feat: agent proactively re-engages users on returning sessions
Two complementary changes to reduce conversation cold-start friction: A. Returning-user re-greeting (backend) - When WS reconnects to a non-empty conversation and last_message_at is older than chat_re_greeting_idle_hours (default 6h), the agent emits a warm continuation message that references prior history instead of staying silent. - Self-debouncing: the AI message updates last_message_at, so reconnects within the window will not re-trigger. - Skipped while profile collection is still pending. D. Topic suggestion chips (backend + Expo) - New WS message type topic_suggestions carries 3-4 quick-start chips derived from the current memoir stage's empty slots (deterministic, no extra LLM cost). Sent alongside opening / re-greeting / resume. - Expo chat screen renders a horizontally-scrollable chip row above the input bar; tapping a chip sends the chip's text as a user message and clears the row. Sending any text/voice also clears the chips.
This commit is contained in:
Claude
@@ -23,6 +23,7 @@ from app.agents.chat.prompt_context import ChatPromptContext
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from app.agents.chat.prompts_conversation import (
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SLOT_NAME_MAP,
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get_opening_prompt,
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get_re_greeting_prompt,
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)
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from app.agents.chat.reply_limits import (
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nonempty_segments_or_fallback,
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@@ -503,3 +504,118 @@ class InterviewAgent:
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except Exception as e:
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logger.error("生成开场白失败: {}", e, exc_info=True)
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return ["你好呀～ 又见面了。今天想从人生里哪一小段回忆开始聊聊？"]
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async def generate_re_greeting_message(
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self,
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conversation_id: str,
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memoir_state: MemoirStateSchema,
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idle_hours: float,
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user_profile_context: str = "",
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background_voice: str = "default",
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occupation: str = "",
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profile_birth_year: Optional[int] = None,
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profile_era_place: str = "",
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) -> List[str]:
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"""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。
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与 generate_opening_message 的差异：prompt 明确告知有历史 + 距上次的时间感受，
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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。
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"""
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if not self.llm:
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return ["上次聊到的事我还记着，今天想继续往下讲讲吗？"]
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try:
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narrative_state = narrative_coverage_state(memoir_state)
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control_state = interview_control_state(memoir_state)
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empty_slots = control_state.prompt_empty_slots_for_stage(
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narrative_state, memoir_state.current_stage
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)
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empty_slots_readable = [SLOT_NAME_MAP.get(s, s) for s in empty_slots]
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persona = normalize_interview_persona(settings.chat_interview_persona)
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prompt = get_re_greeting_prompt(
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current_stage=memoir_state.current_stage,
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empty_slots_readable=empty_slots_readable,
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user_profile_context=user_profile_context,
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persona=persona,
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background_voice=background_voice,
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occupation=occupation,
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profile_birth_year=profile_birth_year,
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profile_era_place=profile_era_place,
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idle_hours=idle_hours,
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)
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hw = await get_history_with_window(
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conversation_id,
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max_pairs=settings.chat_history_max_pairs,
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max_chars=settings.chat_history_max_chars,
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)
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messages: List[Any] = [SystemMessage(content=prompt)]
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messages.extend(hw.window)
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messages.append(
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HumanMessage(
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content=(
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"（用户回到这个已有历史的对话，还没说话。"
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"请基于上文做温和的承接式回访问候。）"
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)
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)
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)
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log_agent_payload(
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logger,
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"InterviewAgent.re_greeting.prompt",
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format_history_string(
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messages,
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omit_system_body=settings.agent_log_omit_system_message_body,
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),
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)
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re_greet_llm = self.llm.bind(
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max_tokens=settings.chat_opening_max_tokens,
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temperature=float(settings.chat_interview_temperature),
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)
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llm_t0 = time.perf_counter()
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with agent_span(
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logger,
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"InterviewAgent.re_greeting.llm",
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conversation_id=conversation_id,
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):
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logger.info(
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"event=chat_prompt_built agent=InterviewAgent.generate_re_greeting_message "
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"prompt_chars={} history_pairs_total={} history_pairs_windowed={} idle_hours={:.2f}",
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_message_contents_char_count(messages),
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hw.turn_total,
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len(hw.window) // 2,
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idle_hours,
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)
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response = await re_greet_llm.ainvoke(messages)
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logger.info(
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"event=chat_llm_done agent=InterviewAgent.generate_re_greeting_message "
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"response_latency_ms={:.2f}",
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(time.perf_counter() - llm_t0) * 1000,
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)
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response_text = (
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response.content if hasattr(response, "content") else str(response)
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)
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log_agent_payload(
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logger, "InterviewAgent.re_greeting.raw_response", response_text
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)
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raw_list = segments_from_llm_response(response_text, max_segments=2)
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if not raw_list:
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raw_list = [response_text.strip()]
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max_chars = int(settings.chat_interview_max_chars_per_segment)
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out = truncate_chat_segments(
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raw_list,
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max_segments=2,
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max_chars_per_segment=max_chars,
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)
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log_agent_summary(
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logger,
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"InterviewAgent.re_greeting segments={} conversation_id={} idle_hours={:.2f}",
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len(out),
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conversation_id,
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idle_hours,
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)
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segments = out if out else [response_text.strip()[:max_chars]]
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return nonempty_segments_or_fallback(
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segments,
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fallback="上次聊到的事我还记着，今天想继续往下讲讲吗？",
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)
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except Exception as e:
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logger.error("生成回访问候失败: {}", e, exc_info=True)
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return ["上次聊到的事我还记着，今天想继续往下讲讲吗？"]
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@@ -477,3 +477,26 @@ class ChatOrchestrator:
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profile_birth_year=profile_birth_year,
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profile_era_place=profile_era_place,
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)
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async def generate_re_greeting_message(
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self,
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conversation_id: str,
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memoir_state: MemoirStateSchema,
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idle_hours: float,
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user_profile_context: str = "",
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background_voice: str = "default",
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occupation: str = "",
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profile_birth_year: Optional[int] = None,
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profile_era_place: str = "",
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) -> List[str]:
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"""委托 InterviewAgent 生成老对话回访问候（持久化由调用方负责）。"""
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return await self.interview_agent.generate_re_greeting_message(
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conversation_id=conversation_id,
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memoir_state=memoir_state,
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idle_hours=idle_hours,
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user_profile_context=user_profile_context,
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background_voice=background_voice,
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occupation=occupation,
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profile_birth_year=profile_birth_year,
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profile_era_place=profile_era_place,
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)
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@@ -305,8 +305,176 @@ def get_guided_conversation_prompt(
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# 旧的超大 system prompt 已拆入 BehaviorPolicy / Context / StyleProfile 三层，此处不再保留快照。
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def get_re_greeting_prompt(
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current_stage: str,
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empty_slots_readable: List[str],
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user_profile_context: str = "",
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persona: str = "default",
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background_voice: str = "default",
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occupation: str = "",
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profile_birth_year: Optional[int] = None,
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profile_era_place: str = "",
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idle_hours: float = 6.0,
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) -> str:
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"""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。"""
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stage_name = STAGE_DISPLAY_ZH.get(current_stage, current_stage)
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bv = normalize_background_voice(background_voice)
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if idle_hours >= 168:
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idle_phrase = "好一阵子没聊了"
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elif idle_hours >= 48:
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idle_phrase = "好几天没聊了"
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elif idle_hours >= 20:
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idle_phrase = "隔了一天"
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else:
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idle_phrase = "今天又见面"
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if empty_slots_readable:
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topics_str = "、".join(empty_slots_readable[:4])
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topic_hint = (
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f"## 当前阶段（{stage_name}）还可以聊\n"
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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}。"
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)
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else:
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topic_hint = (
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f"## 当前阶段（{stage_name}）\n"
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"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。"
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)
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if bv == "cadre":
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style_note = "## 语气\n稳重、敬语适度；问候不油滑、不堆排比。"
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elif bv == "military":
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style_note = "## 语气\n简洁、得体；不过度起势、不堆军事辞藻。"
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else:
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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。"
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profile_lines: List[str] = []
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if user_profile_context.strip():
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profile_lines.append(user_profile_context.strip())
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occ = get_occupation_chat_hint(occupation, background_voice)
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if occ:
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profile_lines.append(occ)
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profile_section = ""
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if profile_lines:
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profile_section = "## 用户信息\n" + "\n".join(profile_lines) + "\n"
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persona_key = normalize_interview_persona(persona)
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persona_tone = get_interview_persona_tone_hint(persona_key)
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voice_tone = get_background_voice_tone_hint(background_voice)
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tone_bits = [t for t in (persona_tone, voice_tone) if t]
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tone_paragraph = ""
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if tone_bits:
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tone_paragraph = " " + " ".join(tone_bits) + "\n\n"
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head = (
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"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。"
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f"语境：距上次消息已经{idle_phrase}。"
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"**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"
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"## 要求\n"
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"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"
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"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"
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"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"
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"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"
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"5. 简短：两三句内，不要排比、不要长段。\n"
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)
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return f"""{head}{tone_paragraph}{profile_section}{topic_hint}
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{style_note}
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## 格式
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- 可用 [SPLIT] 分成最多 2 条；或一条里「承接 + 钩子」。
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- {chat_output_rules()} 不要替用户编回答。
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直接输出（仅自然口语，无 Markdown）："""
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_STAGE_TOPIC_CHIP_BANK: Dict[str, List[tuple[str, str]]] = {
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"childhood": [
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("place", "童年长大的地方"),
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("people", "童年里重要的人"),
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("daily_life", "童年的一天"),
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("turning_event", "童年最难忘的一件事"),
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("emotion", "童年最深的感受"),
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],
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"education": [
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("school", "学生时代的学校"),
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("city", "求学的城市"),
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("motivation", "读书时的动力"),
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("challenge", "求学路上的难关"),
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("change", "求学带来的变化"),
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],
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"career": [
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("job", "做过的工作"),
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("environment", "工作的环境"),
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("decision", "职业里的关键决定"),
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("pressure", "工作中的压力"),
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("growth", "职业上的成长"),
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],
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"family": [
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("relationship", "家人之间的关系"),
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("conflict", "家里的矛盾与化解"),
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("support", "家人之间的相互支持"),
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("responsibility", "肩上的家庭责任"),
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],
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"later_life": [
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("value", "现在最看重的事"),
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("regret", "心里的遗憾"),
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("pride", "最骄傲的事"),
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("lesson", "想留下的人生经验"),
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],
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}
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def build_topic_chips(
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current_stage: str,
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empty_slots: List[str],
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*,
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max_chips: int = 4,
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) -> List[Dict[str, str]]:
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"""根据当前阶段与空 slot 列表生成 quick-start 话题 chips。
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返回结构：[{"id": slot_key, "label": 短标签, "text": 用户点击后发出的句子}]
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"""
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stage_bank = _STAGE_TOPIC_CHIP_BANK.get(current_stage) or []
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seen: set[str] = set()
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chips: List[Dict[str, str]] = []
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# 优先从「当前阶段空 slot」挑选（与开场提问方向一致）
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empty_set = {s for s in empty_slots if s}
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for slot_key, label in stage_bank:
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if slot_key in empty_set and slot_key not in seen:
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chips.append(
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{
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"id": slot_key,
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"label": label,
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"text": f"我想聊聊{label}",
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}
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)
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seen.add(slot_key)
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if len(chips) >= max_chips:
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return chips
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# 不足则用阶段默认话题补齐
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for slot_key, label in stage_bank:
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if slot_key in seen:
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continue
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chips.append(
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{
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"id": slot_key,
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"label": label,
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"text": f"我想聊聊{label}",
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}
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)
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seen.add(slot_key)
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if len(chips) >= max_chips:
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return chips
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return chips
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__all__ = [
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"SLOT_NAME_MAP",
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"build_topic_chips",
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"get_guided_conversation_prompt",
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"get_opening_prompt",
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"get_re_greeting_prompt",
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]
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Reference in New Issue
Block a user