refactor: 进一步重构agents目录结构 符合多agent架构

api/app/agents/chat/conversation_agent.py

@@ -6,7 +6,7 @@ from datetime import datetime
 from typing import Any, Dict, List, Optional
 
 from app.agents.chat.orchestrator import ChatOrchestrator
-from app.agents.prompts import ConversationStage
+from app.agents.chat.prompts_conversation import ConversationStage
 from app.agents.state_schema import MemoirStateSchema
 from app.core.redis import redis_service
 

api/app/agents/chat/interview_agent.py

@@ -8,8 +8,11 @@ from app.core.dependencies import get_llm_provider
 from app.core.logging import get_logger
 
 from app.agents.chat.helpers import format_history_string, get_history_messages
-from app.agents.prompts import get_guided_conversation_prompt, get_opening_prompt
+from app.agents.chat.prompts_conversation import (
-from app.agents.prompts.conversation_prompts import SLOT_NAME_MAP
+    SLOT_NAME_MAP,
+    get_guided_conversation_prompt,
+    get_opening_prompt,
+)
 from app.agents.state_schema import MemoirStateSchema
 
 logger = get_logger(__name__)

api/app/agents/chat/orchestrator.py

@@ -92,7 +92,7 @@ class ChatOrchestrator:
             conversation.conversation_stage = state.current_stage
             await db.commit()
 
-        from app.agents.prompts.profile_prompts import format_user_profile_context
+        from app.agents.chat.prompts_profile import format_user_profile_context
 
         user_profile_context = ""
         if user:

api/app/agents/chat/profile_agent.py

@@ -11,7 +11,7 @@ from app.core.dependencies import get_llm_provider
 from app.core.logging import get_logger
 
 from app.agents.chat.helpers import format_history_string, get_history_messages
-from app.agents.prompts.profile_prompts import (
+from app.agents.chat.prompts_profile import (
     get_profile_extraction_prompt,
     get_profile_followup_prompt,
     get_profile_greeting_prompt,

api/app/agents/chat/prompts.py

@@ -0,0 +1,41 @@
+"""
+Chat 模块提示词：用户资料收集 + 对话访谈
+"""
+# Profile prompts（用户资料收集）
+from app.agents.chat.prompts_profile import (
+    PROFILE_FIELD_NAMES,
+    format_user_profile_context,
+    get_missing_profile_fields,
+    get_profile_extraction_prompt,
+    get_profile_followup_prompt,
+    get_profile_greeting_prompt,
+)
+
+# Conversation prompts（对话访谈）
+from app.agents.chat.prompts_conversation import (
+    ConversationStage,
+    INTERVIEW_QUESTIONS,
+    SLOT_NAME_MAP,
+    get_conversation_prompt,
+    get_guided_conversation_prompt,
+    get_opening_prompt,
+    get_questions_for_stage,
+    get_system_prompt,
+)
+
+__all__ = [
+    "PROFILE_FIELD_NAMES",
+    "format_user_profile_context",
+    "get_missing_profile_fields",
+    "get_profile_extraction_prompt",
+    "get_profile_followup_prompt",
+    "get_profile_greeting_prompt",
+    "ConversationStage",
+    "INTERVIEW_QUESTIONS",
+    "SLOT_NAME_MAP",
+    "get_conversation_prompt",
+    "get_guided_conversation_prompt",
+    "get_opening_prompt",
+    "get_questions_for_stage",
+    "get_system_prompt",
+]

api/app/agents/chat/prompts_conversation.py

@@ -65,12 +65,12 @@ INTERVIEW_QUESTIONS: Dict[ConversationStage, List[str]] = {
 def get_system_prompt(current_stage: ConversationStage, covered_topics: List[str], user_latest_response: str) -> str:
     """
     生成对话 Agent 的系统提示词
-    
+
     Args:
         current_stage: 当前对话阶段
         covered_topics: 已聊过的话题列表
         user_latest_response: 用户最新回答
-    
+
     Returns:
         系统提示词字符串
     """
@@ -82,9 +82,9 @@ def get_system_prompt(current_stage: ConversationStage, covered_topics: List[str
         ConversationStage.BELIEFS: "信念",
         ConversationStage.SUMMARY: "人生总结",
     }
-    
+
     covered_topics_str = "、".join(covered_topics) if covered_topics else "暂无"
-    
+
     prompt = f"""你是「岁月知己」，一位资深的人生故事访谈者，专注于帮助用户回忆和讲述人生经历。
 
 ## 角色定位
@@ -106,7 +106,7 @@ def get_system_prompt(current_stage: ConversationStage, covered_topics: List[str
 已聊话题：{covered_topics_str}
 
 请直接回应用户，不要有任何元描述。"""
-    
+
     return prompt
 
 
@@ -116,46 +116,39 @@ def get_questions_for_stage(stage: ConversationStage) -> List[str]:
 
 
 SLOT_NAME_MAP = {
-    # 童年
     "place": "成长的地方",
     "people": "重要的人",
     "daily_life": "日常生活",
     "emotion": "童年感受",
     "turning_event": "难忘的事",
-    # 教育
     "school": "学校经历",
     "city": "求学的城市",
     "motivation": "学习动力",
     "challenge": "遇到的挑战",
     "change": "成长变化",
-    # 事业
     "job": "工作内容",
     "environment": "工作环境",
     "decision": "重要决定",
     "pressure": "压力与困难",
     "growth": "职业成长",
-    # 家庭
     "relationship": "家人关系",
     "conflict": "矛盾与化解",
     "support": "相互支持",
     "responsibility": "家庭责任",
-    # 信念
     "value": "核心价值观",
     "regret": "遗憾与释怀",
     "pride": "骄傲的事",
     "lesson": "人生经验",
 }
 
-# 阶段关联话题（用于自然过渡）
 STAGE_RELATED_TOPICS = {
-    "childhood": ["family", "education"],  # 童年可以自然聊到家庭、教育
+    "childhood": ["family", "education"],
-    "education": ["childhood", "career"],   # 教育可以聊到童年、事业
+    "education": ["childhood", "career"],
-    "career": ["education", "family", "belief"],  # 事业可以聊到教育、家庭、信念
+    "career": ["education", "family", "belief"],
-    "family": ["childhood", "career", "belief"],  # 家庭可以聊到童年、事业、信念
+    "family": ["childhood", "career", "belief"],
-    "belief": ["career", "family"],  # 信念可以聊到事业、家庭
+    "belief": ["career", "family"],
 }
 
-# 轻松话题（用于调节气氛）
 LIGHT_TOPICS = [
     "有什么爱好或者特别喜欢的消遣方式吗？",
     "最近有什么让你开心的事吗？",
@@ -163,13 +156,12 @@ LIGHT_TOPICS = [
     "平时喜欢看什么书或者电影吗？",
 ]
 
-# 回应风格模板（增加多样性）
 RESPONSE_STYLES = [
-    "empathy",      # 共情式回应
+    "empathy",
-    "curious",      # 好奇追问
+    "curious",
-    "reflection",   # 感慨反思
+    "reflection",
-    "lighthearted", # 轻松调侃
+    "lighthearted",
-    "connection",   # 关联自身（虚构）
+    "connection",
 ]
 
 
@@ -178,10 +170,7 @@ def get_opening_prompt(
     empty_slots_readable: List[str],
     user_profile_context: str = "",
 ) -> str:
-    """
+    """空对话时 AI 先开口的提示词"""
-    空对话时 AI 先开口的提示词（用户通过「打个招呼」进入，尚未发送任何消息）。
-    要求 AI 先发一条问候 + 一个具体问题，引导用户开始分享。
-    """
     stage_name_map = {
         "childhood": "童年时光",
         "education": "求学经历",
@@ -216,10 +205,7 @@ def get_opening_prompt(
 
 
 def _build_era_context(current_stage: str, user_profile_context: str) -> str:
-    """
+    """根据用户的人生阶段和出生年份，生成对应时代的历史/政治/文化背景提示"""
-    根据用户的人生阶段和出生年份，生成对应时代的历史/政治/文化背景提示。
-    让 agent 在对话中自然融入时代感。
-    """
     if not user_profile_context:
         return ""
 
@@ -250,7 +236,6 @@ def _build_era_context(current_stage: str, user_profile_context: str) -> str:
     era_end = birth_year + age_range[1]
 
     era_events = []
-
     decade_events = {
         1950: "新中国成立初期、土地改革、抗美援朝",
         1960: "大跃进、三年自然灾害、中苏关系变化",
@@ -270,7 +255,6 @@ def _build_era_context(current_stage: str, user_profile_context: str) -> str:
         return ""
 
     place_hint = f"（用户来自{birth_place}）" if birth_place else ""
-
     return f"""
 ## 时代背景参考{place_hint}
 用户在这个人生阶段大约经历了 {era_start}-{era_end} 年（{age_range[0]}-{age_range[1]} 岁）：
@@ -293,20 +277,7 @@ def get_guided_conversation_prompt(
     detected_user_stage: str = "",
     user_profile_context: str = "",
 ) -> str:
-    """
+    """生成状态感知的对话提示词"""
-    生成状态感知的对话提示词
-
-    Args:
-        current_stage: 系统当前跟踪的阶段
-        empty_slots: 当前阶段未填充的槽位
-        filled_slots: 当前阶段已填充的槽位
-        user_message: 用户消息
-        conversation_turn: 总对话轮数
-        same_topic_turns: 同一话题的轮数
-        all_stages_coverage: 所有阶段的覆盖情况 {stage: {total, filled, empty, ratio}}
-        detected_user_stage: 检测到用户正在谈论的阶段（可能和 current_stage 不同）
-        user_profile_context: 用户基础资料上下文
-    """
     stage_name_map = {
         "childhood": "童年时光",
         "education": "求学经历",
@@ -317,12 +288,8 @@ def get_guided_conversation_prompt(
 
     current_stage_name = stage_name_map.get(current_stage, current_stage)
     user_stage_name = stage_name_map.get(detected_user_stage, "") if detected_user_stage else ""
-
-    # 判断用户是否在聊一个不同于系统当前阶段的话题
     user_jumped = detected_user_stage and detected_user_stage != current_stage
 
-    # --- 构建当前聊天上下文 ---
-    # 转换 slot 名称为中文
     empty_slots_readable = [SLOT_NAME_MAP.get(s, s) for s in empty_slots]
     empty_slots_str = "、".join(empty_slots_readable) if empty_slots_readable else "已聊得很充分"
 
@@ -332,7 +299,6 @@ def get_guided_conversation_prompt(
         filled_info.append(f"{readable_key}: {value[:50]}..." if len(value) > 50 else f"{readable_key}: {value}")
     filled_slots_str = "\n".join(filled_info) if filled_info else "刚开始聊"
 
-    # --- 构建全局进度概览 ---
     progress_lines = []
     uncovered_stages = []
     if all_stages_coverage:
@@ -350,17 +316,14 @@ def get_guided_conversation_prompt(
                 progress_lines.append(f"  {sname}：已聊得很充分 ✓")
     progress_str = "\n".join(progress_lines) if progress_lines else ""
 
-    # --- 动态策略 ---
     filled_count = len(filled_slots)
     should_switch_topic = same_topic_turns >= 3 or (filled_count >= 2 and same_topic_turns >= 2)
     should_lighten_mood = conversation_turn > 0 and conversation_turn % 5 == 0
     should_try_new_stage = filled_count >= 3 and len(empty_slots) <= 2
 
-    # 获取相关阶段
     related_stages = STAGE_RELATED_TOPICS.get(current_stage, [])
     related_stages_str = "、".join([stage_name_map.get(s, s) for s in related_stages])
 
-    # 选择回应风格
     style = random.choice(RESPONSE_STYLES)
     style_guidance = {
         "empathy": "这次回应要特别体现共情，表达你能理解用户的感受",
@@ -370,7 +333,6 @@ def get_guided_conversation_prompt(
         "connection": "这次回应可以分享一个类似的经历或感受（可以虚构）",
     }.get(style, "")
 
-    # --- 构建动态指导 ---
     dynamic_guidance = ""
     if user_jumped:
         dynamic_guidance += f"""
@@ -385,19 +347,15 @@ def get_guided_conversation_prompt(
         if should_try_new_stage and related_stages:
             dynamic_guidance += f"\n- 如果自然的话，可以尝试聊聊相关的话题，比如{related_stages_str}"
 
-    # --- 缺失章节补充提示（仅在用户没有跳转、且当前话题聊得差不多时） ---
     uncovered_hint = ""
     if not user_jumped and uncovered_stages and should_try_new_stage:
         uncovered_hint = f"\n- 还没聊到的人生阶段有：{'、'.join(uncovered_stages)}，如果聊天中有自然的契机，可以轻轻带一句，但不要刻意"
 
-    # --- 组合 prompt ---
-    # 根据是否跳转，调整主题描述
     if user_jumped:
         topic_desc = f"你们原本在聊「{current_stage_name}」，但用户自然地聊到了「{user_stage_name}」的内容"
     else:
         topic_desc = f"你们聊到了「{current_stage_name}」这个话题"
 
-    # --- 用户资料和时代背景 ---
     profile_section = ""
     if user_profile_context:
         profile_section = f"\n## 用户基本信息\n{user_profile_context}\n"
@@ -460,7 +418,6 @@ def get_guided_conversation_prompt(
     return prompt
 
 
-# 保留向后兼容的函数名
 def get_conversation_prompt(current_stage: ConversationStage, covered_topics: List[str], user_latest_response: str) -> str:
     """向后兼容的函数"""
     return get_system_prompt(current_stage, covered_topics, user_latest_response)

api/app/agents/memoir/classification_agent.py

@@ -8,7 +8,7 @@ from typing import Any, Optional
 
 from app.core.logging import get_logger
 
-from app.agents.prompts.memory_prompts import (
+from app.agents.memoir.prompts import (
     CHAPTER_CATEGORIES,
     get_chapter_classification_prompt,
 )

api/app/agents/memoir/extraction_agent.py

@@ -11,7 +11,7 @@ from typing import Any, Dict
 from app.core.logging import get_logger
 from app.features.memoir.memoir_images.json_payload import extract_json_payload
 
-from app.agents.prompts.memory_prompts import get_state_extraction_prompt
+from app.agents.memoir.prompts import get_state_extraction_prompt
 
 logger = get_logger(__name__)
 

api/app/agents/memoir/memory_agent.py

@@ -8,12 +8,12 @@ from typing import Dict, List, Optional
 from app.core.dependencies import get_llm_provider
 from app.core.logging import get_logger
 
-from app.agents.prompts import (
+from app.agents.memoir.prompts import (
+    CHAPTER_CATEGORIES,
+    STAGE_TO_ORDER,
     get_chapter_classification_prompt,
     get_text_rewrite_prompt,
     inject_image_placeholder_template,
-    CHAPTER_CATEGORIES,
-    STAGE_TO_ORDER,
 )
 
 logger = get_logger(__name__)

api/app/agents/memoir/narrative_agent.py

@@ -8,7 +8,7 @@ from typing import Any, Dict, Optional
 
 from app.core.logging import get_logger
 
-from app.agents.prompts.memory_prompts import (
+from app.agents.memoir.prompts import (
     get_creative_title_prompt,
     get_narrative_prompt,
 )

api/app/agents/memoir/placeholder_agent.py

@@ -3,7 +3,7 @@ PlaceholderInjectAgent：对 narrative 做占位符模板注入。
 对应现有逻辑：inject_image_placeholder_template
 纯函数式，无 LLM 调用。
 """
-from app.agents.prompts.memory_prompts import inject_image_placeholder_template
+from app.agents.memoir.prompts import inject_image_placeholder_template
 
 
 def inject_placeholders(content: str) -> str:

api/app/agents/memoir/processor.py

@@ -13,7 +13,7 @@ from app.core.logging import get_logger
 from app.core.task_tracker import task_tracker
 
 from app.agents.state_schema import MemoirStateSchema
-from app.agents.prompts.memory_prompts import (
+from app.agents.memoir.prompts import (
     get_creative_title_prompt,
     get_narrative_prompt,
     get_state_extraction_prompt,

api/app/agents/memoir/prompts.py

@@ -5,7 +5,6 @@ import json
 import re
 from typing import Optional
 
-# 章节分类映射
 CHAPTER_CATEGORIES = {
     "childhood": "童年与成长背景",
     "education": "教育经历与青年时期",
@@ -17,7 +16,6 @@ CHAPTER_CATEGORIES = {
     "summary": "人生总结",
 }
 
-# 章节顺序
 CHAPTER_ORDER = [
     "childhood",
     "education",
@@ -29,22 +27,19 @@ CHAPTER_ORDER = [
     "summary",
 ]
 
-# 统一的阶段名 → 排序索引映射
-# 兼容 5 阶段简化名（conversation/state 模型）和 8 分类详细名（chapter 模型）
 STAGE_TO_ORDER = {
     "childhood": 0,
     "education": 1,
-    "career": 2,              # 5-stage 简化名
+    "career": 2,
-    "career_early": 2,        # 8-category 详细名
+    "career_early": 2,
     "career_achievement": 3,
     "career_challenge": 4,
     "family": 5,
-    "belief": 6,              # 5-stage 简化名（单数）
+    "belief": 6,
-    "beliefs": 6,             # 8-category 详细名（复数）
+    "beliefs": 6,
     "summary": 7,
 }
 
-# 图片占位符入库前拼接的固定提示词模板（与原先 prompt 中要求一致，改为代码侧统一拼接）
 IMAGE_PLACEHOLDER_TEMPLATE = (
     "温暖怀旧风格，年代感复古色调，柔和光影，朴素温馨氛围，安静治愈，低饱和度，"
     "质感柔和细腻，简约构图，充满岁月沉淀感与故事感，高清唯美插画封面，不要包含文字，"
@@ -53,8 +48,6 @@ IMAGE_PLACEHOLDER_TEMPLATE = (
     "有朦胧怀旧的年代感。"
 )
 
-
-# 匹配任意层数的图片占位符（2/4/6/8...层花括号），整段替换为规范四层，避免多余花括号残留导致客户端显示异常
 _IMAGE_PLACEHOLDER_ANY_BRACES_RE = re.compile(
     r"(\{\{)+IMAGE:\s*([^}]+)(\}\})+",
     re.DOTALL,
@@ -64,9 +57,7 @@ _IMAGE_PLACEHOLDER_ANY_BRACES_RE = re.compile(
 def inject_image_placeholder_template(content: str) -> str:
     """
     入库前对章节正文做占位符处理：用正则匹配所有图片占位符位置，拼上固定模板。
-    支持任意层数花括号（如 {{、{{{{、{{{{{{ 等），输出统一为四层大括号 + 固定模板 + 描述，
+    支持任意层数花括号，输出统一为四层大括号 + 固定模板 + 描述。
-    避免 LLM 输出花括号过多时只替换内层导致多余花括号残留在正文中、在手机端被原样显示。
-    若占位符内已包含固定模板前缀则不再重复拼接。
     """
     if not content or not content.strip():
         return content
@@ -82,6 +73,8 @@ def inject_image_placeholder_template(content: str) -> str:
 
     content = _IMAGE_PLACEHOLDER_ANY_BRACES_RE.sub(replace_one, content)
     return content
+
+
 def get_system_prompt() -> str:
     """获取整理 Agent 的系统提示词"""
     return """你是一位专业的传记作家和文字编辑，擅长将口语化的对话内容整理成优雅的书面语回忆录章节。
@@ -156,9 +149,7 @@ def get_chapter_classification_prompt(segments_text: str) -> str:
 def get_text_rewrite_prompt(segments_text: str, chapter_category: str, existing_content: str = "") -> str:
     """获取文本改写的提示词"""
     chapter_name = CHAPTER_CATEGORIES.get(chapter_category, chapter_category)
-    
     existing_section = f"\n\n已有章节内容：\n{existing_content}" if existing_content else ""
-    
     return f"""{get_system_prompt()}
 
 请将以下口语化的对话内容改写为书面语，归类到"{chapter_name}"章节。
@@ -193,8 +184,6 @@ def get_text_rewrite_prompt(segments_text: str, chapter_category: str, existing_
 def get_state_extraction_prompt(user_message: str, current_stage: str, stage_slots: dict) -> str:
     """抽取结构化信息并判断阶段"""
     slot_keys = list(stage_slots.keys())
-
-    # 提供所有阶段的 slot 参考，帮助 LLM 将内容归类到正确的阶段
     all_stage_slots = {
         "childhood": ["place", "people", "daily_life", "emotion", "turning_event"],
         "education": ["school", "city", "motivation", "challenge", "change"],
@@ -227,10 +216,10 @@ def get_state_extraction_prompt(user_message: str, current_stage: str, stage_slo
 }}
 
 要求：
-1. **应的 slot 列表
+1. **先忽略话语中的语气词、填充词、寒暄、与AI的交互指令等无关内容**，只关注涉及人生经历的实质信息
-4. slots 只填写确实提到的、与人生经历相关的实先忽略话语中的语气词、填充词、寒暄、与AI的交互指令等无关内容**，只关注涉及人生经历的实质信息
 2. **detected_stage 必须根据用户话语的实际内容判断**，不要默认沿用系统当前阶段。用户可能在聊不同阶段的事情
-3. slots 的 key 必须属于 detected_stage 对质内容
+3. slots 的 key 必须属于 detected_stage 对应的 slot 列表
+4. slots 只填写确实提到的、与人生经历相关的实质内容
 5. **snippet 应是提炼后的核心信息**，去除语气词和冗余表达，50 字以内
 6. 如果用户话语中没有任何与人生经历相关的实质内容（如纯粹的寒暄、指令、语气词），slots 为空对象
 """
@@ -275,7 +264,7 @@ def get_creative_title_prompt(
     return f"""{get_system_prompt()}
 
 请根据阶段和情绪生成 1 个有创意的章节标题。
-阶段：{stage}  
+阶段：{stage}
 情绪：{emotion}
 可用信息：{slots}{profile_section}{time_section}
 
@@ -307,9 +296,7 @@ def get_narrative_prompt(
     context_tail = ""
     if existing_content:
         context_tail = existing_content[-300:] if len(existing_content) > 300 else existing_content
-
     context_section = f"\n\n【衔接上下文（已有内容的末尾，仅供参考衔接，不要重复）】：\n{context_tail}" if context_tail else ""
-
     profile_section = f"\n\n用户基本信息：\n{user_profile}" if user_profile else ""
     age_hint = _build_age_hint(stage, birth_year)
     time_section = f"\n时间参考：{age_hint}" if age_hint else ""

api/app/agents/prompts/__init__.py

@@ -1,57 +0,0 @@
-"""
-提示词模块
-"""
-from .conversation_prompts import (
-    ConversationStage,
-    get_system_prompt as get_conversation_prompt,
-    get_questions_for_stage,
-    get_guided_conversation_prompt,
-    get_opening_prompt,
-    INTERVIEW_QUESTIONS,
-)
-from .memory_prompts import (
-    get_system_prompt as get_memory_prompt,
-    get_chapter_classification_prompt,
-    get_text_rewrite_prompt,
-    get_state_extraction_prompt,
-    get_creative_title_prompt,
-    get_narrative_prompt,
-    inject_image_placeholder_template,
-    CHAPTER_CATEGORIES,
-    CHAPTER_ORDER,
-    STAGE_TO_ORDER,
-)
-from .profile_prompts import (
-    get_profile_greeting_prompt,
-    get_profile_extraction_prompt,
-    get_profile_followup_prompt,
-    format_user_profile_context,
-    get_missing_profile_fields,
-    PROFILE_FIELD_NAMES,
-)
-
-__all__ = [
-    "ConversationStage",
-    "get_conversation_prompt",
-    "get_questions_for_stage",
-    "get_guided_conversation_prompt",
-    "get_opening_prompt",
-    "INTERVIEW_QUESTIONS",
-    "get_memory_prompt",
-    "get_chapter_classification_prompt",
-    "get_text_rewrite_prompt",
-    "get_state_extraction_prompt",
-    "get_creative_title_prompt",
-    "get_narrative_prompt",
-    "inject_image_placeholder_template",
-    "CHAPTER_CATEGORIES",
-    "CHAPTER_ORDER",
-    "STAGE_TO_ORDER",
-    "get_profile_greeting_prompt",
-    "get_profile_extraction_prompt",
-    "get_profile_followup_prompt",
-    "format_user_profile_context",
-    "get_missing_profile_fields",
-    "PROFILE_FIELD_NAMES",
-]
-

api/app/features/conversation/ws/profile_collector.py

@@ -6,7 +6,7 @@ from app.features.user.models import User
 
 def get_missing_profile_fields(user: User) -> list:
     """检查用户缺失的资料字段"""
-    from app.agents.prompts.profile_prompts import get_missing_profile_fields as _get_missing
+    from app.agents.chat.prompts_profile import get_missing_profile_fields as _get_missing
     return _get_missing(
         birth_year=user.birth_year,
         birth_place=user.birth_place,

api/app/features/conversation/ws/router.py

@@ -10,7 +10,7 @@ from datetime import datetime, timezone
 from fastapi import WebSocket, WebSocketDisconnect, status
 from starlette.websockets import WebSocketState
 
-from app.agents.prompts.profile_prompts import format_user_profile_context
+from app.agents.chat.prompts_profile import format_user_profile_context
 from app.core.db import AsyncSessionLocal
 from app.core.security import verify_token
 from app.features.conversation.models import Conversation, Segment

api/app/features/memoir/service.py

@@ -8,7 +8,7 @@ from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy.orm import joinedload
 
-from app.agents.prompts.memory_prompts import (
+from app.agents.memoir.prompts import (
     CHAPTER_CATEGORIES,
     CHAPTER_ORDER,
     STAGE_TO_ORDER,

api/app/tasks/memoir_tasks.py

@@ -26,7 +26,7 @@ from app.features.memoir.models import (
 from app.features.user.models import User
 from app.core.dependencies import get_llm_provider
 from app.agents.state_schema import MemoirStateSchema, SlotData, default_state
-from app.agents.prompts.memory_prompts import (
+from app.agents.memoir.prompts import (
     STAGE_TO_ORDER,
     get_narrative_prompt,
     inject_image_placeholder_template,
@@ -34,7 +34,7 @@ from app.agents.prompts.memory_prompts import (
 from app.agents.memoir import MemoirOrchestrator
 from app.agents.memoir.narrative_agent import NarrativeAgent
 from app.agents.memoir.placeholder_agent import inject_placeholders
-from app.agents.prompts.profile_prompts import format_user_profile_context
+from app.agents.chat.prompts_profile import format_user_profile_context
 from app.features.memoir.memoir_images.parser import (
     build_initial_image_assets,
     parse_image_placeholders,

api/routers/chapters.py

@@ -14,7 +14,7 @@ from database import get_async_db
 from database.models import Chapter as ChapterModel, ChapterSection
 from database.models import User as UserModel
 from middleware.auth import get_current_user
-from app.agents.prompts.memory_prompts import CHAPTER_CATEGORIES, CHAPTER_ORDER, STAGE_TO_ORDER
+from app.agents.memoir.prompts import CHAPTER_CATEGORIES, CHAPTER_ORDER, STAGE_TO_ORDER
 from services.memoir_images.schema import (
     completed_image_assets,
     IMAGE_STATUS_COMPLETED,

api/routers/websocket.py

@@ -23,7 +23,7 @@ from database.models import User as UserModel
 from services.auth_service import verify_token
 from services.memoir_state_service import get_or_create_state
 from services import asr_service, redis_service
-from app.agents.prompts.profile_prompts import format_user_profile_context
+from app.agents.chat.prompts_profile import format_user_profile_context
 
 logger = logging.getLogger(__name__)
 LEGACY_VOICE_SESSION_ID = "legacy"
@@ -924,7 +924,7 @@ async def websocket_endpoint(
 
 def _get_missing_profile_fields(user: UserModel) -> list:
     """检查用户缺失的资料字段"""
-    from app.agents.prompts.profile_prompts import get_missing_profile_fields
+    from app.agents.chat.prompts_profile import get_missing_profile_fields
     return get_missing_profile_fields(
         birth_year=user.birth_year,
         birth_place=user.birth_place,
@@ -935,7 +935,7 @@ def _get_missing_profile_fields(user: UserModel) -> list:
 
 def _get_filled_profile_fields(user: UserModel) -> dict:
     """获取用户已有的资料字段（中文展示）"""
-    from app.agents.prompts.profile_prompts import PROFILE_FIELD_NAMES
+    from app.agents.chat.prompts_profile import PROFILE_FIELD_NAMES
     filled = {}
     if user.birth_year:
         filled["birth_year"] = str(user.birth_year)
@@ -1045,7 +1045,7 @@ async def process_user_message(
     # 构建用户资料上下文
     user_profile_context = ""
     if user:
-        from app.agents.prompts.profile_prompts import format_user_profile_context
+        from app.agents.chat.prompts_profile import format_user_profile_context
         user_profile_context = format_user_profile_context(
             birth_year=user.birth_year,
             birth_place=user.birth_place,

api/scripts/reprocess_user_memoir.py

@@ -50,7 +50,7 @@ from app.features.memoir.models import Book, Chapter, ChapterSection, MemoirStat
 from app.features.user.models import User
 from app.core.dependencies import get_llm_provider
 from app.agents.state_schema import MemoirStateSchema, SlotData, default_state
-from app.agents.prompts.memory_prompts import (
+from app.agents.memoir.prompts import (
     get_creative_title_prompt,
     get_narrative_prompt,
     get_state_extraction_prompt,

api/tests/test_memory_prompts_inject.py

@@ -1,7 +1,7 @@
 """测试 memory_prompts.inject_image_placeholder_template：占位符花括号统一为四层，避免多余花括号残留"""
 import unittest
 
-from app.agents.prompts.memory_prompts import (
+from app.agents.memoir.prompts import (
     IMAGE_PLACEHOLDER_TEMPLATE,
     inject_image_placeholder_template,
 )