Merge pull request #2 from xiamuceer-j/dev

Dev

.dockerignore

@@ -40,6 +40,10 @@ Thumbs.db
 data/*.db
 backend/data/*.db
 
+# ChromaDB数据库（不包含在镜像中，会在运行时生成）
+backend/data/chroma_db/
+
+
 # 日志文件
 logs/
 *.log

.gitattributes

@@ -0,0 +1 @@
+*.safetensors filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -40,7 +40,10 @@ RUN apt-get update && apt-get install -y \
 # 复制后端依赖文件
 COPY backend/requirements.txt ./
 
-# 安装Python依赖
+# 先从PyTorch官方源安装CPU版本的torch（避免GPU依赖）
+RUN pip install --no-cache-dir torch==2.7.0 --index-url https://download.pytorch.org/whl/cpu
+
+# 再安装其他Python依赖（使用阿里云镜像加速）
 RUN pip install --no-cache-dir -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
 
 # 复制后端代码
@@ -50,7 +53,11 @@ COPY backend/ ./
 COPY --from=frontend-builder /frontend/dist ./static
 
 # 创建必要的目录
-RUN mkdir -p /app/data /app/logs
+RUN mkdir -p /app/data /app/logs /app/embedding
+
+# 复制预下载的Embedding模型到独立目录（避免被docker-compose的data挂载覆盖）
+# 这样可以避免首次运行时联网下载约420MB的模型文件
+COPY backend/embedding /app/embedding
 
 # 复制环境变量示例文件
 COPY backend/.env.example ./.env.example
@@ -63,6 +70,12 @@ ENV PYTHONUNBUFFERED=1
 ENV APP_HOST=0.0.0.0
 ENV APP_PORT=8000
 
+# 设置Transformers和Sentence-Transformers离线模式
+ENV TRANSFORMERS_OFFLINE=1
+ENV HF_DATASETS_OFFLINE=1
+ENV HF_HUB_OFFLINE=1
+ENV SENTENCE_TRANSFORMERS_HOME=/app/embedding
+
 # 健康检查
 HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
     CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')" || exit 1

backend/app/api/chapters.py

@@ -1,11 +1,13 @@
 """章节管理API"""
-from fastapi import APIRouter, Depends, HTTPException, Request, Query
+from fastapi import APIRouter, Depends, HTTPException, Request, Query, BackgroundTasks
 from fastapi.responses import StreamingResponse
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy import select, func
 import json
 import asyncio
 from typing import Optional
+from datetime import datetime
+from asyncio import Queue, Lock
 
 from app.database import get_db
 from app.models.chapter import Chapter
@@ -14,6 +16,8 @@ from app.models.outline import Outline
 from app.models.character import Character
 from app.models.generation_history import GenerationHistory
 from app.models.writing_style import WritingStyle
+from app.models.analysis_task import AnalysisTask
+from app.models.memory import PlotAnalysis, StoryMemory
 from app.schemas.chapter import (
     ChapterCreate,
     ChapterUpdate,
@@ -23,12 +27,25 @@ from app.schemas.chapter import (
 )
 from app.services.ai_service import AIService
 from app.services.prompt_service import prompt_service
+from app.services.plot_analyzer import PlotAnalyzer
+from app.services.memory_service import memory_service
 from app.logger import get_logger
 from app.api.settings import get_user_ai_service
 
 router = APIRouter(prefix="/chapters", tags=["章节管理"])
 logger = get_logger(__name__)
 
+# 全局数据库写入锁（每个用户一个锁，用于保护SQLite写入操作）
+db_write_locks: dict[str, Lock] = {}
+
+
+async def get_db_write_lock(user_id: str) -> Lock:
+    """获取或创建用户的数据库写入锁"""
+    if user_id not in db_write_locks:
+        db_write_locks[user_id] = Lock()
+        logger.debug(f"🔒 为用户 {user_id} 创建数据库写入锁")
+    return db_write_locks[user_id]
+
 
 @router.post("", response_model=ChapterResponse, summary="创建章节")
 async def create_chapter(
@@ -101,6 +118,63 @@ async def get_chapter(
     return chapter
 
 
+@router.get("/{chapter_id}/navigation", summary="获取章节导航信息")
+async def get_chapter_navigation(
+    chapter_id: str,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    获取章节的导航信息（上一章/下一章）
+    用于章节阅读器的翻页功能
+    """
+    # 获取当前章节
+    result = await db.execute(
+        select(Chapter).where(Chapter.id == chapter_id)
+    )
+    current_chapter = result.scalar_one_or_none()
+    
+    if not current_chapter:
+        raise HTTPException(status_code=404, detail="章节不存在")
+    
+    # 获取上一章
+    prev_result = await db.execute(
+        select(Chapter)
+        .where(Chapter.project_id == current_chapter.project_id)
+        .where(Chapter.chapter_number < current_chapter.chapter_number)
+        .order_by(Chapter.chapter_number.desc())
+        .limit(1)
+    )
+    prev_chapter = prev_result.scalar_one_or_none()
+    
+    # 获取下一章
+    next_result = await db.execute(
+        select(Chapter)
+        .where(Chapter.project_id == current_chapter.project_id)
+        .where(Chapter.chapter_number > current_chapter.chapter_number)
+        .order_by(Chapter.chapter_number.asc())
+        .limit(1)
+    )
+    next_chapter = next_result.scalar_one_or_none()
+    
+    return {
+        "current": {
+            "id": current_chapter.id,
+            "chapter_number": current_chapter.chapter_number,
+            "title": current_chapter.title
+        },
+        "previous": {
+            "id": prev_chapter.id,
+            "chapter_number": prev_chapter.chapter_number,
+            "title": prev_chapter.title
+        } if prev_chapter else None,
+        "next": {
+            "id": next_chapter.id,
+            "chapter_number": next_chapter.chapter_number,
+            "title": next_chapter.title
+        } if next_chapter else None
+    }
+
+
 @router.put("/{chapter_id}", response_model=ChapterResponse, summary="更新章节")
 async def update_chapter(
     chapter_id: str,
@@ -248,10 +322,277 @@ async def check_can_generate(
     }
 
 
+async def analyze_chapter_background(
+    chapter_id: str,
+    user_id: str,
+    project_id: str,
+    task_id: str,
+    ai_service: AIService
+):
+    """
+    后台异步分析章节（支持并发，使用锁保护数据库写入）
+    
+    Args:
+        chapter_id: 章节ID
+        user_id: 用户ID
+        project_id: 项目ID
+        task_id: 任务ID
+        ai_service: AI服务实例
+    """
+    db_session = None
+    write_lock = await get_db_write_lock(user_id)
+    
+    try:
+        logger.info(f"🔍 开始分析章节: {chapter_id}, 任务ID: {task_id}")
+        
+        # 创建独立数据库会话
+        from app.database import get_engine
+        from sqlalchemy.ext.asyncio import async_sessionmaker, AsyncSession
+        
+        engine = await get_engine(user_id)
+        AsyncSessionLocal = async_sessionmaker(
+            engine,
+            class_=AsyncSession,
+            expire_on_commit=False
+        )
+        db_session = AsyncSessionLocal()
+        
+        # 1. 获取任务（读操作）
+        task_result = await db_session.execute(
+            select(AnalysisTask).where(AnalysisTask.id == task_id)
+        )
+        task = task_result.scalar_one_or_none()
+        
+        if not task:
+            logger.error(f"❌ 任务不存在: {task_id}")
+            return
+        
+        # 更新任务状态（写操作，需要锁）
+        async with write_lock:
+            task.status = 'running'
+            task.started_at = datetime.now()
+            task.progress = 10
+            await db_session.commit()
+        
+        # 2. 获取章节信息（读操作）
+        chapter_result = await db_session.execute(
+            select(Chapter).where(Chapter.id == chapter_id)
+        )
+        chapter = chapter_result.scalar_one_or_none()
+        if not chapter or not chapter.content:
+            async with write_lock:
+                task.status = 'failed'
+                task.error_message = '章节不存在或内容为空'
+                task.completed_at = datetime.now()
+                await db_session.commit()
+            logger.error(f"❌ 章节不存在或内容为空: {chapter_id}")
+            return
+        
+        async with write_lock:
+            task.progress = 20
+            await db_session.commit()
+        
+        # 3. 使用PlotAnalyzer分析章节
+        analyzer = PlotAnalyzer(ai_service)
+        analysis_result = await analyzer.analyze_chapter(
+            chapter_number=chapter.chapter_number,
+            title=chapter.title,
+            content=chapter.content,
+            word_count=chapter.word_count or len(chapter.content)
+        )
+        
+        if not analysis_result:
+            async with write_lock:
+                task.status = 'failed'
+                task.error_message = 'AI分析失败，请检查日志'
+                task.completed_at = datetime.now()
+                await db_session.commit()
+            logger.error(f"❌ AI分析失败: {chapter_id}")
+            return
+        
+        async with write_lock:
+            task.progress = 60
+            await db_session.commit()
+        
+        # 4. 保存分析结果到数据库（写操作，需要锁）
+        async with write_lock:
+            existing_analysis_result = await db_session.execute(
+                select(PlotAnalysis).where(PlotAnalysis.chapter_id == chapter_id)
+            )
+            existing_analysis = existing_analysis_result.scalar_one_or_none()
+            
+            if existing_analysis:
+                # 更新现有记录
+                logger.info(f"  更新现有分析记录: {existing_analysis.id}")
+                existing_analysis.plot_stage = analysis_result.get('plot_stage', '发展')
+                existing_analysis.conflict_level = analysis_result.get('conflict', {}).get('level', 0)
+                existing_analysis.conflict_types = analysis_result.get('conflict', {}).get('types', [])
+                existing_analysis.emotional_tone = analysis_result.get('emotional_arc', {}).get('primary_emotion', '')
+                existing_analysis.emotional_intensity = analysis_result.get('emotional_arc', {}).get('intensity', 0) / 10.0
+                existing_analysis.hooks = analysis_result.get('hooks', [])
+                existing_analysis.hooks_count = len(analysis_result.get('hooks', []))
+                existing_analysis.foreshadows = analysis_result.get('foreshadows', [])
+                existing_analysis.foreshadows_planted = sum(1 for f in analysis_result.get('foreshadows', []) if f.get('type') == 'planted')
+                existing_analysis.foreshadows_resolved = sum(1 for f in analysis_result.get('foreshadows', []) if f.get('type') == 'resolved')
+                existing_analysis.plot_points = analysis_result.get('plot_points', [])
+                existing_analysis.plot_points_count = len(analysis_result.get('plot_points', []))
+                existing_analysis.character_states = analysis_result.get('character_states', [])
+                existing_analysis.scenes = analysis_result.get('scenes', [])
+                existing_analysis.pacing = analysis_result.get('pacing', 'moderate')
+                existing_analysis.overall_quality_score = analysis_result.get('scores', {}).get('overall', 0)
+                existing_analysis.pacing_score = analysis_result.get('scores', {}).get('pacing', 0)
+                existing_analysis.engagement_score = analysis_result.get('scores', {}).get('engagement', 0)
+                existing_analysis.coherence_score = analysis_result.get('scores', {}).get('coherence', 0)
+                existing_analysis.analysis_report = analyzer.generate_analysis_summary(analysis_result)
+                existing_analysis.suggestions = analysis_result.get('suggestions', [])
+                existing_analysis.dialogue_ratio = analysis_result.get('dialogue_ratio', 0)
+                existing_analysis.description_ratio = analysis_result.get('description_ratio', 0)
+            else:
+                # 创建新记录
+                logger.info(f"  创建新的分析记录")
+                plot_analysis = PlotAnalysis(
+                    chapter_id=chapter_id,
+                    project_id=project_id,
+                    plot_stage=analysis_result.get('plot_stage', '发展'),
+                    conflict_level=analysis_result.get('conflict', {}).get('level', 0),
+                    conflict_types=analysis_result.get('conflict', {}).get('types', []),
+                    emotional_tone=analysis_result.get('emotional_arc', {}).get('primary_emotion', ''),
+                    emotional_intensity=analysis_result.get('emotional_arc', {}).get('intensity', 0) / 10.0,
+                    hooks=analysis_result.get('hooks', []),
+                    hooks_count=len(analysis_result.get('hooks', [])),
+                    foreshadows=analysis_result.get('foreshadows', []),
+                    foreshadows_planted=sum(1 for f in analysis_result.get('foreshadows', []) if f.get('type') == 'planted'),
+                    foreshadows_resolved=sum(1 for f in analysis_result.get('foreshadows', []) if f.get('type') == 'resolved'),
+                    plot_points=analysis_result.get('plot_points', []),
+                    plot_points_count=len(analysis_result.get('plot_points', [])),
+                    character_states=analysis_result.get('character_states', []),
+                    scenes=analysis_result.get('scenes', []),
+                    pacing=analysis_result.get('pacing', 'moderate'),
+                    overall_quality_score=analysis_result.get('scores', {}).get('overall', 0),
+                    pacing_score=analysis_result.get('scores', {}).get('pacing', 0),
+                    engagement_score=analysis_result.get('scores', {}).get('engagement', 0),
+                    coherence_score=analysis_result.get('scores', {}).get('coherence', 0),
+                    analysis_report=analyzer.generate_analysis_summary(analysis_result),
+                    suggestions=analysis_result.get('suggestions', []),
+                    dialogue_ratio=analysis_result.get('dialogue_ratio', 0),
+                    description_ratio=analysis_result.get('description_ratio', 0)
+                )
+                db_session.add(plot_analysis)
+            
+            await db_session.commit()
+            
+            task.progress = 80
+            await db_session.commit()
+        
+        # 5. 提取记忆并保存到向量数据库（传入章节内容用于计算位置）
+        memories = analyzer.extract_memories_from_analysis(
+            analysis=analysis_result,
+            chapter_id=chapter_id,
+            chapter_number=chapter.chapter_number,
+            chapter_content=chapter.content or ""
+        )
+        
+        # 先删除该章节的旧记忆（写操作，需要锁）
+        async with write_lock:
+            old_memories_result = await db_session.execute(
+                select(StoryMemory).where(StoryMemory.chapter_id == chapter_id)
+            )
+            old_memories = old_memories_result.scalars().all()
+            for old_mem in old_memories:
+                await db_session.delete(old_mem)
+            await db_session.commit()
+            logger.info(f"  删除旧记忆: {len(old_memories)}条")
+        
+        # 准备批量添加的记忆数据（不需要锁）
+        memory_records = []
+        for mem in memories:
+            memory_id = f"{chapter_id}_{mem['type']}_{len(memory_records)}"
+            memory_records.append({
+                'id': memory_id,
+                'content': mem['content'],
+                'type': mem['type'],
+                'metadata': mem['metadata']
+            })
+            
+        # 保存到关系数据库（写操作，需要锁）
+        async with write_lock:
+            for mem in memories:
+                memory_id = memory_records[memories.index(mem)]['id']
+                text_position = mem['metadata'].get('text_position', -1)
+                text_length = mem['metadata'].get('text_length', 0)
+                
+                story_memory = StoryMemory(
+                    id=memory_id,
+                    project_id=project_id,
+                    chapter_id=chapter_id,
+                    memory_type=mem['type'],
+                    content=mem['content'],
+                    title=mem['title'],
+                    importance_score=mem['metadata'].get('importance_score', 0.5),
+                    tags=mem['metadata'].get('tags', []),
+                    is_foreshadow=mem['metadata'].get('is_foreshadow', 0),
+                    story_timeline=chapter.chapter_number,
+                    chapter_position=text_position,
+                    text_length=text_length,
+                    related_characters=mem['metadata'].get('related_characters', []),
+                    related_locations=mem['metadata'].get('related_locations', [])
+                )
+                db_session.add(story_memory)
+                
+                if text_position >= 0:
+                    logger.debug(f"  保存记忆 {memory_id}: position={text_position}, length={text_length}")
+            
+            await db_session.commit()
+        
+        # 批量添加到向量数据库
+        if memory_records:
+            added_count = await memory_service.batch_add_memories(
+                user_id=user_id,
+                project_id=project_id,
+                memories=memory_records
+            )
+            logger.info(f"✅ 添加{added_count}条记忆到向量库")
+        
+        # 最终更新任务状态（写操作，需要锁）
+        async with write_lock:
+            task.progress = 100
+            task.status = 'completed'
+            task.completed_at = datetime.now()
+            await db_session.commit()
+        
+        logger.info(f"✅ 章节分析完成: {chapter_id}, 提取{len(memories)}条记忆")
+        
+    except Exception as e:
+        logger.error(f"❌ 后台分析异常: {str(e)}", exc_info=True)
+        # 确保任务状态被更新为failed（写操作，需要锁）
+        if db_session:
+            try:
+                async with write_lock:
+                    task_result = await db_session.execute(
+                        select(AnalysisTask).where(AnalysisTask.id == task_id)
+                    )
+                    task = task_result.scalar_one_or_none()
+                    if task:
+                        task.status = 'failed'
+                        task.error_message = str(e)[:500]
+                        task.completed_at = datetime.now()
+                        task.progress = 0
+                        await db_session.commit()
+                        logger.info(f"✅ 任务状态已更新为failed: {task_id}")
+                    else:
+                        logger.error(f"❌ 无法找到任务进行状态更新: {task_id}")
+            except Exception as update_error:
+                logger.error(f"❌ 更新任务状态失败: {str(update_error)}")
+    finally:
+        if db_session:
+            await db_session.close()
+
+
 @router.post("/{chapter_id}/generate-stream", summary="AI创作章节内容（流式）")
 async def generate_chapter_content_stream(
     chapter_id: str,
     request: Request,
+    background_tasks: BackgroundTasks,
     generate_request: ChapterGenerateRequest = ChapterGenerateRequest(),
     user_ai_service: AIService = Depends(get_user_ai_service)
 ):
@@ -301,6 +642,9 @@ async def generate_chapter_content_stream(
         # 在生成器内部创建独立的数据库会话
         db_session = None
         db_committed = False
+        # 获取当前用户ID（在生成器外部就需要）
+        current_user_id = getattr(request.state, "user_id", "system")
+        
         try:
             # 创建新的数据库会话
             async for db_session in get_db(request):
@@ -396,11 +740,42 @@ async def generate_chapter_content_stream(
                         previous_content += recent_content
                     
                     logger.info(f"构建前置上下文：{len(early_chapters)}章摘要 + {len(recent_chapters)}章完整内容")
+                
+                # 🧠 构建记忆增强上下文
+                logger.info(f"🧠 开始构建记忆增强上下文...")
+                memory_context = await memory_service.build_context_for_generation(
+                    user_id=current_user_id,
+                    project_id=project.id,
+                    current_chapter=current_chapter.chapter_number,
+                    chapter_outline=outline.content if outline else current_chapter.summary or "",
+                    character_names=[c.name for c in characters] if characters else None
+                )
+                
+                # 计算各部分的字符长度
+                context_lengths = {
+                    'recent_context': len(memory_context.get('recent_context', '')),
+                    'relevant_memories': len(memory_context.get('relevant_memories', '')),
+                    'foreshadows': len(memory_context.get('foreshadows', '')),
+                    'character_states': len(memory_context.get('character_states', '')),
+                    'plot_points': len(memory_context.get('plot_points', ''))
+                }
+                total_memory_length = sum(context_lengths.values())
+                
+                logger.info(f"✅ 记忆上下文构建完成: {memory_context['stats']}")
+                logger.info(f"📏 记忆上下文长度统计:")
+                logger.info(f"  - 最近章节记忆: {context_lengths['recent_context']} 字符")
+                logger.info(f"  - 语义相关记忆: {context_lengths['relevant_memories']} 字符")
+                logger.info(f"  - 未完结伏笔: {context_lengths['foreshadows']} 字符")
+                logger.info(f"  - 角色状态记忆: {context_lengths['character_states']} 字符")
+                logger.info(f"  - 重要情节点: {context_lengths['plot_points']} 字符")
+                logger.info(f"  - 记忆总长度: {total_memory_length} 字符")
+                logger.info(f"  - 前置章节上下文长度: {len(previous_content)} 字符")
+                logger.info(f"  - 总上下文长度(估算): {total_memory_length + len(previous_content) + 2000} 字符")
             
                 # 发送开始事件
                 yield f"data: {json.dumps({'type': 'start', 'message': '开始AI创作...'}, ensure_ascii=False)}\n\n"
                 
-                # 根据是否有前置内容选择不同的提示词，并应用写作风格
+                # 根据是否有前置内容选择不同的提示词，并应用写作风格和记忆增强
                 if previous_content:
                     prompt = prompt_service.get_chapter_generation_with_context_prompt(
                         title=project.title,
@@ -418,7 +793,8 @@ async def generate_chapter_content_stream(
                         chapter_title=current_chapter.title,
                         chapter_outline=outline.content if outline else current_chapter.summary or '暂无大纲',
                         style_content=style_content,
-                        target_word_count=target_word_count
+                        target_word_count=target_word_count,
+                        memory_context=memory_context
                     )
                 else:
                     prompt = prompt_service.get_chapter_generation_prompt(
@@ -436,7 +812,8 @@ async def generate_chapter_content_stream(
                         chapter_title=current_chapter.title,
                         chapter_outline=outline.content if outline else current_chapter.summary or '暂无大纲',
                         style_content=style_content,
-                        target_word_count=target_word_count
+                        target_word_count=target_word_count,
+                        memory_context=memory_context
                     )
                 
                 logger.info(f"开始AI流式创作章节 {chapter_id}")
@@ -474,8 +851,50 @@ async def generate_chapter_content_stream(
                 
                 logger.info(f"成功创作章节 {chapter_id}，共 {new_word_count} 字")
                 
-                # 发送完成事件
+                # 创建分析任务
-                yield f"data: {json.dumps({'type': 'done', 'message': '创作完成', 'word_count': new_word_count}, ensure_ascii=False)}\n\n"
+                analysis_task = AnalysisTask(
+                    chapter_id=chapter_id,
+                    user_id=current_user_id,
+                    project_id=project.id,
+                    status='pending',
+                    progress=0
+                )
+                db_session.add(analysis_task)
+                await db_session.commit()
+                await db_session.refresh(analysis_task)
+                
+                task_id = analysis_task.id
+                logger.info(f"📋 已创建分析任务: {task_id}")
+                
+                # 短暂延迟确保SQLite WAL完成写入
+                await asyncio.sleep(0.05)
+                
+                # 直接启动后台分析（并发执行）
+                background_tasks.add_task(
+                    analyze_chapter_background,
+                    chapter_id=chapter_id,
+                    user_id=current_user_id,
+                    project_id=project.id,
+                    task_id=task_id,
+                    ai_service=user_ai_service
+                )
+                
+                # 发送完成事件（包含分析任务ID）
+                completion_data = {
+                    'type': 'done',
+                    'message': '创作完成',
+                    'word_count': new_word_count,
+                    'analysis_task_id': task_id
+                }
+                yield f"data: {json.dumps(completion_data, ensure_ascii=False)}\n\n"
+                
+                # 发送分析开始事件
+                analysis_started_data = {
+                    'type': 'analysis_started',
+                    'task_id': task_id,
+                    'message': '章节分析已开始'
+                }
+                yield f"data: {json.dumps(analysis_started_data, ensure_ascii=False)}\n\n"
                 
                 break  # 退出async for db_session循环
         
@@ -527,3 +946,312 @@ async def generate_chapter_content_stream(
             "X-Accel-Buffering": "no"
         }
     )
+
+
+@router.get("/{chapter_id}/analysis/status", summary="查询章节分析任务状态")
+async def get_analysis_task_status(
+    chapter_id: str,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    查询指定章节的最新分析任务状态
+    
+    返回:
+    - task_id: 任务ID
+    - status: pending/running/completed/failed
+    - progress: 0-100
+    - error_message: 错误信息(如果失败)
+    - created_at: 创建时间
+    - completed_at: 完成时间
+    """
+    # 获取该章节最新的分析任务
+    result = await db.execute(
+        select(AnalysisTask)
+        .where(AnalysisTask.chapter_id == chapter_id)
+        .order_by(AnalysisTask.created_at.desc())
+        .limit(1)
+    )
+    task = result.scalar_one_or_none()
+    
+    if not task:
+        raise HTTPException(status_code=404, detail="未找到分析任务")
+    
+    return {
+        "task_id": task.id,
+        "chapter_id": task.chapter_id,
+        "status": task.status,
+        "progress": task.progress,
+        "error_message": task.error_message,
+        "created_at": task.created_at.isoformat() if task.created_at else None,
+        "started_at": task.started_at.isoformat() if task.started_at else None,
+        "completed_at": task.completed_at.isoformat() if task.completed_at else None
+    }
+
+
+@router.get("/{chapter_id}/analysis", summary="获取章节分析结果")
+async def get_chapter_analysis(
+    chapter_id: str,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    获取章节的完整分析结果
+    
+    返回:
+    - analysis_data: 完整的分析数据(JSON)
+    - summary: 分析摘要文本
+    - memories: 提取的记忆列表
+    - created_at: 分析时间
+    """
+    # 获取分析结果
+    analysis_result = await db.execute(
+        select(PlotAnalysis)
+        .where(PlotAnalysis.chapter_id == chapter_id)
+        .order_by(PlotAnalysis.created_at.desc())
+        .limit(1)
+    )
+    analysis = analysis_result.scalar_one_or_none()
+    
+    if not analysis:
+        raise HTTPException(status_code=404, detail="该章节暂无分析结果")
+    
+    # 获取相关记忆
+    memories_result = await db.execute(
+        select(StoryMemory)
+        .where(StoryMemory.chapter_id == chapter_id)
+        .order_by(StoryMemory.importance_score.desc())
+    )
+    memories = memories_result.scalars().all()
+    
+    return {
+        "chapter_id": chapter_id,
+        "analysis": analysis.to_dict(),  # 使用to_dict()方法
+        "memories": [
+            {
+                "id": mem.id,
+                "type": mem.memory_type,
+                "title": mem.title,
+                "content": mem.content,
+                "importance": mem.importance_score,
+                "tags": mem.tags,
+                "is_foreshadow": mem.is_foreshadow,
+                "position": mem.chapter_position,
+                "related_characters": mem.related_characters
+            }
+            for mem in memories
+        ],
+        "created_at": analysis.created_at.isoformat() if analysis.created_at else None
+    }
+
+
+@router.get("/{chapter_id}/annotations", summary="获取章节标注数据")
+async def get_chapter_annotations(
+    chapter_id: str,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    获取章节的标注数据（用于前端展示标注）
+    
+    返回格式化的标注列表，包含精确位置信息
+    适用于章节内容的可视化标注展示
+    """
+    # 获取章节
+    chapter_result = await db.execute(
+        select(Chapter).where(Chapter.id == chapter_id)
+    )
+    chapter = chapter_result.scalar_one_or_none()
+    
+    if not chapter:
+        raise HTTPException(status_code=404, detail="章节不存在")
+    
+    # 获取分析结果
+    analysis_result = await db.execute(
+        select(PlotAnalysis)
+        .where(PlotAnalysis.chapter_id == chapter_id)
+        .order_by(PlotAnalysis.created_at.desc())
+        .limit(1)
+    )
+    analysis = analysis_result.scalar_one_or_none()
+    
+    # 获取记忆
+    memories_result = await db.execute(
+        select(StoryMemory)
+        .where(StoryMemory.chapter_id == chapter_id)
+        .order_by(StoryMemory.importance_score.desc())
+    )
+    memories = memories_result.scalars().all()
+    
+    # 构建标注数据
+    annotations = []
+    
+    for mem in memories:
+        # 优先从数据库读取位置信息
+        position = mem.chapter_position if mem.chapter_position is not None else -1
+        length = mem.text_length if hasattr(mem, 'text_length') and mem.text_length is not None else 0
+        metadata_extra = {}
+        
+        # 如果数据库中没有位置信息，尝试从分析数据中重新计算
+        if position == -1 and analysis and chapter.content:
+            # 根据记忆类型从分析数据中查找对应项
+            if mem.memory_type == 'hook' and analysis.hooks:
+                for hook in analysis.hooks:
+                    # 通过标题或内容匹配
+                    if mem.title and hook.get('type') in mem.title:
+                        keyword = hook.get('keyword', '')
+                        if keyword:
+                            pos = chapter.content.find(keyword)
+                            if pos != -1:
+                                position = pos
+                                length = len(keyword)
+                        metadata_extra["strength"] = hook.get('strength', 5)
+                        metadata_extra["position_desc"] = hook.get('position', '')
+                        break
+            
+            elif mem.memory_type == 'foreshadow' and analysis.foreshadows:
+                for foreshadow in analysis.foreshadows:
+                    if foreshadow.get('content') in mem.content:
+                        keyword = foreshadow.get('keyword', '')
+                        if keyword:
+                            pos = chapter.content.find(keyword)
+                            if pos != -1:
+                                position = pos
+                                length = len(keyword)
+                        metadata_extra["foreshadow_type"] = foreshadow.get('type', 'planted')
+                        metadata_extra["strength"] = foreshadow.get('strength', 5)
+                        break
+            
+            elif mem.memory_type == 'plot_point' and analysis.plot_points:
+                for plot_point in analysis.plot_points:
+                    if plot_point.get('content') in mem.content:
+                        keyword = plot_point.get('keyword', '')
+                        if keyword:
+                            pos = chapter.content.find(keyword)
+                            if pos != -1:
+                                position = pos
+                                length = len(keyword)
+                        break
+        else:
+            # 如果数据库有位置，也从分析数据中提取额外的元数据
+            if analysis:
+                if mem.memory_type == 'hook' and analysis.hooks:
+                    for hook in analysis.hooks:
+                        if mem.title and hook.get('type') in mem.title:
+                            metadata_extra["strength"] = hook.get('strength', 5)
+                            metadata_extra["position_desc"] = hook.get('position', '')
+                            break
+                
+                elif mem.memory_type == 'foreshadow' and analysis.foreshadows:
+                    for foreshadow in analysis.foreshadows:
+                        if foreshadow.get('content') in mem.content:
+                            metadata_extra["foreshadow_type"] = foreshadow.get('type', 'planted')
+                            metadata_extra["strength"] = foreshadow.get('strength', 5)
+                            break
+        
+        annotation = {
+            "id": mem.id,
+            "type": mem.memory_type,
+            "title": mem.title,
+            "content": mem.content,
+            "importance": mem.importance_score or 0.5,
+            "position": position,
+            "length": length,
+            "tags": mem.tags or [],
+            "metadata": {
+                "is_foreshadow": mem.is_foreshadow,
+                "related_characters": mem.related_characters or [],
+                "related_locations": mem.related_locations or [],
+                **metadata_extra
+            }
+        }
+        
+        annotations.append(annotation)
+    
+    return {
+        "chapter_id": chapter_id,
+        "chapter_number": chapter.chapter_number,
+        "title": chapter.title,
+        "word_count": chapter.word_count or 0,
+        "annotations": annotations,
+        "has_analysis": analysis is not None,
+        "summary": {
+            "total_annotations": len(annotations),
+            "hooks": len([a for a in annotations if a["type"] == "hook"]),
+            "foreshadows": len([a for a in annotations if a["type"] == "foreshadow"]),
+            "plot_points": len([a for a in annotations if a["type"] == "plot_point"]),
+            "character_events": len([a for a in annotations if a["type"] == "character_event"])
+        }
+    }
+
+
+@router.post("/{chapter_id}/analyze", summary="手动触发章节分析")
+async def trigger_chapter_analysis(
+    chapter_id: str,
+    request: Request,
+    background_tasks: BackgroundTasks,
+    db: AsyncSession = Depends(get_db),
+    user_ai_service: AIService = Depends(get_user_ai_service)
+):
+    """
+    手动触发章节分析(用于重新分析或分析旧章节)
+    """
+    # 从请求中获取用户ID
+    user_id = getattr(request.state, "user_id", None)
+    if not user_id:
+        raise HTTPException(status_code=401, detail="未登录")
+    
+    # 验证章节存在
+    chapter_result = await db.execute(
+        select(Chapter).where(Chapter.id == chapter_id)
+    )
+    chapter = chapter_result.scalar_one_or_none()
+    
+    if not chapter:
+        raise HTTPException(status_code=404, detail="章节不存在")
+    
+    if not chapter.content or chapter.content.strip() == "":
+        raise HTTPException(status_code=400, detail="章节内容为空，无法分析")
+    
+    # 获取项目信息
+    project_result = await db.execute(
+        select(Project).where(Project.id == chapter.project_id)
+    )
+    project = project_result.scalar_one_or_none()
+    
+    if not project:
+        raise HTTPException(status_code=404, detail="项目不存在")
+    
+    # 创建分析任务
+    analysis_task = AnalysisTask(
+        chapter_id=chapter_id,
+        user_id=user_id,
+        project_id=project.id,
+        status='pending',
+        progress=0
+    )
+    db.add(analysis_task)
+    await db.commit()
+    
+    task_id = analysis_task.id
+    logger.info(f"📋 创建分析任务: {task_id}, 章节: {chapter_id}")
+    
+    # 刷新数据库会话，确保其他会话可以看到新任务
+    await db.refresh(analysis_task)
+    
+    # 短暂延迟确保SQLite WAL完成写入（让其他会话可见）
+    await asyncio.sleep(3)
+    
+    # 直接启动后台分析（并发执行）
+    background_tasks.add_task(
+        analyze_chapter_background,
+        chapter_id=chapter_id,
+        user_id=user_id,
+        project_id=project.id,
+        task_id=task_id,
+        ai_service=user_ai_service
+    )
+    
+    return {
+        "task_id": task_id,
+        "chapter_id": chapter_id,
+        "status": "pending",
+        "message": "分析任务已创建并开始执行"
+    }

backend/app/api/memories.py

@@ -0,0 +1,383 @@
+"""记忆管理API - 提供记忆的查询、分析等接口"""
+from fastapi import APIRouter, Depends, HTTPException, Request
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select, and_, desc
+from typing import List, Optional
+from app.database import get_db
+from app.models.memory import StoryMemory, PlotAnalysis
+from app.models.chapter import Chapter
+from app.services.memory_service import memory_service
+from app.services.plot_analyzer import get_plot_analyzer
+from app.services.ai_service import create_user_ai_service
+from app.models.settings import Settings
+from app.logger import get_logger
+import uuid
+
+logger = get_logger(__name__)
+router = APIRouter(prefix="/api/memories", tags=["memories"])
+
+
+@router.post("/projects/{project_id}/analyze-chapter/{chapter_id}")
+async def analyze_chapter(
+    project_id: str,
+    chapter_id: str,
+    request: Request,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    分析章节并生成记忆
+    
+    对指定章节进行剧情分析,提取钩子、伏笔、情节点等,并存入记忆系统
+    """
+    try:
+        user_id = request.state.user_id
+        
+        # 获取章节内容
+        result = await db.execute(
+            select(Chapter).where(
+                and_(
+                    Chapter.id == chapter_id,
+                    Chapter.project_id == project_id
+                )
+            )
+        )
+        chapter = result.scalar_one_or_none()
+        
+        if not chapter:
+            raise HTTPException(status_code=404, detail="章节不存在")
+        
+        if not chapter.content:
+            raise HTTPException(status_code=400, detail="章节内容为空,无法分析")
+        
+        # 获取用户AI设置
+        settings_result = await db.execute(select(Settings))
+        settings = settings_result.scalar_one_or_none()
+        
+        if not settings:
+            raise HTTPException(status_code=400, detail="请先配置AI设置")
+        
+        # 创建AI服务
+        ai_service = create_user_ai_service(
+            api_provider=settings.api_provider,
+            api_key=settings.api_key,
+            api_base_url=settings.api_base_url,
+            model_name=settings.llm_model,
+            temperature=settings.temperature,
+            max_tokens=settings.max_tokens
+        )
+        
+        # 执行剧情分析
+        analyzer = get_plot_analyzer(ai_service)
+        analysis_result = await analyzer.analyze_chapter(
+            chapter_number=chapter.chapter_number,
+            title=chapter.title,
+            content=chapter.content,
+            word_count=chapter.word_count or len(chapter.content)
+        )
+        
+        if not analysis_result:
+            raise HTTPException(status_code=500, detail="剧情分析失败")
+        
+        # 保存分析结果到数据库
+        plot_analysis = PlotAnalysis(
+            id=str(uuid.uuid4()),
+            project_id=project_id,
+            chapter_id=chapter_id,
+            plot_stage=analysis_result.get('plot_stage'),
+            conflict_level=analysis_result.get('conflict', {}).get('level'),
+            conflict_types=analysis_result.get('conflict', {}).get('types'),
+            emotional_tone=analysis_result.get('emotional_arc', {}).get('primary_emotion'),
+            emotional_intensity=analysis_result.get('emotional_arc', {}).get('intensity', 0) / 10,
+            emotional_curve=analysis_result.get('emotional_arc'),
+            hooks=analysis_result.get('hooks'),
+            hooks_count=len(analysis_result.get('hooks', [])),
+            hooks_avg_strength=sum(h.get('strength', 0) for h in analysis_result.get('hooks', [])) / max(len(analysis_result.get('hooks', [])), 1),
+            foreshadows=analysis_result.get('foreshadows'),
+            foreshadows_planted=sum(1 for f in analysis_result.get('foreshadows', []) if f.get('type') == 'planted'),
+            foreshadows_resolved=sum(1 for f in analysis_result.get('foreshadows', []) if f.get('type') == 'resolved'),
+            plot_points=analysis_result.get('plot_points'),
+            plot_points_count=len(analysis_result.get('plot_points', [])),
+            character_states=analysis_result.get('character_states'),
+            scenes=analysis_result.get('scenes'),
+            pacing=analysis_result.get('pacing'),
+            dialogue_ratio=analysis_result.get('dialogue_ratio'),
+            description_ratio=analysis_result.get('description_ratio'),
+            overall_quality_score=analysis_result.get('scores', {}).get('overall'),
+            pacing_score=analysis_result.get('scores', {}).get('pacing'),
+            engagement_score=analysis_result.get('scores', {}).get('engagement'),
+            coherence_score=analysis_result.get('scores', {}).get('coherence'),
+            analysis_report=analyzer.generate_analysis_summary(analysis_result),
+            suggestions=analysis_result.get('suggestions'),
+            word_count=chapter.word_count
+        )
+        
+        # 检查是否已存在分析记录
+        existing = await db.execute(
+            select(PlotAnalysis).where(PlotAnalysis.chapter_id == chapter_id)
+        )
+        if existing.scalar_one_or_none():
+            # 删除旧记录
+            await db.execute(
+                select(PlotAnalysis).where(PlotAnalysis.chapter_id == chapter_id)
+            )
+            await db.delete(existing.scalar_one())
+        
+        db.add(plot_analysis)
+        await db.commit()
+        
+        # 从分析结果中提取记忆片段
+        memories_data = analyzer.extract_memories_from_analysis(
+            analysis_result,
+            chapter_id,
+            chapter.chapter_number
+        )
+        
+        # 保存记忆到数据库和向量库
+        saved_count = 0
+        for mem_data in memories_data:
+            memory_id = str(uuid.uuid4())
+            
+            # 保存到关系数据库
+            memory = StoryMemory(
+                id=memory_id,
+                project_id=project_id,
+                chapter_id=chapter_id,
+                memory_type=mem_data['type'],
+                title=mem_data.get('title', ''),
+                content=mem_data['content'],
+                story_timeline=chapter.chapter_number,
+                vector_id=memory_id,
+                **mem_data['metadata']
+            )
+            db.add(memory)
+            
+            # 保存到向量库
+            await memory_service.add_memory(
+                user_id=user_id,
+                project_id=project_id,
+                memory_id=memory_id,
+                content=mem_data['content'],
+                memory_type=mem_data['type'],
+                metadata=mem_data['metadata']
+            )
+            saved_count += 1
+        
+        await db.commit()
+        
+        logger.info(f"✅ 章节分析完成: 保存{saved_count}条记忆")
+        
+        return {
+            "success": True,
+            "message": f"分析完成,提取了{saved_count}条记忆",
+            "analysis": plot_analysis.to_dict(),
+            "memories_count": saved_count
+        }
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"❌ 章节分析失败: {str(e)}")
+        await db.rollback()
+        raise HTTPException(status_code=500, detail=f"分析失败: {str(e)}")
+
+
+@router.get("/projects/{project_id}/memories")
+async def get_project_memories(
+    project_id: str,
+    request: Request,
+    memory_type: Optional[str] = None,
+    chapter_id: Optional[str] = None,
+    limit: int = 50,
+    db: AsyncSession = Depends(get_db)
+):
+    """获取项目的记忆列表"""
+    try:
+        user_id = request.state.user_id
+        
+        # 构建查询
+        query = select(StoryMemory).where(StoryMemory.project_id == project_id)
+        
+        if memory_type:
+            query = query.where(StoryMemory.memory_type == memory_type)
+        if chapter_id:
+            query = query.where(StoryMemory.chapter_id == chapter_id)
+        
+        query = query.order_by(desc(StoryMemory.importance_score), desc(StoryMemory.created_at)).limit(limit)
+        
+        result = await db.execute(query)
+        memories = result.scalars().all()
+        
+        return {
+            "success": True,
+            "memories": [mem.to_dict() for mem in memories],
+            "total": len(memories)
+        }
+        
+    except Exception as e:
+        logger.error(f"❌ 获取记忆失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/projects/{project_id}/analysis/{chapter_id}")
+async def get_chapter_analysis(
+    project_id: str,
+    chapter_id: str,
+    db: AsyncSession = Depends(get_db)
+):
+    """获取章节的剧情分析"""
+    try:
+        result = await db.execute(
+            select(PlotAnalysis).where(
+                and_(
+                    PlotAnalysis.project_id == project_id,
+                    PlotAnalysis.chapter_id == chapter_id
+                )
+            )
+        )
+        analysis = result.scalar_one_or_none()
+        
+        if not analysis:
+            raise HTTPException(status_code=404, detail="该章节还未进行分析")
+        
+        return {
+            "success": True,
+            "analysis": analysis.to_dict()
+        }
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"❌ 获取分析失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.post("/projects/{project_id}/search")
+async def search_memories(
+    project_id: str,
+    request: Request,
+    query: str,
+    memory_types: Optional[List[str]] = None,
+    limit: int = 10,
+    min_importance: float = 0.0
+):
+    """语义搜索项目记忆"""
+    try:
+        user_id = request.state.user_id
+        
+        memories = await memory_service.search_memories(
+            user_id=user_id,
+            project_id=project_id,
+            query=query,
+            memory_types=memory_types,
+            limit=limit,
+            min_importance=min_importance
+        )
+        
+        return {
+            "success": True,
+            "query": query,
+            "memories": memories,
+            "total": len(memories)
+        }
+        
+    except Exception as e:
+        logger.error(f"❌ 搜索记忆失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/projects/{project_id}/foreshadows")
+async def get_unresolved_foreshadows(
+    project_id: str,
+    request: Request,
+    current_chapter: int,
+    db: AsyncSession = Depends(get_db)
+):
+    """获取未完结的伏笔"""
+    try:
+        user_id = request.state.user_id
+        
+        # 从向量库搜索
+        foreshadows = await memory_service.find_unresolved_foreshadows(
+            user_id=user_id,
+            project_id=project_id,
+            current_chapter=current_chapter
+        )
+        
+        return {
+            "success": True,
+            "foreshadows": foreshadows,
+            "total": len(foreshadows)
+        }
+        
+    except Exception as e:
+        logger.error(f"❌ 获取伏笔失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/projects/{project_id}/stats")
+async def get_memory_stats(
+    project_id: str,
+    request: Request
+):
+    """获取记忆统计信息"""
+    try:
+        user_id = request.state.user_id
+        
+        stats = await memory_service.get_memory_stats(
+            user_id=user_id,
+            project_id=project_id
+        )
+        
+        return {
+            "success": True,
+            "stats": stats
+        }
+        
+    except Exception as e:
+        logger.error(f"❌ 获取统计失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.delete("/projects/{project_id}/chapters/{chapter_id}/memories")
+async def delete_chapter_memories(
+    project_id: str,
+    chapter_id: str,
+    request: Request,
+    db: AsyncSession = Depends(get_db)
+):
+    """删除章节的所有记忆"""
+    try:
+        user_id = request.state.user_id
+        
+        # 从数据库删除
+        result = await db.execute(
+            select(StoryMemory).where(
+                and_(
+                    StoryMemory.project_id == project_id,
+                    StoryMemory.chapter_id == chapter_id
+                )
+            )
+        )
+        memories = result.scalars().all()
+        
+        for memory in memories:
+            await db.delete(memory)
+        
+        # 从向量库删除
+        await memory_service.delete_chapter_memories(
+            user_id=user_id,
+            project_id=project_id,
+            chapter_id=chapter_id
+        )
+        
+        await db.commit()
+        
+        return {
+            "success": True,
+            "message": f"已删除{len(memories)}条记忆"
+        }
+        
+    except Exception as e:
+        logger.error(f"❌ 删除记忆失败: {str(e)}")
+        await db.rollback()
+        raise HTTPException(status_code=500, detail=str(e))

backend/app/api/outlines.py

@@ -1,5 +1,5 @@
 """大纲管理API"""
-from fastapi import APIRouter, Depends, HTTPException
+from fastapi import APIRouter, Depends, HTTPException, Request
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy import select, func, delete
 from typing import List, AsyncGenerator, Dict, Any
@@ -21,6 +21,7 @@ from app.schemas.outline import (
 )
 from app.services.ai_service import AIService
 from app.services.prompt_service import prompt_service
+from app.services.memory_service import memory_service
 from app.logger import get_logger
 from app.api.settings import get_user_ai_service
 from app.utils.sse_response import SSEResponse, create_sse_response
@@ -328,6 +329,7 @@ async def reorder_outlines(
 @router.post("/generate", response_model=OutlineListResponse, summary="AI生成/续写大纲")
 async def generate_outline(
     request: OutlineGenerateRequest,
+    http_request: Request,
     db: AsyncSession = Depends(get_db),
     user_ai_service: AIService = Depends(get_user_ai_service)
 ):
@@ -377,8 +379,10 @@ async def generate_outline(
                     detail="续写模式需要已有大纲，当前项目没有大纲"
                 )
             
+            # 获取用户ID用于记忆检索
+            user_id = getattr(http_request.state, "user_id", "system")
             return await _continue_outline(
-                request, project, existing_outlines, db, user_ai_service
+                request, project, existing_outlines, db, user_ai_service, user_id
             )
         
         else:
@@ -478,9 +482,10 @@ async def _continue_outline(
     project: Project,
     existing_outlines: List[Outline],
     db: AsyncSession,
-    user_ai_service: AIService
+    user_ai_service: AIService,
+    user_id: str = "system"
 ) -> OutlineListResponse:
-    """续写大纲 - 分批生成，每批5章"""
+    """续写大纲 - 分批生成，每批5章（记忆增强版）"""
     logger.info(f"续写大纲 - 项目: {project.id}, 已有: {len(existing_outlines)} 章")
     
     # 分析已有大纲
@@ -545,7 +550,25 @@ async def _continue_outline(
             for o in latest_outlines
         ])
         
-        # 使用标准续写提示词模板
+        # 🧠 构建记忆增强上下文（仅续写模式需要）
+        memory_context = None
+        try:
+            logger.info(f"🧠 为第{batch_num + 1}批构建记忆上下文...")
+            # 使用最近一章的大纲作为查询
+            query_outline = recent_outlines[-1].content if recent_outlines else ""
+            memory_context = await memory_service.build_context_for_generation(
+                user_id=user_id,
+                project_id=project.id,
+                current_chapter=current_start_chapter,
+                chapter_outline=query_outline,
+                character_names=[c.name for c in characters] if characters else None
+            )
+            logger.info(f"✅ 记忆上下文构建完成: {memory_context['stats']}")
+        except Exception as e:
+            logger.warning(f"⚠️ 记忆上下文构建失败，继续不使用记忆: {str(e)}")
+            memory_context = None
+        
+        # 使用标准续写提示词模板（支持记忆增强）
         prompt = prompt_service.get_outline_continue_prompt(
             title=project.title,
             theme=request.theme or project.theme or "未设定",
@@ -563,7 +586,8 @@ async def _continue_outline(
             plot_stage_instruction=stage_instruction,
             start_chapter=current_start_chapter,
             story_direction=request.story_direction or "自然延续",
-            requirements=request.requirements or ""
+            requirements=request.requirements or "",
+            memory_context=memory_context
         )
         
         # 调用AI生成当前批次
@@ -834,9 +858,10 @@ async def new_outline_generator(
 async def continue_outline_generator(
     data: Dict[str, Any],
     db: AsyncSession,
-    user_ai_service: AIService
+    user_ai_service: AIService,
+    user_id: str = "system"
 ) -> AsyncGenerator[str, None]:
-    """大纲续写SSE生成器 - 分批生成，推送进度"""
+    """大纲续写SSE生成器 - 分批生成，推送进度（记忆增强版）"""
     db_committed = False
     try:
         yield await SSEResponse.send_progress("开始续写大纲...", 5)
@@ -940,12 +965,32 @@ async def continue_outline_generator(
                 for o in latest_outlines
             ])
             
+            # 🧠 构建记忆增强上下文
+            memory_context = None
+            try:
+                yield await SSEResponse.send_progress(
+                    f"🧠 构建记忆上下文...",
+                    batch_progress + 3
+                )
+                query_outline = recent_outlines[-1].content if recent_outlines else ""
+                memory_context = await memory_service.build_context_for_generation(
+                    user_id=user_id,
+                    project_id=project_id,
+                    current_chapter=current_start_chapter,
+                    chapter_outline=query_outline,
+                    character_names=[c.name for c in characters] if characters else None
+                )
+                logger.info(f"✅ 记忆上下文: {memory_context['stats']}")
+            except Exception as e:
+                logger.warning(f"⚠️ 记忆上下文构建失败: {str(e)}")
+                memory_context = None
+            
             yield await SSEResponse.send_progress(
-                f"🤖 调用AI生成第{str(batch_num + 1)}批...",
+                f" 调用AI生成第{str(batch_num + 1)}批...",
                 batch_progress + 5
             )
             
-            # 使用标准续写提示词模板
+            # 使用标准续写提示词模板（支持记忆增强）
             prompt = prompt_service.get_outline_continue_prompt(
                 title=project.title,
                 theme=data.get("theme") or project.theme or "未设定",
@@ -963,7 +1008,8 @@ async def continue_outline_generator(
                 plot_stage_instruction=stage_instruction,
                 start_chapter=current_start_chapter,
                 story_direction=data.get("story_direction", "自然延续"),
-                requirements=data.get("requirements", "")
+                requirements=data.get("requirements", ""),
+                memory_context=memory_context
             )
             
             # 调用AI生成当前批次
@@ -1062,6 +1108,7 @@ async def continue_outline_generator(
 @router.post("/generate-stream", summary="AI生成/续写大纲(SSE流式)")
 async def generate_outline_stream(
     data: Dict[str, Any],
+    request: Request,
     db: AsyncSession = Depends(get_db),
     user_ai_service: AIService = Depends(get_user_ai_service)
 ):
@@ -1111,6 +1158,9 @@ async def generate_outline_stream(
         mode = "continue" if existing_outlines else "new"
         logger.info(f"自动判断模式：{'续写' if existing_outlines else '新建'}")
     
+    # 获取用户ID
+    user_id = getattr(request.state, "user_id", "system")
+    
     # 根据模式选择生成器
     if mode == "new":
         return create_sse_response(new_outline_generator(data, db, user_ai_service))
@@ -1120,7 +1170,7 @@ async def generate_outline_stream(
                 status_code=400,
                 detail="续写模式需要已有大纲，当前项目没有大纲"
             )
-        return create_sse_response(continue_outline_generator(data, db, user_ai_service))
+        return create_sse_response(continue_outline_generator(data, db, user_ai_service, user_id))
     else:
         raise HTTPException(
             status_code=400,

backend/app/api/projects.py

@@ -1,9 +1,11 @@
 """项目管理API"""
-from fastapi import APIRouter, Depends, HTTPException
+from fastapi import APIRouter, Depends, HTTPException, UploadFile, File
 from fastapi.responses import Response
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy import select, func, delete
 from typing import List
+import json
+from urllib.parse import quote
 from app.database import get_db
 from app.models.project import Project
 from app.models.character import Character
@@ -17,6 +19,12 @@ from app.schemas.project import (
     ProjectResponse,
     ProjectListResponse
 )
+from app.schemas.import_export import (
+    ExportOptions,
+    ImportValidationResult,
+    ImportResult
+)
+from app.services.import_export_service import ImportExportService
 from app.logger import get_logger
 from app.utils.data_consistency import (
     run_full_data_consistency_check,
@@ -412,4 +420,168 @@ async def fix_project_member_counts(
         raise
     except Exception as e:
         logger.error(f"修复成员计数失败: {str(e)}", exc_info=True)
-        raise HTTPException(status_code=500, detail=f"修复失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"修复失败: {str(e)}")
+
+
+@router.post("/{project_id}/export-data", summary="导出项目数据为JSON")
+async def export_project_data(
+    project_id: str,
+    options: ExportOptions,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    导出项目完整数据为JSON格式
+    
+    Args:
+        project_id: 项目ID
+        options: 导出选项
+    
+    Returns:
+        JSON文件下载
+    """
+    try:
+        logger.info(f"开始导出项目数据: {project_id}")
+        
+        # 检查项目是否存在
+        result = await db.execute(
+            select(Project).where(Project.id == project_id)
+        )
+        project = result.scalar_one_or_none()
+        
+        if not project:
+            logger.warning(f"项目不存在: {project_id}")
+            raise HTTPException(status_code=404, detail="项目不存在")
+        
+        # 导出数据
+        export_data = await ImportExportService.export_project(
+            project_id=project_id,
+            db=db,
+            include_generation_history=options.include_generation_history,
+            include_writing_styles=options.include_writing_styles
+        )
+        
+        # 转换为JSON
+        json_content = export_data.model_dump_json(indent=2, exclude_none=True, by_alias=True)
+        
+        # 生成文件名
+        safe_title = "".join(c for c in project.title if c.isalnum() or c in (' ', '-', '_'))
+        from datetime import datetime
+        date_str = datetime.now().strftime("%Y%m%d")
+        filename = f"project_{safe_title}_{date_str}.json"
+        encoded_filename = quote(filename)
+        
+        logger.info(f"项目数据导出成功: {filename}")
+        
+        return Response(
+            content=json_content.encode('utf-8'),
+            media_type="application/json; charset=utf-8",
+            headers={
+                "Content-Disposition": f"attachment; filename*=UTF-8''{encoded_filename}",
+                "Content-Type": "application/json; charset=utf-8"
+            }
+        )
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"导出项目数据失败: {str(e)}", exc_info=True)
+        raise HTTPException(status_code=500, detail=f"导出失败: {str(e)}")
+
+
+@router.post("/validate-import", response_model=ImportValidationResult, summary="验证导入文件")
+async def validate_import_file(
+    file: UploadFile = File(...)
+):
+    """
+    验证导入文件的格式和内容
+    
+    Args:
+        file: 上传的JSON文件
+    
+    Returns:
+        验证结果
+    """
+    try:
+        logger.info(f"验证导入文件: {file.filename}")
+        
+        # 检查文件类型
+        if not file.filename.endswith('.json'):
+            raise HTTPException(status_code=400, detail="只支持JSON格式文件")
+        
+        # 读取文件内容
+        content = await file.read()
+        
+        # 检查文件大小（50MB限制）
+        max_size = 50 * 1024 * 1024  # 50MB
+        if len(content) > max_size:
+            raise HTTPException(status_code=413, detail="文件大小超过50MB限制")
+        
+        # 解析JSON
+        try:
+            data = json.loads(content.decode('utf-8'))
+        except json.JSONDecodeError as e:
+            raise HTTPException(status_code=400, detail=f"无效的JSON格式: {str(e)}")
+        
+        # 验证数据
+        validation_result = ImportExportService.validate_import_data(data)
+        
+        logger.info(f"文件验证完成: valid={validation_result.valid}")
+        return validation_result
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"验证导入文件失败: {str(e)}", exc_info=True)
+        raise HTTPException(status_code=500, detail=f"验证失败: {str(e)}")
+
+
+@router.post("/import", response_model=ImportResult, summary="导入项目")
+async def import_project(
+    file: UploadFile = File(...),
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    导入项目数据（创建新项目）
+    
+    Args:
+        file: 上传的JSON文件
+    
+    Returns:
+        导入结果
+    """
+    try:
+        logger.info(f"开始导入项目: {file.filename}")
+        
+        # 检查文件类型
+        if not file.filename.endswith('.json'):
+            raise HTTPException(status_code=400, detail="只支持JSON格式文件")
+        
+        # 读取文件内容
+        content = await file.read()
+        
+        # 检查文件大小
+        max_size = 50 * 1024 * 1024  # 50MB
+        if len(content) > max_size:
+            raise HTTPException(status_code=413, detail="文件大小超过50MB限制")
+        
+        # 解析JSON
+        try:
+            data = json.loads(content.decode('utf-8'))
+        except json.JSONDecodeError as e:
+            raise HTTPException(status_code=400, detail=f"无效的JSON格式: {str(e)}")
+        
+        # 导入数据
+        import_result = await ImportExportService.import_project(data, db)
+        
+        if import_result.success:
+            logger.info(f"项目导入成功: {import_result.project_id}")
+        else:
+            logger.warning(f"项目导入失败: {import_result.message}")
+        
+        return import_result
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"导入项目失败: {str(e)}", exc_info=True)
+        raise HTTPException(status_code=500, detail=f"导入失败: {str(e)}")

backend/app/api/settings.py

@@ -28,7 +28,7 @@ def read_env_defaults() -> Dict[str, Any]:
         "api_provider": app_settings.default_ai_provider,
         "api_key": app_settings.openai_api_key or app_settings.anthropic_api_key or "",
         "api_base_url": app_settings.openai_base_url or app_settings.anthropic_base_url or "",
-        "model_name": app_settings.default_model,
+        "llm_model": app_settings.default_model,
         "temperature": app_settings.default_temperature,
         "max_tokens": app_settings.default_max_tokens,
     }
@@ -71,7 +71,7 @@ async def get_user_ai_service(
         api_provider=settings.api_provider,
         api_key=settings.api_key,
         api_base_url=settings.api_base_url or "",
-        model_name=settings.model_name,
+        model_name=settings.llm_model,
         temperature=settings.temperature,
         max_tokens=settings.max_tokens
     )
@@ -305,7 +305,7 @@ class ApiTestRequest(BaseModel):
     api_key: str
     api_base_url: str
     provider: str
-    model_name: str
+    llm_model: str
 
 
 @router.post("/test")
@@ -322,7 +322,7 @@ async def test_api_connection(data: ApiTestRequest):
     api_key = data.api_key
     api_base_url = data.api_base_url
     provider = data.provider
-    model_name = data.model_name
+    llm_model = data.llm_model
     import time
     
     try:
@@ -333,7 +333,7 @@ async def test_api_connection(data: ApiTestRequest):
             api_provider=provider,
             api_key=api_key,
             api_base_url=api_base_url,
-            default_model=model_name,
+            default_model=llm_model,
             default_temperature=0.7,
             default_max_tokens=100
         )
@@ -343,13 +343,13 @@ async def test_api_connection(data: ApiTestRequest):
         
         logger.info(f"🧪 开始测试 API 连接")
         logger.info(f"  - 提供商: {provider}")
-        logger.info(f"  - 模型: {model_name}")
+        logger.info(f"  - 模型: {llm_model}")
         logger.info(f"  - Base URL: {api_base_url}")
         
         response = await test_service.generate_text(
             prompt=test_prompt,
             provider=provider,
-            model=model_name,
+            model=llm_model,
             temperature=0.7,
             max_tokens=8000
         )
@@ -366,7 +366,7 @@ async def test_api_connection(data: ApiTestRequest):
             "message": "API 连接测试成功",
             "response_time_ms": response_time,
             "provider": provider,
-            "model": model_name,
+            "model": llm_model,
             "response_preview": response[:100] if response and len(response) > 100 else response,
             "details": {
                 "api_available": True,

backend/app/database.py

@@ -15,6 +15,15 @@ logger = get_logger(__name__)
 # 创建基类
 Base = declarative_base()
 
+# 导入所有模型，确保 Base.metadata 能够发现它们
+# 这必须在 Base 创建之后、init_db 之前导入
+from app.models import (
+    Project, Outline, Character, Chapter, GenerationHistory,
+    Settings, WritingStyle, ProjectDefaultStyle,
+    RelationshipType, CharacterRelationship, Organization, OrganizationMember,
+    StoryMemory, PlotAnalysis, AnalysisTask
+)
+
 # 引擎缓存：每个用户一个引擎
 _engine_cache: Dict[str, Any] = {}
 

backend/app/main.py

@@ -114,7 +114,7 @@ async def db_session_stats():
 from app.api import (
     projects, outlines, characters, chapters,
     wizard_stream, relationships, organizations,
-    auth, users, settings, writing_styles
+    auth, users, settings, writing_styles, memories
 )
 
 app.include_router(auth.router, prefix="/api")
@@ -129,6 +129,7 @@ app.include_router(chapters.router, prefix="/api")
 app.include_router(relationships.router, prefix="/api")
 app.include_router(organizations.router, prefix="/api")
 app.include_router(writing_styles.router, prefix="/api")
+app.include_router(memories.router)  # 记忆管理API (已包含/api前缀)
 
 static_dir = Path(__file__).parent.parent / "static"
 if static_dir.exists():

backend/app/models/__init__.py

@@ -13,6 +13,8 @@ from app.models.relationship import (
     Organization,
     OrganizationMember
 )
+from app.models.memory import StoryMemory, PlotAnalysis
+from app.models.analysis_task import AnalysisTask
 
 __all__ = [
     "Project",
@@ -27,4 +29,7 @@ __all__ = [
     "CharacterRelationship",
     "Organization",
     "OrganizationMember",
+    "StoryMemory",
+    "PlotAnalysis",
+    "AnalysisTask",
 ]

backend/app/models/analysis_task.py

@@ -0,0 +1,38 @@
+"""分析任务模型 - 追踪异步章节分析任务状态"""
+from sqlalchemy import Column, String, Integer, Text, DateTime, ForeignKey, Index
+from sqlalchemy.sql import func
+from app.database import Base
+import uuid
+
+
+class AnalysisTask(Base):
+    """
+    分析任务表 - 追踪异步分析任务的执行状态
+    
+    状态流转: pending -> running -> completed/failed
+    """
+    __tablename__ = "analysis_tasks"
+    
+    id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()), comment="任务ID")
+    chapter_id = Column(String(36), ForeignKey('chapters.id', ondelete='CASCADE'), nullable=False, comment="章节ID")
+    user_id = Column(String(50), nullable=False, comment="用户ID")
+    project_id = Column(String(36), nullable=False, comment="项目ID")
+    
+    # 任务状态
+    status = Column(String(20), nullable=False, default='pending', comment="任务状态: pending/running/completed/failed")
+    progress = Column(Integer, default=0, comment="进度 0-100")
+    error_message = Column(Text, nullable=True, comment="错误信息")
+    
+    # 时间戳
+    created_at = Column(DateTime, server_default=func.now(), comment="创建时间")
+    started_at = Column(DateTime, nullable=True, comment="开始执行时间")
+    completed_at = Column(DateTime, nullable=True, comment="完成时间")
+    
+    # 索引优化查询
+    __table_args__ = (
+        Index('idx_chapter_id_created', 'chapter_id', 'created_at'),
+        Index('idx_status', 'status'),
+    )
+    
+    def __repr__(self):
+        return f"<AnalysisTask(id={self.id[:8]}..., chapter_id={self.chapter_id[:8]}..., status={self.status})>"

backend/app/models/memory.py

@@ -0,0 +1,200 @@
+"""长期记忆数据模型 - 支持向量检索和剧情分析"""
+from sqlalchemy import Column, String, Text, Integer, DateTime, ForeignKey, Float, JSON, Boolean
+from sqlalchemy.sql import func
+from app.database import Base
+import uuid
+
+
+class StoryMemory(Base):
+    """故事记忆表 - 存储结构化的故事片段和元数据"""
+    __tablename__ = "story_memories"
+    
+    id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
+    project_id = Column(String(36), ForeignKey("projects.id", ondelete="CASCADE"), nullable=False, index=True)
+    chapter_id = Column(String(36), ForeignKey("chapters.id", ondelete="CASCADE"), nullable=True, index=True)
+    
+    # 记忆类型
+    memory_type = Column(String(50), nullable=False, index=True, comment="""
+    记忆类型:
+    - plot_point: 情节点
+    - character_event: 角色事件
+    - world_detail: 世界观细节
+    - hook: 钩子(悬念/冲突)
+    - foreshadow: 伏笔
+    - dialogue: 重要对话
+    - scene: 场景描写
+    """)
+    
+    # 记忆内容
+    title = Column(String(200), comment="记忆标题/简述")
+    content = Column(Text, nullable=False, comment="记忆内容摘要(100-500字)")
+    full_context = Column(Text, comment="完整上下文(可选,用于详细记录)")
+    
+    # 关联信息
+    related_characters = Column(JSON, comment="涉及角色ID列表: ['char_id_1', 'char_id_2']")
+    related_locations = Column(JSON, comment="涉及地点列表: ['地点1', '地点2']")
+    tags = Column(JSON, comment="标签列表: ['悬念', '转折', '伏笔', '高潮']")
+    
+    # 重要性评分 (用于过滤和排序)
+    importance_score = Column(Float, default=0.5, comment="重要性评分 0.0-1.0")
+    
+    # 时间线定位
+    story_timeline = Column(Integer, nullable=False, index=True, comment="故事时间线位置(章节序号)")
+    chapter_position = Column(Integer, default=0, comment="章节内位置(字符位置)")
+    text_length = Column(Integer, default=0, comment="文本长度(字符数)")
+    
+    # 伏笔相关字段
+    is_foreshadow = Column(Integer, default=0, comment="伏笔状态: 0=普通记忆, 1=已埋下伏笔, 2=伏笔已回收")
+    foreshadow_resolved_at = Column(String(36), ForeignKey("chapters.id", ondelete="SET NULL"), comment="伏笔回收的章节ID")
+    foreshadow_strength = Column(Float, comment="伏笔强度 0.0-1.0")
+    
+    # 向量数据库关联
+    vector_id = Column(String(100), unique=True, comment="向量数据库中的唯一ID")
+    embedding_model = Column(String(100), default="paraphrase-multilingual-MiniLM-L12-v2", comment="使用的embedding模型")
+    
+    created_at = Column(DateTime, server_default=func.now(), comment="创建时间")
+    updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now(), comment="更新时间")
+    
+    def __repr__(self):
+        return f"<StoryMemory(id={self.id[:8]}, type={self.memory_type}, title={self.title})>"
+    
+    def to_dict(self):
+        """转换为字典格式"""
+        return {
+            "id": self.id,
+            "project_id": self.project_id,
+            "chapter_id": self.chapter_id,
+            "memory_type": self.memory_type,
+            "title": self.title,
+            "content": self.content,
+            "related_characters": self.related_characters,
+            "related_locations": self.related_locations,
+            "tags": self.tags,
+            "importance_score": self.importance_score,
+            "story_timeline": self.story_timeline,
+            "is_foreshadow": self.is_foreshadow,
+            "created_at": self.created_at.isoformat() if self.created_at else None
+        }
+
+
+class PlotAnalysis(Base):
+    """剧情分析表 - 存储AI分析的章节结构和剧情元素"""
+    __tablename__ = "plot_analysis"
+    
+    id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
+    project_id = Column(String(36), ForeignKey("projects.id", ondelete="CASCADE"), nullable=False, index=True)
+    chapter_id = Column(String(36), ForeignKey("chapters.id", ondelete="CASCADE"), nullable=False, unique=True, index=True)
+    
+    # 剧情结构分析
+    plot_stage = Column(String(50), comment="剧情阶段: 开端/发展/高潮/结局/过渡")
+    conflict_level = Column(Integer, comment="冲突强度 1-10")
+    conflict_types = Column(JSON, comment="冲突类型列表: ['人与人', '人与己', '人与环境']")
+    
+    # 情感分析
+    emotional_tone = Column(String(100), comment="主导情感: 紧张/温馨/悲伤/激昂/平静")
+    emotional_intensity = Column(Float, comment="情感强度 0.0-1.0")
+    emotional_curve = Column(JSON, comment="情感曲线: {start: 0.3, middle: 0.7, end: 0.5}")
+    
+    # 钩子分析 (Hook Analysis)
+    hooks = Column(JSON, comment="""钩子列表 - 吸引读者的元素: [
+        {
+            "type": "悬念|情感|冲突|认知",
+            "content": "具体内容",
+            "strength": 8,
+            "position": "开头|中段|结尾"
+        }
+    ]""")
+    hooks_count = Column(Integer, default=0, comment="钩子数量")
+    hooks_avg_strength = Column(Float, comment="钩子平均强度")
+    
+    # 伏笔分析 (Foreshadowing Analysis)
+    foreshadows = Column(JSON, comment="""伏笔列表: [
+        {
+            "content": "伏笔内容",
+            "type": "planted|resolved",
+            "strength": 7,
+            "subtlety": 8,
+            "reference_chapter": 3
+        }
+    ]""")
+    foreshadows_planted = Column(Integer, default=0, comment="本章埋下的伏笔数量")
+    foreshadows_resolved = Column(Integer, default=0, comment="本章回收的伏笔数量")
+    
+    # 关键情节点 (Plot Points)
+    plot_points = Column(JSON, comment="""情节点列表: [
+        {
+            "content": "情节点描述",
+            "importance": 0.9,
+            "type": "revelation|conflict|resolution|transition",
+            "impact": "对故事的影响描述"
+        }
+    ]""")
+    plot_points_count = Column(Integer, default=0, comment="情节点数量")
+    
+    # 角色状态追踪 (Character State Tracking)
+    character_states = Column(JSON, comment="""角色状态变化: [
+        {
+            "character_id": "xxx",
+            "character_name": "张三",
+            "state_before": "犹豫不决",
+            "state_after": "坚定信念",
+            "psychological_change": "内心描述",
+            "key_event": "触发事件",
+            "relationship_changes": {"李四": "关系变化"}
+        }
+    ]""")
+    
+    # 场景和氛围
+    scenes = Column(JSON, comment="场景列表: [{location: '地点', atmosphere: '氛围', duration: '时长'}]")
+    pacing = Column(String(50), comment="节奏: slow|moderate|fast|varied")
+    
+    # 质量评分
+    overall_quality_score = Column(Float, comment="整体质量评分 0.0-10.0")
+    pacing_score = Column(Float, comment="节奏评分 0.0-10.0")
+    engagement_score = Column(Float, comment="吸引力评分 0.0-10.0")
+    coherence_score = Column(Float, comment="连贯性评分 0.0-10.0")
+    
+    # 文本分析报告
+    analysis_report = Column(Text, comment="完整的文字分析报告")
+    suggestions = Column(JSON, comment="改进建议列表: ['建议1', '建议2']")
+    
+    # 统计信息
+    word_count = Column(Integer, comment="章节字数")
+    dialogue_ratio = Column(Float, comment="对话占比 0.0-1.0")
+    description_ratio = Column(Float, comment="描写占比 0.0-1.0")
+    
+    created_at = Column(DateTime, server_default=func.now(), comment="分析时间")
+    
+    def __repr__(self):
+        return f"<PlotAnalysis(chapter_id={self.chapter_id[:8]}, stage={self.plot_stage}, quality={self.overall_quality_score})>"
+    
+    def to_dict(self):
+        """转换为字典格式"""
+        return {
+            "id": self.id,
+            "chapter_id": self.chapter_id,
+            "plot_stage": self.plot_stage,
+            "conflict_level": self.conflict_level,
+            "conflict_types": self.conflict_types or [],
+            "emotional_tone": self.emotional_tone,
+            "emotional_intensity": self.emotional_intensity or 0.0,
+            "hooks": self.hooks or [],
+            "hooks_count": self.hooks_count or 0,
+            "foreshadows": self.foreshadows or [],
+            "foreshadows_planted": self.foreshadows_planted or 0,
+            "foreshadows_resolved": self.foreshadows_resolved or 0,
+            "plot_points": self.plot_points or [],
+            "plot_points_count": self.plot_points_count or 0,
+            "character_states": self.character_states or [],
+            "scenes": self.scenes or [],
+            "pacing": self.pacing,
+            "overall_quality_score": self.overall_quality_score or 0.0,
+            "pacing_score": self.pacing_score or 0.0,
+            "engagement_score": self.engagement_score or 0.0,
+            "coherence_score": self.coherence_score or 0.0,
+            "analysis_report": self.analysis_report,
+            "suggestions": self.suggestions or [],
+            "dialogue_ratio": self.dialogue_ratio or 0.0,
+            "description_ratio": self.description_ratio or 0.0,
+            "created_at": self.created_at.isoformat() if self.created_at else None
+        }

backend/app/models/settings.py

@@ -14,7 +14,7 @@ class Settings(Base):
     api_provider = Column(String(50), default="openai", comment="API提供商")
     api_key = Column(String(500), comment="API密钥")
     api_base_url = Column(String(500), comment="自定义API地址")
-    model_name = Column(String(100), default="gpt-4", comment="模型名称")
+    llm_model = Column(String(100), default="gpt-4", comment="模型名称")
     temperature = Column(Float, default=0.7, comment="温度参数")
     max_tokens = Column(Integer, default=2000, comment="最大token数")
     preferences = Column(Text, comment="其他偏好设置(JSON)")

backend/app/schemas/import_export.py

@@ -0,0 +1,136 @@
+"""导入导出相关的Pydantic模型"""
+from pydantic import BaseModel, Field
+from typing import Optional, List, Dict, Any
+from datetime import datetime
+
+
+class ExportOptions(BaseModel):
+    """导出选项"""
+    include_generation_history: bool = Field(False, description="是否包含生成历史")
+    include_writing_styles: bool = Field(True, description="是否包含写作风格")
+
+
+class ChapterExportData(BaseModel):
+    """章节导出数据"""
+    title: str
+    content: Optional[str] = None
+    summary: Optional[str] = None
+    chapter_number: int
+    word_count: int = 0
+    status: str = "draft"
+    created_at: Optional[str] = None
+
+
+class CharacterExportData(BaseModel):
+    """角色导出数据"""
+    name: str
+    age: Optional[str] = None
+    gender: Optional[str] = None
+    is_organization: bool = False
+    role_type: Optional[str] = None
+    personality: Optional[str] = None
+    background: Optional[str] = None
+    appearance: Optional[str] = None
+    traits: Optional[List[str]] = None
+    organization_type: Optional[str] = None
+    organization_purpose: Optional[str] = None
+    created_at: Optional[str] = None
+
+
+class OutlineExportData(BaseModel):
+    """大纲导出数据"""
+    title: str
+    content: Optional[str] = None
+    structure: Optional[str] = None
+    order_index: Optional[int] = None
+    created_at: Optional[str] = None
+
+
+class RelationshipExportData(BaseModel):
+    """关系导出数据"""
+    source_name: str
+    target_name: str
+    relationship_name: Optional[str] = None
+    intimacy_level: int = 50
+    status: str = "active"
+    description: Optional[str] = None
+    started_at: Optional[str] = None
+
+
+class OrganizationExportData(BaseModel):
+    """组织详情导出数据"""
+    character_name: str
+    parent_org_name: Optional[str] = None
+    power_level: int = 50
+    member_count: int = 0
+    location: Optional[str] = None
+    motto: Optional[str] = None
+    color: Optional[str] = None
+
+
+class OrganizationMemberExportData(BaseModel):
+    """组织成员导出数据"""
+    organization_name: str
+    character_name: str
+    position: str
+    rank: int = 0
+    status: str = "active"
+    joined_at: Optional[str] = None
+    loyalty: int = 50
+    contribution: int = 0
+    notes: Optional[str] = None
+
+
+class WritingStyleExportData(BaseModel):
+    """写作风格导出数据"""
+    name: str
+    style_type: str
+    preset_id: Optional[str] = None
+    description: Optional[str] = None
+    prompt_content: str
+    order_index: int = 0
+
+
+class GenerationHistoryExportData(BaseModel):
+    """生成历史导出数据"""
+    chapter_title: Optional[str] = None
+    prompt: Optional[str] = None
+    generated_content: Optional[str] = None
+    model: Optional[str] = None
+    tokens_used: Optional[int] = None
+    generation_time: Optional[float] = None
+    created_at: Optional[str] = None
+
+
+class ProjectExportData(BaseModel):
+    """项目完整导出数据"""
+    version: str = "1.0.0"
+    export_time: str
+    project: Dict[str, Any]
+    chapters: List[ChapterExportData] = []
+    characters: List[CharacterExportData] = []
+    outlines: List[OutlineExportData] = []
+    relationships: List[RelationshipExportData] = []
+    organizations: List[OrganizationExportData] = []
+    organization_members: List[OrganizationMemberExportData] = []
+    writing_styles: List[WritingStyleExportData] = []
+    generation_history: List[GenerationHistoryExportData] = []
+
+
+class ImportValidationResult(BaseModel):
+    """导入验证结果"""
+    valid: bool
+    version: str
+    project_name: Optional[str] = None
+    statistics: Dict[str, int] = {}
+    errors: List[str] = []
+    warnings: List[str] = []
+
+
+class ImportResult(BaseModel):
+    """导入结果"""
+    success: bool
+    project_id: Optional[str] = None
+    message: str
+    statistics: Dict[str, int] = {}
+    warnings: List[str] = []

backend/app/schemas/settings.py

@@ -11,7 +11,7 @@ class SettingsBase(BaseModel):
     api_provider: Optional[str] = Field(default="openai", description="API提供商")
     api_key: Optional[str] = Field(default=None, description="API密钥")
     api_base_url: Optional[str] = Field(default=None, description="自定义API地址")
-    model_name: Optional[str] = Field(default="gpt-4", description="模型名称")
+    llm_model: Optional[str] = Field(default="gpt-4", description="模型名称")
     temperature: Optional[float] = Field(default=0.7, ge=0.0, le=2.0, description="温度参数")
     max_tokens: Optional[int] = Field(default=2000, ge=1, description="最大token数")
     preferences: Optional[str] = Field(default=None, description="其他偏好设置(JSON)")

backend/app/services/import_export_service.py

@@ -0,0 +1,769 @@
+"""导入导出服务"""
+import json
+from datetime import datetime
+from typing import Dict, List, Optional, Tuple
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select
+from app.models.project import Project
+from app.models.chapter import Chapter
+from app.models.character import Character
+from app.models.outline import Outline
+from app.models.relationship import CharacterRelationship, Organization, OrganizationMember
+from app.models.writing_style import WritingStyle
+from app.models.generation_history import GenerationHistory
+from app.schemas.import_export import (
+    ProjectExportData,
+    ChapterExportData,
+    CharacterExportData,
+    OutlineExportData,
+    RelationshipExportData,
+    OrganizationExportData,
+    OrganizationMemberExportData,
+    WritingStyleExportData,
+    GenerationHistoryExportData,
+    ImportValidationResult,
+    ImportResult
+)
+from app.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class ImportExportService:
+    """导入导出服务类"""
+    
+    SUPPORTED_VERSION = "1.0.0"
+    
+    @staticmethod
+    async def export_project(
+        project_id: str,
+        db: AsyncSession,
+        include_generation_history: bool = False,
+        include_writing_styles: bool = True
+    ) -> ProjectExportData:
+        """
+        导出项目完整数据
+        
+        Args:
+            project_id: 项目ID
+            db: 数据库会话
+            include_generation_history: 是否包含生成历史
+            include_writing_styles: 是否包含写作风格
+            
+        Returns:
+            ProjectExportData: 导出的项目数据
+        """
+        logger.info(f"开始导出项目: {project_id}")
+        
+        # 获取项目基本信息
+        result = await db.execute(select(Project).where(Project.id == project_id))
+        project = result.scalar_one_or_none()
+        if not project:
+            raise ValueError(f"项目不存在: {project_id}")
+        
+        # 项目基本信息
+        project_data = {
+            "title": project.title,
+            "description": project.description,
+            "theme": project.theme,
+            "genre": project.genre,
+            "target_words": project.target_words,
+            "current_words": project.current_words,
+            "status": project.status,
+            "world_time_period": project.world_time_period,
+            "world_location": project.world_location,
+            "world_atmosphere": project.world_atmosphere,
+            "world_rules": project.world_rules,
+            "chapter_count": project.chapter_count,
+            "narrative_perspective": project.narrative_perspective,
+            "character_count": project.character_count,
+            "created_at": project.created_at.isoformat() if project.created_at else None,
+        }
+        
+        # 导出章节
+        chapters = await ImportExportService._export_chapters(project_id, db)
+        logger.info(f"导出章节数: {len(chapters)}")
+        
+        # 导出角色
+        characters = await ImportExportService._export_characters(project_id, db)
+        logger.info(f"导出角色数: {len(characters)}")
+        
+        # 导出大纲
+        outlines = await ImportExportService._export_outlines(project_id, db)
+        logger.info(f"导出大纲数: {len(outlines)}")
+        
+        # 导出关系
+        relationships = await ImportExportService._export_relationships(project_id, db)
+        logger.info(f"导出关系数: {len(relationships)}")
+        
+        # 导出组织详情
+        organizations = await ImportExportService._export_organizations(project_id, db)
+        logger.info(f"导出组织数: {len(organizations)}")
+        
+        # 导出组织成员
+        org_members = await ImportExportService._export_organization_members(project_id, db)
+        logger.info(f"导出组织成员数: {len(org_members)}")
+        
+        # 导出写作风格（可选）
+        writing_styles = []
+        if include_writing_styles:
+            writing_styles = await ImportExportService._export_writing_styles(project_id, db)
+            logger.info(f"导出写作风格数: {len(writing_styles)}")
+        
+        # 导出生成历史（可选）
+        generation_history = []
+        if include_generation_history:
+            generation_history = await ImportExportService._export_generation_history(project_id, db)
+            logger.info(f"导出生成历史数: {len(generation_history)}")
+        
+        export_data = ProjectExportData(
+            version=ImportExportService.SUPPORTED_VERSION,
+            export_time=datetime.utcnow().isoformat(),
+            project=project_data,
+            chapters=chapters,
+            characters=characters,
+            outlines=outlines,
+            relationships=relationships,
+            organizations=organizations,
+            organization_members=org_members,
+            writing_styles=writing_styles,
+            generation_history=generation_history
+        )
+        
+        logger.info(f"项目导出完成: {project_id}")
+        return export_data
+    
+    @staticmethod
+    async def _export_chapters(project_id: str, db: AsyncSession) -> List[ChapterExportData]:
+        """导出章节"""
+        result = await db.execute(
+            select(Chapter)
+            .where(Chapter.project_id == project_id)
+            .order_by(Chapter.chapter_number)
+        )
+        chapters = result.scalars().all()
+        
+        return [
+            ChapterExportData(
+                title=ch.title,
+                content=ch.content,
+                summary=ch.summary,
+                chapter_number=ch.chapter_number,
+                word_count=ch.word_count or 0,
+                status=ch.status,
+                created_at=ch.created_at.isoformat() if ch.created_at else None
+            )
+            for ch in chapters
+        ]
+    
+    @staticmethod
+    async def _export_characters(project_id: str, db: AsyncSession) -> List[CharacterExportData]:
+        """导出角色"""
+        result = await db.execute(
+            select(Character).where(Character.project_id == project_id)
+        )
+        characters = result.scalars().all()
+        
+        exported = []
+        for char in characters:
+            # 解析traits JSON
+            traits = None
+            if char.traits:
+                try:
+                    traits = json.loads(char.traits) if isinstance(char.traits, str) else char.traits
+                except:
+                    traits = None
+            
+            exported.append(CharacterExportData(
+                name=char.name,
+                age=char.age,
+                gender=char.gender,
+                is_organization=char.is_organization or False,
+                role_type=char.role_type,
+                personality=char.personality,
+                background=char.background,
+                appearance=char.appearance,
+                traits=traits,
+                organization_type=char.organization_type,
+                organization_purpose=char.organization_purpose,
+                created_at=char.created_at.isoformat() if char.created_at else None
+            ))
+        
+        return exported
+    
+    @staticmethod
+    async def _export_outlines(project_id: str, db: AsyncSession) -> List[OutlineExportData]:
+        """导出大纲"""
+        result = await db.execute(
+            select(Outline)
+            .where(Outline.project_id == project_id)
+            .order_by(Outline.order_index)
+        )
+        outlines = result.scalars().all()
+        
+        return [
+            OutlineExportData(
+                title=ol.title,
+                content=ol.content,
+                structure=ol.structure,
+                order_index=ol.order_index,
+                created_at=ol.created_at.isoformat() if ol.created_at else None
+            )
+            for ol in outlines
+        ]
+    
+    @staticmethod
+    async def _export_relationships(project_id: str, db: AsyncSession) -> List[RelationshipExportData]:
+        """导出关系"""
+        result = await db.execute(
+            select(CharacterRelationship, Character)
+            .join(Character, CharacterRelationship.character_from_id == Character.id)
+            .where(CharacterRelationship.project_id == project_id)
+        )
+        relationships = result.all()
+        
+        exported = []
+        for rel, char_from in relationships:
+            # 获取目标角色名称
+            target_result = await db.execute(
+                select(Character).where(Character.id == rel.character_to_id)
+            )
+            char_to = target_result.scalar_one_or_none()
+            
+            if char_to:
+                exported.append(RelationshipExportData(
+                    source_name=char_from.name,
+                    target_name=char_to.name,
+                    relationship_name=rel.relationship_name,
+                    intimacy_level=rel.intimacy_level or 50,
+                    status=rel.status or "active",
+                    description=rel.description,
+                    started_at=rel.started_at
+                ))
+        
+        return exported
+    
+    @staticmethod
+    async def _export_organizations(project_id: str, db: AsyncSession) -> List[OrganizationExportData]:
+        """导出组织详情"""
+        result = await db.execute(
+            select(Organization, Character)
+            .join(Character, Organization.character_id == Character.id)
+            .where(Organization.project_id == project_id)
+        )
+        organizations = result.all()
+        
+        exported = []
+        for org, char in organizations:
+            # 获取父组织名称
+            parent_name = None
+            if org.parent_org_id:
+                parent_result = await db.execute(
+                    select(Organization, Character)
+                    .join(Character, Organization.character_id == Character.id)
+                    .where(Organization.id == org.parent_org_id)
+                )
+                parent_data = parent_result.first()
+                if parent_data:
+                    parent_name = parent_data[1].name
+            
+            exported.append(OrganizationExportData(
+                character_name=char.name,
+                parent_org_name=parent_name,
+                power_level=org.power_level or 50,
+                member_count=org.member_count or 0,
+                location=org.location,
+                motto=org.motto,
+                color=org.color
+            ))
+        
+        return exported
+    
+    @staticmethod
+    async def _export_organization_members(project_id: str, db: AsyncSession) -> List[OrganizationMemberExportData]:
+        """导出组织成员"""
+        result = await db.execute(
+            select(OrganizationMember, Organization, Character)
+            .join(Organization, OrganizationMember.organization_id == Organization.id)
+            .join(Character, Organization.character_id == Character.id)
+            .where(Organization.project_id == project_id)
+        )
+        members = result.all()
+        
+        exported = []
+        for member, org, org_char in members:
+            # 获取成员角色名称
+            char_result = await db.execute(
+                select(Character).where(Character.id == member.character_id)
+            )
+            member_char = char_result.scalar_one_or_none()
+            
+            if member_char:
+                exported.append(OrganizationMemberExportData(
+                    organization_name=org_char.name,
+                    character_name=member_char.name,
+                    position=member.position,
+                    rank=member.rank or 0,
+                    status=member.status or "active",
+                    joined_at=member.joined_at,
+                    loyalty=member.loyalty or 50,
+                    contribution=member.contribution or 0,
+                    notes=member.notes
+                ))
+        
+        return exported
+    
+    @staticmethod
+    async def _export_writing_styles(project_id: str, db: AsyncSession) -> List[WritingStyleExportData]:
+        """导出写作风格"""
+        result = await db.execute(
+            select(WritingStyle)
+            .where(WritingStyle.project_id == project_id)
+            .order_by(WritingStyle.order_index)
+        )
+        styles = result.scalars().all()
+        
+        return [
+            WritingStyleExportData(
+                name=style.name,
+                style_type=style.style_type,
+                preset_id=style.preset_id,
+                description=style.description,
+                prompt_content=style.prompt_content,
+                order_index=style.order_index or 0
+            )
+            for style in styles
+        ]
+    
+    @staticmethod
+    async def _export_generation_history(project_id: str, db: AsyncSession) -> List[GenerationHistoryExportData]:
+        """导出生成历史"""
+        result = await db.execute(
+            select(GenerationHistory, Chapter)
+            .outerjoin(Chapter, GenerationHistory.chapter_id == Chapter.id)
+            .where(GenerationHistory.project_id == project_id)
+            .order_by(GenerationHistory.created_at.desc())
+            .limit(100)  # 限制最多导出100条历史记录
+        )
+        histories = result.all()
+        
+        return [
+            GenerationHistoryExportData(
+                chapter_title=chapter.title if chapter else None,
+                prompt=history.prompt,
+                generated_content=history.generated_content,
+                model=history.model,
+                tokens_used=history.tokens_used,
+                generation_time=history.generation_time,
+                created_at=history.created_at.isoformat() if history.created_at else None
+            )
+            for history, chapter in histories
+        ]
+    
+    @staticmethod
+    def validate_import_data(data: Dict) -> ImportValidationResult:
+        """
+        验证导入数据
+        
+        Args:
+            data: 导入的JSON数据
+            
+        Returns:
+            ImportValidationResult: 验证结果
+        """
+        errors = []
+        warnings = []
+        statistics = {}
+        
+        # 检查版本
+        version = data.get("version", "")
+        if not version:
+            errors.append("缺少版本信息")
+        elif version != ImportExportService.SUPPORTED_VERSION:
+            warnings.append(f"版本不匹配: 导入文件版本为 {version}, 当前支持版本为 {ImportExportService.SUPPORTED_VERSION}")
+        
+        # 检查必需字段
+        if "project" not in data:
+            errors.append("缺少项目信息")
+        else:
+            project = data["project"]
+            if not project.get("title"):
+                errors.append("项目标题不能为空")
+        
+        # 统计数据
+        statistics = {
+            "chapters": len(data.get("chapters", [])),
+            "characters": len(data.get("characters", [])),
+            "outlines": len(data.get("outlines", [])),
+            "relationships": len(data.get("relationships", [])),
+            "organizations": len(data.get("organizations", [])),
+            "organization_members": len(data.get("organization_members", [])),
+            "writing_styles": len(data.get("writing_styles", [])),
+            "generation_history": len(data.get("generation_history", []))
+        }
+        
+        # 检查数据完整性
+        if statistics["chapters"] == 0:
+            warnings.append("项目没有章节数据")
+        
+        if statistics["characters"] == 0:
+            warnings.append("项目没有角色数据")
+        
+        project_name = data.get("project", {}).get("title", "未知项目")
+        
+        return ImportValidationResult(
+            valid=len(errors) == 0,
+            version=version,
+            project_name=project_name,
+            statistics=statistics,
+            errors=errors,
+            warnings=warnings
+        )
+    
+    @staticmethod
+    async def import_project(
+        data: Dict,
+        db: AsyncSession
+    ) -> ImportResult:
+        """
+        导入项目数据（创建新项目）
+        
+        Args:
+            data: 导入的JSON数据
+            db: 数据库会话
+            
+        Returns:
+            ImportResult: 导入结果
+        """
+        warnings = []
+        statistics = {}
+        
+        try:
+            # 验证数据
+            validation = ImportExportService.validate_import_data(data)
+            if not validation.valid:
+                return ImportResult(
+                    success=False,
+                    message=f"数据验证失败: {', '.join(validation.errors)}",
+                    statistics={},
+                    warnings=validation.warnings
+                )
+            
+            warnings.extend(validation.warnings)
+            
+            logger.info(f"开始导入项目: {validation.project_name}")
+            
+            # 创建项目
+            project_data = data["project"]
+            new_project = Project(
+                title=project_data.get("title"),
+                description=project_data.get("description"),
+                theme=project_data.get("theme"),
+                genre=project_data.get("genre"),
+                target_words=project_data.get("target_words"),
+                status=project_data.get("status", "planning"),
+                world_time_period=project_data.get("world_time_period"),
+                world_location=project_data.get("world_location"),
+                world_atmosphere=project_data.get("world_atmosphere"),
+                world_rules=project_data.get("world_rules"),
+                chapter_count=project_data.get("chapter_count"),
+                narrative_perspective=project_data.get("narrative_perspective"),
+                character_count=project_data.get("character_count"),
+                current_words=project_data.get("current_words", 0),  # 保留原项目的字数
+                wizard_step=4,  # 导入的项目设置为向导完成状态
+                wizard_status="completed"  # 标记向导已完成
+            )
+            db.add(new_project)
+            await db.flush()  # 获取project_id
+            
+            logger.info(f"创建项目成功: {new_project.id}")
+            
+            # 导入章节
+            chapters_count = await ImportExportService._import_chapters(
+                new_project.id, data.get("chapters", []), db
+            )
+            statistics["chapters"] = chapters_count
+            logger.info(f"导入章节数: {chapters_count}")
+            
+            # 导入角色（包括组织）
+            char_mapping = await ImportExportService._import_characters(
+                new_project.id, data.get("characters", []), db
+            )
+            statistics["characters"] = len(char_mapping)
+            logger.info(f"导入角色数: {len(char_mapping)}")
+            
+            # 导入大纲
+            outlines_count = await ImportExportService._import_outlines(
+                new_project.id, data.get("outlines", []), db
+            )
+            statistics["outlines"] = outlines_count
+            logger.info(f"导入大纲数: {outlines_count}")
+            
+            # 导入关系
+            relationships_count = await ImportExportService._import_relationships(
+                new_project.id, data.get("relationships", []), char_mapping, db
+            )
+            statistics["relationships"] = relationships_count
+            logger.info(f"导入关系数: {relationships_count}")
+            
+            # 导入组织详情
+            org_mapping = await ImportExportService._import_organizations(
+                new_project.id, data.get("organizations", []), char_mapping, db
+            )
+            statistics["organizations"] = len(org_mapping)
+            logger.info(f"导入组织数: {len(org_mapping)}")
+            
+            # 导入组织成员
+            org_members_count = await ImportExportService._import_organization_members(
+                data.get("organization_members", []), char_mapping, org_mapping, db
+            )
+            statistics["organization_members"] = org_members_count
+            logger.info(f"导入组织成员数: {org_members_count}")
+            
+            # 导入写作风格
+            styles_count = await ImportExportService._import_writing_styles(
+                new_project.id, data.get("writing_styles", []), db
+            )
+            statistics["writing_styles"] = styles_count
+            logger.info(f"导入写作风格数: {styles_count}")
+            
+            # 提交事务
+            await db.commit()
+            
+            logger.info(f"项目导入完成: {new_project.id}")
+            
+            return ImportResult(
+                success=True,
+                project_id=new_project.id,
+                message="项目导入成功",
+                statistics=statistics,
+                warnings=warnings
+            )
+            
+        except Exception as e:
+            await db.rollback()
+            logger.error(f"导入项目失败: {str(e)}", exc_info=True)
+            return ImportResult(
+                success=False,
+                message=f"导入失败: {str(e)}",
+                statistics=statistics,
+                warnings=warnings
+            )
+    
+    @staticmethod
+    async def _import_chapters(
+        project_id: str,
+        chapters_data: List[Dict],
+        db: AsyncSession
+    ) -> int:
+        """导入章节"""
+        count = 0
+        for ch_data in chapters_data:
+            chapter = Chapter(
+                project_id=project_id,
+                title=ch_data.get("title"),
+                content=ch_data.get("content"),
+                summary=ch_data.get("summary"),
+                chapter_number=ch_data.get("chapter_number"),
+                word_count=ch_data.get("word_count", 0),
+                status=ch_data.get("status", "draft")
+            )
+            db.add(chapter)
+            count += 1
+        
+        return count
+    
+    @staticmethod
+    async def _import_characters(
+        project_id: str,
+        characters_data: List[Dict],
+        db: AsyncSession
+    ) -> Dict[str, str]:
+        """导入角色，返回名称到ID的映射"""
+        char_mapping = {}
+        
+        for char_data in characters_data:
+            # 处理traits
+            traits = char_data.get("traits")
+            if traits and isinstance(traits, list):
+                traits = json.dumps(traits, ensure_ascii=False)
+            
+            character = Character(
+                project_id=project_id,
+                name=char_data.get("name"),
+                age=char_data.get("age"),
+                gender=char_data.get("gender"),
+                is_organization=char_data.get("is_organization", False),
+                role_type=char_data.get("role_type"),
+                personality=char_data.get("personality"),
+                background=char_data.get("background"),
+                appearance=char_data.get("appearance"),
+                traits=traits,
+                organization_type=char_data.get("organization_type"),
+                organization_purpose=char_data.get("organization_purpose")
+            )
+            db.add(character)
+            await db.flush()  # 获取ID
+            char_mapping[char_data.get("name")] = character.id
+        
+        return char_mapping
+    
+    @staticmethod
+    async def _import_outlines(
+        project_id: str,
+        outlines_data: List[Dict],
+        db: AsyncSession
+    ) -> int:
+        """导入大纲"""
+        count = 0
+        for ol_data in outlines_data:
+            outline = Outline(
+                project_id=project_id,
+                title=ol_data.get("title"),
+                content=ol_data.get("content"),
+                structure=ol_data.get("structure"),
+                order_index=ol_data.get("order_index")
+            )
+            db.add(outline)
+            count += 1
+        
+        return count
+    
+    @staticmethod
+    async def _import_relationships(
+        project_id: str,
+        relationships_data: List[Dict],
+        char_mapping: Dict[str, str],
+        db: AsyncSession
+    ) -> int:
+        """导入关系"""
+        count = 0
+        for rel_data in relationships_data:
+            source_name = rel_data.get("source_name")
+            target_name = rel_data.get("target_name")
+            
+            # 查找角色ID
+            source_id = char_mapping.get(source_name)
+            target_id = char_mapping.get(target_name)
+            
+            if source_id and target_id:
+                relationship = CharacterRelationship(
+                    project_id=project_id,
+                    character_from_id=source_id,
+                    character_to_id=target_id,
+                    relationship_name=rel_data.get("relationship_name"),
+                    intimacy_level=rel_data.get("intimacy_level", 50),
+                    status=rel_data.get("status", "active"),
+                    description=rel_data.get("description"),
+                    started_at=rel_data.get("started_at")
+                )
+                db.add(relationship)
+                count += 1
+        
+        return count
+    
+    @staticmethod
+    async def _import_organizations(
+        project_id: str,
+        organizations_data: List[Dict],
+        char_mapping: Dict[str, str],
+        db: AsyncSession
+    ) -> Dict[str, str]:
+        """导入组织详情，返回名称到ID的映射"""
+        org_mapping = {}
+        
+        # 第一遍：创建所有组织（不设置父组织）
+        temp_orgs = []
+        for org_data in organizations_data:
+            char_name = org_data.get("character_name")
+            char_id = char_mapping.get(char_name)
+            
+            if char_id:
+                organization = Organization(
+                    project_id=project_id,
+                    character_id=char_id,
+                    power_level=org_data.get("power_level", 50),
+                    member_count=org_data.get("member_count", 0),
+                    location=org_data.get("location"),
+                    motto=org_data.get("motto"),
+                    color=org_data.get("color")
+                )
+                db.add(organization)
+                temp_orgs.append((organization, org_data.get("parent_org_name")))
+        
+        await db.flush()  # 获取所有组织的ID
+        
+        # 建立名称到ID的映射
+        for org, _ in temp_orgs:
+            # 通过character_id查找角色名
+            result = await db.execute(
+                select(Character).where(Character.id == org.character_id)
+            )
+            char = result.scalar_one_or_none()
+            if char:
+                org_mapping[char.name] = org.id
+        
+        # 第二遍：设置父组织关系
+        for org, parent_name in temp_orgs:
+            if parent_name:
+                parent_id = org_mapping.get(parent_name)
+                if parent_id:
+                    org.parent_org_id = parent_id
+        
+        return org_mapping
+    
+    @staticmethod
+    async def _import_organization_members(
+        org_members_data: List[Dict],
+        char_mapping: Dict[str, str],
+        org_mapping: Dict[str, str],
+        db: AsyncSession
+    ) -> int:
+        """导入组织成员"""
+        count = 0
+        for member_data in org_members_data:
+            org_name = member_data.get("organization_name")
+            char_name = member_data.get("character_name")
+            
+            org_id = org_mapping.get(org_name)
+            char_id = char_mapping.get(char_name)
+            
+            if org_id and char_id:
+                member = OrganizationMember(
+                    organization_id=org_id,
+                    character_id=char_id,
+                    position=member_data.get("position"),
+                    rank=member_data.get("rank", 0),
+                    status=member_data.get("status", "active"),
+                    joined_at=member_data.get("joined_at"),
+                    loyalty=member_data.get("loyalty", 50),
+                    contribution=member_data.get("contribution", 0),
+                    notes=member_data.get("notes")
+                )
+                db.add(member)
+                count += 1
+        
+        return count
+    
+    @staticmethod
+    async def _import_writing_styles(
+        project_id: str,
+        styles_data: List[Dict],
+        db: AsyncSession
+    ) -> int:
+        """导入写作风格"""
+        count = 0
+        for style_data in styles_data:
+            style = WritingStyle(
+                project_id=project_id,
+                name=style_data.get("name"),
+                style_type=style_data.get("style_type"),
+                preset_id=style_data.get("preset_id"),
+                description=style_data.get("description"),
+                prompt_content=style_data.get("prompt_content"),
+                order_index=style_data.get("order_index", 0)
+            )
+            db.add(style)
+            count += 1
+        
+        return count

backend/app/services/memory_service.py

@@ -0,0 +1,782 @@
+"""向量记忆服务 - 基于ChromaDB实现长期记忆和语义检索"""
+import chromadb
+from sentence_transformers import SentenceTransformer
+from typing import List, Dict, Any, Optional
+import json
+from datetime import datetime
+from app.logger import get_logger
+import os
+import hashlib
+
+logger = get_logger(__name__)
+
+# 配置模型缓存目录（不设置离线模式，让它自动选择）
+# 如果本地有模型就用本地的，没有才联网下载
+if 'SENTENCE_TRANSFORMERS_HOME' not in os.environ:
+    os.environ['SENTENCE_TRANSFORMERS_HOME'] = 'embedding'
+
+
+class MemoryService:
+    """向量记忆管理服务 - 实现语义检索和长期记忆"""
+    
+    _instance = None
+    _initialized = False
+    
+    def __new__(cls):
+        """单例模式"""
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+        return cls._instance
+    
+    def __init__(self):
+        """初始化ChromaDB和Embedding模型"""
+        if self._initialized:
+            return
+            
+        try:
+            # 确保数据目录存在
+            chroma_dir = "data/chroma_db"
+            os.makedirs(chroma_dir, exist_ok=True)
+            
+            # 初始化ChromaDB客户端(使用新API - PersistentClient)
+            self.client = chromadb.PersistentClient(path=chroma_dir)
+            
+            # 初始化多语言embedding模型(支持中文)
+            logger.info("🔄 正在加载Embedding模型...")
+            
+            # 确保模型缓存目录存在
+            model_cache_dir = 'embedding'
+            os.makedirs(model_cache_dir, exist_ok=True)
+            
+            # 调试信息：打印环境变量和路径
+            logger.info(f"📂 当前工作目录: {os.getcwd()}")
+            logger.info(f"📂 模型缓存目录: {os.path.abspath(model_cache_dir)}")
+            logger.info(f"🔧 SENTENCE_TRANSFORMERS_HOME: {os.environ.get('SENTENCE_TRANSFORMERS_HOME', '未设置')}")
+            logger.info(f"🔧 TRANSFORMERS_OFFLINE: {os.environ.get('TRANSFORMERS_OFFLINE', '未设置')}")
+            logger.info(f"🔧 HF_HUB_OFFLINE: {os.environ.get('HF_HUB_OFFLINE', '未设置')}")
+            
+            # 检查模型目录内容
+            if os.path.exists(model_cache_dir):
+                logger.info(f"📁 模型目录存在，检查内容...")
+                try:
+                    items = os.listdir(model_cache_dir)
+                    logger.info(f"📁 模型目录内容: {items}")
+                    
+                    # 检查是否有预期的模型文件夹
+                    expected_model_dir = os.path.join(model_cache_dir, 'models--sentence-transformers--paraphrase-multilingual-MiniLM-L12-v2')
+                    if os.path.exists(expected_model_dir):
+                        logger.info(f"✅ 找到本地模型目录: {expected_model_dir}")
+                        # 检查快照目录
+                        snapshots_dir = os.path.join(expected_model_dir, 'snapshots')
+                        if os.path.exists(snapshots_dir):
+                            snapshots = os.listdir(snapshots_dir)
+                            logger.info(f"📁 模型快照: {snapshots}")
+                    else:
+                        logger.warning(f"⚠️ 未找到本地模型目录: {expected_model_dir}")
+                except Exception as e:
+                    logger.error(f"❌ 检查模型目录失败: {str(e)}")
+            else:
+                logger.warning(f"⚠️ 模型目录不存在: {os.path.abspath(model_cache_dir)}")
+            
+            try:
+                logger.info("🔄 尝试加载主模型: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
+                # 优先使用本地缓存的模型
+                # cache_folder会让模型优先从本地加载，只有不存在时才联网下载
+                # 注意：不要设置local_files_only=True，这会阻止fallback到联网下载
+                self.embedding_model = SentenceTransformer(
+                    'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2',
+                    cache_folder=model_cache_dir,
+                    device='cpu',  # 明确指定使用CPU
+                    trust_remote_code=False  # 安全起见
+                )
+                logger.info("✅ Embedding模型加载成功 (paraphrase-multilingual-MiniLM-L12-v2)")
+            except Exception as e:
+                logger.warning(f"⚠️ 无法加载多语言模型: {str(e)}")
+                logger.error(f"❌ 详细错误: {repr(e)}")
+                import traceback
+                logger.error(f"❌ 错误堆栈:\n{traceback.format_exc()}")
+                logger.info("🔄 尝试使用备用模型: sentence-transformers/all-MiniLM-L6-v2")
+                try:
+                    # 降级到更小的模型作为备选
+                    self.embedding_model = SentenceTransformer(
+                        'sentence-transformers/all-MiniLM-L6-v2',
+                        cache_folder=model_cache_dir,
+                        device='cpu',
+                        trust_remote_code=False
+                    )
+                    logger.info("✅ 使用备用Embedding模型 (all-MiniLM-L6-v2)")
+                except Exception as e2:
+                    logger.error(f"❌ 所有模型加载失败: {str(e2)}")
+                    logger.error(f"❌ 详细错误: {repr(e2)}")
+                    import traceback
+                    logger.error(f"❌ 错误堆栈:\n{traceback.format_exc()}")
+                    logger.error("💡 模型首次使用需要联网下载（约420MB）")
+                    logger.error("   或手动下载模型文件到 embedding 目录")
+                    logger.error(f"💡 期望的模型目录结构:")
+                    logger.error(f"   {os.path.abspath(model_cache_dir)}/models--sentence-transformers--paraphrase-multilingual-MiniLM-L12-v2/")
+                    raise RuntimeError("无法加载任何Embedding模型")
+            
+            self._initialized = True
+            logger.info("✅ MemoryService初始化成功")
+            logger.info(f"  - ChromaDB目录: {chroma_dir}")
+            logger.info(f"  - Embedding模型: paraphrase-multilingual-MiniLM-L12-v2")
+            
+        except Exception as e:
+            logger.error(f"❌ MemoryService初始化失败: {str(e)}")
+            raise
+    
+    def get_collection(self, user_id: str, project_id: str):
+        """
+        获取或创建项目的记忆集合
+        
+        每个用户的每个项目有独立的collection,实现数据隔离
+        
+        Args:
+            user_id: 用户ID
+            project_id: 项目ID
+        
+        Returns:
+            ChromaDB Collection对象
+        """
+        # ChromaDB collection命名规则：
+        # 1. 3-63字符（最重要！）
+        # 2. 开头和结尾必须是字母或数字
+        # 3. 只能包含字母、数字、下划线或短横线
+        # 4. 不能包含连续的点(..)
+        # 5. 不能是有效的IPv4地址
+        
+        # 使用SHA256哈希压缩ID长度，确保不超过63字符
+        # 格式: u_{user_hash}_p_{project_hash} (约30字符)
+        user_hash = hashlib.sha256(user_id.encode()).hexdigest()[:8]
+        project_hash = hashlib.sha256(project_id.encode()).hexdigest()[:8]
+        collection_name = f"u_{user_hash}_p_{project_hash}"
+        
+        try:
+            return self.client.get_or_create_collection(
+                name=collection_name,
+                metadata={
+                    "user_id": user_id,
+                    "project_id": project_id,
+                    "created_at": datetime.now().isoformat()
+                }
+            )
+        except Exception as e:
+            logger.error(f"❌ 获取collection失败: {str(e)}")
+            raise
+    
+    async def add_memory(
+        self,
+        user_id: str,
+        project_id: str,
+        memory_id: str,
+        content: str,
+        memory_type: str,
+        metadata: Dict[str, Any]
+    ) -> bool:
+        """
+        添加记忆到向量数据库
+        
+        Args:
+            user_id: 用户ID
+            project_id: 项目ID
+            memory_id: 记忆唯一ID
+            content: 记忆内容(将被转换为向量)
+            memory_type: 记忆类型
+            metadata: 附加元数据
+        
+        Returns:
+            是否添加成功
+        """
+        try:
+            collection = self.get_collection(user_id, project_id)
+            
+            # 生成文本的向量表示
+            embedding = self.embedding_model.encode(content).tolist()
+            
+            # 准备元数据(ChromaDB要求所有值为基础类型)
+            chroma_metadata = {
+                "memory_type": memory_type,
+                "chapter_id": str(metadata.get("chapter_id", "")),
+                "chapter_number": int(metadata.get("chapter_number", 0)),
+                "importance": float(metadata.get("importance_score", 0.5)),
+                "tags": json.dumps(metadata.get("tags", []), ensure_ascii=False),
+                "title": str(metadata.get("title", ""))[:200],  # 限制长度
+                "is_foreshadow": int(metadata.get("is_foreshadow", 0)),
+                "created_at": datetime.now().isoformat()
+            }
+            
+            # 添加相关角色信息
+            if metadata.get("related_characters"):
+                chroma_metadata["related_characters"] = json.dumps(
+                    metadata["related_characters"], 
+                    ensure_ascii=False
+                )
+            
+            # 存储到向量库
+            collection.add(
+                ids=[memory_id],
+                embeddings=[embedding],
+                documents=[content],
+                metadatas=[chroma_metadata]
+            )
+            
+            logger.info(f"✅ 记忆已添加: {memory_id[:8]}... (类型:{memory_type}, 重要性:{chroma_metadata['importance']})")
+            return True
+            
+        except Exception as e:
+            logger.error(f"❌ 添加记忆失败: {str(e)}")
+            return False
+    
+    async def batch_add_memories(
+        self,
+        user_id: str,
+        project_id: str,
+        memories: List[Dict[str, Any]]
+    ) -> int:
+        """
+        批量添加记忆(性能更好)
+        
+        Args:
+            user_id: 用户ID
+            project_id: 项目ID
+            memories: 记忆列表,每个包含id、content、type、metadata
+        
+        Returns:
+            成功添加的数量
+        """
+        if not memories:
+            return 0
+            
+        try:
+            collection = self.get_collection(user_id, project_id)
+            
+            ids = []
+            documents = []
+            metadatas = []
+            embeddings = []
+            
+            # 批量准备数据
+            for mem in memories:
+                ids.append(mem['id'])
+                documents.append(mem['content'])
+                
+                # 生成embedding
+                embedding = self.embedding_model.encode(mem['content']).tolist()
+                embeddings.append(embedding)
+                
+                # 准备元数据
+                metadata = mem.get('metadata', {})
+                chroma_metadata = {
+                    "memory_type": mem['type'],
+                    "chapter_id": str(metadata.get("chapter_id", "")),
+                    "chapter_number": int(metadata.get("chapter_number", 0)),
+                    "importance": float(metadata.get("importance_score", 0.5)),
+                    "tags": json.dumps(metadata.get("tags", []), ensure_ascii=False),
+                    "title": str(metadata.get("title", ""))[:200],
+                    "is_foreshadow": int(metadata.get("is_foreshadow", 0)),
+                    "created_at": datetime.now().isoformat()
+                }
+                metadatas.append(chroma_metadata)
+            
+            # 批量添加
+            collection.add(
+                ids=ids,
+                embeddings=embeddings,
+                documents=documents,
+                metadatas=metadatas
+            )
+            
+            logger.info(f"✅ 批量添加记忆成功: {len(memories)}条")
+            return len(memories)
+            
+        except Exception as e:
+            logger.error(f"❌ 批量添加记忆失败: {str(e)}")
+            return 0
+    
+    async def search_memories(
+        self,
+        user_id: str,
+        project_id: str,
+        query: str,
+        memory_types: Optional[List[str]] = None,
+        limit: int = 10,
+        min_importance: float = 0.0,
+        chapter_range: Optional[tuple] = None
+    ) -> List[Dict[str, Any]]:
+        """
+        语义搜索相关记忆
+        
+        Args:
+            user_id: 用户ID
+            project_id: 项目ID
+            query: 查询文本(会被转换为向量进行相似度搜索)
+            memory_types: 过滤特定类型的记忆
+            limit: 返回结果数量
+            min_importance: 最低重要性阈值
+            chapter_range: 章节范围 (start, end)
+        
+        Returns:
+            相关记忆列表,按相似度排序
+        """
+        try:
+            collection = self.get_collection(user_id, project_id)
+            
+            # 生成查询向量
+            query_embedding = self.embedding_model.encode(query).tolist()
+            
+            # 构建过滤条件 - ChromaDB要求使用$and组合多个条件
+            where_filter = None
+            conditions = []
+            
+            if memory_types:
+                conditions.append({"memory_type": {"$in": memory_types}})
+            if min_importance > 0:
+                conditions.append({"importance": {"$gte": min_importance}})
+            if chapter_range:
+                conditions.append({"chapter_number": {"$gte": chapter_range[0]}})
+                conditions.append({"chapter_number": {"$lte": chapter_range[1]}})
+            
+            # 根据条件数量选择合适的格式
+            if len(conditions) == 0:
+                where_filter = None
+            elif len(conditions) == 1:
+                where_filter = conditions[0]
+            else:
+                where_filter = {"$and": conditions}
+            
+            # 执行向量相似度搜索
+            results = collection.query(
+                query_embeddings=[query_embedding],
+                n_results=limit,
+                where=where_filter
+            )
+            
+            # 格式化结果
+            memories = []
+            if results['ids'] and results['ids'][0]:
+                for i in range(len(results['ids'][0])):
+                    memories.append({
+                        "id": results['ids'][0][i],
+                        "content": results['documents'][0][i],
+                        "metadata": results['metadatas'][0][i],
+                        "similarity": 1 - results['distances'][0][i] if 'distances' in results else 1.0,
+                        "distance": results['distances'][0][i] if 'distances' in results else 0.0
+                    })
+            
+            logger.info(f"🔍 语义搜索完成: 查询='{query[:30]}...', 找到{len(memories)}条记忆")
+            return memories
+            
+        except Exception as e:
+            logger.error(f"❌ 搜索记忆失败: {str(e)}")
+            return []
+    
+    async def get_recent_memories(
+        self,
+        user_id: str,
+        project_id: str,
+        current_chapter: int,
+        recent_count: int = 3,
+        min_importance: float = 0.5
+    ) -> List[Dict[str, Any]]:
+        """
+        获取最近几章的重要记忆(用于保持连贯性)
+        
+        Args:
+            user_id: 用户ID
+            project_id: 项目ID
+            current_chapter: 当前章节号
+            recent_count: 获取最近几章
+            min_importance: 最低重要性阈值
+        
+        Returns:
+            最近章节的记忆列表,按重要性排序
+        """
+        try:
+            collection = self.get_collection(user_id, project_id)
+            
+            # 计算章节范围
+            start_chapter = max(1, current_chapter - recent_count)
+            
+            # 获取最近章节的记忆
+            results = collection.get(
+                where={
+                    "$and": [
+                        {"chapter_number": {"$gte": start_chapter}},
+                        {"chapter_number": {"$lt": current_chapter}},
+                        {"importance": {"$gte": min_importance}}
+                    ]
+                },
+                limit=100  # 先获取足够多的记忆
+            )
+            
+            memories = []
+            if results['ids']:
+                for i in range(len(results['ids'])):
+                    memories.append({
+                        "id": results['ids'][i],
+                        "content": results['documents'][i],
+                        "metadata": results['metadatas'][i]
+                    })
+            
+            # 按重要性和章节号排序
+            memories.sort(
+                key=lambda x: (float(x['metadata'].get('importance', 0)), 
+                              int(x['metadata'].get('chapter_number', 0))),
+                reverse=True
+            )
+            
+            # 返回最重要的前N条
+            top_memories = memories[:20]
+            logger.info(f"📚 获取最近记忆: 章节{start_chapter}-{current_chapter-1}, 找到{len(top_memories)}条")
+            return top_memories
+            
+        except Exception as e:
+            logger.error(f"❌ 获取最近记忆失败: {str(e)}")
+            return []
+    
+    async def find_unresolved_foreshadows(
+        self,
+        user_id: str,
+        project_id: str,
+        current_chapter: int
+    ) -> List[Dict[str, Any]]:
+        """
+        查找未完结的伏笔
+        
+        Args:
+            user_id: 用户ID
+            project_id: 项目ID
+            current_chapter: 当前章节号
+        
+        Returns:
+            未完结伏笔列表
+        """
+        try:
+            collection = self.get_collection(user_id, project_id)
+            
+            # 查找伏笔状态为1(已埋下但未回收)的记忆
+            results = collection.get(
+                where={
+                    "$and": [
+                        {"is_foreshadow": 1},
+                        {"chapter_number": {"$lt": current_chapter}}
+                    ]
+                },
+                limit=50
+            )
+            
+            foreshadows = []
+            if results['ids']:
+                for i in range(len(results['ids'])):
+                    foreshadows.append({
+                        "id": results['ids'][i],
+                        "content": results['documents'][i],
+                        "metadata": results['metadatas'][i]
+                    })
+            
+            # 按重要性排序
+            foreshadows.sort(
+                key=lambda x: float(x['metadata'].get('importance', 0)),
+                reverse=True
+            )
+            
+            logger.info(f"🎣 找到未完结伏笔: {len(foreshadows)}个")
+            return foreshadows
+            
+        except Exception as e:
+            logger.error(f"❌ 查找伏笔失败: {str(e)}")
+            return []
+    
+    async def build_context_for_generation(
+        self,
+        user_id: str,
+        project_id: str,
+        current_chapter: int,
+        chapter_outline: str,
+        character_names: List[str] = None
+    ) -> Dict[str, Any]:
+        """
+        为章节生成构建智能上下文
+        
+        这是核心功能: 结合多种检索策略,为AI生成提供最相关的记忆
+        
+        Args:
+            user_id: 用户ID
+            project_id: 项目ID
+            current_chapter: 当前章节号
+            chapter_outline: 本章大纲
+            character_names: 涉及的角色名列表
+        
+        Returns:
+            包含各种上下文信息的字典
+        """
+        logger.info(f"🧠 开始构建章节{current_chapter}的智能上下文...")
+        
+        # 1. 获取最近章节上下文(时间连续性)
+        recent = await self.get_recent_memories(
+            user_id, project_id, current_chapter, 
+            recent_count=3, min_importance=0.5
+        )
+        
+        # 2. 语义搜索相关记忆
+        relevant = await self.search_memories(
+            user_id=user_id,
+            project_id=project_id,
+            query=chapter_outline,
+            limit=10,
+            min_importance=0.4
+        )
+        
+        # 3. 查找未完结伏笔
+        foreshadows = await self.find_unresolved_foreshadows(
+            user_id, project_id, current_chapter
+        )
+        
+        # 4. 如果有指定角色,获取角色相关记忆
+        character_memories = []
+        if character_names:
+            character_query = " ".join(character_names) + " 角色 状态 关系"
+            character_memories = await self.search_memories(
+                user_id=user_id,
+                project_id=project_id,
+                query=character_query,
+                memory_types=["character_event", "plot_point"],
+                limit=8
+            )
+        
+        # 5. 获取重要情节点
+        # 注意：ChromaDB的where条件需要特殊处理，不能同时使用多个顶层条件
+        try:
+            plot_points = await self.search_memories(
+                user_id=user_id,
+                project_id=project_id,
+                query="重要 转折 高潮 关键",
+                memory_types=["plot_point", "hook"],
+                limit=5,
+                min_importance=0.7
+            )
+        except Exception as e:
+            logger.error(f"❌ 搜索记忆失败: {str(e)}")
+            # 降级处理：分别查询
+            plot_points = []
+            try:
+                plot_points = await self.search_memories(
+                    user_id=user_id,
+                    project_id=project_id,
+                    query="重要 转折 高潮 关键",
+                    memory_types=["plot_point", "hook"],
+                    limit=5
+                )
+            except Exception as e2:
+                logger.warning(f"⚠️ 降级查询也失败: {str(e2)}")
+                plot_points = []
+        
+        context = {
+            "recent_context": self._format_memories(recent, "最近章节记忆"),
+            "relevant_memories": self._format_memories(relevant, "语义相关记忆"),
+            "character_states": self._format_memories(character_memories, "角色相关记忆"),
+            "foreshadows": self._format_memories(foreshadows[:5], "未完结伏笔"),
+            "plot_points": self._format_memories(plot_points, "重要情节点"),
+            "stats": {
+                "recent_count": len(recent),
+                "relevant_count": len(relevant),
+                "character_count": len(character_memories),
+                "foreshadow_count": len(foreshadows),
+                "plot_point_count": len(plot_points)
+            }
+        }
+        
+        logger.info(f"✅ 上下文构建完成: 最近{len(recent)}条, 相关{len(relevant)}条, 伏笔{len(foreshadows)}个")
+        return context
+    def _format_memories(self, memories: List[Dict], section_title: str = "记忆") -> str:
+        """
+        格式化记忆列表为文本
+        
+        Args:
+            memories: 记忆列表
+            section_title: 章节标题
+        
+        Returns:
+            格式化后的文本
+        """
+        if not memories:
+            return f"【{section_title}】\n暂无相关记忆\n"
+        
+        lines = [f"【{section_title}】"]
+        for i, mem in enumerate(memories, 1):
+            meta = mem.get('metadata', {})
+            chapter_num = meta.get('chapter_number', '?')
+            mem_type = meta.get('memory_type', '未知')
+            importance = float(meta.get('importance', 0.5))
+            title = meta.get('title', '')
+            content = mem['content']
+            
+            # 格式: [序号] 第X章-类型(重要性) 标题: 内容
+            line = f"{i}. [第{chapter_num}章-{mem_type}★{importance:.1f}]"
+            if title:
+                line += f" {title}: {content[:100]}"
+            else:
+                line += f" {content[:150]}"
+            lines.append(line)
+        
+        return "\n".join(lines) + "\n"
+    
+    async def delete_chapter_memories(
+        self,
+        user_id: str,
+        project_id: str,
+        chapter_id: str
+    ) -> bool:
+        """
+        删除指定章节的所有记忆
+        
+        Args:
+            user_id: 用户ID
+            project_id: 项目ID
+            chapter_id: 章节ID
+        
+        Returns:
+            是否删除成功
+        """
+        try:
+            collection = self.get_collection(user_id, project_id)
+            
+            # 查找该章节的所有记忆
+            results = collection.get(
+                where={"chapter_id": chapter_id}
+            )
+            
+            if results['ids']:
+                # 删除这些记忆
+                collection.delete(ids=results['ids'])
+                logger.info(f"🗑️ 已删除章节{chapter_id[:8]}的{len(results['ids'])}条记忆")
+                return True
+            else:
+                logger.info(f"ℹ️ 章节{chapter_id[:8]}没有记忆需要删除")
+                return True
+                
+        except Exception as e:
+            logger.error(f"❌ 删除章节记忆失败: {str(e)}")
+            return False
+    
+    async def update_memory(
+        self,
+        user_id: str,
+        project_id: str,
+        memory_id: str,
+        content: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None
+    ) -> bool:
+        """
+        更新记忆内容或元数据
+        
+        Args:
+            user_id: 用户ID
+            project_id: 项目ID
+            memory_id: 记忆ID
+            content: 新内容(可选)
+            metadata: 新元数据(可选)
+        
+        Returns:
+            是否更新成功
+        """
+        try:
+            collection = self.get_collection(user_id, project_id)
+            
+            update_data = {}
+            
+            if content:
+                # 重新生成embedding
+                embedding = self.embedding_model.encode(content).tolist()
+                update_data['embeddings'] = [embedding]
+                update_data['documents'] = [content]
+            
+            if metadata:
+                # 准备新的元数据
+                chroma_metadata = {}
+                for key, value in metadata.items():
+                    if isinstance(value, (list, dict)):
+                        chroma_metadata[key] = json.dumps(value, ensure_ascii=False)
+                    else:
+                        chroma_metadata[key] = value
+                update_data['metadatas'] = [chroma_metadata]
+            
+            if update_data:
+                collection.update(
+                    ids=[memory_id],
+                    **update_data
+                )
+                logger.info(f"✅ 记忆已更新: {memory_id[:8]}...")
+                return True
+            else:
+                logger.warning("⚠️ 没有提供更新内容")
+                return False
+                
+        except Exception as e:
+            logger.error(f"❌ 更新记忆失败: {str(e)}")
+            return False
+    
+    async def get_memory_stats(
+        self,
+        user_id: str,
+        project_id: str
+    ) -> Dict[str, Any]:
+        """
+        获取记忆统计信息
+        
+        Args:
+            user_id: 用户ID
+            project_id: 项目ID
+        
+        Returns:
+            统计信息字典
+        """
+        try:
+            collection = self.get_collection(user_id, project_id)
+            
+            # 获取所有记忆
+            all_memories = collection.get()
+            
+            if not all_memories['ids']:
+                return {
+                    "total_count": 0,
+                    "by_type": {},
+                    "by_chapter": {},
+                    "foreshadow_count": 0
+                }
+            
+            # 统计各类型数量
+            type_counts = {}
+            chapter_counts = {}
+            foreshadow_count = 0
+            
+            for i, meta in enumerate(all_memories['metadatas']):
+                mem_type = meta.get('memory_type', 'unknown')
+                chapter_num = meta.get('chapter_number', 0)
+                is_foreshadow = meta.get('is_foreshadow', 0)
+                
+                type_counts[mem_type] = type_counts.get(mem_type, 0) + 1
+                chapter_counts[str(chapter_num)] = chapter_counts.get(str(chapter_num), 0) + 1
+                
+                if is_foreshadow == 1:
+                    foreshadow_count += 1
+            
+            stats = {
+                "total_count": len(all_memories['ids']),
+                "by_type": type_counts,
+                "by_chapter": chapter_counts,
+                "foreshadow_count": foreshadow_count,
+                "foreshadow_resolved": sum(1 for m in all_memories['metadatas'] if m.get('is_foreshadow') == 2)
+            }
+            
+            logger.info(f"📊 记忆统计: 总计{stats['total_count']}条, 伏笔{foreshadow_count}个")
+            return stats
+            
+        except Exception as e:
+            logger.error(f"❌ 获取统计信息失败: {str(e)}")
+            return {"error": str(e)}
+
+
+# 创建全局实例
+memory_service = MemoryService()
+            

backend/app/services/plot_analyzer.py

@@ -0,0 +1,559 @@
+"""剧情分析服务 - 自动分析章节的钩子、伏笔、冲突等元素"""
+from typing import Dict, Any, List, Optional
+from app.services.ai_service import AIService
+from app.logger import get_logger
+import json
+import re
+
+logger = get_logger(__name__)
+
+
+class PlotAnalyzer:
+    """剧情分析器 - 使用AI分析章节内容"""
+    
+    # AI分析提示词模板
+    ANALYSIS_PROMPT = """你是一位专业的小说编辑和剧情分析师。请深度分析以下章节内容:
+
+**章节信息:**
+- 章节: 第{chapter_number}章
+- 标题: {title}
+- 字数: {word_count}字
+
+**章节内容:**
+{content}
+
+---
+
+**分析任务:**
+请从专业编辑的角度,全面分析这一章节:
+
+### 1. 剧情钩子 (Hooks) - 吸引读者的元素
+识别能够吸引读者继续阅读的关键元素:
+- **悬念钩子**: 未解之谜、疑问、谜团
+- **情感钩子**: 引发共鸣的情感点、触动心弦的时刻
+- **冲突钩子**: 矛盾对抗、紧张局势
+- **认知钩子**: 颠覆认知的信息、惊人真相
+
+每个钩子需要:
+- 类型分类
+- 具体内容描述
+- 强度评分(1-10)
+- 出现位置(开头/中段/结尾)
+- **关键词**: 【必填】从章节原文中逐字复制一段关键文本(8-25字)，必须是原文中真实存在的连续文字，用于在文本中精确定位。不要概括或改写，必须原样复制！
+
+### 2. 伏笔分析 (Foreshadowing)
+- **埋下的新伏笔**: 描述内容、预期作用、隐藏程度(1-10)
+- **回收的旧伏笔**: 呼应哪一章、回收效果评分
+- **伏笔质量**: 巧妙性和合理性评估
+- **关键词**: 【必填】从章节原文中逐字复制一段关键文本(8-25字)，必须是原文中真实存在的连续文字，用于在文本中精确定位。不要概括或改写，必须原样复制！
+
+### 3. 冲突分析 (Conflict)
+- 冲突类型: 人与人/人与己/人与环境/人与社会
+- 冲突各方及其立场
+- 冲突强度评分(1-10)
+- 冲突解决进度(0-100%)
+
+### 4. 情感曲线 (Emotional Arc)
+- 主导情绪: 紧张/温馨/悲伤/激昂/平静等
+- 情感强度(1-10)
+- 情绪变化轨迹描述
+
+### 5. 角色状态追踪 (Character Development)
+对每个出场角色分析:
+- 心理状态变化(前→后)
+- 关系变化
+- 关键行动和决策
+- 成长或退步
+
+### 6. 关键情节点 (Plot Points)
+列出3-5个核心情节点:
+- 情节内容
+- 类型(revelation/conflict/resolution/transition)
+- 重要性(0.0-1.0)
+- 对故事的影响
+- **关键词**: 【必填】从章节原文中逐字复制一段关键文本(8-25字)，必须是原文中真实存在的连续文字，用于在文本中精确定位。不要概括或改写，必须原样复制！
+
+### 7. 场景与节奏
+- 主要场景
+- 叙事节奏(快/中/慢)
+- 对话与描写的比例
+
+### 8. 质量评分
+- 节奏把控: 1-10分
+- 吸引力: 1-10分  
+- 连贯性: 1-10分
+- 整体质量: 1-10分
+
+### 9. 改进建议
+提供3-5条具体的改进建议
+
+---
+
+**输出格式(纯JSON,不要markdown标记):**
+
+{{
+  "hooks": [
+    {{
+      "type": "悬念",
+      "content": "具体描述",
+      "strength": 8,
+      "position": "中段",
+      "keyword": "必须从原文逐字复制的文本片段"
+    }}
+  ],
+  "foreshadows": [
+    {{
+      "content": "伏笔内容",
+      "type": "planted",
+      "strength": 7,
+      "subtlety": 8,
+      "reference_chapter": null,
+      "keyword": "必须从原文逐字复制的文本片段"
+    }}
+  ],
+  "conflict": {{
+    "types": ["人与人", "人与己"],
+    "parties": ["主角-复仇", "反派-维护现状"],
+    "level": 8,
+    "description": "冲突描述",
+    "resolution_progress": 0.3
+  }},
+  "emotional_arc": {{
+    "primary_emotion": "紧张",
+    "intensity": 8,
+    "curve": "平静→紧张→高潮→释放",
+    "secondary_emotions": ["期待", "焦虑"]
+  }},
+  "character_states": [
+    {{
+      "character_name": "张三",
+      "state_before": "犹豫",
+      "state_after": "坚定",
+      "psychological_change": "心理变化描述",
+      "key_event": "触发事件",
+      "relationship_changes": {{"李四": "关系改善"}}
+    }}
+  ],
+  "plot_points": [
+    {{
+      "content": "情节点描述",
+      "type": "revelation",
+      "importance": 0.9,
+      "impact": "推动故事发展",
+      "keyword": "必须从原文逐字复制的文本片段"
+    }}
+  ],
+  "scenes": [
+    {{
+      "location": "地点",
+      "atmosphere": "氛围",
+      "duration": "时长估计"
+    }}
+  ],
+  "pacing": "varied",
+  "dialogue_ratio": 0.4,
+  "description_ratio": 0.3,
+  "scores": {{
+    "pacing": 8,
+    "engagement": 9,
+    "coherence": 8,
+    "overall": 8.5
+  }},
+  "plot_stage": "发展",
+  "suggestions": [
+    "具体建议1",
+    "具体建议2"
+  ]
+}}
+
+**重要提示:**
+1. 每个钩子、伏笔、情节点的keyword字段是必填的，不能为空
+2. keyword必须是从章节原文中逐字复制的文本，长度8-25字
+3. keyword用于在前端标注文本位置，所以必须能在原文中精确找到
+4. 不要使用概括性语句或改写后的文字作为keyword
+
+只返回JSON,不要其他说明。"""
+    
+    def __init__(self, ai_service: AIService):
+        """
+        初始化剧情分析器
+        
+        Args:
+            ai_service: AI服务实例
+        """
+        self.ai_service = ai_service
+        logger.info("✅ PlotAnalyzer初始化成功")
+    
+    async def analyze_chapter(
+        self,
+        chapter_number: int,
+        title: str,
+        content: str,
+        word_count: int
+    ) -> Optional[Dict[str, Any]]:
+        """
+        分析单章内容
+        
+        Args:
+            chapter_number: 章节号
+            title: 章节标题
+            content: 章节内容
+            word_count: 字数
+        
+        Returns:
+            分析结果字典,失败返回None
+        """
+        try:
+            logger.info(f"🔍 开始分析第{chapter_number}章: {title}")
+            
+            # 如果内容过长,截取前8000字(避免超token)
+            analysis_content = content[:8000] if len(content) > 8000 else content
+            
+            # 构建提示词
+            prompt = self.ANALYSIS_PROMPT.format(
+                chapter_number=chapter_number,
+                title=title,
+                word_count=word_count,
+                content=analysis_content
+            )
+            
+            # 调用AI进行分析
+            # 注意：不指定max_tokens，使用用户在设置中配置的值
+            logger.info(f"  调用AI分析(内容长度: {len(analysis_content)}字)...")
+            response = await self.ai_service.generate_text(
+                prompt=prompt,
+                temperature=0.3  # 降低温度以获得更稳定的JSON输出
+            )
+            
+            # 解析JSON结果
+            analysis_result = self._parse_analysis_response(response)
+            
+            if analysis_result:
+                logger.info(f"✅ 第{chapter_number}章分析完成")
+                logger.info(f"  - 钩子: {len(analysis_result.get('hooks', []))}个")
+                logger.info(f"  - 伏笔: {len(analysis_result.get('foreshadows', []))}个")
+                logger.info(f"  - 情节点: {len(analysis_result.get('plot_points', []))}个")
+                logger.info(f"  - 整体评分: {analysis_result.get('scores', {}).get('overall', 'N/A')}")
+                return analysis_result
+            else:
+                logger.error(f"❌ 第{chapter_number}章分析失败: JSON解析错误")
+                return None
+                
+        except Exception as e:
+            logger.error(f"❌ 章节分析异常: {str(e)}")
+            return None
+    
+    def _parse_analysis_response(self, response: str) -> Optional[Dict[str, Any]]:
+        """
+        解析AI返回的分析结果
+        
+        Args:
+            response: AI返回的文本
+        
+        Returns:
+            解析后的字典,失败返回None
+        """
+        try:
+            # 清理响应文本
+            cleaned = response.strip()
+            
+            # 移除可能的markdown标记
+            cleaned = re.sub(r'^```json\s*', '', cleaned)
+            cleaned = re.sub(r'^```\s*', '', cleaned)
+            cleaned = re.sub(r'\s*```$', '', cleaned)
+            
+            # 尝试解析JSON
+            result = json.loads(cleaned)
+            
+            # 验证必要字段
+            required_fields = ['hooks', 'plot_points', 'scores']
+            for field in required_fields:
+                if field not in result:
+                    logger.warning(f"⚠️ 分析结果缺少字段: {field}")
+                    result[field] = [] if field != 'scores' else {}
+            
+            return result
+            
+        except json.JSONDecodeError as e:
+            logger.error(f"❌ JSON解析失败: {str(e)}")
+            logger.error(f"  原始响应(前500字): {response[:500]}")
+            
+            # 尝试提取JSON部分
+            json_match = re.search(r'\{[\s\S]*\}', response)
+            if json_match:
+                try:
+                    result = json.loads(json_match.group())
+                    logger.info("✅ 通过正则提取成功解析JSON")
+                    return result
+                except:
+                    pass
+            
+            return None
+        except Exception as e:
+            logger.error(f"❌ 解析异常: {str(e)}")
+            return None
+    
+    def extract_memories_from_analysis(
+        self,
+        analysis: Dict[str, Any],
+        chapter_id: str,
+        chapter_number: int,
+        chapter_content: str = ""
+    ) -> List[Dict[str, Any]]:
+        """
+        从分析结果中提取记忆片段
+        
+        Args:
+            analysis: 分析结果
+            chapter_id: 章节ID
+            chapter_number: 章节号
+            chapter_content: 章节完整内容(用于计算位置)
+        
+        Returns:
+            记忆片段列表
+        """
+        memories = []
+        
+        try:
+            # 1. 提取钩子作为记忆
+            for i, hook in enumerate(analysis.get('hooks', [])):
+                if hook.get('strength', 0) >= 6:  # 只保存强度>=6的钩子
+                    keyword = hook.get('keyword', '')
+                    position, length = self._find_text_position(chapter_content, keyword)
+                    
+                    logger.info(f"  钩子位置: keyword='{keyword[:30]}...', pos={position}, len={length}")
+                    
+                    memories.append({
+                        'type': 'hook',
+                        'content': f"[{hook.get('type', '未知')}钩子] {hook.get('content', '')}",
+                        'title': f"{hook.get('type', '钩子')} - {hook.get('position', '')}",
+                        'metadata': {
+                            'chapter_id': chapter_id,
+                            'chapter_number': chapter_number,
+                            'importance_score': min(hook.get('strength', 5) / 10, 1.0),
+                            'tags': [hook.get('type', '钩子'), hook.get('position', '')],
+                            'is_foreshadow': 0,
+                            'keyword': keyword,
+                            'text_position': position,
+                            'text_length': length,
+                            'strength': hook.get('strength', 5),
+                            'position_desc': hook.get('position', '')
+                        }
+                    })
+            
+            # 2. 提取伏笔作为记忆
+            for i, foreshadow in enumerate(analysis.get('foreshadows', [])):
+                is_planted = foreshadow.get('type') == 'planted'
+                keyword = foreshadow.get('keyword', '')
+                position, length = self._find_text_position(chapter_content, keyword)
+                
+                logger.info(f"  伏笔位置: keyword='{keyword[:30]}...', pos={position}, len={length}")
+                
+                memories.append({
+                    'type': 'foreshadow',
+                    'content': foreshadow.get('content', ''),
+                    'title': f"{'埋下伏笔' if is_planted else '回收伏笔'}",
+                    'metadata': {
+                        'chapter_id': chapter_id,
+                        'chapter_number': chapter_number,
+                        'importance_score': min(foreshadow.get('strength', 5) / 10, 1.0),
+                        'tags': ['伏笔', foreshadow.get('type', 'planted')],
+                        'is_foreshadow': 1 if is_planted else 2,
+                        'reference_chapter': foreshadow.get('reference_chapter'),
+                        'keyword': keyword,
+                        'text_position': position,
+                        'text_length': length,
+                        'foreshadow_type': foreshadow.get('type', 'planted'),
+                        'strength': foreshadow.get('strength', 5)
+                    }
+                })
+            
+            # 3. 提取关键情节点
+            for i, plot_point in enumerate(analysis.get('plot_points', [])):
+                if plot_point.get('importance', 0) >= 0.6:  # 只保存重要性>=0.6的情节点
+                    keyword = plot_point.get('keyword', '')
+                    position, length = self._find_text_position(chapter_content, keyword)
+                    
+                    logger.info(f"  情节点位置: keyword='{keyword[:30]}...', pos={position}, len={length}")
+                    
+                    memories.append({
+                        'type': 'plot_point',
+                        'content': f"{plot_point.get('content', '')}。影响: {plot_point.get('impact', '')}",
+                        'title': f"情节点 - {plot_point.get('type', '未知')}",
+                        'metadata': {
+                            'chapter_id': chapter_id,
+                            'chapter_number': chapter_number,
+                            'importance_score': plot_point.get('importance', 0.5),
+                            'tags': ['情节点', plot_point.get('type', '未知')],
+                            'is_foreshadow': 0,
+                            'keyword': keyword,
+                            'text_position': position,
+                            'text_length': length
+                        }
+                    })
+            
+            # 4. 提取角色状态变化
+            for i, char_state in enumerate(analysis.get('character_states', [])):
+                char_name = char_state.get('character_name', '未知角色')
+                memories.append({
+                    'type': 'character_event',
+                    'content': f"{char_name}的状态变化: {char_state.get('state_before', '')} → {char_state.get('state_after', '')}。{char_state.get('psychological_change', '')}",
+                    'title': f"{char_name}的变化",
+                    'metadata': {
+                        'chapter_id': chapter_id,
+                        'chapter_number': chapter_number,
+                        'importance_score': 0.7,
+                        'tags': ['角色', char_name, '状态变化'],
+                        'related_characters': [char_name],
+                        'is_foreshadow': 0
+                    }
+                })
+            
+            # 5. 如果有重要冲突,也记录下来
+            conflict = analysis.get('conflict', {})
+            
+            if conflict and conflict.get('level', 0) >= 7:
+                # 确保 parties 和 types 都是字符串列表
+                parties = conflict.get('parties', [])
+                if parties and isinstance(parties, list):
+                    parties = [str(p) for p in parties]
+                
+                types = conflict.get('types', [])
+                if types and isinstance(types, list):
+                    types = [str(t) for t in types]
+                
+                memories.append({
+                    'type': 'plot_point',
+                    'content': f"重要冲突: {conflict.get('description', '')}。冲突各方: {', '.join(parties)}",
+                    'title': f"冲突 - 强度{conflict.get('level', 0)}",
+                    'metadata': {
+                        'chapter_id': chapter_id,
+                        'chapter_number': chapter_number,
+                        'importance_score': min(conflict.get('level', 5) / 10, 1.0),
+                        'tags': ['冲突'] + types,
+                        'is_foreshadow': 0
+                    }
+                })
+            
+            logger.info(f"📝 从分析中提取了{len(memories)}条记忆")
+            return memories
+            
+        except Exception as e:
+            logger.error(f"❌ 提取记忆失败: {str(e)}")
+            return []
+    
+    def _find_text_position(self, full_text: str, keyword: str) -> tuple[int, int]:
+        """
+        在全文中查找关键词位置
+        
+        Args:
+            full_text: 完整文本
+            keyword: 关键词
+        
+        Returns:
+            (起始位置, 长度) 如果未找到返回(-1, 0)
+        """
+        if not keyword or not full_text:
+            return (-1, 0)
+        
+        try:
+            # 1. 精确匹配
+            pos = full_text.find(keyword)
+            if pos != -1:
+                return (pos, len(keyword))
+            
+            # 2. 去除标点符号后匹配
+            import re
+            clean_keyword = re.sub(r'[，。！？、；：""''（）《》【】]', '', keyword)
+            clean_text = re.sub(r'[，。！？、；：""''（）《》【】]', '', full_text)
+            pos = clean_text.find(clean_keyword)
+            
+            if pos != -1:
+                # 反向映射到原文位置（简化处理）
+                return (pos, len(clean_keyword))
+            
+            # 3. 模糊匹配：查找关键词的前半部分
+            if len(keyword) > 10:
+                partial = keyword[:min(15, len(keyword))]
+                pos = full_text.find(partial)
+                if pos != -1:
+                    return (pos, len(partial))
+            
+            # 4. 未找到
+            logger.debug(f"未找到关键词位置: {keyword[:30]}...")
+            return (-1, 0)
+            
+        except Exception as e:
+            logger.error(f"查找位置失败: {str(e)}")
+            return (-1, 0)
+    
+    def generate_analysis_summary(self, analysis: Dict[str, Any]) -> str:
+        """
+        生成分析摘要文本
+        
+        Args:
+            analysis: 分析结果
+        
+        Returns:
+            格式化的摘要文本
+        """
+        try:
+            lines = ["=== 章节分析报告 ===\n"]
+            
+            # 整体评分
+            scores = analysis.get('scores', {})
+            lines.append(f"【整体评分】")
+            lines.append(f"  整体质量: {scores.get('overall', 'N/A')}/10")
+            lines.append(f"  节奏把控: {scores.get('pacing', 'N/A')}/10")
+            lines.append(f"  吸引力: {scores.get('engagement', 'N/A')}/10")
+            lines.append(f"  连贯性: {scores.get('coherence', 'N/A')}/10\n")
+            
+            # 剧情阶段
+            lines.append(f"【剧情阶段】{analysis.get('plot_stage', '未知')}\n")
+            
+            # 钩子统计
+            hooks = analysis.get('hooks', [])
+            if hooks:
+                lines.append(f"【钩子分析】共{len(hooks)}个")
+                for hook in hooks[:3]:  # 只显示前3个
+                    lines.append(f"  • [{hook.get('type')}] {hook.get('content', '')[:50]}... (强度:{hook.get('strength', 0)})")
+                lines.append("")
+            
+            # 伏笔统计
+            foreshadows = analysis.get('foreshadows', [])
+            if foreshadows:
+                planted = sum(1 for f in foreshadows if f.get('type') == 'planted')
+                resolved = sum(1 for f in foreshadows if f.get('type') == 'resolved')
+                lines.append(f"【伏笔分析】埋下{planted}个, 回收{resolved}个\n")
+            
+            # 冲突分析
+            conflict = analysis.get('conflict', {})
+            if conflict:
+                lines.append(f"【冲突分析】")
+                lines.append(f"  类型: {', '.join(conflict.get('types', []))}")
+                lines.append(f"  强度: {conflict.get('level', 0)}/10")
+                lines.append(f"  进度: {int(conflict.get('resolution_progress', 0) * 100)}%\n")
+            
+            # 改进建议
+            suggestions = analysis.get('suggestions', [])
+            if suggestions:
+                lines.append(f"【改进建议】")
+                for i, sug in enumerate(suggestions, 1):
+                    lines.append(f"  {i}. {sug}")
+            
+            return "\n".join(lines)
+            
+        except Exception as e:
+            logger.error(f"❌ 生成摘要失败: {str(e)}")
+            return "分析摘要生成失败"
+
+
+# 创建全局实例(需要时手动初始化)
+_plot_analyzer_instance = None
+
+def get_plot_analyzer(ai_service: AIService) -> PlotAnalyzer:
+    """获取剧情分析器实例"""
+    global _plot_analyzer_instance
+    if _plot_analyzer_instance is None:
+        _plot_analyzer_instance = PlotAnalyzer(ai_service)
+    return _plot_analyzer_instance

backend/app/services/prompt_service.py

@@ -315,7 +315,7 @@ class PromptService:
 2. 数组中要包含{chapter_count}个章节对象
 3. 文本中不要使用中文引号（""），改用【】或《》"""
     
-    # 大纲续写提示词
+    # 大纲续写提示词（记忆增强版）
     OUTLINE_CONTINUE_GENERATION = """你是一位经验丰富的小说作家和编剧。请基于以下信息续写小说大纲：
 
 【项目信息】
@@ -340,6 +340,11 @@ class PromptService:
 【最近剧情】
 {recent_plot}
 
+【🧠 智能记忆系统 - 续写参考】
+以下是从故事记忆库中检索到的相关信息，请在续写大纲时参考：
+
+{memory_context}
+
 【续写指导】
 - 当前情节阶段：{plot_stage_instruction}
 - 起始章节编号：第{start_chapter}章
@@ -348,10 +353,12 @@ class PromptService:
 
 请生成第{start_chapter}章到第{end_chapter}章的大纲。
 要求：
-- 与前文自然衔接，保持故事连贯性
+- **剧情连贯性**：与前文自然衔接，保持故事连贯性
-- 遵循情节阶段的发展要求
+- **记忆参考**：适当参考记忆系统中的伏笔、钩子和情节点
-- 保持与已有章节相同的风格和详细程度
+- **伏笔回收**：可以考虑回收未完结的伏笔，制造呼应
-- 推进角色成长和情节发展
+- **角色发展**：遵循角色在前文中的成长轨迹
+- **情节阶段**：遵循情节阶段的发展要求
+- **风格一致**：保持与已有章节相同的风格和详细程度
 
 **重要格式要求：**
 1. 只返回纯JSON数组格式，不要包含任何markdown标记、代码块标记或其他说明文字
@@ -465,7 +472,7 @@ class PromptService:
 
 请直接输出章节正文内容，不要包含章节标题和其他说明文字。"""
 
-    # 章节完整创作提示词（带前置章节上下文）
+    # 章节完整创作提示词（带前置章节上下文和记忆增强）
     CHAPTER_GENERATION_WITH_CONTEXT = """你是一位专业的小说作家。请根据以下信息创作本章内容：
 
 项目信息：
@@ -489,6 +496,11 @@ class PromptService:
 【已完成的前置章节内容】
 {previous_content}
 
+【🧠 智能记忆系统 - 重要参考】
+以下是从故事记忆库中检索到的相关信息，请在创作时适当参考和呼应：
+
+{memory_context}
+
 本章信息：
 - 章节序号：第{chapter_number}章
 - 章节标题：{chapter_title}
@@ -518,8 +530,15 @@ class PromptService:
 - 体现世界观特色
 
 5. **承上启下**：
-- 开头自然衔接上一章结尾
+   - 开头自然衔接上一章结尾
-- 结尾为下一章做好铺垫
+   - 结尾为下一章做好铺垫
+
+6. **记忆系统使用指南**：
+   - **最近章节记忆**：保持情节连贯，注意角色状态和剧情发展
+   - **语义相关记忆**：参考相似情节的处理方式
+   - **未完结伏笔**：适当时机可以回收伏笔，制造呼应效果
+   - **角色状态记忆**：确保角色行为符合其发展轨迹
+   - **重要情节点**：与关键剧情保持一致
 
 请直接输出章节正文内容，不要包含章节标题和其他说明文字。"""
 
@@ -746,14 +765,26 @@ class PromptService:
                                       characters_info: str, outlines_context: str,
                                       chapter_number: int, chapter_title: str,
                                       chapter_outline: str, style_content: str = "",
-                                      target_word_count: int = 3000) -> str:
+                                      target_word_count: int = 3000,
+                                      memory_context: dict = None) -> str:
         """
         获取章节完整创作提示词
         
         Args:
             style_content: 写作风格要求内容，如果提供则会追加到提示词中
             target_word_count: 目标字数，默认3000字
+            memory_context: 记忆上下文（可选）
         """
+        # 格式化记忆上下文
+        memory_text = ""
+        if memory_context:
+            memory_text = "\n【🧠 智能记忆系统 - 重要参考】\n"
+            memory_text += memory_context.get('recent_context', '')
+            memory_text += "\n" + memory_context.get('relevant_memories', '')
+            memory_text += "\n" + memory_context.get('foreshadows', '')
+            memory_text += "\n" + memory_context.get('character_states', '')
+            memory_text += "\n" + memory_context.get('plot_points', '')
+        
         base_prompt = cls.format_prompt(
             cls.CHAPTER_GENERATION,
             title=title,
@@ -772,6 +803,13 @@ class PromptService:
             target_word_count=target_word_count
         )
         
+        # 插入记忆上下文
+        if memory_text:
+            base_prompt = base_prompt.replace(
+                "本章信息：",
+                memory_text + "\n\n本章信息："
+            )
+        
         # 如果有风格要求，应用到提示词中
         if style_content:
             return WritingStyleManager.apply_style_to_prompt(base_prompt, style_content)
@@ -786,14 +824,27 @@ class PromptService:
                                                    previous_content: str, chapter_number: int,
                                                    chapter_title: str, chapter_outline: str,
                                                    style_content: str = "",
-                                                   target_word_count: int = 3000) -> str:
+                                                   target_word_count: int = 3000,
+                                                   memory_context: dict = None) -> str:
         """
-        获取章节完整创作提示词（带前置章节上下文）
+        获取章节完整创作提示词（带前置章节上下文和记忆增强）
         
         Args:
             style_content: 写作风格要求内容，如果提供则会追加到提示词中
             target_word_count: 目标字数，默认3000字
+            memory_context: 记忆上下文（可选）
         """
+        # 格式化记忆上下文
+        memory_text = ""
+        if memory_context:
+            memory_text = memory_context.get('recent_context', '')
+            memory_text += "\n" + memory_context.get('relevant_memories', '')
+            memory_text += "\n" + memory_context.get('foreshadows', '')
+            memory_text += "\n" + memory_context.get('character_states', '')
+            memory_text += "\n" + memory_context.get('plot_points', '')
+        else:
+            memory_text = "暂无相关记忆"
+        
         base_prompt = cls.format_prompt(
             cls.CHAPTER_GENERATION_WITH_CONTEXT,
             title=title,
@@ -810,7 +861,8 @@ class PromptService:
             chapter_number=chapter_number,
             chapter_title=chapter_title,
             chapter_outline=chapter_outline,
-            target_word_count=target_word_count
+            target_word_count=target_word_count,
+            memory_context=memory_text
         )
         
         # 如果有风格要求，应用到提示词中
@@ -839,9 +891,22 @@ class PromptService:
                                     current_chapter_count: int, all_chapters_brief: str,
                                     recent_plot: str, plot_stage_instruction: str,
                                     start_chapter: int, story_direction: str,
-                                    requirements: str = "") -> str:
+                                    requirements: str = "",
-        """获取大纲续写提示词"""
+                                    memory_context: dict = None) -> str:
+        """获取大纲续写提示词（支持记忆增强）"""
         end_chapter = start_chapter + chapter_count - 1
+        
+        # 格式化记忆上下文
+        memory_text = ""
+        if memory_context:
+            memory_text = memory_context.get('recent_context', '')
+            memory_text += "\n" + memory_context.get('relevant_memories', '')
+            memory_text += "\n" + memory_context.get('foreshadows', '')
+            memory_text += "\n" + memory_context.get('character_states', '')
+            memory_text += "\n" + memory_context.get('plot_points', '')
+        else:
+            memory_text = "暂无相关记忆（可能是首次续写或记忆库为空）"
+        
         return cls.format_prompt(
             cls.OUTLINE_CONTINUE_GENERATION,
             title=title,
@@ -861,7 +926,8 @@ class PromptService:
             start_chapter=start_chapter,
             end_chapter=end_chapter,
             story_direction=story_direction,
-            requirements=requirements or "无特殊要求"
+            requirements=requirements or "无特殊要求",
+            memory_context=memory_text
         )
     
     @classmethod

backend/embedding/models--sentence-transformers--paraphrase-multilingual-MiniLM-L12-v2/refs/main

@@ -0,0 +1 @@
+86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d

backend/embedding/models--sentence-transformers--paraphrase-multilingual-MiniLM-L12-v2/snapshots/86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d/1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

backend/embedding/models--sentence-transformers--paraphrase-multilingual-MiniLM-L12-v2/snapshots/86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d/README.md

@@ -0,0 +1,156 @@
+---
+language:
+- multilingual
+- ar
+- bg
+- ca
+- cs
+- da
+- de
+- el
+- en
+- es
+- et
+- fa
+- fi
+- fr
+- gl
+- gu
+- he
+- hi
+- hr
+- hu
+- hy
+- id
+- it
+- ja
+- ka
+- ko
+- ku
+- lt
+- lv
+- mk
+- mn
+- mr
+- ms
+- my
+- nb
+- nl
+- pl
+- pt
+- ro
+- ru
+- sk
+- sl
+- sq
+- sr
+- sv
+- th
+- tr
+- uk
+- ur
+- vi
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+language_bcp47:
+- fr-ca
+- pt-br
+- zh-cn
+- zh-tw
+pipeline_tag: sentence-similarity
+---
+
+# sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+
+## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+
+# Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
+model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling. In this case, max pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+)
+```
+
+## Citing & Authors
+
+This model was trained by [sentence-transformers](https://www.sbert.net/). 
+        
+If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
+```bibtex 
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "http://arxiv.org/abs/1908.10084",
+}
+```

backend/embedding/models--sentence-transformers--paraphrase-multilingual-MiniLM-L12-v2/snapshots/86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d/config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "old_models/paraphrase-multilingual-MiniLM-L12-v2/0_Transformer",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.7.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

backend/embedding/models--sentence-transformers--paraphrase-multilingual-MiniLM-L12-v2/snapshots/86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d/config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

backend/embedding/models--sentence-transformers--paraphrase-multilingual-MiniLM-L12-v2/snapshots/86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eaa086f0ffee582aeb45b36e34cdd1fe2d6de2bef61f8a559a1bbc9bd955917b
+size 470641600

backend/embedding/models--sentence-transformers--paraphrase-multilingual-MiniLM-L12-v2/snapshots/86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d/modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

backend/embedding/models--sentence-transformers--paraphrase-multilingual-MiniLM-L12-v2/snapshots/86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

backend/embedding/models--sentence-transformers--paraphrase-multilingual-MiniLM-L12-v2/snapshots/86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d/special_tokens_map.json

@@ -0,0 +1 @@
+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false}}

backend/embedding/models--sentence-transformers--paraphrase-multilingual-MiniLM-L12-v2/snapshots/86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d/tokenizer_config.json

@@ -0,0 +1 @@
+{"do_lower_case": true, "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "tokenize_chinese_chars": true, "strip_accents": null, "bos_token": "<s>", "eos_token": "</s>", "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "old_models/paraphrase-multilingual-MiniLM-L12-v2/0_Transformer"}

backend/requirements.txt

@@ -12,9 +12,22 @@ pydantic==2.5.3
 pydantic-settings==2.1.0
 
 # AI服务
-openai==1.10.0
+openai==2.7.0
-anthropic==0.18.0
+anthropic==0.72.0
 
 # 工具库
-httpx==0.26.0
+httpx==0.28.1
-python-dotenv==1.0.0
+python-dotenv==1.0.0
+
+# NumPy版本锁定（兼容性要求）
+numpy==1.26.4
+
+# 向量数据库和Embedding (长期记忆系统)
+chromadb==1.3.2
+
+
+# Transformers（锁定兼容版本）
+transformers==4.35.2
+
+# Sentence Transformers（基于PyTorch的文本embedding库）
+sentence-transformers==2.3.1

frontend/src/App.tsx

@@ -10,6 +10,8 @@ import Characters from './pages/Characters';
 import Relationships from './pages/Relationships';
 import Organizations from './pages/Organizations';
 import Chapters from './pages/Chapters';
+import ChapterReader from './pages/ChapterReader';
+import ChapterAnalysis from './pages/ChapterAnalysis';
 import WritingStyles from './pages/WritingStyles';
 import Settings from './pages/Settings';
 // import Polish from './pages/Polish';
@@ -34,6 +36,7 @@ function App() {
           <Route path="/" element={<ProtectedRoute><ProjectList /></ProtectedRoute>} />
           <Route path="/wizard" element={<ProtectedRoute><ProjectWizardNew /></ProtectedRoute>} />
           <Route path="/settings" element={<ProtectedRoute><Settings /></ProtectedRoute>} />
+          <Route path="/chapters/:chapterId/reader" element={<ProtectedRoute><ChapterReader /></ProtectedRoute>} />
           <Route path="/project/:projectId" element={<ProtectedRoute><ProjectDetail /></ProtectedRoute>}>
             <Route index element={<Navigate to="world-setting" replace />} />
             <Route path="world-setting" element={<WorldSetting />} />
@@ -42,6 +45,7 @@ function App() {
             <Route path="relationships" element={<Relationships />} />
             <Route path="organizations" element={<Organizations />} />
             <Route path="chapters" element={<Chapters />} />
+            <Route path="chapter-analysis" element={<ChapterAnalysis />} />
             <Route path="writing-styles" element={<WritingStyles />} />
             {/* <Route path="polish" element={<Polish />} /> */}
           </Route>

frontend/src/components/AnnotatedText.tsx

@@ -0,0 +1,253 @@
+import React, { useMemo, useEffect, useRef } from 'react';
+import { Tooltip } from 'antd';
+
+// 标注数据类型
+export interface MemoryAnnotation {
+  id: string;
+  type: 'hook' | 'foreshadow' | 'plot_point' | 'character_event';
+  title: string;
+  content: string;
+  importance: number;
+  position: number;
+  length: number;
+  tags: string[];
+  metadata: {
+    strength?: number;
+    foreshadowType?: 'planted' | 'resolved';
+    relatedCharacters?: string[];
+    [key: string]: any;
+  };
+}
+
+// 文本片段类型
+interface TextSegment {
+  type: 'text' | 'annotated';
+  content: string;
+  annotation?: MemoryAnnotation;
+}
+
+interface AnnotatedTextProps {
+  content: string;
+  annotations: MemoryAnnotation[];
+  onAnnotationClick?: (annotation: MemoryAnnotation) => void;
+  activeAnnotationId?: string;
+  scrollToAnnotation?: string;
+}
+
+// 类型颜色映射
+const TYPE_COLORS = {
+  hook: '#ff6b6b',
+  foreshadow: '#6b7bff',
+  plot_point: '#51cf66',
+  character_event: '#ffd93d',
+};
+
+// 类型图标映射
+const TYPE_ICONS = {
+  hook: '🎣',
+  foreshadow: '🌟',
+  plot_point: '💎',
+  character_event: '👤',
+};
+
+/**
+ * 带标注的文本组件
+ * 将记忆标注可视化地展示在章节文本中
+ */
+const AnnotatedText: React.FC<AnnotatedTextProps> = ({
+  content,
+  annotations,
+  onAnnotationClick,
+  activeAnnotationId,
+  scrollToAnnotation,
+}) => {
+  const annotationRefs = useRef<Record<string, HTMLSpanElement | null>>({});
+
+  // 当需要滚动到特定标注时
+  useEffect(() => {
+    if (scrollToAnnotation && annotationRefs.current[scrollToAnnotation]) {
+      const element = annotationRefs.current[scrollToAnnotation];
+      element?.scrollIntoView({
+        behavior: 'smooth',
+        block: 'center',
+      });
+    }
+  }, [scrollToAnnotation]);
+  // 处理标注重叠和排序
+  const processedAnnotations = useMemo(() => {
+    if (!annotations || annotations.length === 0) {
+      console.log('AnnotatedText: 没有标注数据');
+      return [];
+    }
+    
+    console.log(`AnnotatedText: 收到${annotations.length}个标注，内容长度${content.length}`);
+    
+    // 过滤掉无效位置的标注
+    const validAnnotations = annotations.filter(
+      (a) => a.position >= 0 && a.position < content.length
+    );
+    
+    const invalidCount = annotations.length - validAnnotations.length;
+    if (invalidCount > 0) {
+      console.warn(`AnnotatedText: ${invalidCount}个标注位置无效，有效标注${validAnnotations.length}个`);
+      console.log('无效标注:', annotations.filter(a => a.position < 0 || a.position >= content.length));
+    }
+    
+    // 按位置排序
+    return validAnnotations.sort((a, b) => a.position - b.position);
+  }, [annotations, content]);
+
+  // 将文本分割为带标注的片段
+  const segments = useMemo(() => {
+    if (processedAnnotations.length === 0) {
+      return [{ type: 'text' as const, content }];
+    }
+
+    const result: TextSegment[] = [];
+    let lastPos = 0;
+
+    for (const annotation of processedAnnotations) {
+      const { position, length } = annotation;
+      
+      // 添加普通文本片段
+      if (position > lastPos) {
+        result.push({
+          type: 'text',
+          content: content.slice(lastPos, position),
+        });
+      }
+
+      // 添加标注片段
+      const annotatedContent = content.slice(
+        position,
+        position + (length > 0 ? length : 30) // 如果没有长度，默认30字符
+      );
+      
+      result.push({
+        type: 'annotated',
+        content: annotatedContent,
+        annotation,
+      });
+
+      lastPos = position + (length > 0 ? length : 30);
+    }
+
+    // 添加剩余文本
+    if (lastPos < content.length) {
+      result.push({
+        type: 'text',
+        content: content.slice(lastPos),
+      });
+    }
+
+    return result;
+  }, [content, processedAnnotations]);
+
+  // 渲染标注片段
+  const renderAnnotatedSegment = (segment: TextSegment, index: number) => {
+    if (segment.type === 'text') {
+      return <span key={index}>{segment.content}</span>;
+    }
+
+    const { annotation } = segment;
+    if (!annotation) return null;
+
+    const color = TYPE_COLORS[annotation.type];
+    const icon = TYPE_ICONS[annotation.type];
+    const isActive = activeAnnotationId === annotation.id;
+
+    // 工具提示内容
+    const tooltipContent = (
+      <div style={{ maxWidth: 300 }}>
+        <div style={{ fontWeight: 'bold', marginBottom: 4 }}>
+          {icon} {annotation.title}
+        </div>
+        <div style={{ fontSize: 12, opacity: 0.9 }}>
+          {annotation.content.slice(0, 100)}
+          {annotation.content.length > 100 ? '...' : ''}
+        </div>
+        <div style={{ marginTop: 8, fontSize: 11, opacity: 0.7 }}>
+          重要性: {(annotation.importance * 10).toFixed(1)}/10
+        </div>
+        {annotation.tags && annotation.tags.length > 0 && (
+          <div style={{ marginTop: 4, fontSize: 11 }}>
+            {annotation.tags.map((tag, i) => (
+              <span
+                key={i}
+                style={{
+                  display: 'inline-block',
+                  background: 'rgba(255,255,255,0.2)',
+                  padding: '2px 6px',
+                  borderRadius: 3,
+                  marginRight: 4,
+                }}
+              >
+                {tag}
+              </span>
+            ))}
+          </div>
+        )}
+      </div>
+    );
+
+    return (
+      <Tooltip key={index} title={tooltipContent} placement="top">
+        <span
+          ref={(el) => {
+            if (annotation) {
+              annotationRefs.current[annotation.id] = el;
+            }
+          }}
+          data-annotation-id={annotation?.id}
+          className={`annotated-text ${isActive ? 'active' : ''}`}
+          style={{
+            position: 'relative',
+            borderBottom: `2px solid ${color}`,
+            cursor: 'pointer',
+            backgroundColor: isActive ? `${color}22` : 'transparent',
+            transition: 'all 0.2s',
+            padding: '2px 0',
+          }}
+          onClick={() => onAnnotationClick?.(annotation)}
+          onMouseEnter={(e) => {
+            e.currentTarget.style.backgroundColor = `${color}33`;
+          }}
+          onMouseLeave={(e) => {
+            e.currentTarget.style.backgroundColor = isActive
+              ? `${color}22`
+              : 'transparent';
+          }}
+        >
+          {segment.content}
+          <span
+            style={{
+              position: 'absolute',
+              top: -20,
+              left: '50%',
+              transform: 'translateX(-50%)',
+              fontSize: 14,
+              pointerEvents: 'none',
+            }}
+          >
+            {icon}
+          </span>
+        </span>
+      </Tooltip>
+    );
+  };
+
+  return (
+    <div
+      style={{
+        lineHeight: 2,
+        fontSize: 16,
+        whiteSpace: 'pre-wrap',
+        wordBreak: 'break-word',
+      }}
+    >
+      {segments.map((segment, index) => renderAnnotatedSegment(segment, index))}
+    </div>
+  );
+};
+
+export default AnnotatedText;

frontend/src/components/ChapterAnalysis.tsx

@@ -0,0 +1,537 @@
+import { useState, useEffect } from 'react';
+import { Modal, Progress, Spin, Alert, Tabs, Card, Tag, List, Empty, Statistic, Row, Col, Button } from 'antd';
+import {
+  ThunderboltOutlined,
+  BulbOutlined,
+  FireOutlined,
+  HeartOutlined,
+  TeamOutlined,
+  TrophyOutlined,
+  CheckCircleOutlined,
+  ClockCircleOutlined,
+  CloseCircleOutlined,
+  ReloadOutlined
+} from '@ant-design/icons';
+import type { AnalysisTask, ChapterAnalysisResponse } from '../types';
+
+interface ChapterAnalysisProps {
+  chapterId: string;
+  visible: boolean;
+  onClose: () => void;
+}
+
+export default function ChapterAnalysis({ chapterId, visible, onClose }: ChapterAnalysisProps) {
+  const [task, setTask] = useState<AnalysisTask | null>(null);
+  const [analysis, setAnalysis] = useState<ChapterAnalysisResponse | null>(null);
+  const [loading, setLoading] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+
+  useEffect(() => {
+    if (visible && chapterId) {
+      fetchAnalysisStatus();
+    }
+    
+    // 清理函数：组件卸载或关闭时清除轮询
+    return () => {
+      // 清除可能存在的轮询
+    };
+  }, [visible, chapterId]);
+
+  const fetchAnalysisStatus = async () => {
+    try {
+      setLoading(true);
+      setError(null);
+      
+      const response = await fetch(`/api/chapters/${chapterId}/analysis/status`);
+      
+      if (response.status === 404) {
+        setTask(null);
+        setError('该章节还未进行分析');
+        return;
+      }
+      
+      if (!response.ok) {
+        throw new Error('获取分析状态失败');
+      }
+      
+      const taskData: AnalysisTask = await response.json();
+      setTask(taskData);
+      
+      if (taskData.status === 'completed') {
+        await fetchAnalysisResult();
+      } else if (taskData.status === 'running' || taskData.status === 'pending') {
+        // 开始轮询
+        startPolling();
+      }
+    } catch (err) {
+      setError((err as Error).message);
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  const fetchAnalysisResult = async () => {
+    try {
+      const response = await fetch(`/api/chapters/${chapterId}/analysis`);
+      if (!response.ok) {
+        throw new Error('获取分析结果失败');
+      }
+      const data: ChapterAnalysisResponse = await response.json();
+      setAnalysis(data);
+    } catch (err) {
+      setError((err as Error).message);
+    }
+  };
+
+  const startPolling = () => {
+    const pollInterval = setInterval(async () => {
+      try {
+        const response = await fetch(`/api/chapters/${chapterId}/analysis/status`);
+        if (!response.ok) return;
+        
+        const taskData: AnalysisTask = await response.json();
+        setTask(taskData);
+        
+        if (taskData.status === 'completed') {
+          clearInterval(pollInterval);
+          await fetchAnalysisResult();
+        } else if (taskData.status === 'failed') {
+          clearInterval(pollInterval);
+          setError(taskData.error_message || '分析失败');
+        }
+      } catch (err) {
+        console.error('轮询错误:', err);
+      }
+    }, 2000);
+
+    // 5分钟超时
+    setTimeout(() => clearInterval(pollInterval), 300000);
+  };
+
+  const triggerAnalysis = async () => {
+    try {
+      setLoading(true);
+      setError(null);
+      
+      const response = await fetch(`/api/chapters/${chapterId}/analyze`, {
+        method: 'POST'
+      });
+      
+      if (!response.ok) {
+        const errorData = await response.json();
+        throw new Error(errorData.detail || '触发分析失败');
+      }
+      
+      // 触发成功后立即关闭Modal，让父组件的状态管理接管
+      onClose();
+    } catch (err) {
+      setError((err as Error).message);
+    } finally {
+      setLoading(false);
+    }
+  };
+
+
+  const renderStatusIcon = () => {
+    if (!task) return null;
+    
+    switch (task.status) {
+      case 'pending':
+        return <ClockCircleOutlined style={{ color: '#faad14' }} />;
+      case 'running':
+        return <Spin />;
+      case 'completed':
+        return <CheckCircleOutlined style={{ color: '#52c41a' }} />;
+      case 'failed':
+        return <CloseCircleOutlined style={{ color: '#ff4d4f' }} />;
+      default:
+        return null;
+    }
+  };
+
+  const renderProgress = () => {
+    if (!task || task.status === 'completed') return null;
+    
+    return (
+      <div style={{ padding: '24px' }}>
+        <div style={{ display: 'flex', alignItems: 'center', marginBottom: 16 }}>
+          {renderStatusIcon()}
+          <span style={{ marginLeft: 8, fontSize: 16 }}>
+            {task.status === 'pending' && '等待分析...'}
+            {task.status === 'running' && 'AI正在分析中...'}
+            {task.status === 'failed' && '分析失败'}
+          </span>
+        </div>
+        <Progress 
+          percent={task.progress} 
+          status={task.status === 'failed' ? 'exception' : 'active'}
+        />
+        {task.status === 'failed' && task.error_message && (
+          <Alert 
+            message="分析失败" 
+            description={task.error_message} 
+            type="error" 
+            showIcon 
+            style={{ marginTop: 16 }}
+          />
+        )}
+      </div>
+    );
+  };
+
+  const renderAnalysisResult = () => {
+    if (!analysis) return null;
+    
+    const { analysis: analysis_data, memories } = analysis;
+    
+    return (
+      <Tabs
+        defaultActiveKey="overview"
+        style={{ height: '100%' }}
+        items={[
+          {
+            key: 'overview',
+            label: '概览',
+            icon: <TrophyOutlined />,
+            children: (
+              <div style={{ height: 'calc(90vh - 220px)', overflowY: 'auto', paddingRight: '8px' }}>
+                <Card title="整体评分" style={{ marginBottom: 16 }}>
+                  <Row gutter={16}>
+                    <Col span={6}>
+                      <Statistic
+                        title="整体质量"
+                        value={analysis_data.overall_quality_score || 0}
+                        suffix="/ 10"
+                        valueStyle={{ color: '#3f8600' }}
+                      />
+                    </Col>
+                    <Col span={6}>
+                      <Statistic
+                        title="节奏把控"
+                        value={analysis_data.pacing_score || 0}
+                        suffix="/ 10"
+                      />
+                    </Col>
+                    <Col span={6}>
+                      <Statistic
+                        title="吸引力"
+                        value={analysis_data.engagement_score || 0}
+                        suffix="/ 10"
+                      />
+                    </Col>
+                    <Col span={6}>
+                      <Statistic
+                        title="连贯性"
+                        value={analysis_data.coherence_score || 0}
+                        suffix="/ 10"
+                      />
+                    </Col>
+                  </Row>
+                </Card>
+                
+                {analysis_data.analysis_report && (
+                  <Card title="分析摘要" style={{ marginBottom: 16 }}>
+                    <pre style={{ whiteSpace: 'pre-wrap', fontFamily: 'inherit' }}>
+                      {analysis_data.analysis_report}
+                    </pre>
+                  </Card>
+                )}
+                
+                {analysis_data.suggestions && analysis_data.suggestions.length > 0 && (
+                  <Card title={<><BulbOutlined /> 改进建议</>}>
+                    <List
+                      dataSource={analysis_data.suggestions}
+                      renderItem={(item, index) => (
+                        <List.Item>
+                          <span>{index + 1}. {item}</span>
+                        </List.Item>
+                      )}
+                    />
+                  </Card>
+                )}
+              </div>
+            )
+          },
+          {
+            key: 'hooks',
+            label: `钩子 (${analysis_data.hooks?.length || 0})`,
+            icon: <ThunderboltOutlined />,
+            children: (
+              <div style={{ height: 'calc(90vh - 220px)', overflowY: 'auto', paddingRight: '8px' }}>
+                <Card>
+                {analysis_data.hooks && analysis_data.hooks.length > 0 ? (
+                  <List
+                    dataSource={analysis_data.hooks}
+                    renderItem={(hook) => (
+                      <List.Item>
+                        <List.Item.Meta
+                          title={
+                            <div>
+                              <Tag color="blue">{hook.type}</Tag>
+                              <Tag color="orange">{hook.position}</Tag>
+                              <Tag color="red">强度: {hook.strength}/10</Tag>
+                            </div>
+                          }
+                          description={hook.content}
+                        />
+                      </List.Item>
+                    )}
+                  />
+                ) : (
+                  <Empty description="暂无钩子" />
+                )}
+                </Card>
+              </div>
+            )
+          },
+          {
+            key: 'foreshadows',
+            label: `伏笔 (${analysis_data.foreshadows?.length || 0})`,
+            icon: <FireOutlined />,
+            children: (
+              <div style={{ height: 'calc(90vh - 220px)', overflowY: 'auto', paddingRight: '8px' }}>
+                <Card>
+                {analysis_data.foreshadows && analysis_data.foreshadows.length > 0 ? (
+                  <List
+                    dataSource={analysis_data.foreshadows}
+                    renderItem={(foreshadow) => (
+                      <List.Item>
+                        <List.Item.Meta
+                          title={
+                            <div>
+                              <Tag color={foreshadow.type === 'planted' ? 'green' : 'purple'}>
+                                {foreshadow.type === 'planted' ? '已埋下' : '已回收'}
+                              </Tag>
+                              <Tag>强度: {foreshadow.strength}/10</Tag>
+                              <Tag>隐藏度: {foreshadow.subtlety}/10</Tag>
+                              {foreshadow.reference_chapter && (
+                                <Tag color="cyan">呼应第{foreshadow.reference_chapter}章</Tag>
+                              )}
+                            </div>
+                          }
+                          description={foreshadow.content}
+                        />
+                      </List.Item>
+                    )}
+                  />
+                ) : (
+                  <Empty description="暂无伏笔" />
+                )}
+                </Card>
+              </div>
+            )
+          },
+          {
+            key: 'emotion',
+            label: '情感曲线',
+            icon: <HeartOutlined />,
+            children: (
+              <div style={{ height: 'calc(90vh - 220px)', overflowY: 'auto', paddingRight: '8px' }}>
+                <Card>
+                {analysis_data.emotional_tone ? (
+                  <div>
+                    <Row gutter={16} style={{ marginBottom: 24 }}>
+                      <Col span={12}>
+                        <Statistic
+                          title="主导情绪"
+                          value={analysis_data.emotional_tone}
+                        />
+                      </Col>
+                      <Col span={12}>
+                        <Statistic
+                          title="情感强度"
+                          value={(analysis_data.emotional_intensity * 10).toFixed(1)}
+                          suffix="/ 10"
+                        />
+                      </Col>
+                    </Row>
+                    <Card type="inner" title="剧情阶段" size="small">
+                      <p><strong>阶段：</strong>{analysis_data.plot_stage}</p>
+                      <p><strong>冲突等级：</strong>{analysis_data.conflict_level} / 10</p>
+                      {analysis_data.conflict_types && analysis_data.conflict_types.length > 0 && (
+                        <div style={{ marginTop: 8 }}>
+                          <strong>冲突类型：</strong>
+                          {analysis_data.conflict_types.map((type, idx) => (
+                            <Tag key={idx} color="red" style={{ margin: 4 }}>
+                              {type}
+                            </Tag>
+                          ))}
+                        </div>
+                      )}
+                    </Card>
+                  </div>
+                ) : (
+                  <Empty description="暂无情感分析" />
+                )}
+                </Card>
+              </div>
+            )
+          },
+          {
+            key: 'characters',
+            label: `角色 (${analysis_data.character_states?.length || 0})`,
+            icon: <TeamOutlined />,
+            children: (
+              <div style={{ height: 'calc(90vh - 220px)', overflowY: 'auto', paddingRight: '8px' }}>
+                <Card>
+                {analysis_data.character_states && analysis_data.character_states.length > 0 ? (
+                  <List
+                    dataSource={analysis_data.character_states}
+                    renderItem={(char) => (
+                      <List.Item>
+                        <Card 
+                          type="inner" 
+                          title={char.character_name}
+                          size="small"
+                          style={{ width: '100%' }}
+                        >
+                          <p><strong>状态变化：</strong>{char.state_before} → {char.state_after}</p>
+                          <p><strong>心理变化：</strong>{char.psychological_change}</p>
+                          <p><strong>关键事件：</strong>{char.key_event}</p>
+                          {char.relationship_changes && Object.keys(char.relationship_changes).length > 0 && (
+                            <div>
+                              <strong>关系变化：</strong>
+                              {Object.entries(char.relationship_changes).map(([name, change]) => (
+                                <Tag key={name} color="blue" style={{ margin: 4 }}>
+                                  与{name}: {change}
+                                </Tag>
+                              ))}
+                            </div>
+                          )}
+                        </Card>
+                      </List.Item>
+                    )}
+                  />
+                ) : (
+                  <Empty description="暂无角色分析" />
+                )}
+                </Card>
+              </div>
+            )
+          },
+          {
+            key: 'memories',
+            label: `记忆 (${memories?.length || 0})`,
+            icon: <FireOutlined />,
+            children: (
+              <div style={{ height: 'calc(90vh - 220px)', overflowY: 'auto', paddingRight: '8px' }}>
+                <Card>
+                {memories && memories.length > 0 ? (
+                  <List
+                    dataSource={memories}
+                    renderItem={(memory) => (
+                      <List.Item>
+                        <List.Item.Meta
+                          title={
+                            <div>
+                              <Tag color="blue">{memory.type}</Tag>
+                              <Tag color="orange">重要性: {memory.importance.toFixed(1)}</Tag>
+                              {memory.is_foreshadow === 1 && <Tag color="green">已埋下伏笔</Tag>}
+                              {memory.is_foreshadow === 2 && <Tag color="purple">已回收伏笔</Tag>}
+                              <span style={{ marginLeft: 8 }}>{memory.title}</span>
+                            </div>
+                          }
+                          description={
+                            <div>
+                              <p>{memory.content}</p>
+                              <div>
+                                {memory.tags.map((tag, idx) => (
+                                  <Tag key={idx} style={{ margin: 2 }}>{tag}</Tag>
+                                ))}
+                              </div>
+                            </div>
+                          }
+                        />
+                      </List.Item>
+                    )}
+                  />
+                ) : (
+                  <Empty description="暂无记忆片段" />
+                )}
+                </Card>
+              </div>
+            )
+          }
+        ]}
+      />
+    );
+  };
+
+  return (
+    <Modal
+      title="章节分析"
+      open={visible}
+      onCancel={onClose}
+      width="90%"
+      centered
+      style={{
+        maxWidth: '1400px',
+        paddingBottom: 0
+      }}
+      styles={{
+        body: {
+          padding: '24px',
+          paddingBottom: 0
+        }
+      }}
+      footer={[
+        <Button key="close" onClick={onClose}>
+          关闭
+        </Button>,
+        !task && !loading && (
+          <Button
+            key="analyze"
+            type="primary"
+            icon={<ReloadOutlined />}
+            onClick={triggerAnalysis}
+            loading={loading}
+          >
+            开始分析
+          </Button>
+        ),
+        task && (task.status === 'failed') && (
+          <Button
+            key="reanalyze"
+            type="primary"
+            icon={<ReloadOutlined />}
+            onClick={triggerAnalysis}
+            loading={loading}
+            danger
+          >
+            重新分析
+          </Button>
+        ),
+        task && task.status === 'completed' && (
+          <Button
+            key="reanalyze"
+            type="default"
+            icon={<ReloadOutlined />}
+            onClick={triggerAnalysis}
+            loading={loading}
+          >
+            重新分析
+          </Button>
+        )
+      ].filter(Boolean)}
+    >
+      {loading && !task && (
+        <div style={{ textAlign: 'center', padding: '48px' }}>
+          <Spin size="large" />
+          <p style={{ marginTop: 16 }}>加载中...</p>
+        </div>
+      )}
+      
+      {error && (
+        <Alert
+          message="错误"
+          description={error}
+          type="error"
+          showIcon
+        />
+      )}
+      
+      {task && task.status !== 'completed' && renderProgress()}
+      {task && task.status === 'completed' && analysis && renderAnalysisResult()}
+    </Modal>
+  );
+}

frontend/src/components/MemorySidebar.tsx

@@ -0,0 +1,250 @@
+import React, { useMemo, useEffect, useRef } from 'react';
+import { Card, Tag, Badge, Empty, Collapse, Divider } from 'antd';
+import {
+  FireOutlined,
+  StarOutlined,
+  ThunderboltOutlined,
+  UserOutlined,
+} from '@ant-design/icons';
+import type { MemoryAnnotation } from './AnnotatedText';
+
+const { Panel } = Collapse;
+
+interface MemorySidebarProps {
+  annotations: MemoryAnnotation[];
+  activeAnnotationId?: string;
+  onAnnotationClick?: (annotation: MemoryAnnotation) => void;
+  scrollToAnnotation?: string;
+}
+
+// 类型配置
+const TYPE_CONFIG = {
+  hook: {
+    label: '钩子',
+    icon: <FireOutlined />,
+    color: '#ff6b6b',
+  },
+  foreshadow: {
+    label: '伏笔',
+    icon: <StarOutlined />,
+    color: '#6b7bff',
+  },
+  plot_point: {
+    label: '情节点',
+    icon: <ThunderboltOutlined />,
+    color: '#51cf66',
+  },
+  character_event: {
+    label: '角色事件',
+    icon: <UserOutlined />,
+    color: '#ffd93d',
+  },
+};
+
+/**
+ * 记忆侧边栏组件
+ * 展示章节的所有记忆标注
+ */
+const MemorySidebar: React.FC<MemorySidebarProps> = ({
+  annotations,
+  activeAnnotationId,
+  onAnnotationClick,
+  scrollToAnnotation,
+}) => {
+  const cardRefs = useRef<Record<string, HTMLDivElement | null>>({});
+
+  // 当需要滚动到特定标注卡片时
+  useEffect(() => {
+    if (scrollToAnnotation && cardRefs.current[scrollToAnnotation]) {
+      const element = cardRefs.current[scrollToAnnotation];
+      element?.scrollIntoView({
+        behavior: 'smooth',
+        block: 'center',
+      });
+    }
+  }, [scrollToAnnotation]);
+  // 按类型分组
+  const groupedAnnotations = useMemo(() => {
+    const groups: Record<string, MemoryAnnotation[]> = {
+      hook: [],
+      foreshadow: [],
+      plot_point: [],
+      character_event: [],
+    };
+
+    annotations.forEach((annotation) => {
+      if (groups[annotation.type]) {
+        groups[annotation.type].push(annotation);
+      }
+    });
+
+    // 每组按重要性排序
+    Object.keys(groups).forEach((type) => {
+      groups[type].sort((a, b) => b.importance - a.importance);
+    });
+
+    return groups;
+  }, [annotations]);
+
+  // 统计信息
+  const stats = useMemo(() => {
+    return {
+      total: annotations.length,
+      hooks: groupedAnnotations.hook.length,
+      foreshadows: groupedAnnotations.foreshadow.length,
+      plotPoints: groupedAnnotations.plot_point.length,
+      characterEvents: groupedAnnotations.character_event.length,
+    };
+  }, [annotations, groupedAnnotations]);
+
+  // 渲染单个记忆卡片
+  const renderMemoryCard = (annotation: MemoryAnnotation) => {
+    const config = TYPE_CONFIG[annotation.type];
+    const isActive = activeAnnotationId === annotation.id;
+
+    return (
+      <div
+        key={annotation.id}
+        ref={(el) => {
+          cardRefs.current[annotation.id] = el;
+        }}
+      >
+        <Card
+          size="small"
+          hoverable
+          onClick={() => onAnnotationClick?.(annotation)}
+          style={{
+            marginBottom: 12,
+            borderLeft: `4px solid ${config.color}`,
+            backgroundColor: isActive ? `${config.color}11` : 'transparent',
+            cursor: 'pointer',
+            transition: 'all 0.2s',
+          }}
+          bodyStyle={{ padding: 12 }}
+        >
+        <div style={{ marginBottom: 8 }}>
+          <Badge
+            count={`${(annotation.importance * 10).toFixed(1)}`}
+            style={{
+              backgroundColor: config.color,
+              float: 'right',
+            }}
+          />
+          <div style={{ fontWeight: 600, fontSize: 14, paddingRight: 50 }}>
+            {config.icon} {annotation.title}
+          </div>
+        </div>
+
+        <div
+          style={{
+            fontSize: 13,
+            color: '#666',
+            lineHeight: 1.6,
+            marginBottom: 8,
+          }}
+        >
+          {annotation.content.length > 100
+            ? `${annotation.content.slice(0, 100)}...`
+            : annotation.content}
+        </div>
+
+        {annotation.tags && annotation.tags.length > 0 && (
+          <div>
+            {annotation.tags.map((tag, index) => (
+              <Tag key={index} style={{ fontSize: 11, margin: '2px 4px 2px 0' }}>
+                {tag}
+              </Tag>
+            ))}
+          </div>
+        )}
+
+        {/* 特殊元数据 */}
+        {annotation.metadata.strength && (
+          <div style={{ marginTop: 4, fontSize: 11, color: '#999' }}>
+            强度: {annotation.metadata.strength}/10
+          </div>
+        )}
+        {annotation.metadata.foreshadowType && (
+          <Tag
+            color={annotation.metadata.foreshadowType === 'planted' ? 'blue' : 'green'}
+            style={{ marginTop: 4 }}
+          >
+            {annotation.metadata.foreshadowType === 'planted' ? '已埋下' : '已回收'}
+          </Tag>
+        )}
+        </Card>
+      </div>
+    );
+  };
+
+  if (annotations.length === 0) {
+    return (
+      <div style={{ padding: 24 }}>
+        <Empty description="暂无分析数据" />
+      </div>
+    );
+  }
+
+  return (
+    <div style={{ height: '100%', overflowY: 'auto', padding: '16px' }}>
+      {/* 统计概览 */}
+      <Card size="small" style={{ marginBottom: 16 }}>
+        <div style={{ fontWeight: 600, marginBottom: 12 }}>📊 分析概览</div>
+        <div style={{ display: 'grid', gridTemplateColumns: '1fr 1fr', gap: 8 }}>
+          <div>
+            <div style={{ fontSize: 12, color: '#999' }}>钩子</div>
+            <div style={{ fontSize: 20, fontWeight: 600, color: TYPE_CONFIG.hook.color }}>
+              {stats.hooks}
+            </div>
+          </div>
+          <div>
+            <div style={{ fontSize: 12, color: '#999' }}>伏笔</div>
+            <div style={{ fontSize: 20, fontWeight: 600, color: TYPE_CONFIG.foreshadow.color }}>
+              {stats.foreshadows}
+            </div>
+          </div>
+          <div>
+            <div style={{ fontSize: 12, color: '#999' }}>情节点</div>
+            <div style={{ fontSize: 20, fontWeight: 600, color: TYPE_CONFIG.plot_point.color }}>
+              {stats.plotPoints}
+            </div>
+          </div>
+          <div>
+            <div style={{ fontSize: 12, color: '#999' }}>角色事件</div>
+            <div
+              style={{ fontSize: 20, fontWeight: 600, color: TYPE_CONFIG.character_event.color }}
+            >
+              {stats.characterEvents}
+            </div>
+          </div>
+        </div>
+      </Card>
+
+      <Divider style={{ margin: '16px 0' }} />
+
+      {/* 分类展示 */}
+      <Collapse defaultActiveKey={['hook', 'foreshadow', 'plot_point']} ghost>
+        {Object.entries(groupedAnnotations).map(([type, items]) => {
+          if (items.length === 0) return null;
+
+          const config = TYPE_CONFIG[type as keyof typeof TYPE_CONFIG];
+
+          return (
+            <Panel
+              key={type}
+              header={
+                <span style={{ fontWeight: 600 }}>
+                  {config.icon} {config.label} ({items.length})
+                </span>
+              }
+            >
+              {items.map((annotation) => renderMemoryCard(annotation))}
+            </Panel>
+          );
+        })}
+      </Collapse>
+    </div>
+  );
+};
+
+export default MemorySidebar;

frontend/src/pages/ChapterAnalysis.tsx

@@ -0,0 +1,374 @@
+import React, { useState, useEffect } from 'react';
+import { Card, List, Button, Space, Empty, Tag, Spin, Alert, Switch, Drawer, message } from 'antd';
+import {
+  EyeOutlined,
+  EyeInvisibleOutlined,
+  MenuOutlined,
+  LeftOutlined,
+  RightOutlined,
+} from '@ant-design/icons';
+import { useParams } from 'react-router-dom';
+import api from '../services/api';
+import AnnotatedText, { type MemoryAnnotation } from '../components/AnnotatedText';
+import MemorySidebar from '../components/MemorySidebar';
+
+interface ChapterItem {
+  id: string;
+  chapter_number: number;
+  title: string;
+  content: string;
+  word_count: number;
+  status: string;
+}
+
+interface AnnotationsData {
+  chapter_id: string;
+  chapter_number: number;
+  title: string;
+  word_count: number;
+  annotations: MemoryAnnotation[];
+  has_analysis: boolean;
+  summary: {
+    total_annotations: number;
+    hooks: number;
+    foreshadows: number;
+    plot_points: number;
+    character_events: number;
+  };
+}
+
+interface NavigationData {
+  current: {
+    id: string;
+    chapter_number: number;
+    title: string;
+  };
+  previous: {
+    id: string;
+    chapter_number: number;
+    title: string;
+  } | null;
+  next: {
+    id: string;
+    chapter_number: number;
+    title: string;
+  } | null;
+}
+
+/**
+ * 项目内的章节剧情分析页面
+ * 显示章节列表和带标注的章节内容
+ */
+const ChapterAnalysis: React.FC = () => {
+  const { projectId } = useParams<{ projectId: string }>();
+  
+  const [chapters, setChapters] = useState<ChapterItem[]>([]);
+  const [selectedChapter, setSelectedChapter] = useState<ChapterItem | null>(null);
+  const [annotationsData, setAnnotationsData] = useState<AnnotationsData | null>(null);
+  const [navigation, setNavigation] = useState<NavigationData | null>(null);
+  const [loading, setLoading] = useState(true);
+  const [contentLoading, setContentLoading] = useState(false);
+  const [showAnnotations, setShowAnnotations] = useState(true);
+  const [activeAnnotationId, setActiveAnnotationId] = useState<string | undefined>();
+  const [sidebarVisible, setSidebarVisible] = useState(false);
+  const [scrollToContentAnnotation, setScrollToContentAnnotation] = useState<string | undefined>();
+  const [scrollToSidebarAnnotation, setScrollToSidebarAnnotation] = useState<string | undefined>();
+
+  // 加载章节列表
+  useEffect(() => {
+    const loadChapters = async () => {
+      if (!projectId) return;
+      
+      try {
+        setLoading(true);
+        const response = await api.get(`/chapters/project/${projectId}`);
+        // API 拦截器已经解析了 response.data，所以直接使用
+        const data = response.data || response;
+        const chapterList = data.items || [];
+        setChapters(chapterList);
+        
+        // 自动选择第一个有内容的章节
+        const firstChapterWithContent = chapterList.find((ch: ChapterItem) => ch.content && ch.content.trim() !== '');
+        if (firstChapterWithContent) {
+          loadChapterContent(firstChapterWithContent.id);
+        }
+      } catch (error) {
+        console.error('加载章节列表失败:', error);
+        message.error('加载章节列表失败');
+      } finally {
+        setLoading(false);
+      }
+    };
+
+    loadChapters();
+  }, [projectId]);
+
+  // 加载章节内容和标注
+  const loadChapterContent = async (chapterId: string) => {
+    try {
+      setContentLoading(true);
+      
+      const [chapterResponse, annotationsResponse, navigationResponse] = await Promise.all([
+        api.get(`/chapters/${chapterId}`),
+        api.get(`/chapters/${chapterId}/annotations`).catch(() => null),
+        api.get(`/chapters/${chapterId}/navigation`).catch(() => null),
+      ]);
+
+      // 提取 data 属性
+      setSelectedChapter(chapterResponse.data || chapterResponse);
+      setAnnotationsData(annotationsResponse ? (annotationsResponse.data || annotationsResponse) : null);
+      setNavigation(navigationResponse ? (navigationResponse.data || navigationResponse) : null);
+    } catch (error) {
+      console.error('加载章节内容失败:', error);
+      message.error('加载章节内容失败');
+    } finally {
+      setContentLoading(false);
+    }
+  };
+
+  const handleChapterSelect = (chapterId: string) => {
+    loadChapterContent(chapterId);
+  };
+
+  const handlePreviousChapter = () => {
+    if (navigation?.previous) {
+      loadChapterContent(navigation.previous.id);
+    }
+  };
+
+  const handleNextChapter = () => {
+    if (navigation?.next) {
+      loadChapterContent(navigation.next.id);
+    }
+  };
+
+  const handleAnnotationClick = (annotation: MemoryAnnotation, source: 'content' | 'sidebar' = 'content') => {
+    setActiveAnnotationId(annotation.id);
+    
+    if (source === 'content') {
+      // 从内容区点击，滚动到侧边栏
+      setScrollToSidebarAnnotation(annotation.id);
+      // 清除滚动状态
+      setTimeout(() => setScrollToSidebarAnnotation(undefined), 100);
+      
+      if (window.innerWidth < 768) {
+        setSidebarVisible(true);
+      }
+    } else {
+      // 从侧边栏点击，滚动到内容区
+      setScrollToContentAnnotation(annotation.id);
+      // 清除滚动状态
+      setTimeout(() => setScrollToContentAnnotation(undefined), 100);
+    }
+  };
+
+  const hasAnnotations = annotationsData && annotationsData.annotations.length > 0;
+
+  if (loading) {
+    return (
+      <div style={{ textAlign: 'center', padding: '100px 0' }}>
+        <Spin size="large" tip="加载章节中..." />
+      </div>
+    );
+  }
+
+  return (
+    <div style={{ display: 'flex', height: '100%', gap: 16 }}>
+      {/* 左侧章节列表 */}
+      <Card
+        title="章节列表"
+        style={{ width: 280, height: '100%', overflow: 'hidden' }}
+        bodyStyle={{ padding: 0, height: 'calc(100% - 57px)', overflow: 'auto' }}
+      >
+        {chapters.length === 0 ? (
+          <Empty description="暂无章节" style={{ marginTop: 60 }} />
+        ) : (
+          <List
+            dataSource={chapters}
+            renderItem={(chapter) => (
+              <List.Item
+                key={chapter.id}
+                onClick={() => handleChapterSelect(chapter.id)}
+                style={{
+                  cursor: 'pointer',
+                  padding: '12px 16px',
+                  background: selectedChapter?.id === chapter.id ? '#e6f7ff' : 'transparent',
+                  borderLeft: selectedChapter?.id === chapter.id ? '3px solid #1890ff' : '3px solid transparent',
+                }}
+              >
+                <List.Item.Meta
+                  title={
+                    <span style={{ fontSize: 14, fontWeight: selectedChapter?.id === chapter.id ? 600 : 400 }}>
+                      第{chapter.chapter_number}章: {chapter.title}
+                    </span>
+                  }
+                  description={
+                    <Space size={4}>
+                      <Tag color={chapter.content && chapter.content.trim() !== '' ? 'success' : 'default'}>
+                        {chapter.word_count || 0}字
+                      </Tag>
+                    </Space>
+                  }
+                />
+              </List.Item>
+            )}
+          />
+        )}
+      </Card>
+
+      {/* 右侧内容区域 */}
+      <div style={{ flex: 1, display: 'flex', flexDirection: 'column', height: '100%', overflow: 'hidden' }}>
+        {!selectedChapter ? (
+          <Card style={{ height: '100%' }}>
+            <Empty description="请从左侧选择一个章节查看" style={{ marginTop: 100 }} />
+          </Card>
+        ) : (
+          <>
+            {/* 工具栏 */}
+            <Card size="small" style={{ marginBottom: 16 }}>
+              <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center' }}>
+                <Space>
+                  <Button
+                    icon={<LeftOutlined />}
+                    onClick={handlePreviousChapter}
+                    disabled={!navigation?.previous}
+                    title={navigation?.previous ? `上一章: ${navigation.previous.title}` : '已是第一章'}
+                  >
+                    上一章
+                  </Button>
+                  <span style={{ fontSize: 16, fontWeight: 600 }}>
+                    第{selectedChapter.chapter_number}章: {selectedChapter.title}
+                  </span>
+                  <Button
+                    icon={<RightOutlined />}
+                    onClick={handleNextChapter}
+                    disabled={!navigation?.next}
+                    title={navigation?.next ? `下一章: ${navigation.next.title}` : '已是最后一章'}
+                  >
+                    下一章
+                  </Button>
+                </Space>
+
+                <Space>
+                  {hasAnnotations && (
+                    <>
+                      <Switch
+                        checked={showAnnotations}
+                        onChange={setShowAnnotations}
+                        checkedChildren={<EyeOutlined />}
+                        unCheckedChildren={<EyeInvisibleOutlined />}
+                      />
+                      <span style={{ fontSize: 13, color: '#666' }}>显示标注</span>
+                      <Button
+                        icon={<MenuOutlined />}
+                        onClick={() => setSidebarVisible(true)}
+                        style={{ display: window.innerWidth < 768 ? 'inline-block' : 'none' }}
+                      >
+                        分析
+                      </Button>
+                    </>
+                  )}
+                </Space>
+              </div>
+
+              {hasAnnotations && annotationsData && (
+                <div style={{ marginTop: 12, fontSize: 12, color: '#999' }}>
+                  共有 {annotationsData.summary.total_annotations} 个标注：
+                  {annotationsData.summary.hooks > 0 && ` 🎣${annotationsData.summary.hooks}个钩子`}
+                  {annotationsData.summary.foreshadows > 0 &&
+                    ` 🌟${annotationsData.summary.foreshadows}个伏笔`}
+                  {annotationsData.summary.plot_points > 0 &&
+                    ` 💎${annotationsData.summary.plot_points}个情节点`}
+                  {annotationsData.summary.character_events > 0 &&
+                    ` 👤${annotationsData.summary.character_events}个角色事件`}
+                </div>
+              )}
+            </Card>
+
+            {/* 内容区域 */}
+            <div style={{ flex: 1, display: 'flex', gap: 16, overflow: 'hidden' }}>
+              {/* 章节内容 */}
+              <Card
+                style={{ flex: 1, overflow: 'auto' }}
+                loading={contentLoading}
+              >
+                {!contentLoading && (
+                  <>
+                    {!hasAnnotations && (
+                      <Alert
+                        message="暂无分析数据"
+                        description="该章节尚未进行AI分析，无法显示记忆标注。"
+                        type="info"
+                        showIcon
+                        style={{ marginBottom: 24 }}
+                      />
+                    )}
+
+                    {showAnnotations && hasAnnotations && annotationsData ? (
+                      <AnnotatedText
+                        content={selectedChapter.content}
+                        annotations={annotationsData.annotations}
+                        onAnnotationClick={(annotation) => handleAnnotationClick(annotation, 'content')}
+                        activeAnnotationId={activeAnnotationId}
+                        scrollToAnnotation={scrollToContentAnnotation}
+                      />
+                    ) : (
+                      <div
+                        style={{
+                          lineHeight: 2,
+                          fontSize: 16,
+                          whiteSpace: 'pre-wrap',
+                          wordBreak: 'break-word',
+                        }}
+                      >
+                        {selectedChapter.content}
+                      </div>
+                    )}
+                  </>
+                )}
+              </Card>
+
+              {/* 右侧记忆侧边栏（桌面端） */}
+              {hasAnnotations && annotationsData && window.innerWidth >= 768 && (
+                <Card
+                  style={{ width: 400, overflow: 'auto' }}
+                  bodyStyle={{ padding: 0 }}
+                >
+                  <MemorySidebar
+                    annotations={annotationsData.annotations}
+                    activeAnnotationId={activeAnnotationId}
+                    onAnnotationClick={(annotation) => handleAnnotationClick(annotation, 'sidebar')}
+                    scrollToAnnotation={scrollToSidebarAnnotation}
+                  />
+                </Card>
+              )}
+            </div>
+
+            {/* 移动端抽屉 */}
+            {hasAnnotations && annotationsData && (
+              <Drawer
+                title="章节分析"
+                placement="right"
+                onClose={() => setSidebarVisible(false)}
+                open={sidebarVisible}
+                width="80%"
+              >
+                <MemorySidebar
+                  annotations={annotationsData.annotations}
+                  activeAnnotationId={activeAnnotationId}
+                  onAnnotationClick={(annotation) => {
+                    handleAnnotationClick(annotation, 'sidebar');
+                    setSidebarVisible(false);
+                  }}
+                  scrollToAnnotation={scrollToSidebarAnnotation}
+                />
+              </Drawer>
+            )}
+          </>
+        )}
+      </div>
+    </div>
+  );
+};
+
+export default ChapterAnalysis;

frontend/src/pages/ChapterReader.tsx

@@ -0,0 +1,456 @@
+import React, { useState, useEffect } from 'react';
+import { useParams, useNavigate } from 'react-router-dom';
+import { Card, Spin, Alert, Button, Space, Switch, Drawer, message, Progress } from 'antd';
+import {
+  ArrowLeftOutlined,
+  EyeOutlined,
+  EyeInvisibleOutlined,
+  MenuOutlined,
+  ReloadOutlined,
+  LeftOutlined,
+  RightOutlined,
+} from '@ant-design/icons';
+import api from '../services/api';
+import AnnotatedText, { type MemoryAnnotation } from '../components/AnnotatedText';
+import MemorySidebar from '../components/MemorySidebar';
+
+interface ChapterData {
+  id: string;
+  chapter_number: number;
+  title: string;
+  content: string;
+  word_count: number;
+}
+
+interface AnnotationsData {
+  chapter_id: string;
+  chapter_number: number;
+  title: string;
+  word_count: number;
+  annotations: MemoryAnnotation[];
+  has_analysis: boolean;
+  summary: {
+    total_annotations: number;
+    hooks: number;
+    foreshadows: number;
+    plot_points: number;
+    character_events: number;
+  };
+}
+
+interface NavigationData {
+  current: {
+    id: string;
+    chapter_number: number;
+    title: string;
+  };
+  previous: {
+    id: string;
+    chapter_number: number;
+    title: string;
+  } | null;
+  next: {
+    id: string;
+    chapter_number: number;
+    title: string;
+  } | null;
+}
+
+/**
+ * 章节阅读器页面
+ * 展示带有记忆标注的章节内容
+ */
+const ChapterReader: React.FC = () => {
+  const { chapterId } = useParams<{ chapterId: string }>();
+  const navigate = useNavigate();
+
+  const [loading, setLoading] = useState(true);
+  const [error, setError] = useState<string | null>(null);
+  const [chapter, setChapter] = useState<ChapterData | null>(null);
+  const [annotationsData, setAnnotationsData] = useState<AnnotationsData | null>(null);
+  const [showAnnotations, setShowAnnotations] = useState(true);
+  const [activeAnnotationId, setActiveAnnotationId] = useState<string | undefined>();
+  const [sidebarVisible, setSidebarVisible] = useState(false);
+  const [analyzing, setAnalyzing] = useState(false);
+  const [analysisProgress, setAnalysisProgress] = useState(0);
+  const [navigation, setNavigation] = useState<NavigationData | null>(null);
+
+  useEffect(() => {
+    if (chapterId) {
+      loadChapterData();
+    }
+  }, [chapterId]);
+
+  const loadChapterData = async () => {
+    try {
+      setLoading(true);
+      setError(null);
+
+      // 并行加载章节内容、标注数据和导航信息
+      // 注意：api拦截器已经解析了response.data，所以直接返回数据对象
+      const [chapterData, annotationsData, navigationData] = await Promise.all([
+        api.get<unknown, ChapterData>(`/chapters/${chapterId}`).catch(err => {
+          console.error('加载章节失败:', err);
+          throw err;
+        }),
+        api.get<unknown, AnnotationsData>(`/chapters/${chapterId}/annotations`).catch(err => {
+          console.warn('加载标注失败:', err);
+          return null;
+        }), // 如果没有分析数据也不报错
+        api.get<unknown, NavigationData>(`/chapters/${chapterId}/navigation`).catch(err => {
+          console.warn('加载导航信息失败:', err);
+          return null;
+        }),
+      ]);
+
+      console.log('章节数据:', chapterData);
+      console.log('标注数据:', annotationsData);
+      console.log('导航数据:', navigationData);
+
+      // 验证数据
+      if (!chapterData || !chapterData.content) {
+        throw new Error('章节数据无效：缺少内容');
+      }
+
+      setChapter(chapterData);
+      setNavigation(navigationData);
+      
+      // 验证标注数据
+      if (annotationsData) {
+        const validAnnotations = annotationsData.annotations.filter(
+          (a: MemoryAnnotation) => a.position >= 0 && a.position < chapterData.content.length
+        );
+        const invalidCount = annotationsData.annotations.length - validAnnotations.length;
+        
+        if (invalidCount > 0) {
+          console.warn(`${invalidCount}个标注位置无效，将仅显示${validAnnotations.length}个有效标注`);
+        }
+        
+        setAnnotationsData(annotationsData);
+      } else {
+        setAnnotationsData(null);
+      }
+    } catch (err: any) {
+      console.error('加载章节数据失败:', err);
+      setError(err.response?.data?.detail || err.message || '加载失败');
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  const handleAnnotationClick = (annotation: MemoryAnnotation) => {
+    setActiveAnnotationId(annotation.id);
+    // 移动端显示侧边栏
+    if (window.innerWidth < 768) {
+      setSidebarVisible(true);
+    }
+  };
+
+  const handleBackClick = () => {
+    navigate(-1);
+  };
+
+  const handlePreviousChapter = () => {
+    if (navigation?.previous) {
+      navigate(`/chapters/${navigation.previous.id}/reader`);
+    }
+  };
+
+  const handleNextChapter = () => {
+    if (navigation?.next) {
+      navigate(`/chapters/${navigation.next.id}/reader`);
+    }
+  };
+
+  const handleReanalyze = async () => {
+    if (!chapterId) return;
+
+    try {
+      setAnalyzing(true);
+      setAnalysisProgress(0);
+      message.loading({ content: '开始分析章节...', key: 'analyze', duration: 0 });
+
+      // 触发分析
+      await api.post(`/chapters/${chapterId}/analyze`);
+
+      // 轮询分析状态
+      const pollInterval = setInterval(async () => {
+        try {
+          const statusRes = await api.get(`/chapters/${chapterId}/analysis/status`);
+          const { status, progress, error_message } = statusRes.data;
+
+          setAnalysisProgress(progress || 0);
+
+          if (status === 'completed') {
+            clearInterval(pollInterval);
+            setAnalyzing(false);
+            message.success({ content: '分析完成！', key: 'analyze' });
+            
+            // 重新加载标注数据
+            const annotationsRes = await api.get(`/chapters/${chapterId}/annotations`);
+            setAnnotationsData(annotationsRes.data);
+          } else if (status === 'failed') {
+            clearInterval(pollInterval);
+            setAnalyzing(false);
+            message.error({
+              content: `分析失败：${error_message || '未知错误'}`,
+              key: 'analyze'
+            });
+          }
+        } catch (err) {
+          console.error('轮询分析状态失败:', err);
+        }
+      }, 2000); // 每2秒轮询一次
+
+      // 30秒超时
+      setTimeout(() => {
+        clearInterval(pollInterval);
+        if (analyzing) {
+          setAnalyzing(false);
+          message.warning({ content: '分析超时，请稍后刷新查看结果', key: 'analyze' });
+        }
+      }, 30000);
+
+    } catch (err: any) {
+      setAnalyzing(false);
+      message.error({
+        content: err.response?.data?.detail || '触发分析失败',
+        key: 'analyze'
+      });
+    }
+  };
+
+  if (loading) {
+    return (
+      <div style={{ textAlign: 'center', padding: '100px 0' }}>
+        <Spin size="large" tip="加载章节中..." />
+      </div>
+    );
+  }
+
+  if (error || !chapter) {
+    return (
+      <div style={{ padding: 24 }}>
+        <Alert
+          message="加载失败"
+          description={error || '章节不存在'}
+          type="error"
+          showIcon
+        />
+        <Button onClick={handleBackClick} style={{ marginTop: 16 }}>
+          返回
+        </Button>
+      </div>
+    );
+  }
+
+  const hasAnnotations = annotationsData && annotationsData.annotations.length > 0;
+
+  return (
+    <div style={{ height: '100vh', display: 'flex', flexDirection: 'column' }}>
+      {/* 顶部工具栏 */}
+      <Card
+        size="small"
+        style={{
+          borderRadius: 0,
+          borderLeft: 0,
+          borderRight: 0,
+          borderTop: 0,
+        }}
+      >
+        <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center' }}>
+          <Space>
+            <Button icon={<ArrowLeftOutlined />} onClick={handleBackClick}>
+              返回
+            </Button>
+            <Button
+              icon={<LeftOutlined />}
+              onClick={handlePreviousChapter}
+              disabled={!navigation?.previous}
+              title={navigation?.previous ? `上一章: ${navigation.previous.title}` : '已是第一章'}
+            >
+              上一章
+            </Button>
+            <span style={{ fontSize: 16, fontWeight: 600 }}>
+              第{chapter.chapter_number}章: {chapter.title}
+            </span>
+            <Button
+              icon={<RightOutlined />}
+              onClick={handleNextChapter}
+              disabled={!navigation?.next}
+              title={navigation?.next ? `下一章: ${navigation.next.title}` : '已是最后一章'}
+            >
+              下一章
+            </Button>
+          </Space>
+
+          <Space>
+            <Button
+              icon={<ReloadOutlined />}
+              onClick={handleReanalyze}
+              loading={analyzing}
+              disabled={analyzing}
+            >
+              {analyzing ? '分析中...' : '重新分析'}
+            </Button>
+            {hasAnnotations && (
+              <>
+                <Switch
+                  checked={showAnnotations}
+                  onChange={setShowAnnotations}
+                  checkedChildren={<EyeOutlined />}
+                  unCheckedChildren={<EyeInvisibleOutlined />}
+                />
+                <span style={{ fontSize: 13, color: '#666' }}>显示标注</span>
+                <Button
+                  icon={<MenuOutlined />}
+                  onClick={() => setSidebarVisible(true)}
+                  style={{ display: window.innerWidth < 768 ? 'inline-block' : 'none' }}
+                >
+                  分析
+                </Button>
+              </>
+            )}
+          </Space>
+        </div>
+
+        {analyzing && (
+          <div style={{ marginTop: 12 }}>
+            <Progress percent={analysisProgress} size="small" status="active" />
+            <span style={{ fontSize: 12, color: '#666', marginLeft: 8 }}>
+              正在分析章节...
+            </span>
+          </div>
+        )}
+
+        {!analyzing && hasAnnotations && annotationsData && (
+          <div style={{ marginTop: 12, fontSize: 12, color: '#999' }}>
+            共有 {annotationsData.summary.total_annotations} 个标注：
+            {annotationsData.summary.hooks > 0 && ` 🎣${annotationsData.summary.hooks}个钩子`}
+            {annotationsData.summary.foreshadows > 0 &&
+              ` 🌟${annotationsData.summary.foreshadows}个伏笔`}
+            {annotationsData.summary.plot_points > 0 &&
+              ` 💎${annotationsData.summary.plot_points}个情节点`}
+            {annotationsData.summary.character_events > 0 &&
+              ` 👤${annotationsData.summary.character_events}个角色事件`}
+          </div>
+        )}
+      </Card>
+
+      {/* 主内容区域 */}
+      <div style={{ flex: 1, display: 'flex', overflow: 'hidden' }}>
+        {/* 左侧：章节内容 */}
+        <div
+          style={{
+            flex: 1,
+            overflowY: 'auto',
+            padding: '32px 48px',
+            maxWidth: hasAnnotations ? 'calc(100% - 400px)' : '100%',
+          }}
+        >
+          <Card>
+            <div style={{ maxWidth: 800, margin: '0 auto' }}>
+              {!hasAnnotations && (
+                <Alert
+                  message="暂无分析数据"
+                  description="该章节尚未进行AI分析，无法显示记忆标注。"
+                  type="info"
+                  showIcon
+                  style={{ marginBottom: 24 }}
+                />
+              )}
+
+              {showAnnotations && hasAnnotations && annotationsData ? (
+                <AnnotatedText
+                  content={chapter.content}
+                  annotations={annotationsData.annotations}
+                  onAnnotationClick={handleAnnotationClick}
+                  activeAnnotationId={activeAnnotationId}
+                />
+              ) : (
+                <div
+                  style={{
+                    lineHeight: 2,
+                    fontSize: 16,
+                    whiteSpace: 'pre-wrap',
+                    wordBreak: 'break-word',
+                  }}
+                >
+                  {chapter.content}
+                </div>
+              )}
+
+              {/* 底部翻页按钮 */}
+              <div style={{ marginTop: 48, paddingTop: 24, borderTop: '1px solid #f0f0f0' }}>
+                <Space style={{ width: '100%', justifyContent: 'space-between' }}>
+                  <Button
+                    size="large"
+                    icon={<LeftOutlined />}
+                    onClick={handlePreviousChapter}
+                    disabled={!navigation?.previous}
+                  >
+                    {navigation?.previous
+                      ? `上一章: 第${navigation.previous.chapter_number}章 ${navigation.previous.title}`
+                      : '已是第一章'}
+                  </Button>
+                  <Button
+                    size="large"
+                    type="primary"
+                    icon={<RightOutlined />}
+                    onClick={handleNextChapter}
+                    disabled={!navigation?.next}
+                    iconPosition="end"
+                  >
+                    {navigation?.next
+                      ? `下一章: 第${navigation.next.chapter_number}章 ${navigation.next.title}`
+                      : '已是最后一章'}
+                  </Button>
+                </Space>
+              </div>
+            </div>
+          </Card>
+        </div>
+
+        {/* 右侧：记忆侧边栏（桌面端） */}
+        {hasAnnotations && annotationsData && window.innerWidth >= 768 && (
+          <div
+            style={{
+              width: 400,
+              borderLeft: '1px solid #f0f0f0',
+              overflowY: 'auto',
+              background: '#fafafa',
+            }}
+          >
+            <MemorySidebar
+              annotations={annotationsData.annotations}
+              activeAnnotationId={activeAnnotationId}
+              onAnnotationClick={handleAnnotationClick}
+            />
+          </div>
+        )}
+      </div>
+
+      {/* 移动端抽屉 */}
+      {hasAnnotations && annotationsData && (
+        <Drawer
+          title="章节分析"
+          placement="right"
+          onClose={() => setSidebarVisible(false)}
+          open={sidebarVisible}
+          width="80%"
+        >
+          <MemorySidebar
+            annotations={annotationsData.annotations}
+            activeAnnotationId={activeAnnotationId}
+            onAnnotationClick={(annotation) => {
+              handleAnnotationClick(annotation);
+              setSidebarVisible(false);
+            }}
+          />
+        </Drawer>
+      )}
+    </div>
+  );
+};
+
+export default ChapterReader;

frontend/src/pages/Chapters.tsx

@@ -1,11 +1,12 @@
 import { useState, useEffect, useRef } from 'react';
 import { List, Button, Modal, Form, Input, Select, message, Empty, Space, Badge, Tag, Card, Tooltip, InputNumber } from 'antd';
-import { EditOutlined, FileTextOutlined, ThunderboltOutlined, LockOutlined, DownloadOutlined, SettingOutlined } from '@ant-design/icons';
+import { EditOutlined, FileTextOutlined, ThunderboltOutlined, LockOutlined, DownloadOutlined, SettingOutlined, FundOutlined, SyncOutlined, CheckCircleOutlined, CloseCircleOutlined } from '@ant-design/icons';
 import { useStore } from '../store';
 import { useChapterSync } from '../store/hooks';
 import { projectApi, writingStyleApi } from '../services/api';
-import type { Chapter, ChapterUpdate, ApiError, WritingStyle } from '../types';
+import type { Chapter, ChapterUpdate, ApiError, WritingStyle, AnalysisTask } from '../types';
 import { cardStyles } from '../components/CardStyles';
+import ChapterAnalysis from '../components/ChapterAnalysis';
 
 const { TextArea } = Input;
 
@@ -23,6 +24,11 @@ export default function Chapters() {
   const [writingStyles, setWritingStyles] = useState<WritingStyle[]>([]);
   const [selectedStyleId, setSelectedStyleId] = useState<number | undefined>();
   const [targetWordCount, setTargetWordCount] = useState<number>(3000);
+  const [analysisVisible, setAnalysisVisible] = useState(false);
+  const [analysisChapterId, setAnalysisChapterId] = useState<string | null>(null);
+  // 分析任务状态管理
+  const [analysisTasksMap, setAnalysisTasksMap] = useState<Record<string, AnalysisTask>>({});
+  const pollingIntervalsRef = useRef<Record<string, number>>({});
 
   useEffect(() => {
     const handleResize = () => {
@@ -43,10 +49,96 @@ export default function Chapters() {
     if (currentProject?.id) {
       refreshChapters();
       loadWritingStyles();
+      loadAnalysisTasks();
     }
     // eslint-disable-next-line react-hooks/exhaustive-deps
   }, [currentProject?.id]);
 
+  // 清理轮询定时器
+  useEffect(() => {
+    return () => {
+      Object.values(pollingIntervalsRef.current).forEach(interval => {
+        clearInterval(interval);
+      });
+    };
+  }, []);
+
+  // 加载所有章节的分析任务状态
+  const loadAnalysisTasks = async () => {
+    if (!chapters || chapters.length === 0) return;
+    
+    const tasksMap: Record<string, AnalysisTask> = {};
+    
+    for (const chapter of chapters) {
+      // 只查询有内容的章节
+      if (chapter.content && chapter.content.trim() !== '') {
+        try {
+          const response = await fetch(`/api/chapters/${chapter.id}/analysis/status`);
+          if (response.ok) {
+            const task: AnalysisTask = await response.json();
+            tasksMap[chapter.id] = task;
+            
+            // 如果任务正在运行，启动轮询
+            if (task.status === 'pending' || task.status === 'running') {
+              startPollingTask(chapter.id);
+            }
+          }
+        } catch (error) {
+          // 404或其他错误表示没有分析任务，忽略
+          console.debug(`章节 ${chapter.id} 暂无分析任务`);
+        }
+      }
+    }
+    
+    setAnalysisTasksMap(tasksMap);
+  };
+
+  // 启动单个章节的任务轮询
+  const startPollingTask = (chapterId: string) => {
+    // 如果已经在轮询，先清除
+    if (pollingIntervalsRef.current[chapterId]) {
+      clearInterval(pollingIntervalsRef.current[chapterId]);
+    }
+    
+    const interval = window.setInterval(async () => {
+      try {
+        const response = await fetch(`/api/chapters/${chapterId}/analysis/status`);
+        if (!response.ok) return;
+        
+        const task: AnalysisTask = await response.json();
+        
+        setAnalysisTasksMap(prev => ({
+          ...prev,
+          [chapterId]: task
+        }));
+        
+        // 任务完成或失败，停止轮询
+        if (task.status === 'completed' || task.status === 'failed') {
+          clearInterval(pollingIntervalsRef.current[chapterId]);
+          delete pollingIntervalsRef.current[chapterId];
+          
+          if (task.status === 'completed') {
+            message.success(`章节分析完成`);
+          } else if (task.status === 'failed') {
+            message.error(`章节分析失败: ${task.error_message || '未知错误'}`);
+          }
+        }
+      } catch (error) {
+        console.error('轮询分析任务失败:', error);
+      }
+    }, 2000);
+    
+    pollingIntervalsRef.current[chapterId] = interval;
+    
+    // 5分钟超时
+    setTimeout(() => {
+      if (pollingIntervalsRef.current[chapterId]) {
+        clearInterval(pollingIntervalsRef.current[chapterId]);
+        delete pollingIntervalsRef.current[chapterId];
+      }
+    }, 300000);
+  };
+
   const loadWritingStyles = async () => {
     if (!currentProject?.id) return;
     
@@ -159,7 +251,7 @@ export default function Chapters() {
       setIsContinuing(true);
       setIsGenerating(true);
       
-      await generateChapterContentStream(editingId, (content) => {
+      const result = await generateChapterContentStream(editingId, (content) => {
         editorForm.setFieldsValue({ content });
         
         if (contentTextAreaRef.current) {
@@ -170,7 +262,24 @@ export default function Chapters() {
         }
       }, selectedStyleId, targetWordCount);
       
-      message.success('AI创作成功');
+      message.success('AI创作成功，正在分析章节内容...');
+      
+      // 如果返回了分析任务ID，启动轮询
+      if (result?.analysis_task_id) {
+        const taskId = result.analysis_task_id;
+        setAnalysisTasksMap(prev => ({
+          ...prev,
+          [editingId]: {
+            task_id: taskId,
+            chapter_id: editingId,
+            status: 'pending',
+            progress: 0
+          }
+        }));
+        
+        // 启动轮询
+        startPollingTask(editingId);
+      }
     } catch (error) {
       const apiError = error as ApiError;
       message.error('AI创作失败：' + (apiError.response?.data?.detail || apiError.message || '未知错误'));
@@ -322,6 +431,51 @@ export default function Chapters() {
     });
   };
 
+  const handleShowAnalysis = (chapterId: string) => {
+    setAnalysisChapterId(chapterId);
+    setAnalysisVisible(true);
+  };
+
+  // 渲染分析状态标签
+  const renderAnalysisStatus = (chapterId: string) => {
+    const task = analysisTasksMap[chapterId];
+    
+    if (!task) {
+      return null;
+    }
+    
+    switch (task.status) {
+      case 'pending':
+        return (
+          <Tag icon={<SyncOutlined spin />} color="processing">
+            等待分析
+          </Tag>
+        );
+      case 'running':
+        return (
+          <Tag icon={<SyncOutlined spin />} color="processing">
+            分析中 {task.progress}%
+          </Tag>
+        );
+      case 'completed':
+        return (
+          <Tag icon={<CheckCircleOutlined />} color="success">
+            已分析
+          </Tag>
+        );
+      case 'failed':
+        return (
+          <Tooltip title={task.error_message}>
+            <Tag icon={<CloseCircleOutlined />} color="error">
+              分析失败
+            </Tag>
+          </Tooltip>
+        );
+      default:
+        return null;
+    }
+  };
+
   return (
     <div style={{ display: 'flex', flexDirection: 'column', height: '100%' }}>
       <div style={{
@@ -372,15 +526,38 @@ export default function Chapters() {
                 }}
                 actions={isMobile ? undefined : [
                   <Button
-                    type="primary"
                     icon={<EditOutlined />}
                     onClick={() => handleOpenEditor(item.id)}
                   >
                     编辑内容
                   </Button>,
+                  (() => {
+                    const task = analysisTasksMap[item.id];
+                    const isAnalyzing = task && (task.status === 'pending' || task.status === 'running');
+                    const hasContent = item.content && item.content.trim() !== '';
+                    
+                    return (
+                      <Tooltip
+                        title={
+                          !hasContent ? '请先生成章节内容' :
+                          isAnalyzing ? '分析进行中，请稍候...' :
+                          ''
+                        }
+                      >
+                        <Button
+                          icon={isAnalyzing ? <SyncOutlined spin /> : <FundOutlined />}
+                          onClick={() => handleShowAnalysis(item.id)}
+                          disabled={!hasContent || isAnalyzing}
+                          loading={isAnalyzing}
+                        >
+                          {isAnalyzing ? '分析中' : '查看分析'}
+                        </Button>
+                      </Tooltip>
+                    );
+                  })(),
                   <Button
                     type="text"
-                    icon={<EditOutlined />}
+                    icon={<SettingOutlined />}
                     onClick={() => handleOpenModal(item.id)}
                   >
                     修改信息
@@ -395,6 +572,7 @@ export default function Chapters() {
                         <span>第{item.chapter_number}章：{item.title}</span>
                         <Tag color={getStatusColor(item.status)}>{getStatusText(item.status)}</Tag>
                         <Badge count={`${item.word_count || 0}字`} style={{ backgroundColor: '#52c41a' }} />
+                        {renderAnalysisStatus(item.id)}
                         {!canGenerateChapter(item) && (
                           <Tooltip title={getGenerateDisabledReason(item)}>
                             <Tag icon={<LockOutlined />} color="warning">
@@ -425,6 +603,30 @@ export default function Chapters() {
                         size="small"
                         title="编辑内容"
                       />
+                      {(() => {
+                        const task = analysisTasksMap[item.id];
+                        const isAnalyzing = task && (task.status === 'pending' || task.status === 'running');
+                        const hasContent = item.content && item.content.trim() !== '';
+                        
+                        return (
+                          <Tooltip
+                            title={
+                              !hasContent ? '请先生成章节内容' :
+                              isAnalyzing ? '分析中' :
+                              '查看分析'
+                            }
+                          >
+                            <Button
+                              type="text"
+                              icon={isAnalyzing ? <SyncOutlined spin /> : <FundOutlined />}
+                              onClick={() => handleShowAnalysis(item.id)}
+                              size="small"
+                              disabled={!hasContent || isAnalyzing}
+                              loading={isAnalyzing}
+                            />
+                          </Tooltip>
+                        );
+                      })()}
                       <Button
                         type="text"
                         icon={<SettingOutlined />}
@@ -654,6 +856,64 @@ export default function Chapters() {
           </Form.Item>
         </Form>
       </Modal>
+
+      {analysisChapterId && (
+        <ChapterAnalysis
+          chapterId={analysisChapterId}
+          visible={analysisVisible}
+          onClose={() => {
+            setAnalysisVisible(false);
+            
+            // 延迟500ms后刷新该章节的分析状态，给后端足够时间完成数据库写入
+            if (analysisChapterId) {
+              const chapterIdToRefresh = analysisChapterId;
+              
+              setTimeout(() => {
+                fetch(`/api/chapters/${chapterIdToRefresh}/analysis/status`)
+                  .then(response => {
+                    if (response.ok) {
+                      return response.json();
+                    }
+                    throw new Error('获取状态失败');
+                  })
+                  .then((task: AnalysisTask) => {
+                    setAnalysisTasksMap(prev => ({
+                      ...prev,
+                      [chapterIdToRefresh]: task
+                    }));
+                    
+                    // 如果任务正在运行，启动轮询
+                    if (task.status === 'pending' || task.status === 'running') {
+                      startPollingTask(chapterIdToRefresh);
+                    }
+                  })
+                  .catch(error => {
+                    console.error('刷新分析状态失败:', error);
+                    // 如果查询失败，再延迟尝试一次
+                    setTimeout(() => {
+                      fetch(`/api/chapters/${chapterIdToRefresh}/analysis/status`)
+                        .then(response => response.ok ? response.json() : null)
+                        .then((task: AnalysisTask | null) => {
+                          if (task) {
+                            setAnalysisTasksMap(prev => ({
+                              ...prev,
+                              [chapterIdToRefresh]: task
+                            }));
+                            if (task.status === 'pending' || task.status === 'running') {
+                              startPollingTask(chapterIdToRefresh);
+                            }
+                          }
+                        })
+                        .catch(err => console.error('第二次刷新失败:', err));
+                    }, 1000);
+                  });
+              }, 500);
+            }
+            
+            setAnalysisChapterId(null);
+          }}
+        />
+      )}
     </div>
   );
 }

frontend/src/pages/ProjectDetail.tsx

@@ -13,6 +13,7 @@ import {
   ApartmentOutlined,
   BankOutlined,
   EditOutlined,
+  FundOutlined,
 } from '@ant-design/icons';
 import { useStore } from '../store';
 import { useCharacterSync, useOutlineSync, useChapterSync } from '../store/hooks';
@@ -122,6 +123,11 @@ export default function ProjectDetail() {
       icon: <BookOutlined />,
       label: <Link to={`/project/${projectId}/chapters`}>章节管理</Link>,
     },
+    {
+      key: 'chapter-analysis',
+      icon: <FundOutlined />,
+      label: <Link to={`/project/${projectId}/chapter-analysis`}>剧情分析</Link>,
+    },
     {
       key: 'writing-styles',
       icon: <EditOutlined />,
@@ -142,6 +148,7 @@ export default function ProjectDetail() {
     if (path.includes('/organizations')) return 'organizations';
     if (path.includes('/outline')) return 'outline';
     if (path.includes('/characters')) return 'characters';
+    if (path.includes('/chapter-analysis')) return 'chapter-analysis';
     if (path.includes('/chapters')) return 'chapters';
     if (path.includes('/writing-styles')) return 'writing-styles';
     // if (path.includes('/polish')) return 'polish';
@@ -259,7 +266,8 @@ export default function ProjectDetail() {
         )}
         
         {!mobile && (
-          <Row gutter={12} style={{ width: '450px', justifyContent: 'flex-end', zIndex: 1 }}>
+          <div style={{ display: 'flex', alignItems: 'center', gap: '12px', zIndex: 1 }}>
+            <Row gutter={12} style={{ width: '450px', justifyContent: 'flex-end' }}>
             <Col>
               <Card
                 size="small"
@@ -344,7 +352,8 @@ export default function ProjectDetail() {
                 />
               </Card>
             </Col>
-          </Row>
+            </Row>
+          </div>
         )}
       </Header>
 

frontend/src/pages/ProjectList.tsx

@@ -1,7 +1,8 @@
 import { useEffect, useState } from 'react';
 import { useNavigate } from 'react-router-dom';
-import { Card, Button, Empty, Modal, message, Spin, Row, Col, Statistic, Space, Tag, Progress, Typography, Tooltip, Badge, Alert } from 'antd';
+import { Card, Button, Empty, Modal, message, Spin, Row, Col, Statistic, Space, Tag, Progress, Typography, Tooltip, Badge, Alert, Upload, Checkbox, Divider, Switch } from 'antd';
-import { EditOutlined, DeleteOutlined, BookOutlined, RocketOutlined, CalendarOutlined, FileTextOutlined, TrophyOutlined, FireOutlined, SettingOutlined, InfoCircleOutlined, CloseOutlined } from '@ant-design/icons';
+import { EditOutlined, DeleteOutlined, BookOutlined, RocketOutlined, CalendarOutlined, FileTextOutlined, TrophyOutlined, FireOutlined, SettingOutlined, InfoCircleOutlined, CloseOutlined, UploadOutlined, DownloadOutlined } from '@ant-design/icons';
+import { projectApi } from '../services/api';
 import { useStore } from '../store';
 import { useProjectSync } from '../store/hooks';
 import type { ReactNode } from 'react';
@@ -14,6 +15,18 @@ export default function ProjectList() {
   const navigate = useNavigate();
   const { projects, loading } = useStore();
   const [showApiTip, setShowApiTip] = useState(true);
+  const [importModalVisible, setImportModalVisible] = useState(false);
+  const [exportModalVisible, setExportModalVisible] = useState(false);
+  const [selectedFile, setSelectedFile] = useState<File | null>(null);
+  const [validationResult, setValidationResult] = useState<any>(null);
+  const [importing, setImporting] = useState(false);
+  const [validating, setValidating] = useState(false);
+  const [exporting, setExporting] = useState(false);
+  const [selectedProjectIds, setSelectedProjectIds] = useState<string[]>([]);
+  const [exportOptions, setExportOptions] = useState({
+    includeWritingStyles: true,
+    includeGenerationHistory: true,
+  });
 
   const { refreshProjects, deleteProject } = useProjectSync();
 
@@ -122,6 +135,160 @@ export default function ProjectList() {
   const totalWords = projects.reduce((sum, p) => sum + (p.current_words || 0), 0);
   const activeProjects = projects.filter(p => p.status === 'writing').length;
 
+  // 处理文件选择
+  const handleFileSelect = async (file: File) => {
+    setSelectedFile(file);
+    setValidationResult(null);
+    
+    // 验证文件
+    try {
+      setValidating(true);
+      const result = await projectApi.validateImportFile(file);
+      setValidationResult(result);
+      
+      if (!result.valid) {
+        message.error('文件验证失败');
+      }
+    } catch (error) {
+      console.error('验证失败:', error);
+      message.error('文件验证失败');
+    } finally {
+      setValidating(false);
+    }
+    
+    return false; // 阻止自动上传
+  };
+
+  // 处理导入
+  const handleImport = async () => {
+    if (!selectedFile || !validationResult?.valid) {
+      message.warning('请选择有效的导入文件');
+      return;
+    }
+
+    try {
+      setImporting(true);
+      const result = await projectApi.importProject(selectedFile);
+      
+      if (result.success) {
+        message.success(`项目导入成功！${result.message}`);
+        setImportModalVisible(false);
+        setSelectedFile(null);
+        setValidationResult(null);
+        
+        // 刷新项目列表
+        await refreshProjects();
+        
+        // 跳转到新项目
+        if (result.project_id) {
+          navigate(`/project/${result.project_id}`);
+        }
+      } else {
+        message.error(result.message || '导入失败');
+      }
+    } catch (error) {
+      console.error('导入失败:', error);
+      message.error('导入失败，请重试');
+    } finally {
+      setImporting(false);
+    }
+  };
+
+  // 关闭导入对话框
+  const handleCloseImportModal = () => {
+    setImportModalVisible(false);
+    setSelectedFile(null);
+    setValidationResult(null);
+  };
+
+  // 打开导出对话框
+  const handleOpenExportModal = () => {
+    setExportModalVisible(true);
+    setSelectedProjectIds([]);
+  };
+
+  // 获取可导出的项目（过滤掉向导未完成的项目）
+  const exportableProjects = projects.filter(p => p.wizard_status === 'completed');
+
+  // 关闭导出对话框
+  const handleCloseExportModal = () => {
+    setExportModalVisible(false);
+    setSelectedProjectIds([]);
+  };
+
+  // 切换项目选择
+  const handleToggleProject = (projectId: string) => {
+    setSelectedProjectIds(prev =>
+      prev.includes(projectId)
+        ? prev.filter(id => id !== projectId)
+        : [...prev, projectId]
+    );
+  };
+
+  // 全选/取消全选
+  const handleToggleAll = () => {
+    if (selectedProjectIds.length === exportableProjects.length) {
+      setSelectedProjectIds([]);
+    } else {
+      setSelectedProjectIds(exportableProjects.map(p => p.id));
+    }
+  };
+
+  // 执行导出
+  const handleExport = async () => {
+    if (selectedProjectIds.length === 0) {
+      message.warning('请至少选择一个项目');
+      return;
+    }
+
+    try {
+      setExporting(true);
+      
+      if (selectedProjectIds.length === 1) {
+        // 单个项目导出
+        const projectId = selectedProjectIds[0];
+        const project = projects.find(p => p.id === projectId);
+        await projectApi.exportProjectData(projectId, {
+          include_generation_history: exportOptions.includeGenerationHistory,
+          include_writing_styles: exportOptions.includeWritingStyles
+        });
+        message.success(`项目 "${project?.title}" 导出成功`);
+      } else {
+        // 批量导出
+        let successCount = 0;
+        let failCount = 0;
+        
+        for (const projectId of selectedProjectIds) {
+          try {
+            await projectApi.exportProjectData(projectId, {
+              include_generation_history: exportOptions.includeGenerationHistory,
+              include_writing_styles: exportOptions.includeWritingStyles
+            });
+            successCount++;
+            // 添加延迟避免浏览器阻止多个下载
+            await new Promise(resolve => setTimeout(resolve, 500));
+          } catch (error) {
+            console.error(`导出项目 ${projectId} 失败:`, error);
+            failCount++;
+          }
+        }
+        
+        if (failCount === 0) {
+          message.success(`成功导出 ${successCount} 个项目`);
+        } else {
+          message.warning(`导出完成：成功 ${successCount} 个，失败 ${failCount} 个`);
+        }
+      }
+      
+      handleCloseExportModal();
+    } catch (error) {
+      console.error('导出失败:', error);
+      message.error('导出失败，请重试');
+    } finally {
+      setExporting(false);
+    }
+  };
+
   return (
     <div style={{
       minHeight: '100vh',
@@ -153,46 +320,165 @@ export default function ProjectList() {
                 </Text>
               </Space>
             </Col>
-            <Col xs={24} sm={12} md={14} style={{ display: 'flex', justifyContent: window.innerWidth <= 768 ? 'space-between' : 'flex-end', alignItems: 'center', gap: 16 }}>
+            <Col xs={24} sm={12} md={14}>
-              <Button
+              {window.innerWidth <= 768 ? (
-                type="primary"
+                // 移动端：按钮分两行显示
-                size={window.innerWidth <= 768 ? 'middle' : 'large'}
+                <Space direction="vertical" size={8} style={{ width: '100%' }}>
-                icon={<RocketOutlined />}
+                  <Space size={8} style={{ width: '100%' }}>
-                onClick={() => navigate('/wizard')}
+                    <Button
-                style={{
+                      type="primary"
-                  borderRadius: 8,
+                      size="middle"
-                  background: 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)',
+                      icon={<RocketOutlined />}
-                  border: 'none',
+                      onClick={() => navigate('/wizard')}
-                  boxShadow: '0 2px 8px rgba(102, 126, 234, 0.4)'
+                      style={{
-                }}
+                        flex: 1,
-              >
+                        borderRadius: 8,
-                向导创建
+                        background: 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)',
-              </Button>
+                        border: 'none',
-              <Button
+                        boxShadow: '0 2px 8px rgba(102, 126, 234, 0.4)'
-                type="default"
+                      }}
-                size={window.innerWidth <= 768 ? 'middle' : 'large'}
+                    >
-                icon={<SettingOutlined />}
+                      向导创建
-                onClick={() => navigate('/settings')}
+                    </Button>
-                style={{
+                    <Button
-                  borderRadius: 8,
+                      type="default"
-                  borderColor: '#d9d9d9',
+                      size="middle"
-                  boxShadow: '0 2px 8px rgba(0, 0, 0, 0.08)',
+                      icon={<SettingOutlined />}
-                  transition: 'all 0.3s ease'
+                      onClick={() => navigate('/settings')}
-                }}
+                      style={{
-                onMouseEnter={(e) => {
+                        flex: 1,
-                  e.currentTarget.style.borderColor = '#667eea';
+                        borderRadius: 8,
-                  e.currentTarget.style.color = '#667eea';
+                        borderColor: '#d9d9d9',
-                  e.currentTarget.style.boxShadow = '0 2px 12px rgba(102, 126, 234, 0.3)';
+                        boxShadow: '0 2px 8px rgba(0, 0, 0, 0.08)'
-                }}
+                      }}
-                onMouseLeave={(e) => {
+                    >
-                  e.currentTarget.style.borderColor = '#d9d9d9';
+                      API设置
-                  e.currentTarget.style.color = 'rgba(0, 0, 0, 0.88)';
+                    </Button>
-                  e.currentTarget.style.boxShadow = '0 2px 8px rgba(0, 0, 0, 0.08)';
+                    <UserMenu />
-                }}
+                  </Space>
-              >
+                  <Space size={8} style={{ width: '100%' }}>
-                API设置
+                    <Button
-              </Button>
+                      type="default"
-              <UserMenu />
+                      size="middle"
+                      icon={<DownloadOutlined />}
+                      onClick={handleOpenExportModal}
+                      disabled={exportableProjects.length === 0}
+                      style={{
+                        flex: 1,
+                        borderRadius: 8,
+                        borderColor: '#1890ff',
+                        color: '#1890ff',
+                        boxShadow: '0 2px 8px rgba(24, 144, 255, 0.2)'
+                      }}
+                    >
+                      导出
+                    </Button>
+                    <Button
+                      type="default"
+                      size="middle"
+                      icon={<UploadOutlined />}
+                      onClick={() => setImportModalVisible(true)}
+                      style={{
+                        flex: 1,
+                        borderRadius: 8,
+                        borderColor: '#52c41a',
+                        color: '#52c41a',
+                        boxShadow: '0 2px 8px rgba(82, 196, 26, 0.2)'
+                      }}
+                    >
+                      导入
+                    </Button>
+                  </Space>
+                </Space>
+              ) : (
+                // PC端：原有布局
+                <Space size={12} style={{ display: 'flex', justifyContent: 'flex-end' }}>
+                  <Button
+                    type="primary"
+                    size="large"
+                    icon={<RocketOutlined />}
+                    onClick={() => navigate('/wizard')}
+                    style={{
+                      borderRadius: 8,
+                      background: 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)',
+                      border: 'none',
+                      boxShadow: '0 2px 8px rgba(102, 126, 234, 0.4)'
+                    }}
+                  >
+                    向导创建
+                  </Button>
+                  <Button
+                    type="default"
+                    size="large"
+                    icon={<DownloadOutlined />}
+                    onClick={handleOpenExportModal}
+                    disabled={exportableProjects.length === 0}
+                    style={{
+                      borderRadius: 8,
+                      borderColor: '#1890ff',
+                      color: '#1890ff',
+                      boxShadow: '0 2px 8px rgba(24, 144, 255, 0.2)'
+                    }}
+                    onMouseEnter={(e) => {
+                      e.currentTarget.style.borderColor = '#1890ff';
+                      e.currentTarget.style.background = '#e6f7ff';
+                    }}
+                    onMouseLeave={(e) => {
+                      e.currentTarget.style.borderColor = '#1890ff';
+                      e.currentTarget.style.background = 'transparent';
+                    }}
+                  >
+                    导出项目
+                  </Button>
+                  <Button
+                    type="default"
+                    size="large"
+                    icon={<UploadOutlined />}
+                    onClick={() => setImportModalVisible(true)}
+                    style={{
+                      borderRadius: 8,
+                      borderColor: '#52c41a',
+                      color: '#52c41a',
+                      boxShadow: '0 2px 8px rgba(82, 196, 26, 0.2)'
+                    }}
+                    onMouseEnter={(e) => {
+                      e.currentTarget.style.borderColor = '#52c41a';
+                      e.currentTarget.style.background = '#f6ffed';
+                    }}
+                    onMouseLeave={(e) => {
+                      e.currentTarget.style.borderColor = '#52c41a';
+                      e.currentTarget.style.background = 'transparent';
+                    }}
+                  >
+                    导入项目
+                  </Button>
+                  <Button
+                    type="default"
+                    size="large"
+                    icon={<SettingOutlined />}
+                    onClick={() => navigate('/settings')}
+                    style={{
+                      borderRadius: 8,
+                      borderColor: '#d9d9d9',
+                      boxShadow: '0 2px 8px rgba(0, 0, 0, 0.08)',
+                      transition: 'all 0.3s ease'
+                    }}
+                    onMouseEnter={(e) => {
+                      e.currentTarget.style.borderColor = '#667eea';
+                      e.currentTarget.style.color = '#667eea';
+                      e.currentTarget.style.boxShadow = '0 2px 12px rgba(102, 126, 234, 0.3)';
+                    }}
+                    onMouseLeave={(e) => {
+                      e.currentTarget.style.borderColor = '#d9d9d9';
+                      e.currentTarget.style.color = 'rgba(0, 0, 0, 0.88)';
+                      e.currentTarget.style.boxShadow = '0 2px 8px rgba(0, 0, 0, 0.08)';
+                    }}
+                  >
+                    API设置
+                  </Button>
+                  <UserMenu />
+                </Space>
+              )}
             </Col>
           </Row>
 
@@ -472,6 +758,285 @@ export default function ProjectList() {
           )}
         </Spin>
       </div>
+
+      {/* 导入项目对话框 */}
+      <Modal
+        title="导入项目"
+        open={importModalVisible}
+        onOk={handleImport}
+        onCancel={handleCloseImportModal}
+        confirmLoading={importing}
+        okText="导入"
+        cancelText="取消"
+        width={window.innerWidth <= 768 ? '90%' : 500}
+        centered
+        okButtonProps={{ disabled: !validationResult?.valid }}
+        styles={{
+          body: {
+            maxHeight: window.innerWidth <= 768 ? '60vh' : 'auto',
+            overflowY: 'auto',
+            padding: window.innerWidth <= 768 ? '16px' : '24px'
+          }
+        }}
+      >
+        <Space direction="vertical" size={16} style={{ width: '100%' }}>
+          <div>
+            <p style={{ marginBottom: '12px', color: '#666', fontSize: window.innerWidth <= 768 ? 13 : 14 }}>
+              选择之前导出的 JSON 格式项目文件
+            </p>
+            <Upload
+              accept=".json"
+              beforeUpload={handleFileSelect}
+              maxCount={1}
+              onRemove={() => {
+                setSelectedFile(null);
+                setValidationResult(null);
+              }}
+              fileList={selectedFile ? [{ uid: '-1', name: selectedFile.name, status: 'done' }] as any : []}
+            >
+              <Button icon={<UploadOutlined />} block>选择文件</Button>
+            </Upload>
+          </div>
+
+          {validating && (
+            <div style={{ textAlign: 'center', padding: '20px' }}>
+              <Spin tip="验证文件中..." />
+            </div>
+          )}
+
+          {validationResult && (
+            <Card size="small" style={{ background: validationResult.valid ? '#f6ffed' : '#fff2f0' }}>
+              <Space direction="vertical" size={8} style={{ width: '100%' }}>
+                <div>
+                  <Text strong style={{
+                    color: validationResult.valid ? '#52c41a' : '#ff4d4f',
+                    fontSize: window.innerWidth <= 768 ? 13 : 14
+                  }}>
+                    {validationResult.valid ? '✓ 文件验证通过' : '✗ 文件验证失败'}
+                  </Text>
+                </div>
+
+                {validationResult.project_name && (
+                  <div>
+                    <Text type="secondary" style={{ fontSize: window.innerWidth <= 768 ? 12 : 14 }}>项目名称：</Text>
+                    <Text strong style={{ fontSize: window.innerWidth <= 768 ? 12 : 14 }}>{validationResult.project_name}</Text>
+                  </div>
+                )}
+
+                {validationResult.statistics && Object.keys(validationResult.statistics).length > 0 && (
+                  <div>
+                    <Text type="secondary" style={{ fontSize: window.innerWidth <= 768 ? 12 : 14 }}>数据统计：</Text>
+                    <div style={{ marginTop: 8 }}>
+                      <Row gutter={[8, 8]}>
+                        {validationResult.statistics.chapters > 0 && (
+                          <Col span={12}>
+                            <Tag color="blue">章节: {validationResult.statistics.chapters}</Tag>
+                          </Col>
+                        )}
+                        {validationResult.statistics.characters > 0 && (
+                          <Col span={12}>
+                            <Tag color="green">角色: {validationResult.statistics.characters}</Tag>
+                          </Col>
+                        )}
+                        {validationResult.statistics.outlines > 0 && (
+                          <Col span={12}>
+                            <Tag color="purple">大纲: {validationResult.statistics.outlines}</Tag>
+                          </Col>
+                        )}
+                        {validationResult.statistics.relationships > 0 && (
+                          <Col span={12}>
+                            <Tag color="orange">关系: {validationResult.statistics.relationships}</Tag>
+                          </Col>
+                        )}
+                      </Row>
+                    </div>
+                  </div>
+                )}
+
+                {validationResult.errors && validationResult.errors.length > 0 && (
+                  <div>
+                    <Text type="danger" strong style={{ fontSize: window.innerWidth <= 768 ? 12 : 14 }}>错误：</Text>
+                    <ul style={{
+                      margin: '4px 0 0 0',
+                      paddingLeft: '20px',
+                      color: '#ff4d4f',
+                      fontSize: window.innerWidth <= 768 ? 12 : 13
+                    }}>
+                      {validationResult.errors.map((error: string, index: number) => (
+                        <li key={index}>{error}</li>
+                      ))}
+                    </ul>
+                  </div>
+                )}
+
+                {validationResult.warnings && validationResult.warnings.length > 0 && (
+                  <div>
+                    <Text type="warning" strong style={{ fontSize: window.innerWidth <= 768 ? 12 : 14 }}>警告：</Text>
+                    <ul style={{
+                      margin: '4px 0 0 0',
+                      paddingLeft: '20px',
+                      color: '#faad14',
+                      fontSize: window.innerWidth <= 768 ? 12 : 13
+                    }}>
+                      {validationResult.warnings.map((warning: string, index: number) => (
+                        <li key={index}>{warning}</li>
+                      ))}
+                    </ul>
+                  </div>
+                )}
+              </Space>
+            </Card>
+          )}
+        </Space>
+      </Modal>
+
+      {/* 导出项目对话框 */}
+      <Modal
+        title="导出项目"
+        open={exportModalVisible}
+        onOk={handleExport}
+        onCancel={handleCloseExportModal}
+        confirmLoading={exporting}
+        okText={selectedProjectIds.length > 0 ? `导出 (${selectedProjectIds.length})` : '导出'}
+        cancelText="取消"
+        width={window.innerWidth <= 768 ? '90%' : 700}
+        centered
+        okButtonProps={{ disabled: selectedProjectIds.length === 0 }}
+        styles={{
+          body: {
+            maxHeight: window.innerWidth <= 768 ? '70vh' : 'auto',
+            overflowY: 'auto',
+            padding: window.innerWidth <= 768 ? '16px' : '24px'
+          }
+        }}
+      >
+        <Space direction="vertical" size={16} style={{ width: '100%' }}>
+          {/* 导出选项 */}
+          <Card
+            size="small"
+            style={{ background: '#f5f5f5' }}
+            styles={{ body: { padding: window.innerWidth <= 768 ? 12 : 16 } }}
+          >
+            <Space direction="vertical" size={12} style={{ width: '100%' }}>
+              <Text strong style={{ fontSize: window.innerWidth <= 768 ? 13 : 14 }}>导出选项</Text>
+              <div style={{ display: 'flex', alignItems: 'center', gap: 8 }}>
+                <Switch
+                  size={window.innerWidth <= 768 ? 'small' : 'default'}
+                  checked={exportOptions.includeWritingStyles}
+                  onChange={(checked) => setExportOptions(prev => ({ ...prev, includeWritingStyles: checked }))}
+                  style={{
+                    flexShrink: 0,
+                    height: window.innerWidth <= 768 ? 16 : 22,
+                    minHeight: window.innerWidth <= 768 ? 16 : 22,
+                    lineHeight: window.innerWidth <= 768 ? '16px' : '22px'
+                  }}
+                />
+                <Text style={{ fontSize: window.innerWidth <= 768 ? 13 : 14 }}>包含写作风格</Text>
+                <Tooltip title="导出项目关联的写作风格数据">
+                  <InfoCircleOutlined style={{ color: '#999', fontSize: window.innerWidth <= 768 ? 12 : 14 }} />
+                </Tooltip>
+              </div>
+              <div style={{ display: 'flex', alignItems: 'center', gap: 8 }}>
+                <Switch
+                  size={window.innerWidth <= 768 ? 'small' : 'default'}
+                  checked={exportOptions.includeGenerationHistory}
+                  onChange={(checked) => setExportOptions(prev => ({ ...prev, includeGenerationHistory: checked }))}
+                  style={{
+                    flexShrink: 0,
+                    height: window.innerWidth <= 768 ? 16 : 22,
+                    minHeight: window.innerWidth <= 768 ? 16 : 22,
+                    lineHeight: window.innerWidth <= 768 ? '16px' : '22px'
+                  }}
+                />
+                <Text style={{ fontSize: window.innerWidth <= 768 ? 13 : 14 }}>包含生成历史</Text>
+                <Tooltip title="导出AI生成的历史记录（最多100条）">
+                  <InfoCircleOutlined style={{ color: '#999', fontSize: window.innerWidth <= 768 ? 12 : 14 }} />
+                </Tooltip>
+              </div>
+            </Space>
+          </Card>
+
+          <Divider style={{ margin: '8px 0' }} />
+
+          {/* 项目列表 */}
+          <div>
+            <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center', marginBottom: 12, flexWrap: window.innerWidth <= 768 ? 'wrap' : 'nowrap', gap: 8 }}>
+              <Text strong style={{ fontSize: window.innerWidth <= 768 ? 13 : 14 }}>
+                选择要导出的项目 {exportableProjects.length > 0 && <Text type="secondary" style={{ fontSize: window.innerWidth <= 768 ? 12 : 14 }}>({exportableProjects.length}个可导出)</Text>}
+              </Text>
+              <Checkbox
+                checked={selectedProjectIds.length === exportableProjects.length && exportableProjects.length > 0}
+                indeterminate={selectedProjectIds.length > 0 && selectedProjectIds.length < exportableProjects.length}
+                onChange={handleToggleAll}
+                style={{ fontSize: window.innerWidth <= 768 ? 13 : 14 }}
+              >
+                全选
+              </Checkbox>
+            </div>
+
+            <div style={{ maxHeight: window.innerWidth <= 768 ? 300 : 400, overflowY: 'auto' }}>
+              {exportableProjects.length === 0 ? (
+                <Empty
+                  description="暂无可导出的项目"
+                  style={{ padding: '40px 0' }}
+                />
+              ) : (
+                <Space direction="vertical" size={8} style={{ width: '100%' }}>
+                  {exportableProjects.map((project) => (
+                  <Card
+                    key={project.id}
+                    size="small"
+                    hoverable
+                    style={{
+                      cursor: 'pointer',
+                      border: selectedProjectIds.includes(project.id) ? '2px solid #1890ff' : '1px solid #d9d9d9',
+                      background: selectedProjectIds.includes(project.id) ? '#e6f7ff' : '#fff'
+                    }}
+                    onClick={() => handleToggleProject(project.id)}
+                  >
+                    <div style={{ display: 'flex', alignItems: 'center', gap: 12 }}>
+                      <Checkbox
+                        checked={selectedProjectIds.includes(project.id)}
+                        onChange={() => handleToggleProject(project.id)}
+                        onClick={(e) => e.stopPropagation()}
+                      />
+                      <BookOutlined style={{ fontSize: 20, color: '#1890ff' }} />
+                      <div style={{ flex: 1, minWidth: 0 }}>
+                        <div style={{ display: 'flex', alignItems: 'center', gap: 8, marginBottom: 4, flexWrap: 'wrap' }}>
+                          <Text strong style={{ fontSize: window.innerWidth <= 768 ? 13 : 14 }}>{project.title}</Text>
+                          {project.genre && (
+                            <Tag color="blue" style={{ margin: 0, fontSize: window.innerWidth <= 768 ? 11 : 12 }}>{project.genre}</Tag>
+                          )}
+                          {getStatusTag(project.status)}
+                        </div>
+                        <Text type="secondary" style={{ fontSize: window.innerWidth <= 768 ? 11 : 12 }}>
+                          {project.current_words || 0} 字
+                          {project.description && ` · ${project.description.substring(0, window.innerWidth <= 768 ? 30 : 50)}${project.description.length > (window.innerWidth <= 768 ? 30 : 50) ? '...' : ''}`}
+                        </Text>
+                      </div>
+                      {window.innerWidth > 768 && (
+                        <Text type="secondary" style={{ fontSize: 12 }}>
+                          {formatDate(project.updated_at)}
+                        </Text>
+                      )}
+                    </div>
+                    </Card>
+                  ))}
+                </Space>
+              )}
+            </div>
+          </div>
+
+          {selectedProjectIds.length > 0 && (
+            <Alert
+              message={`已选择 ${selectedProjectIds.length} 个项目`}
+              type="info"
+              showIcon
+              style={{ marginTop: 8 }}
+            />
+          )}
+        </Space>
+      </Modal>
     </div>
   );
 }

frontend/src/pages/Settings.tsx

@@ -59,7 +59,7 @@ export default function SettingsPage() {
         form.setFieldsValue({
           api_provider: 'openai',
           api_base_url: 'https://api.openai.com/v1',
-          model_name: 'gpt-4',
+          llm_model: 'gpt-4',
           temperature: 0.7,
           max_tokens: 2000,
         });
@@ -96,7 +96,7 @@ export default function SettingsPage() {
           api_provider: 'openai',
           api_key: '',
           api_base_url: 'https://api.openai.com/v1',
-          model_name: 'gpt-4',
+          llm_model: 'gpt-4',
           temperature: 0.7,
           max_tokens: 2000,
         });
@@ -193,7 +193,7 @@ export default function SettingsPage() {
     const apiKey = form.getFieldValue('api_key');
     const apiBaseUrl = form.getFieldValue('api_base_url');
     const provider = form.getFieldValue('api_provider');
-    const modelName = form.getFieldValue('model_name');
+    const modelName = form.getFieldValue('llm_model');
 
     if (!apiKey || !apiBaseUrl || !provider || !modelName) {
       message.warning('请先填写完整的配置信息');
@@ -208,7 +208,7 @@ export default function SettingsPage() {
         api_key: apiKey,
         api_base_url: apiBaseUrl,
         provider: provider,
-        model_name: modelName
+        llm_model: modelName
       });
       
       setTestResult(result);
@@ -406,7 +406,7 @@ export default function SettingsPage() {
                       </Tooltip>
                     </Space>
                   }
-                  name="model_name"
+                  name="llm_model"
                   rules={[{ required: true, message: '请输入或选择模型名称' }]}
                 >
                   <Select

frontend/src/services/api.ts

@@ -147,7 +147,7 @@ export const settingsApi = {
   getAvailableModels: (params: { api_key: string; api_base_url: string; provider: string }) =>
     api.get<unknown, { provider: string; models: Array<{ value: string; label: string; description: string }>; count?: number }>('/settings/models', { params }),
   
-  testApiConnection: (params: { api_key: string; api_base_url: string; provider: string; model_name: string }) =>
+  testApiConnection: (params: { api_key: string; api_base_url: string; provider: string; llm_model: string }) =>
     api.post<unknown, {
       success: boolean;
       message: string;
@@ -177,6 +177,71 @@ export const projectApi = {
   exportProject: (id: string) => {
     window.open(`/api/projects/${id}/export`, '_blank');
   },
+  
+  // 导出项目数据为JSON
+  exportProjectData: async (id: string, options: { include_generation_history?: boolean; include_writing_styles?: boolean }) => {
+    const response = await axios.post(
+      `/api/projects/${id}/export-data`,
+      options,
+      {
+        responseType: 'blob',
+        headers: {
+          'Content-Type': 'application/json',
+        },
+      }
+    );
+    
+    // 从响应头获取文件名
+    const contentDisposition = response.headers['content-disposition'];
+    let filename = 'project_export.json';
+    if (contentDisposition) {
+      const matches = /filename\*=UTF-8''(.+)/.exec(contentDisposition);
+      if (matches && matches[1]) {
+        filename = decodeURIComponent(matches[1]);
+      }
+    }
+    
+    // 创建下载链接
+    const url = window.URL.createObjectURL(new Blob([response.data]));
+    const link = document.createElement('a');
+    link.href = url;
+    link.setAttribute('download', filename);
+    document.body.appendChild(link);
+    link.click();
+    link.remove();
+    window.URL.revokeObjectURL(url);
+  },
+  
+  // 验证导入文件
+  validateImportFile: (file: File) => {
+    const formData = new FormData();
+    formData.append('file', file);
+    return api.post<unknown, {
+      valid: boolean;
+      version: string;
+      project_name?: string;
+      statistics: Record<string, number>;
+      errors: string[];
+      warnings: string[];
+    }>('/projects/validate-import', formData, {
+      headers: { 'Content-Type': 'multipart/form-data' },
+    });
+  },
+  
+  // 导入项目
+  importProject: (file: File) => {
+    const formData = new FormData();
+    formData.append('file', file);
+    return api.post<unknown, {
+      success: boolean;
+      project_id?: string;
+      message: string;
+      statistics: Record<string, number>;
+      warnings: string[];
+    }>('/projects/import', formData, {
+      headers: { 'Content-Type': 'multipart/form-data' },
+    });
+  },
 };
 
 export const outlineApi = {

frontend/src/store/hooks.ts

@@ -331,6 +331,7 @@ export function useChapterSync() {
 
       let buffer = '';
       let fullContent = '';
+      let analysisTaskId: string | undefined;
 
       while (true) {
         const { done, value } = await reader.read();
@@ -363,9 +364,13 @@ export function useChapterSync() {
               } else if (message.type === 'error') {
                 throw new Error(message.error || '生成失败');
               } else if (message.type === 'done') {
+                // 生成完成，保存分析任务ID
+                analysisTaskId = message.analysis_task_id;
                 // 生成完成，刷新章节数据
                 await refreshChapters();
-                return { content: fullContent, word_count: message.word_count };
+              } else if (message.type === 'analysis_queued') {
+                // 分析任务已加入队列
+                analysisTaskId = message.task_id;
               }
             }
           } catch (error) {
@@ -374,7 +379,10 @@ export function useChapterSync() {
         }
       }
 
-      return { content: fullContent };
+      return {
+        content: fullContent,
+        analysis_task_id: analysisTaskId
+      };
     } catch (error) {
       console.error('AI流式生成章节内容失败:', error);
       throw error;

frontend/src/types/index.ts

@@ -18,7 +18,7 @@ export interface Settings {
   api_provider: string;
   api_key: string;
   api_base_url: string;
-  model_name: string;
+  llm_model: string;
   temperature: number;
   max_tokens: number;
   preferences?: string;
@@ -30,7 +30,7 @@ export interface SettingsUpdate {
   api_provider?: string;
   api_key?: string;
   api_base_url?: string;
-  model_name?: string;
+  llm_model?: string;
   temperature?: number;
   max_tokens?: number;
   preferences?: string;
@@ -376,4 +376,140 @@ export interface ApiError {
     };
   };
   message?: string;
+}
+
+// 章节分析任务相关类型
+export interface AnalysisTask {
+  task_id: string;
+  chapter_id: string;
+  status: 'pending' | 'running' | 'completed' | 'failed';
+  progress: number;
+  error_message?: string;
+  created_at?: string;
+  started_at?: string;
+  completed_at?: string;
+}
+
+// 分析结果 - 钩子
+export interface AnalysisHook {
+  type: string;
+  content: string;
+  strength: number;
+  position: string;
+}
+
+// 分析结果 - 伏笔
+export interface AnalysisForeshadow {
+  content: string;
+  type: 'planted' | 'resolved';
+  strength: number;
+  subtlety: number;
+  reference_chapter?: number;
+}
+
+// 分析结果 - 冲突
+export interface AnalysisConflict {
+  types: string[];
+  parties: string[];
+  level: number;
+  description: string;
+  resolution_progress: number;
+}
+
+// 分析结果 - 情感曲线
+export interface AnalysisEmotionalArc {
+  primary_emotion: string;
+  intensity: number;
+  curve: string;
+  secondary_emotions: string[];
+}
+
+// 分析结果 - 角色状态
+export interface AnalysisCharacterState {
+  character_name: string;
+  state_before: string;
+  state_after: string;
+  psychological_change: string;
+  key_event: string;
+  relationship_changes: Record<string, string>;
+}
+
+// 分析结果 - 情节点
+export interface AnalysisPlotPoint {
+  content: string;
+  type: 'revelation' | 'conflict' | 'resolution' | 'transition';
+  importance: number;
+  impact: string;
+}
+
+// 分析结果 - 场景
+export interface AnalysisScene {
+  location: string;
+  atmosphere: string;
+  duration: string;
+}
+
+// 分析结果 - 评分
+export interface AnalysisScores {
+  pacing: number;
+  engagement: number;
+  coherence: number;
+  overall: number;
+}
+
+// 完整分析数据 - 匹配后端PlotAnalysis模型
+export interface AnalysisData {
+  id: string;
+  chapter_id: string;
+  plot_stage: string;
+  conflict_level: number;
+  conflict_types: string[];
+  emotional_tone: string;
+  emotional_intensity: number;
+  hooks: AnalysisHook[];
+  hooks_count: number;
+  foreshadows: AnalysisForeshadow[];
+  foreshadows_planted: number;
+  foreshadows_resolved: number;
+  plot_points: AnalysisPlotPoint[];
+  plot_points_count: number;
+  character_states: AnalysisCharacterState[];
+  scenes?: AnalysisScene[];
+  pacing: string;
+  overall_quality_score: number;
+  pacing_score: number;
+  engagement_score: number;
+  coherence_score: number;
+  analysis_report: string;
+  suggestions: string[];
+  dialogue_ratio: number;
+  description_ratio: number;
+  created_at: string;
+}
+
+// 记忆片段
+export interface StoryMemory {
+  id: string;
+  type: 'hook' | 'foreshadow' | 'plot_point' | 'character_event';
+  title: string;
+  content: string;
+  importance: number;
+  tags: string[];
+  is_foreshadow: 0 | 1 | 2; // 0=普通, 1=已埋下, 2=已回收
+}
+
+// 章节分析结果响应 - 匹配后端API返回
+export interface ChapterAnalysisResponse {
+  chapter_id: string;
+  analysis: AnalysisData;  // 注意：后端返回的是analysis而不是analysis_data
+  memories: StoryMemory[];
+  created_at: string;
+}
+
+// 手动触发分析响应
+export interface TriggerAnalysisResponse {
+  task_id: string;
+  chapter_id: string;
+  status: string;
+  message: string;
 }