Files
life-echo/api/app/agents/memoir/narrative_agent.py
Kevin ccdc4e4277 feat(i18n): persist language preference and thread through chat, memoir, TTS
- 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>
2026-05-11 16:16:49 +08:00

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"""
NarrativeAgent生成创意标题和叙事改写。
叙事正文走 `get_narrative_json_prompt` / `get_narrative_merge_json_prompt`(传记作家式书面语 + 事实边界)。
"""
from __future__ import annotations
from typing import Any, Dict, Optional
from app.agents.memoir.prompts import (
get_creative_title_json_prompt,
get_narrative_json_prompt,
get_narrative_merge_json_prompt,
)
from app.agents.memoir.schemas import MemoirTitleOutput
from app.agents.stage_constants import CHAPTER_CATEGORIES, chapter_category_display
from app.core.config import settings
from app.core.langchain_llm import invoke_json_object
from app.core.llm_call import llm_json_call
from app.core.logging import get_logger
logger = get_logger(__name__)
def _default_title_for(stage: str, language: str) -> str:
if language == "en":
cat = chapter_category_display(stage, language="en") or stage
return f"{cat} Memory"
return f"{CHAPTER_CATEGORIES.get(stage, stage)} 回忆"
class NarrativeAgent:
"""生成章节标题和叙事正文"""
def generate_title(
self,
stage: str,
emotion: str,
slots: Dict[str, str],
user_profile: str = "",
birth_year: Optional[int] = None,
llm: Any = None,
language: str = "zh",
) -> str:
"""生成创意标题。若无 LLM 则返回默认标题"""
if not llm:
return _default_title_for(stage, language)
try:
prompt = get_creative_title_json_prompt(
stage=stage,
emotion=emotion,
slots=slots,
user_profile=user_profile,
birth_year=birth_year,
language=language,
)
default_title = _default_title_for(stage, language)
def _title_fallback() -> MemoirTitleOutput:
return MemoirTitleOutput(title=default_title)
out = llm_json_call(
llm,
prompt,
MemoirTitleOutput,
max_tokens=settings.memoir_title_max_tokens,
agent="NarrativeAgent.generate_title",
fallback_factory=_title_fallback,
)
title = (out.title or "").strip()
if title:
return title.strip('"')
return default_title
except Exception as e:
logger.warning("NarrativeAgent 生成标题失败: {}", e)
return _default_title_for(stage, language)
def generate_narrative(
self,
stage: str,
slots: Dict[str, str],
new_content: str,
existing_content: str = "",
user_profile: str = "",
birth_year: Optional[int] = None,
llm: Any = None,
background_voice: str = "default",
occupation: str = "",
*,
fallback_plain_oral: str = "",
language: str = "zh",
) -> str:
"""将新对话改写为叙述。若无 LLM 则直接拼接。
若 `existing_content` 非空append 路径),使用整篇合并提示,输出覆盖全篇的有序段落。
`fallback_plain_oral`:仅含本段口述(勿传含 evidence 的组装串。LLM 异常时只回退到
口述/旧正文拼接,避免把「本段用户口述+摘录」整包写入 story。
"""
oral_fb = (fallback_plain_oral or "").strip()
if not llm:
if existing_content:
if oral_fb:
return f"{existing_content}\n\n{oral_fb}"
return f"{existing_content}\n\n{new_content}"
return oral_fb or new_content
try:
merge_mode = bool((existing_content or "").strip())
if merge_mode:
prompt = get_narrative_merge_json_prompt(
stage=stage,
slots=slots,
new_content=new_content,
existing_content=existing_content,
user_profile=user_profile,
birth_year=birth_year,
background_voice=background_voice,
occupation=occupation,
language=language,
)
max_tokens = int(settings.memoir_narrative_merge_max_tokens)
agent_name = "NarrativeAgent.generate_narrative_merge"
else:
prompt = get_narrative_json_prompt(
stage=stage,
slots=slots,
new_content=new_content,
existing_content=existing_content,
user_profile=user_profile,
birth_year=birth_year,
background_voice=background_voice,
occupation=occupation,
language=language,
)
max_tokens = int(settings.memoir_narrative_max_tokens)
agent_name = "NarrativeAgent.generate_narrative"
return invoke_json_object(
llm,
prompt,
max_tokens=max_tokens,
agent=agent_name,
).strip()
except Exception as e:
logger.warning("NarrativeAgent 生成叙事失败: {}", e)
ex = (existing_content or "").strip()
if ex and oral_fb:
return f"{existing_content}\n\n{oral_fb}"
if oral_fb:
return oral_fb
if ex:
return str(existing_content)
return ""