ccdc4e4277
- Add users.language_preference (Alembic 0018, default zh); capture at signup/SMS only; expose on auth and profile APIs - Lite English prompts for chat and memoir; localized stage labels and agent names (Life Echo / 岁月知己) - Tencent TTS: language-aware synthesis, ModelType=1 for 501004, English chunking - WebSocket pipeline: emit all AGENT_RESPONSE segments when TTS cancels; INFO logs for tts_this_turn and TTS decisions; on-demand TTS logging - Expo: device language on auth, i18n tiers/agent name, [SPLIT] streaming UX fixes - Tests for migration, prompts, pipeline, router tts_this_turn, reply segments Co-authored-by: Cursor <cursoragent@cursor.com>
515 lines
20 KiB
Python
515 lines
20 KiB
Python
"""
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对话 Agent 提示词模板（场景化承接 + 细节深挖 + 人物串联）。
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"""
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from typing import Dict, List, Optional
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from app.agents.chat.background_voice import (
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get_background_voice_tone_hint,
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normalize_background_voice,
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)
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from app.agents.chat.occupation_context import get_occupation_chat_hint
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from app.agents.chat.output_rules import (
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chat_output_rules,
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chat_output_rules_en,
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)
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from app.agents.chat.personas import (
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AGENT_NAME_EN,
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AGENT_NAME_ZH,
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get_interview_persona_tone_hint,
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normalize_interview_persona,
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)
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from app.agents.chat.prompt_layers import (
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assemble_guided_prompt,
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build_absolute_donts_block,
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build_behavior_policy_block,
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build_context_block,
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build_question_outline_block,
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build_reply_strategy_block,
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build_style_profile_block,
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)
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from app.agents.stage_constants import (
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STAGE_ERA_HINTS,
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stage_display_name,
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)
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from app.agents.state_schema import KnownFact, PersonaThread
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from app.core.config import settings
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# 风格示例的单一事实源已迁至 `app.agents.style_profiles.ChatStyleProfile.reply_style_examples`；
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# 这里**不再**维护具体字面示例，避免同一模块被当作 few-shot 锚点反复注入，导致风格过拟合。
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SLOT_NAME_MAP = {
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"place": "成长的地方",
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"people": "重要的人",
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"daily_life": "日常生活",
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"emotion": "童年感受",
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"turning_event": "难忘的事",
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"school": "学校经历",
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"city": "求学的城市",
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"motivation": "学习动力",
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"challenge": "遇到的挑战",
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"change": "成长变化",
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"job": "工作内容",
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"environment": "工作环境",
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"decision": "重要决定",
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"pressure": "压力与困难",
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"growth": "职业成长",
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"relationship": "家人关系",
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"conflict": "矛盾与化解",
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"support": "相互支持",
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"responsibility": "家庭责任",
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"value": "核心价值观",
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"regret": "遗憾与释怀",
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"pride": "骄傲的事",
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"lesson": "人生经验",
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}
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SLOT_NAME_MAP_EN = {
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"place": "where you grew up",
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"people": "important people",
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"daily_life": "everyday life",
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"emotion": "childhood feelings",
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"turning_event": "memorable moments",
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"school": "school experiences",
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"city": "the city you studied in",
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"motivation": "what drove you",
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"challenge": "challenges you faced",
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"change": "how you changed",
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"job": "what you did at work",
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"environment": "your work environment",
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"decision": "important decisions",
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"pressure": "pressure and hardship",
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"growth": "career growth",
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"relationship": "family relationships",
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"conflict": "conflicts and resolutions",
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"support": "mutual support",
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"responsibility": "family responsibilities",
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"value": "core values",
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"regret": "regrets and acceptance",
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"pride": "moments you're proud of",
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"lesson": "life lessons",
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}
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def slot_name_map_for(language: str) -> Dict[str, str]:
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return SLOT_NAME_MAP_EN if language == "en" else SLOT_NAME_MAP
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def _compact_era_hint(
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current_stage: str,
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*,
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birth_year: int | None = None,
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era_place: str = "",
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) -> str:
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if not birth_year:
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return ""
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birth_place = (era_place or "").strip()
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age_range = STAGE_ERA_HINTS.get(current_stage, (0, 30))
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era_start = birth_year + age_range[0]
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era_end = birth_year + age_range[1]
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era_events = []
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decade_events = {
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1950: "新中国成立初期、土地改革、抗美援朝",
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1960: "大跃进、三年自然灾害、中苏关系变化",
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1970: "文化大革命、知青上山下乡、中美建交",
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1980: "改革开放、恢复高考、个体经济兴起、电视普及",
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1990: "社会主义市场经济、下海潮、香港回归、互联网初期",
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2000: "加入WTO、房地产兴起、手机普及、北京奥运",
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2010: "移动互联网爆发、微信时代、共享经济、双创浪潮",
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2020: "新冠疫情、直播经济、人工智能崛起",
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}
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for decade, events in decade_events.items():
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if era_start <= decade + 9 and era_end >= decade:
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era_events.append(f"{decade}年代：{events}")
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parts: List[str] = []
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if era_events:
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place_hint = f" {birth_place}" if birth_place else ""
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parts.append(
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f"时代联想（口述里一两句带过即可）：约 {era_start}-{era_end} 年{place_hint}；"
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f"可提及 {era_events[0]}"
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+ (f"；{era_events[1]}" if len(era_events) > 1 else "")
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+ "。"
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)
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parts.append(
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"时代与流行文化（开放式，自然带入）：\n"
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"- 可从当时的街景、媒介、校园与市井、年节习俗等**泛泛**起头，邀请用户讲自己的版本，勿替用户断言细节。\n"
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"- **优先开放式**问法；少用「你是不是也……」式半封闭逼认。\n"
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"- 与大事记呼应时点到为止，勿展开成长串史实。"
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)
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return "\n".join(parts) + "\n"
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def _get_opening_prompt_en(
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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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profile_birth_year: Optional[int] = None,
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profile_era_place: str = "",
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) -> str:
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"""English-lite opening prompt; ignores persona/background-voice nuances."""
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stage_name = stage_display_name(current_stage, language="en")
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if empty_slots_readable:
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topics_str = ", ".join(empty_slots_readable)
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topics_heading = (
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f"## Suggested topics for this stage ({stage_name})\n"
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f"Pick one of these to ask about: {topics_str}"
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)
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task_question = (
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"2. You are a **warm, host-style confidant**: ask one **specific, "
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"easy-to-answer, vivid** question that pulls the user into telling a "
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"life memory; ideally land on one of the topics above. Avoid vague "
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"openers like \"How have you been?\" Open the door with one small "
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"anchor (a place, a person, an object, or a tiny scene from a day)."
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)
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else:
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topics_heading = (
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f"## Current stage ({stage_name})\n"
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"The main topics for this stage are largely covered. Open with "
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"something tied to a previous memory or a fresh small angle of this "
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"stage; do not interrogate from the start."
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)
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task_question = (
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"2. **Greeting + a memory hook**: after a warm acknowledgement, "
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"drop a light, concrete question tied to recollection — never "
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"small-talk filler."
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)
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profile_section = ""
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if user_profile_context.strip():
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profile_section = "## About the user\n" + user_profile_context.strip() + "\n"
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return f"""You are "{AGENT_NAME_EN}" — a warm host-style friend. The user just opened the chat and **has not said anything yet**; you speak first. Tone like an old friend, but your job is to help the user start telling their life story; in two or three short sentences, give a greeting plus **one vivid, recollection-oriented question** tied to the current stage or suggested topics. No flowery prose, no long literary descriptions, no generic small-talk.
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{profile_section}{topics_heading}
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## Task
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1. Brief greeting.
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{task_question}
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3. Sound natural and warm.
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## Format
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- Use `[SPLIT]` to break into at most two short bubbles, or keep greeting + question in one short bubble.
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- {chat_output_rules_en()} Do not write the user's answer for them.
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Output (spoken-style English only, no Markdown):"""
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def get_opening_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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language: str = "zh",
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) -> str:
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"""空对话时 AI 先开口的提示词"""
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if language == "en":
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return _get_opening_prompt_en(
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current_stage,
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empty_slots_readable,
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user_profile_context=user_profile_context,
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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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stage_name = stage_display_name(current_stage, language="zh")
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bv_open = normalize_background_voice(background_voice)
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if empty_slots_readable:
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topics_str = "、".join(empty_slots_readable)
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topics_heading = (
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f"## 当前建议话题（{stage_name}）\n可以从中选一个来问：{topics_str}"
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)
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task_question = (
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"2. 你是**主持式知己**：接着问一个**具体、好回答、有画面感**的问题，帮用户进入**人生回忆**叙述；"
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"优先落在上述还未聊透的方向上。不要问太宽泛的「有什么想聊的」「最近怎么样」。"
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"像把门敞开请人讲自己的故事，不要像面试第一题；一句里带一个小锚（地方、人物、物件或一天里的片段即可）。"
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"不要用「下面我们聊聊…」类未承接的硬切。好问题举例：「说到童年，你脑海里最先蹦出来的是哪个画面？」"
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)
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else:
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topics_heading = (
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f"## 当前阶段（{stage_name}）\n"
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"这一阶段的主要话题在素材侧**已有覆盖**。"
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"开场仍要**回到人生故事线**：优先接续上次聊过的片段、（若有）记忆线索里出现过的事，或当前阶段里**新鲜的一小角**；"
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"**禁止**为了凑问题而从「童年在哪长大」等已覆盖模板重头盘问；**也不要**把泛泛近况（「今天忙吗」「最近好吗」）当成默认主线。"
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)
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task_question = (
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"2. **问候 + 回忆向勾子**：温暖接话后，带一个与**口述回忆**有关的轻巧引子或具体问题；"
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"若接不上具体事，就用当前阶段的一个**有画面的开放式起头**，仍落在人生经历上，而非纯社交寒暄。"
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)
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if bv_open == "cadre":
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opening_style_rules = (
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"## 语境与语气（干部/机关）\n"
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"- 问候稳重、敬语适度；避免官样排比与过轻佻的网络撒娇语气。\n"
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)
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elif bv_open == "military":
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opening_style_rules = (
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"## 语境与语气（军队相关口述常见交流方式）\n"
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"- 简洁、得体；不用「嗨～」类过轻佻起势；不堆军事辞藻、不编军旅细节。\n"
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)
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else:
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opening_style_rules = (
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"## 风格\n"
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"- 像**温暖的谈话场主持人**：口语、自然、能接住人，但默认把用户带进**人生回忆**叙述；"
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"可轻快，允许一点画面感，不要排比和长段文学描写。\n"
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)
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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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opening_head = (
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f"你是「{AGENT_NAME_ZH}」——主持式知己：用户刚进对话，**还没说话**，请你先开口。"
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"语气像老朋友，但**职责是帮对方开口讲人生故事**；两三句内问候 + **一个落在当前阶段或建议话题上的、有画面感的问题**；"
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"不要排比、不要长段文学描写，**不要**把泛泛问近况当主菜。\n\n"
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)
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if bv_open != "default":
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opening_head = (
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f"你是「{AGENT_NAME_ZH}」——主持式知己：用户刚进对话，**还没说话**，请你先开口。"
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"**短**；两三句内问候 + **一个回忆向的具体问题**；不要排比、不要文学描写。\n\n"
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)
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era_opening_line = ""
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if (
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settings.chat_era_context_enabled
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and profile_birth_year is not None
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and _compact_era_hint(
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current_stage,
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birth_year=profile_birth_year,
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era_place=profile_era_place,
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)
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):
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era_opening_line = (
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"4. 用户资料里已有出生年份与时代参考时，问候里的具体问题可**轻轻带一点年代氛围**（点到为止），"
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"勿写成长段描写或排比。\n"
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)
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return f"""{opening_head}{tone_paragraph}{profile_section}{topics_heading}
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## 任务
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1. 简短问候。
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{task_question}
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3. 自然、温暖。
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{era_opening_line}
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## 格式
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- 可用 [SPLIT] 分成最多 2 条；或一条里「问候 + 问题」。
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- {chat_output_rules()} 不要替用户编回答。
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{opening_style_rules}
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直接输出（仅自然口语，无 Markdown）："""
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def _get_guided_conversation_prompt_en(
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current_stage: str,
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empty_slots: List[str],
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filled_slots: Dict[str, str],
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detected_user_stage: str = "",
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user_profile_context: str = "",
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memory_evidence_text: str = "",
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recent_questions: list[str] | None = None,
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turn_directive_block: str = "",
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) -> str:
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"""English-lite guided interview prompt (no persona/voice nuances)."""
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stage_name = stage_display_name(current_stage, language="en")
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detected_name = (
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stage_display_name(detected_user_stage, language="en")
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if detected_user_stage and detected_user_stage != current_stage
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else ""
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)
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empty_readable = [SLOT_NAME_MAP_EN.get(s, s) for s in empty_slots]
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filled_lines = []
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for k, v in (filled_slots or {}).items():
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name = SLOT_NAME_MAP_EN.get(k, k)
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if v:
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filled_lines.append(f"- {name}: {v}")
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filled_block = "\n".join(filled_lines) if filled_lines else "(none yet)"
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suggested_block = (
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"Suggested still-open angles for this stage: " + ", ".join(empty_readable)
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if empty_readable
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else "Main angles for this stage are largely covered."
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)
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detected_line = (
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f"\nThe user is currently talking about: **{detected_name}** (system was tracking **{stage_name}**)."
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if detected_name
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else ""
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)
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profile_section = ""
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if user_profile_context.strip():
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profile_section = "\n## About the user\n" + user_profile_context.strip()
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memory_section = ""
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if (memory_evidence_text or "").strip():
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memory_section = (
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"\n## Reference memory snippets (for continuity only — do NOT write them as the user's first-person experience this turn)\n"
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+ memory_evidence_text.strip()
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)
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recent_q_section = ""
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if recent_questions:
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last = recent_questions[-4:]
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recent_q_section = (
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"\n## Recently asked questions (do NOT repeat these; offer a new angle)\n"
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+ "\n".join(f"- {q}" for q in last)
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)
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directive_block = (turn_directive_block or "").strip()
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directive_section = (
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f"\n## This turn's plan\n{directive_block}\n" if directive_block else ""
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)
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return f"""{directive_section}You are "{AGENT_NAME_EN}," a warm host-style friend helping the user record a memoir. Reply in conversational English.
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## Stage context
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Currently tracking life stage: **{stage_name}**.{detected_line}
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{suggested_block}
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## Already gathered for this stage
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{filled_block}{profile_section}{memory_section}{recent_q_section}
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## Behaviour
|
||
- Pick up the **specific** detail the user just said (one tangible noun or short phrase) and gently push one step deeper before asking your next question.
|
||
- Prefer ONE clear, specific question per reply. Open-ended over forced A/B options.
|
||
- If the user is in the middle of a story, follow that thread; do not switch topics for the sake of coverage.
|
||
- If you previously asked about something and the user already answered, do not re-ask.
|
||
- Stay short and precise. One acknowledgement sentence + one question is the default shape.
|
||
|
||
## Strict rules
|
||
- {chat_output_rules_en()}
|
||
|
||
## Format
|
||
- Use `[SPLIT]` to split into at most two short bubbles when natural.
|
||
|
||
Reply in English only. Do not output Markdown headings."""
|
||
|
||
|
||
def get_guided_conversation_prompt(
|
||
current_stage: str,
|
||
empty_slots: List[str],
|
||
filled_slots: Dict[str, str],
|
||
all_stages_coverage: Optional[Dict[str, Dict]] = None,
|
||
detected_user_stage: str = "",
|
||
user_profile_context: str = "",
|
||
persona: str = "default",
|
||
memory_evidence_text: str = "",
|
||
background_voice: str = "default",
|
||
occupation: str = "",
|
||
profile_birth_year: Optional[int] = None,
|
||
profile_era_place: str = "",
|
||
known_facts: list[KnownFact] | None = None,
|
||
persona_threads: list[PersonaThread] | None = None,
|
||
recent_questions: list[str] | None = None,
|
||
turn_directive_block: str = "",
|
||
language: str = "zh",
|
||
) -> str:
|
||
"""生成状态感知的对话提示词；用户原话仅以 HumanMessage 传入，不写入本 system 文本。"""
|
||
if language == "en":
|
||
return _get_guided_conversation_prompt_en(
|
||
current_stage=current_stage,
|
||
empty_slots=empty_slots,
|
||
filled_slots=filled_slots,
|
||
detected_user_stage=detected_user_stage,
|
||
user_profile_context=user_profile_context,
|
||
memory_evidence_text=memory_evidence_text,
|
||
recent_questions=recent_questions,
|
||
turn_directive_block=turn_directive_block,
|
||
)
|
||
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_line = ""
|
||
if tone_bits:
|
||
tone_line = " " + " ".join(tone_bits)
|
||
|
||
user_jumped = bool(detected_user_stage and detected_user_stage != current_stage)
|
||
active_stage = (
|
||
detected_user_stage if user_jumped and detected_user_stage else current_stage
|
||
)
|
||
|
||
era_line = ""
|
||
if settings.chat_era_context_enabled:
|
||
era_line = _compact_era_hint(
|
||
active_stage,
|
||
birth_year=profile_birth_year,
|
||
era_place=profile_era_place,
|
||
)
|
||
|
||
empty_slots_readable = [SLOT_NAME_MAP.get(s, s) for s in empty_slots]
|
||
|
||
# ---- Context 层：纯状态与素材 ----
|
||
topic_and_context_block = build_context_block(
|
||
current_stage=current_stage,
|
||
detected_user_stage=detected_user_stage,
|
||
empty_slots_readable=empty_slots_readable,
|
||
filled_slots=filled_slots,
|
||
slot_name_map=SLOT_NAME_MAP,
|
||
all_stages_coverage=all_stages_coverage,
|
||
user_profile_context=user_profile_context,
|
||
occupation=occupation,
|
||
background_voice=background_voice,
|
||
known_facts=known_facts,
|
||
persona_threads=persona_threads,
|
||
recent_questions=recent_questions,
|
||
memory_evidence_text=memory_evidence_text,
|
||
era_line=era_line,
|
||
)
|
||
|
||
question_outline_block = build_question_outline_block(current_stage, empty_slots)
|
||
|
||
# ---- BehaviorPolicy 层：通用行为规则（本轮模式由 TurnPlan 单独注入） ----
|
||
behavior_policy_block = build_behavior_policy_block()
|
||
reply_strategy_block = build_reply_strategy_block()
|
||
absolute_donts_block = build_absolute_donts_block(chat_output_rules())
|
||
|
||
# ---- StyleProfile 层：口吻 + 文采密度 + 成稿质量导向 ----
|
||
style_profile_block = build_style_profile_block(
|
||
persona=persona, background_voice=background_voice
|
||
)
|
||
|
||
return assemble_guided_prompt(
|
||
turn_directive_block=turn_directive_block,
|
||
topic_and_context_block=topic_and_context_block,
|
||
question_outline_block=question_outline_block,
|
||
behavior_policy_block=behavior_policy_block,
|
||
style_profile_block=style_profile_block,
|
||
reply_strategy_block=reply_strategy_block,
|
||
absolute_donts_block=absolute_donts_block,
|
||
intro_tone_line=tone_line,
|
||
)
|
||
|
||
|
||
# 运行时 prompt 生成走 `prompt_layers.assemble_guided_prompt`。
|
||
# 旧的超大 system prompt 已拆入 BehaviorPolicy / Context / StyleProfile 三层，此处不再保留快照。
|
||
|
||
|
||
__all__ = [
|
||
"SLOT_NAME_MAP",
|
||
"SLOT_NAME_MAP_EN",
|
||
"slot_name_map_for",
|
||
"get_guided_conversation_prompt",
|
||
"get_opening_prompt",
|
||
]
|