    if bv == "cadre":
+        style_note = "## Tone\nSteady and respectful; no slick slogans or stacked parallelisms."
+    elif bv == "military":
+        style_note = "## Tone\nCrisp and appropriate; no dramatic military flourishes."
+    else:
+        style_note = "## Tone\nLike an old friend you have not seen in a bit: warm, restrained, no preachy lists."
+
+    profile_lines: List[str] = []
+    if user_profile_context.strip():
+        profile_lines.append(user_profile_context.strip())
+    occ = get_occupation_chat_hint(occupation, background_voice)
+    if occ:
+        profile_lines.append(occ)
+    profile_section = ""
+    if profile_lines:
+        profile_section = "## About the user\n" + "\n".join(profile_lines) + "\n"
+
+    head = (
+        f'You are "{AGENT_NAME_EN}" — a host-style confidant. The user returns with **existing chat history** and has **not spoken yet** — you speak first. '
+        f"Context: {idle_phrase} since their last message.\n\n"
+        "**Job**: offer a warm reopening that shows you remember something specific they shared, then a light, memory-oriented hook you hand back to them.\n\n"
+        "## Requirements\n"
+        "1. **Must** reference one or two concrete details from the history (a person, place, object, or beat) — do not genericize with \"we had a great chat last time.\"\n"
+        "2. **Do not** reuse a brand-new-chat hello; something like \"Last time you mentioned X — want to continue?\" fits better.\n"
+        "3. The hook should be **specific, easy to answer, and visual**, tied to life memory — not \"how have you been\" small talk.\n"
+        "4. If history offers no usable threads, pick a small landing from the stage hints; still avoid vague interrogation.\n"
+        "5. Keep it short: two or three sentences, no long paragraphs.\n"
+    )
+
+    return f"""{head}{profile_section}{topic_hint}
+{style_note}
+## Format
+- Use `[SPLIT]` for at most two short bubbles, or one bubble with reopening + hook.
+- {chat_output_rules_en()} Do not write the user's answer for them.
+
+Output (spoken English only, no Markdown):"""
+
+
+def get_re_greeting_prompt(
+    current_stage: str,
+    empty_slots_readable: List[str],
+    user_profile_context: str = "",
+    persona: str = "default",
+    background_voice: str = "default",
+    occupation: str = "",
+    profile_birth_year: Optional[int] = None,
+    profile_era_place: str = "",
+    idle_hours: float = 6.0,
+    language: str = "zh",
+) -> str:
+    """老对话回访问候提示词：用户带着已有历史回到对话，AI 先开口做承接式问候。"""
+    if language == "en":
+        return _get_re_greeting_prompt_en(
+            current_stage=current_stage,
+            empty_slots_readable=empty_slots_readable,
+            user_profile_context=user_profile_context,
+            background_voice=background_voice,
+            occupation=occupation,
+            idle_hours=idle_hours,
+        )
+    stage_name = stage_display_name(current_stage, language="zh")
+    bv = normalize_background_voice(background_voice)
+
+    if idle_hours >= 168:
+        idle_phrase = "好一阵子没聊了"
+    elif idle_hours >= 48:
+        idle_phrase = "好几天没聊了"
+    elif idle_hours >= 20:
+        idle_phrase = "隔了一天"
+    else:
+        idle_phrase = "今天又见面"
+
+    if empty_slots_readable:
+        topics_str = "、".join(empty_slots_readable[:4])
+        topic_hint = (
+            f"## 当前阶段（{stage_name}）还可以聊\n"
+            f"如果上次聊过的事不便直接接续，可从这些方向里挑一个落点：{topics_str}。"
+        )
+    else:
+        topic_hint = (
+            f"## 当前阶段（{stage_name}）\n"
+            "这一阶段主要话题已有覆盖；优先回到上次聊过的人/事/地方，做温和的承接。"
+        )
+
+    if bv == "cadre":
+        style_note = "## 语气\n稳重、敬语适度；问候不油滑、不堆排比。"
+    elif bv == "military":
+        style_note = "## 语气\n简洁、得体；不过度起势、不堆军事辞藻。"
+    else:
+        style_note = "## 语气\n像许久未见的老朋友，温暖而克制；不要排比、不要长段文学描写。"
+
+    profile_lines: List[str] = []
+    if user_profile_context.strip():
+        profile_lines.append(user_profile_context.strip())
+    occ = get_occupation_chat_hint(occupation, background_voice)
+    if occ:
+        profile_lines.append(occ)
+    profile_section = ""
+    if profile_lines:
+        profile_section = "## 用户信息\n" + "\n".join(profile_lines) + "\n"
+
+    persona_key = normalize_interview_persona(persona)
+    persona_tone = get_interview_persona_tone_hint(persona_key)
+    voice_tone = get_background_voice_tone_hint(background_voice)
+    tone_bits = [t for t in (persona_tone, voice_tone) if t]
+    tone_paragraph = ""
+    if tone_bits:
+        tone_paragraph = " " + " ".join(tone_bits) + "\n\n"
+
+    head = (
+        "你是「岁月知己」——主持式知己。用户带着**已有的对话历史**回到这里，**还没说话**，请你先开口。"
+        f"语境：距上次消息已经{idle_phrase}。"
+        "**职责**：用一句温暖的承接打招呼，让对方感到「我记得你上次说过的事」，再轻轻递上一个**回忆向**的钩子，把话头交还给他。\n\n"
+        "## 要求\n"
+        "1. **必须**轻轻引用历史里的具体人/事/地方/物件做承接（一两个细节即可，不要罗列），不要空喊「上次聊得很好」。\n"
+        "2. **不要**用与刚开新对话相同的「您好/你好呀」式硬开场；像「上次你说到 X，今天想接着讲讲吗？」更合适。\n"
+        "3. 钩子要**具体、好回答、有画面感**，落在人生回忆里；不要问「最近怎么样」「今天忙吗」这种纯社交寒暄。\n"
+        "4. 若历史里没有可用细节，可从「当前阶段还可以聊」里挑一个轻巧落点；仍要避免泛泛盘问。\n"
+        "5. 简短：两三句内，不要排比、不要长段。\n"
+    )
+
+    return f"""{head}{tone_paragraph}{profile_section}{topic_hint}
+{style_note}
+## 格式
+- 可用 [SPLIT] 分成最多 2 条；或一条里「承接 + 钩子」。
+- {chat_output_rules()} 不要替用户编回答。
+
+直接输出（仅自然口语，无 Markdown）："""
+
+
+_STAGE_TOPIC_CHIP_BANK: Dict[str, List[tuple[str, str]]] = {
+    "childhood": [
+        ("place", "童年长大的地方"),
+        ("people", "童年里重要的人"),
+        ("daily_life", "童年的一天"),
+        ("turning_event", "童年最难忘的一件事"),
+        ("emotion", "童年最深的感受"),
+    ],
+    "education": [
+        ("school", "学生时代的学校"),
+        ("city", "求学的城市"),
+        ("motivation", "读书时的动力"),
+        ("challenge", "求学路上的难关"),
+        ("change", "求学带来的变化"),
+    ],
+    "career": [
+        ("job", "做过的工作"),
+        ("environment", "工作的环境"),
+        ("decision", "职业里的关键决定"),
+        ("pressure", "工作中的压力"),
+        ("growth", "职业上的成长"),
+    ],
+    "family": [
+        ("relationship", "家人之间的关系"),
+        ("conflict", "家里的矛盾与化解"),
+        ("support", "家人之间的相互支持"),
+        ("responsibility", "肩上的家庭责任"),
+    ],
+    "later_life": [
+        ("value", "现在最看重的事"),
+        ("regret", "心里的遗憾"),
+        ("pride", "最骄傲的事"),
+        ("lesson", "想留下的人生经验"),
+    ],
+}
+
+
+def build_topic_chips(
+    current_stage: str,
+    empty_slots: List[str],
+    *,
+    max_chips: int = 4,
+) -> List[Dict[str, str]]:
+    """根据当前阶段与空 slot 列表生成 quick-start 话题 chips。
+
+    返回结构：[{"id": slot_key, "label": 短标签, "text": 用户点击后发出的句子}]
+    """
+    stage_bank = _STAGE_TOPIC_CHIP_BANK.get(current_stage) or []
+    seen: set[str] = set()
+    chips: List[Dict[str, str]] = []
+
+    # 优先从「当前阶段空 slot」挑选（与开场提问方向一致）
+    empty_set = {s for s in empty_slots if s}
+    for slot_key, label in stage_bank:
+        if slot_key in empty_set and slot_key not in seen:
+            chips.append(
+                {
+                    "id": slot_key,
+                    "label": label,
+                    "text": f"我想聊聊{label}",
+                }
+            )
+            seen.add(slot_key)
+            if len(chips) >= max_chips:
+                return chips
+
+    # 不足则用阶段默认话题补齐
+    for slot_key, label in stage_bank:
+        if slot_key in seen:
+            continue
+        chips.append(
+            {
+                "id": slot_key,
+                "label": label,
+                "text": f"我想聊聊{label}",
+            }
+        )
+        seen.add(slot_key)
+        if len(chips) >= max_chips:
+            return chips
+
+    return chips
+
+
 __all__ = [
     "SLOT_NAME_MAP",
     "SLOT_NAME_MAP_EN",
     "slot_name_map_for",
+    "build_topic_chips",
     "get_guided_conversation_prompt",
     "get_opening_prompt",
+    "get_re_greeting_prompt",
 ]