From e4f90d5da0d7f8b1ad38a6a8809ee6d04389e4fb Mon Sep 17 00:00:00 2001 From: xiamuceer Date: Tue, 4 Nov 2025 14:38:59 +0800 Subject: [PATCH 01/11] =?UTF-8?q?update:1.=E6=9B=B4=E6=96=B0=E5=AF=BC?= =?UTF-8?q?=E5=85=A5=E5=AF=BC=E5=87=BA=E5=8A=9F=E8=83=BD=202.=E5=AE=9E?= =?UTF-8?q?=E7=8E=B0RAG=E8=AE=B0=E5=BF=86=E5=8A=9F=E8=83=BD=EF=BC=8C?= =?UTF-8?q?=E5=BC=95=E5=85=A5=E5=89=A7=E6=83=85=E5=88=86=E6=9E=90=E5=8A=9F?= =?UTF-8?q?=E8=83=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- backend/app/api/chapters.py | 718 +++++++++++++++- backend/app/api/memories.py | 383 +++++++++ backend/app/api/outlines.py | 74 +- backend/app/api/projects.py | 176 +++- backend/app/database.py | 9 + backend/app/main.py | 3 +- backend/app/models/__init__.py | 5 + backend/app/models/analysis_task.py | 38 + backend/app/models/memory.py | 200 +++++ backend/app/schemas/import_export.py | 136 ++++ backend/app/services/import_export_service.py | 769 ++++++++++++++++++ backend/app/services/memory_service.py | 739 +++++++++++++++++ backend/app/services/plot_analyzer.py | 559 +++++++++++++ backend/app/services/prompt_service.py | 96 ++- backend/requirements.txt | 6 +- frontend/src/App.tsx | 4 + frontend/src/components/AnnotatedText.tsx | 253 ++++++ frontend/src/components/ChapterAnalysis.tsx | 533 ++++++++++++ frontend/src/components/MemorySidebar.tsx | 250 ++++++ frontend/src/pages/ChapterAnalysis.tsx | 494 +++++++++++ frontend/src/pages/ChapterReader.tsx | 456 +++++++++++ frontend/src/pages/Chapters.tsx | 42 +- frontend/src/pages/ProjectDetail.tsx | 13 +- frontend/src/pages/ProjectList.tsx | 649 ++++++++++++++- frontend/src/services/api.ts | 65 ++ frontend/src/types/index.ts | 136 ++++ 26 files changed, 6722 insertions(+), 84 deletions(-) create mode 100644 backend/app/api/memories.py create mode 100644 backend/app/models/analysis_task.py create mode 100644 backend/app/models/memory.py create mode 100644 backend/app/schemas/import_export.py create mode 100644 backend/app/services/import_export_service.py create mode 100644 backend/app/services/memory_service.py create mode 100644 backend/app/services/plot_analyzer.py create mode 100644 frontend/src/components/AnnotatedText.tsx create mode 100644 frontend/src/components/ChapterAnalysis.tsx create mode 100644 frontend/src/components/MemorySidebar.tsx create mode 100644 frontend/src/pages/ChapterAnalysis.tsx create mode 100644 frontend/src/pages/ChapterReader.tsx diff --git a/backend/app/api/chapters.py b/backend/app/api/chapters.py index 0bb5803..01c2775 100644 --- a/backend/app/api/chapters.py +++ b/backend/app/api/chapters.py @@ -1,11 +1,12 @@ """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 app.database import get_db from app.models.chapter import Chapter @@ -14,6 +15,8 @@ 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, @@ -23,6 +26,8 @@ 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 @@ -101,6 +106,63 @@ 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 +310,273 @@ 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 + + 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 + try: + logger.info(f"🔍 开始后台分析章节: {chapter_id}") + + # 等待一小段时间，确保主会话的commit已经持久化到磁盘 + await asyncio.sleep(0.1) + + # 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（添加重试逻辑） + task = None + for retry in range(3): + task_result = await db_session.execute( + select(AnalysisTask).where(AnalysisTask.id == task_id) + ) + task = task_result.scalar_one_or_none() + if task: + break + if retry < 2: + logger.warning(f"⚠️ 第{retry+1}次未找到任务 {task_id}，等待后重试...") + await asyncio.sleep(0.2) + + if not task: + logger.error(f"❌ 任务不存在: {task_id}") + return + + task.status = 'running' + task.started_at = datetime.now() + task.progress = 10 + await db_session.commit() + + # 2. 获取章节信息 + 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: + 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 + + 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: + 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 + + task.progress = 60 + await db_session.commit() + + # 4. 保存分析结果到数据库（先检查是否已存在） + 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（支持重新分析） + 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) + logger.info(f" 删除旧记忆: {len(old_memories)}条") + + # 准备批量添加的记忆数据 + 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'] + }) + + # 从metadata中提取位置信息 + text_position = mem['metadata'].get('text_position', -1) + text_length = mem['metadata'].get('text_length', 0) + + # 同时保存到关系数据库 + 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, # 使用章节序号作为时间线 + chapter_position=text_position, # 保存文本位置 + text_length=text_length, # 保存文本长度 + related_characters=mem['metadata'].get('related_characters', []), + related_locations=mem['metadata'].get('related_locations', []) + ) + db_session.add(story_memory) + + # 记录日志便于调试 + 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") + + 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，避免前端一直轮询 + if db_session: + try: + # 重新获取任务以确保有最新状态 + 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 # 重置进度 + 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 +626,9 @@ 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 +724,42 @@ 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 +777,8 @@ 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 +796,8 @@ 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 +835,48 @@ 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 - yield f"data: {json.dumps({'type': 'done', 'message': '创作完成', 'word_count': new_word_count}, ensure_ascii=False)}\n\n" + # 创建分析任务并启动后台分析 + 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() + # 不需要refresh，只需要获取ID + + task_id = analysis_task.id + + # 启动后台分析任务 + 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 + ) + + logger.info(f"📋 已创建分析任务: {task_id}") + + # 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" + + # 发送分析排队事件 + analysis_queued_data = { + 'type': 'analysis_queued', + 'task_id': task_id, + 'message': '章节分析已加入队列' + } + yield f"data: {json.dumps(analysis_queued_data, ensure_ascii=False)}\n\n" break # 退出async for db_session循环 @@ -527,3 +928,308 @@ 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() + # 注意：不需要refresh，因为我们只需要id，而id在commit后已经生成 + + task_id = analysis_task.id + + # 启动后台分析任务 + 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 + ) + + logger.info(f"📋 手动触发分析任务: {task_id}") + + return { + "task_id": task_id, + "chapter_id": chapter_id, + "status": "pending", + "message": "分析任务已创建并加入队列" + } diff --git a/backend/app/api/memories.py b/backend/app/api/memories.py new file mode 100644 index 0000000..47a10b9 --- /dev/null +++ b/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.model_name, + 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)) \ No newline at end of file diff --git a/backend/app/api/outlines.py b/backend/app/api/outlines.py index aca7d50..1d86ba5 100644 --- a/backend/app/api/outlines.py +++ b/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, diff --git a/backend/app/api/projects.py b/backend/app/api/projects.py index f824c19..ffa4e81 100644 --- a/backend/app/api/projects.py +++ b/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)}") \ No newline at end of file + 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)}") \ No newline at end of file diff --git a/backend/app/database.py b/backend/app/database.py index be098fa..93f0b18 100644 --- a/backend/app/database.py +++ b/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] = {} diff --git a/backend/app/main.py b/backend/app/main.py index 756a23c..719f7e4 100644 --- a/backend/app/main.py +++ b/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(): diff --git a/backend/app/models/__init__.py b/backend/app/models/__init__.py index 38ca856..61f7e8c 100644 --- a/backend/app/models/__init__.py +++ b/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", ] \ No newline at end of file diff --git a/backend/app/models/analysis_task.py b/backend/app/models/analysis_task.py new file mode 100644 index 0000000..4303781 --- /dev/null +++ b/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"" \ No newline at end of file diff --git a/backend/app/models/memory.py b/backend/app/models/memory.py new file mode 100644 index 0000000..881ad4b --- /dev/null +++ b/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"" + + 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"" + + 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 + } \ No newline at end of file diff --git a/backend/app/schemas/import_export.py b/backend/app/schemas/import_export.py new file mode 100644 index 0000000..0e3dba3 --- /dev/null +++ b/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] = [] \ No newline at end of file diff --git a/backend/app/services/import_export_service.py b/backend/app/services/import_export_service.py new file mode 100644 index 0000000..ec9e3f0 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/backend/app/services/memory_service.py b/backend/app/services/memory_service.py new file mode 100644 index 0000000..566235c --- /dev/null +++ b/backend/app/services/memory_service.py @@ -0,0 +1,739 @@ +"""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__) + +# 配置离线模式，避免联网检查 +os.environ['TRANSFORMERS_OFFLINE'] = '1' +os.environ['HF_DATASETS_OFFLINE'] = '1' + + +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 = 'data/models' + os.makedirs(model_cache_dir, exist_ok=True) + + try: + # 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 + self.embedding_model = SentenceTransformer( + 'paraphrase-multilingual-MiniLM-L12-v2', + cache_folder=model_cache_dir, + device='cpu' # 明确指定使用CPU + ) + logger.info("✅ Embedding模型加载成功") + except Exception as e: + logger.warning(f"⚠️ 无法加载多语言模型: {str(e)}") + logger.info("🔄 尝试使用备用模型...") + try: + # 降级到更小的模型作为备选 + self.embedding_model = SentenceTransformer( + 'all-MiniLM-L6-v2', + cache_folder=model_cache_dir, + device='cpu' + ) + logger.info("✅ 使用备用Embedding模型") + except Exception as e2: + logger.error(f"❌ 所有模型加载失败: {str(e2)}") + 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 data/models 目录") + 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() + \ No newline at end of file diff --git a/backend/app/services/plot_analyzer.py b/backend/app/services/plot_analyzer.py new file mode 100644 index 0000000..1f8e809 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/backend/app/services/prompt_service.py b/backend/app/services/prompt_service.py index be1b4a9..ecc47d7 100644 --- a/backend/app/services/prompt_service.py +++ b/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 -- 遵循情节阶段的发展要求 -- 保持与已有章节相同的风格和详细程度 -- 推进角色成长和情节发展 +- **剧情连贯性**：与前文自然衔接，保持故事连贯性 +- **记忆参考**：适当参考记忆系统中的伏笔、钩子和情节点 +- **伏笔回收**：可以考虑回收未完结的伏笔，制造呼应 +- **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 -- 结尾为下一章做好铺垫 + - 开头自然衔接上一章结尾 + - 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: - """获取大纲续写提示词""" + requirements: str = "", + 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 diff --git a/backend/requirements.txt b/backend/requirements.txt index 6a4af8f..94eec74 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -17,4 +17,8 @@ anthropic==0.18.0 # 工具库 httpx==0.26.0 -python-dotenv==1.0.0 \ No newline at end of file +python-dotenv==1.0.0 + +# 向量数据库和Embedding (长期记忆系统) +chromadb>=0.5.0 +sentence-transformers>=2.3.1 \ No newline at end of file diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx index e5ada3d..383598e 100644 --- a/frontend/src/App.tsx +++ b/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() { } /> } /> } /> + } /> }> } /> } /> @@ -42,6 +45,7 @@ function App() { } /> } /> } /> + } /> } /> {/* } /> */} diff --git a/frontend/src/components/AnnotatedText.tsx b/frontend/src/components/AnnotatedText.tsx new file mode 100644 index 0000000..52c745d --- /dev/null +++ b/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 = ({ + content, + annotations, + onAnnotationClick, + activeAnnotationId, + scrollToAnnotation, +}) => { + const annotationRefs = useRef>({}); + + // 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 {segment.content}; + } + + 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 = ( +
+
+ {icon} {annotation.title} +
+
+ {annotation.content.slice(0, 100)} + {annotation.content.length > 100 ? '...' : ''} +
+
+ 重要性: {(annotation.importance * 10).toFixed(1)}/10 +
+ {annotation.tags && annotation.tags.length > 0 && ( +
+ {annotation.tags.map((tag, i) => ( + + {tag} + + ))} +
+ )} +
+ ); + + return ( + + { + 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} + + {icon} + + + + ); + }; + + return ( +
+ {segments.map((segment, index) => renderAnnotatedSegment(segment, index))} +
+ ); +}; + +export default AnnotatedText; \ No newline at end of file diff --git a/frontend/src/components/ChapterAnalysis.tsx b/frontend/src/components/ChapterAnalysis.tsx new file mode 100644 index 0000000..e2eb082 --- /dev/null +++ b/frontend/src/components/ChapterAnalysis.tsx @@ -0,0 +1,533 @@ +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(null); + const [analysis, setAnalysis] = useState(null); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(null); + + useEffect(() => { + if (visible && chapterId) { + fetchAnalysisStatus(); + } + }, [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'); + } + + const result = await response.json(); + setTask({ + task_id: result.task_id, + chapter_id: chapterId, + status: 'pending', + progress: 0 + }); + + // 开始轮询 + startPolling(); + } catch (err) { + setError((err as Error).message); + } finally { + setLoading(false); + } + }; + + const renderStatusIcon = () => { + if (!task) return null; + + switch (task.status) { + case 'pending': + return ; + case 'running': + return ; + case 'completed': + return ; + case 'failed': + return ; + default: + return null; + } + }; + + const renderProgress = () => { + if (!task || task.status === 'completed') return null; + + return ( +
+
+ {renderStatusIcon()} + + {task.status === 'pending' && '等待分析...'} + {task.status === 'running' && 'AI正在分析中...'} + {task.status === 'failed' && '分析失败'} + +
+ + {task.status === 'failed' && task.error_message && ( + + )} +
+ ); + }; + + const renderAnalysisResult = () => { + if (!analysis) return null; + + const { analysis: analysis_data, memories } = analysis; + + return ( + , + children: ( +
+ + + + + + + + + + + + + + + + + + {analysis_data.analysis_report && ( + + 
+                      {analysis_data.analysis_report}
+
+ + )} + + {analysis_data.suggestions && analysis_data.suggestions.length > 0 && ( + 改进建议}> + ( + + {index + 1}. {item} + + )} + /> + + )} +
+ ) + }, + { + key: 'hooks', + label: `钩子 (${analysis_data.hooks?.length || 0})`, + icon: , + children: ( +
+ + {analysis_data.hooks && analysis_data.hooks.length > 0 ? ( + ( + + + {hook.type} + {hook.position} + 强度: {hook.strength}/10 +
+ } + description={hook.content} + /> + + )} + /> + ) : ( + + )} + + + ) + }, + { + key: 'foreshadows', + label: `伏笔 (${analysis_data.foreshadows?.length || 0})`, + icon: , + children: ( +
+ + {analysis_data.foreshadows && analysis_data.foreshadows.length > 0 ? ( + ( + + + + {foreshadow.type === 'planted' ? '已埋下' : '已回收'} + + 强度: {foreshadow.strength}/10 + 隐藏度: {foreshadow.subtlety}/10 + {foreshadow.reference_chapter && ( + 呼应第{foreshadow.reference_chapter}章 + )} +
+ } + description={foreshadow.content} + /> + + )} + /> + ) : ( + + )} + + + ) + }, + { + key: 'emotion', + label: '情感曲线', + icon: , + children: ( +
+ + {analysis_data.emotional_tone ? ( +
+ + + + + + + + + +

阶段：{analysis_data.plot_stage}

+

冲突等级：{analysis_data.conflict_level} / 10

+ {analysis_data.conflict_types && analysis_data.conflict_types.length > 0 && ( +
+ 冲突类型： + {analysis_data.conflict_types.map((type, idx) => ( + + {type} + + ))} +
+ )} + +
+ ) : ( + + )} + +
+ ) + }, + { + key: 'characters', + label: `角色 (${analysis_data.character_states?.length || 0})`, + icon: , + children: ( +
+ + {analysis_data.character_states && analysis_data.character_states.length > 0 ? ( + ( + + +

状态变化：{char.state_before} → {char.state_after}

+

心理变化：{char.psychological_change}

+

关键事件：{char.key_event}

+ {char.relationship_changes && Object.keys(char.relationship_changes).length > 0 && ( +
+ 关系变化： + {Object.entries(char.relationship_changes).map(([name, change]) => ( + + 与{name}: {change} + + ))} +
+ )} + + + )} + /> + ) : ( + + )} + +
+ ) + }, + { + key: 'memories', + label: `记忆 (${memories?.length || 0})`, + icon: , + children: ( +
+ + {memories && memories.length > 0 ? ( + ( + + + {memory.type} + 重要性: {memory.importance.toFixed(1)} + {memory.is_foreshadow === 1 && 已埋下伏笔} + {memory.is_foreshadow === 2 && 已回收伏笔} + {memory.title} +
+ } + description={ +
+

{memory.content}

+
+ {memory.tags.map((tag, idx) => ( + {tag} + ))} +
+
+ } + /> + + )} + /> + ) : ( + + )} + + + ) + } + ]} + /> + ); + }; + + return ( + + 关闭 + , + !task && ( + + ), + task && (task.status === 'failed' || task.status === 'completed') && ( + + ) + ]} + > + {loading && !task && ( +
+ +

加载中...

+
+ )} + + {error && ( + + 开始分析 + + } + /> + )} + + {task && task.status !== 'completed' && renderProgress()} + {task && task.status === 'completed' && analysis && renderAnalysisResult()} + + ); +} \ No newline at end of file diff --git a/frontend/src/components/MemorySidebar.tsx b/frontend/src/components/MemorySidebar.tsx new file mode 100644 index 0000000..c1b79bd --- /dev/null +++ b/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: , + color: '#ff6b6b', + }, + foreshadow: { + label: '伏笔', + icon: , + color: '#6b7bff', + }, + plot_point: { + label: '情节点', + icon: , + color: '#51cf66', + }, + character_event: { + label: '角色事件', + icon: , + color: '#ffd93d', + }, +}; + +/** + * 记忆侧边栏组件 + * 展示章节的所有记忆标注 + */ +const MemorySidebar: React.FC = ({ + annotations, + activeAnnotationId, + onAnnotationClick, + scrollToAnnotation, +}) => { + const cardRefs = useRef>({}); + + // 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 = { + 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 ( +
{ + cardRefs.current[annotation.id] = el; + }} + > + 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 }} + > +
+ +
+ {config.icon} {annotation.title} +
+
+ +
+ {annotation.content.length > 100 + ? `${annotation.content.slice(0, 100)}...` + : annotation.content} +
+ + {annotation.tags && annotation.tags.length > 0 && ( +
+ {annotation.tags.map((tag, index) => ( + + {tag} + + ))} +
+ )} + + {/* 特殊元数据 */} + {annotation.metadata.strength && ( +
+ 强度: {annotation.metadata.strength}/10 +
+ )} + {annotation.metadata.foreshadowType && ( + + {annotation.metadata.foreshadowType === 'planted' ? '已埋下' : '已回收'} + + )} + +
+ ); + }; + + if (annotations.length === 0) { + return ( +
+ +
+ ); + } + + return ( +
+ {/* 统计概览 */} + +
📊 分析概览
+
+
+
钩子
+
+ {stats.hooks} +
+
+
+
伏笔
+
+ {stats.foreshadows} +
+
+
+
情节点
+
+ {stats.plotPoints} +
+
+
+
角色事件
+
+ {stats.characterEvents} +
+
+
+ + + + + {/* 分类展示 */} + + {Object.entries(groupedAnnotations).map(([type, items]) => { + if (items.length === 0) return null; + + const config = TYPE_CONFIG[type as keyof typeof TYPE_CONFIG]; + + return ( + + {config.icon} {config.label} ({items.length}) + + } + > + {items.map((annotation) => renderMemoryCard(annotation))} + + ); + })} + +
+ ); +}; + +export default MemorySidebar; \ No newline at end of file diff --git a/frontend/src/pages/ChapterAnalysis.tsx b/frontend/src/pages/ChapterAnalysis.tsx new file mode 100644 index 0000000..1ebe07e --- /dev/null +++ b/frontend/src/pages/ChapterAnalysis.tsx @@ -0,0 +1,494 @@ +import React, { useState, useEffect } from 'react'; +import { Card, List, Button, Space, Empty, Tag, Spin, Alert, Switch, Drawer, message, Progress } from 'antd'; +import { + EyeOutlined, + EyeInvisibleOutlined, + MenuOutlined, + ReloadOutlined, + 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([]); + const [selectedChapter, setSelectedChapter] = useState(null); + const [annotationsData, setAnnotationsData] = useState(null); + const [navigation, setNavigation] = useState(null); + const [loading, setLoading] = useState(true); + const [contentLoading, setContentLoading] = useState(false); + const [showAnnotations, setShowAnnotations] = useState(true); + const [activeAnnotationId, setActiveAnnotationId] = useState(); + const [sidebarVisible, setSidebarVisible] = useState(false); + const [analyzing, setAnalyzing] = useState(false); + const [analysisProgress, setAnalysisProgress] = useState(0); + const [scrollToContentAnnotation, setScrollToContentAnnotation] = useState(); + const [scrollToSidebarAnnotation, setScrollToSidebarAnnotation] = useState(); + + // 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/${chapterId}/navigation`).catch(() => null), + ]); + + // 提取 data 属性 + setSelectedChapter(chapterResponse.data || chapterResponse); + setAnnotationsData(annotationsResponse ? (annotationsResponse.data || annotationsResponse) : null); + setNavigation(navigationResponse ? (navigationResponse.data || navigationResponse) : null); + } catch (error) { + console.error('加载章节内容失败:', error); + message.error('加载章节内容失败'); + } finally { + setContentLoading(false); + } + }; + + const handleChapterSelect = (chapterId: string) => { + loadChapterContent(chapterId); + }; + + const handlePreviousChapter = () => { + if (navigation?.previous) { + loadChapterContent(navigation.previous.id); + } + }; + + const handleNextChapter = () => { + if (navigation?.next) { + loadChapterContent(navigation.next.id); + } + }; + + const handleAnnotationClick = (annotation: MemoryAnnotation, source: 'content' | 'sidebar' = 'content') => { + setActiveAnnotationId(annotation.id); + + if (source === 'content') { + // 从内容区点击，滚动到侧边栏 + setScrollToSidebarAnnotation(annotation.id); + // 清除滚动状态 + setTimeout(() => setScrollToSidebarAnnotation(undefined), 100); + + if (window.innerWidth < 768) { + setSidebarVisible(true); + } + } else { + // 从侧边栏点击，滚动到内容区 + setScrollToContentAnnotation(annotation.id); + // 清除滚动状态 + setTimeout(() => setScrollToContentAnnotation(undefined), 100); + } + }; + + const handleReanalyze = async () => { + if (!selectedChapter) return; + + let pollInterval: number | null = null; + let timeoutId: number | null = null; + + try { + setAnalyzing(true); + setAnalysisProgress(0); + message.loading({ content: '开始分析章节...', key: 'analyze', duration: 0 }); + + // 触发分析任务 + const triggerRes = await api.post(`/chapters/${selectedChapter.id}/analyze`); + const triggerData = triggerRes.data || triggerRes; + const taskId = triggerData.task_id; + + console.log('分析任务已创建:', taskId); + + // 开始轮询状态 + let pollCount = 0; + const maxPolls = 60; // 最多轮询60次（2分钟） + + pollInterval = setInterval(async () => { + pollCount++; + + if (pollCount > maxPolls) { + if (pollInterval) clearInterval(pollInterval); + if (timeoutId) clearTimeout(timeoutId); + setAnalyzing(false); + message.warning({ content: '分析超时，请稍后刷新页面查看结果', key: 'analyze' }); + return; + } + + try { + const statusRes = await api.get(`/chapters/${selectedChapter.id}/analysis/status`); + const responseData = statusRes.data || statusRes; + + if (!responseData) { + console.warn(`第${pollCount}次轮询：响应数据为空`); + return; + } + + const { status, progress, error_message } = responseData; + console.log(`第${pollCount}次轮询：status=${status}, progress=${progress}`); + + setAnalysisProgress(progress || 0); + + if (status === 'completed') { + if (pollInterval) clearInterval(pollInterval); + if (timeoutId) clearTimeout(timeoutId); + setAnalyzing(false); + message.success({ content: '分析完成！', key: 'analyze' }); + + // 重新加载标注数据 + try { + const annotationsRes = await api.get(`/chapters/${selectedChapter.id}/annotations`); + setAnnotationsData(annotationsRes.data || annotationsRes); + } catch (annotErr) { + console.error('加载标注数据失败:', annotErr); + message.warning('分析完成，但加载标注数据失败，请刷新页面'); + } + } else if (status === 'failed') { + if (pollInterval) clearInterval(pollInterval); + if (timeoutId) clearTimeout(timeoutId); + setAnalyzing(false); + message.error({ + content: `分析失败：${error_message || '未知错误'}`, + key: 'analyze' + }); + } + // pending 或 running 状态继续轮询 + } catch (pollErr) { + console.error(`第${pollCount}次轮询失败:`, pollErr); + // 轮询错误不中断，继续下一次轮询 + } + }, 2000); + + // 设置总超时（2分钟） + timeoutId = setTimeout(() => { + if (pollInterval) clearInterval(pollInterval); + setAnalyzing(false); + message.warning({ content: '分析超时，请稍后刷新页面查看结果', key: 'analyze' }); + }, 120000); + + } catch (err: any) { + // 清理定时器 + if (pollInterval) clearInterval(pollInterval); + if (timeoutId) clearTimeout(timeoutId); + + setAnalyzing(false); + const errorMsg = err.response?.data?.detail || err.message || '触发分析失败'; + console.error('触发分析失败:', errorMsg, err); + message.error({ + content: errorMsg, + key: 'analyze' + }); + } + }; + + const hasAnnotations = annotationsData && annotationsData.annotations.length > 0; + + if (loading) { + return ( +
+ +
+ ); + } + + return ( +
+ {/* 左侧章节列表 */} + + {chapters.length === 0 ? ( + + ) : ( + ( + 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', + }} + > + + 第{chapter.chapter_number}章: {chapter.title} + + } + description={ + + + {chapter.word_count || 0}字 + + + } + /> + + )} + /> + )} + + + {/* 右侧内容区域 */} +
+ {!selectedChapter ? ( + + + + ) : ( + <> + {/* 工具栏 */} + +
+ + + + 第{selectedChapter.chapter_number}章: {selectedChapter.title} + + + + + + + {hasAnnotations && ( + <> + } + unCheckedChildren={} + /> + 显示标注 + + + )} + +
+ + {analyzing && ( +
+ + + 正在分析章节... + +
+ )} + + {!analyzing && hasAnnotations && annotationsData && ( +
+ 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`} +
+ )} + + + {/* 内容区域 */} +
+ {/* 章节内容 */} + + {!contentLoading && ( + <> + {!hasAnnotations && ( + + )} + + {showAnnotations && hasAnnotations && annotationsData ? ( + handleAnnotationClick(annotation, 'content')} + activeAnnotationId={activeAnnotationId} + scrollToAnnotation={scrollToContentAnnotation} + /> + ) : ( +
+ {selectedChapter.content} +
+ )} + + )} + + + {/* 右侧记忆侧边栏（桌面端） */} + {hasAnnotations && annotationsData && window.innerWidth >= 768 && ( + + handleAnnotationClick(annotation, 'sidebar')} + scrollToAnnotation={scrollToSidebarAnnotation} + /> + + )} +
+ + {/* 移动端抽屉 */} + {hasAnnotations && annotationsData && ( + setSidebarVisible(false)} + open={sidebarVisible} + width="80%" + > + { + handleAnnotationClick(annotation, 'sidebar'); + setSidebarVisible(false); + }} + scrollToAnnotation={scrollToSidebarAnnotation} + /> + + )} + + )} +
+
+ ); +}; + +export default ChapterAnalysis; \ No newline at end of file diff --git a/frontend/src/pages/ChapterReader.tsx b/frontend/src/pages/ChapterReader.tsx new file mode 100644 index 0000000..d733d23 --- /dev/null +++ b/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(null); + const [chapter, setChapter] = useState(null); + const [annotationsData, setAnnotationsData] = useState(null); + const [showAnnotations, setShowAnnotations] = useState(true); + const [activeAnnotationId, setActiveAnnotationId] = useState(); + const [sidebarVisible, setSidebarVisible] = useState(false); + const [analyzing, setAnalyzing] = useState(false); + const [analysisProgress, setAnalysisProgress] = useState(0); + const [navigation, setNavigation] = useState(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(`/chapters/${chapterId}`).catch(err => { + console.error('加载章节失败:', err); + throw err; + }), + api.get(`/chapters/${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(`/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：${error_message || '未知错误'}`, + key: 'analyze' + }); + } + } catch (err) { + console.error('轮询分析状态失败:', err); + } + }, 2000); // 每2秒轮询一次 + + // 30秒超时 + setTimeout(() => { + clearInterval(pollInterval); + if (analyzing) { + setAnalyzing(false); + message.warning({ content: '分析超时，请稍后刷新查看结果', key: 'analyze' }); + } + }, 30000); + + } catch (err: any) { + setAnalyzing(false); + message.error({ + content: err.response?.data?.detail || '触发分析失败', + key: 'analyze' + }); + } + }; + + if (loading) { + return ( +
+ +
+ ); + } + + if (error || !chapter) { + return ( +
+ + +
+ ); + } + + const hasAnnotations = annotationsData && annotationsData.annotations.length > 0; + + return ( +
+ {/* 顶部工具栏 */} + +
+ + + + + 第{chapter.chapter_number}章: {chapter.title} + + + + + + + {hasAnnotations && ( + <> + } + unCheckedChildren={} + /> + 显示标注 + + + )} + +
+ + {analyzing && ( +
+ + + 正在分析章节... + +
+ )} + + {!analyzing && hasAnnotations && annotationsData && ( +
+ 共有 {annotationsData.summary.total_annotations} 个标注： + {annotationsData.summary.hooks > 0 && ` 🎣${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`} +
+ )} + + + {/* 主内容区域 */} +
+ {/* 左侧：章节内容 */} +
+ +
+ {!hasAnnotations && ( + + )} + + {showAnnotations && hasAnnotations && annotationsData ? ( + + ) : ( +
+ {chapter.content} +
+ )} + + {/* 底部翻页按钮 */} +
+ + + + +
+
+ +
+ + {/* 右侧：记忆侧边栏（桌面端） */} + {hasAnnotations && annotationsData && window.innerWidth >= 768 && ( +
+ +
+ )} +
+ + {/* 移动端抽屉 */} + {hasAnnotations && annotationsData && ( + setSidebarVisible(false)} + open={sidebarVisible} + width="80%" + > + { + handleAnnotationClick(annotation); + setSidebarVisible(false); + }} + /> + + )} +
+ ); +}; + +export default ChapterReader; \ No newline at end of file diff --git a/frontend/src/pages/Chapters.tsx b/frontend/src/pages/Chapters.tsx index 5dbb574..afac3c7 100644 --- a/frontend/src/pages/Chapters.tsx +++ b/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 } 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 { cardStyles } from '../components/CardStyles'; +import ChapterAnalysis from '../components/ChapterAnalysis'; const { TextArea } = Input; @@ -23,6 +24,8 @@ export default function Chapters() { const [writingStyles, setWritingStyles] = useState([]); const [selectedStyleId, setSelectedStyleId] = useState(); const [targetWordCount, setTargetWordCount] = useState(3000); + const [analysisVisible, setAnalysisVisible] = useState(false); + const [analysisChapterId, setAnalysisChapterId] = useState(null); useEffect(() => { const handleResize = () => { @@ -322,6 +325,11 @@ export default function Chapters() { }); }; + const handleShowAnalysis = (chapterId: string) => { + setAnalysisChapterId(chapterId); + setAnalysisVisible(true); + }; + return (
} onClick={() => handleOpenEditor(item.id)} > 编辑内容 , + + + ,
); } \ No newline at end of file diff --git a/frontend/src/pages/ProjectDetail.tsx b/frontend/src/pages/ProjectDetail.tsx index 04e7d13..137aaea 100644 --- a/frontend/src/pages/ProjectDetail.tsx +++ b/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: , label: 章节管理, }, + { + key: 'chapter-analysis', + icon: , + label: 剧情分析, + }, { key: 'writing-styles', icon: , @@ -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 && ( - +
+ - + +
)} diff --git a/frontend/src/pages/ProjectList.tsx b/frontend/src/pages/ProjectList.tsx index 1b43cbc..80be97c 100644 --- a/frontend/src/pages/ProjectList.tsx +++ b/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 { EditOutlined, DeleteOutlined, BookOutlined, RocketOutlined, CalendarOutlined, FileTextOutlined, TrophyOutlined, FireOutlined, SettingOutlined, InfoCircleOutlined, CloseOutlined } from '@ant-design/icons'; +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, 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(null); + const [validationResult, setValidationResult] = useState(null); + const [importing, setImporting] = useState(false); + const [validating, setValidating] = useState(false); + const [exporting, setExporting] = useState(false); + const [selectedProjectIds, setSelectedProjectIds] = useState([]); + 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 (
- - - - + + {window.innerWidth <= 768 ? ( + // 移动端：按钮分两行显示 + + + + + + + + + + + + ) : ( + // PC端：原有布局 + + + + + + + + )} @@ -472,6 +758,285 @@ export default function ProjectList() { )}
+ + {/* 导入项目对话框 */} + + +
+

+ 选择之前导出的 JSON 格式项目文件 +

+ { + setSelectedFile(null); + setValidationResult(null); + }} + fileList={selectedFile ? [{ uid: '-1', name: selectedFile.name, status: 'done' }] as any : []} + > + + +
+ + {validating && ( +
+ +
+ )} + + {validationResult && ( + + +
+ + {validationResult.valid ? '✓ 文件验证通过' : '✗ 文件验证失败'} + +
+ + {validationResult.project_name && ( +
+ 项目名称： + {validationResult.project_name} +
+ )} + + {validationResult.statistics && Object.keys(validationResult.statistics).length > 0 && ( +
+ 数据统计： +
+ + {validationResult.statistics.chapters > 0 && ( + + 章节: {validationResult.statistics.chapters} + + )} + {validationResult.statistics.characters > 0 && ( + + 角色: {validationResult.statistics.characters} + + )} + {validationResult.statistics.outlines > 0 && ( + + 大纲: {validationResult.statistics.outlines} + + )} + {validationResult.statistics.relationships > 0 && ( + + 关系: {validationResult.statistics.relationships} + + )} + +
+
+ )} + + {validationResult.errors && validationResult.errors.length > 0 && ( +
+ 错误： +
    + {validationResult.errors.map((error: string, index: number) => ( +
  • {error}
    • + ))} +
+
+ )} + + {validationResult.warnings && validationResult.warnings.length > 0 && ( +
+ 警告： +
    + {validationResult.warnings.map((warning: string, index: number) => ( +
  • {warning}
    • + ))} +
+
+ )} + + + )} + + + + {/* 导出项目对话框 */} + 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' + } + }} + > + + {/* 导出选项 */} + + + 导出选项 +
+ 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' + }} + /> + 包含写作风格 + + + +
+
+ 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' + }} + /> + 包含生成历史 + + + +
+ + + + + + {/* 项目列表 */} +
+
+ + 选择要导出的项目 {exportableProjects.length > 0 && ({exportableProjects.length}个可导出)} + + 0} + indeterminate={selectedProjectIds.length > 0 && selectedProjectIds.length < exportableProjects.length} + onChange={handleToggleAll} + style={{ fontSize: window.innerWidth <= 768 ? 13 : 14 }} + > + 全选 + +
+ +
+ {exportableProjects.length === 0 ? ( + + ) : ( + + {exportableProjects.map((project) => ( + handleToggleProject(project.id)} + > +
+ handleToggleProject(project.id)} + onClick={(e) => e.stopPropagation()} + /> + +
+
+ {project.title} + {project.genre && ( + {project.genre} + )} + {getStatusTag(project.status)} +
+ + {project.current_words || 0} 字 + {project.description && ` · ${project.description.substring(0, window.innerWidth <= 768 ? 30 : 50)}${project.description.length > (window.innerWidth <= 768 ? 30 : 50) ? '...' : ''}`} + +
+ {window.innerWidth > 768 && ( + + {formatDate(project.updated_at)} + + )} +
+ + ))} + + )} +
+
+ + {selectedProjectIds.length > 0 && ( + + )} + +
); } \ No newline at end of file diff --git a/frontend/src/services/api.ts b/frontend/src/services/api.ts index 303a65d..ee3ae28 100644 --- a/frontend/src/services/api.ts +++ b/frontend/src/services/api.ts @@ -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; + 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; + warnings: string[]; + }>('/projects/import', formData, { + headers: { 'Content-Type': 'multipart/form-data' }, + }); + }, }; export const outlineApi = { diff --git a/frontend/src/types/index.ts b/frontend/src/types/index.ts index 8cb0179..ca9eab1 100644 --- a/frontend/src/types/index.ts +++ b/frontend/src/types/index.ts @@ -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; +} + +// 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; } \ No newline at end of file From e524e9e0ed81983374571bdcb52556a95f20a8f0 Mon Sep 17 00:00:00 2001 From: xiamuceer Date: Tue, 4 Nov 2025 16:01:48 +0800 Subject: [PATCH 02/11] =?UTF-8?q?fix:numpy=E6=9E=84=E5=BB=BA=E6=8A=A5?= =?UTF-8?q?=E9=94=99?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- backend/app/api/memories.py | 2 +- backend/app/api/settings.py | 16 ++++++++-------- backend/app/models/settings.py | 2 +- backend/app/schemas/settings.py | 2 +- backend/requirements.txt | 3 ++- 5 files changed, 13 insertions(+), 12 deletions(-) diff --git a/backend/app/api/memories.py b/backend/app/api/memories.py index 47a10b9..68e0d16 100644 --- a/backend/app/api/memories.py +++ b/backend/app/api/memories.py @@ -61,7 +61,7 @@ async def analyze_chapter( api_provider=settings.api_provider, api_key=settings.api_key, api_base_url=settings.api_base_url, - model_name=settings.model_name, + model_name=settings.llm_model, temperature=settings.temperature, max_tokens=settings.max_tokens ) diff --git a/backend/app/api/settings.py b/backend/app/api/settings.py index e5a6d20..0f3bf20 100644 --- a/backend/app/api/settings.py +++ b/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, diff --git a/backend/app/models/settings.py b/backend/app/models/settings.py index 7aaa646..eaf7b4c 100644 --- a/backend/app/models/settings.py +++ b/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)") diff --git a/backend/app/schemas/settings.py b/backend/app/schemas/settings.py index 442a967..03b48f6 100644 --- a/backend/app/schemas/settings.py +++ b/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)") diff --git a/backend/requirements.txt b/backend/requirements.txt index 94eec74..e88ef46 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -21,4 +21,5 @@ python-dotenv==1.0.0 # 向量数据库和Embedding (长期记忆系统) chromadb>=0.5.0 -sentence-transformers>=2.3.1 \ No newline at end of file +sentence-transformers>=2.3.1 +numpy==1。26.4 \ No newline at end of file From b197ca480dbbfad461f0be5c59d1c622fec2e44e Mon Sep 17 00:00:00 2001 From: xiamuceer Date: Tue, 4 Nov 2025 16:02:52 +0800 Subject: [PATCH 03/11] =?UTF-8?q?fix:numpy=E6=9E=84=E5=BB=BA=E6=8A=A5?= =?UTF-8?q?=E9=94=99?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- backend/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/backend/requirements.txt b/backend/requirements.txt index e88ef46..baa91db 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -22,4 +22,4 @@ python-dotenv==1.0.0 # 向量数据库和Embedding (长期记忆系统) chromadb>=0.5.0 sentence-transformers>=2.3.1 -numpy==1。26.4 \ No newline at end of file +numpy==1.26.4 \ No newline at end of file From ad951054177751895fb101882a938291019e19e3 Mon Sep 17 00:00:00 2001 From: xiamuceer Date: Tue, 4 Nov 2025 16:28:53 +0800 Subject: [PATCH 04/11] =?UTF-8?q?fix:=E6=9B=B4=E6=96=B0=E4=BE=9D=E8=B5=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- backend/requirements.txt | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/backend/requirements.txt b/backend/requirements.txt index baa91db..11db975 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -12,14 +12,13 @@ pydantic==2.5.3 pydantic-settings==2.1.0 # AI服务 -openai==1.10.0 -anthropic==0.18.0 +openai==2.7.0 +anthropic==0.72.0 # 工具库 -httpx==0.26.0 +httpx==0.28.1 python-dotenv==1.0.0 # 向量数据库和Embedding (长期记忆系统) -chromadb>=0.5.0 -sentence-transformers>=2.3.1 -numpy==1.26.4 \ No newline at end of file +chromadb==1.3.2 +sentence-transformers>=2.3.1 \ No newline at end of file From 29d8d5fb94e411207cfe275418a3da754b184005 Mon Sep 17 00:00:00 2001 From: xiamuceer Date: Tue, 4 Nov 2025 17:27:39 +0800 Subject: [PATCH 05/11] =?UTF-8?q?fix:=E4=BF=AE=E5=A4=8Ddocker=E9=95=9C?= =?UTF-8?q?=E5=83=8F=E6=9E=84=E5=BB=BA=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .dockerignore | 6 ++++++ Dockerfile | 15 ++++++++++++++- backend/app/services/memory_service.py | 2 ++ backend/requirements.txt | 11 ++++++++++- frontend/src/pages/Settings.tsx | 10 +++++----- frontend/src/services/api.ts | 2 +- frontend/src/types/index.ts | 4 ++-- 7 files changed, 40 insertions(+), 10 deletions(-) diff --git a/.dockerignore b/.dockerignore index 7dad3b1..e7c354e 100644 --- a/.dockerignore +++ b/.dockerignore @@ -40,6 +40,12 @@ 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/ + +# 注意：backend/data/models/ 目录需要打包（包含预下载的Embedding模型） +# 所以不要添加到 .dockerignore 中 + # 日志文件 logs/ *.log diff --git a/Dockerfile b/Dockerfile index 60a4447..23877c5 100644 --- a/Dockerfile +++ b/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 @@ -52,6 +55,10 @@ COPY --from=frontend-builder /frontend/dist ./static # 创建必要的目录 RUN mkdir -p /app/data /app/logs +# 复制预下载的Embedding模型（如果存在） +# 这样可以避免首次运行时联网下载约420MB的模型文件 +COPY backend/data/models /app/data/models + # 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/data/models + # 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 diff --git a/backend/app/services/memory_service.py b/backend/app/services/memory_service.py index 566235c..9700827 100644 --- a/backend/app/services/memory_service.py +++ b/backend/app/services/memory_service.py @@ -13,6 +13,8 @@ logger = get_logger(__name__) # 配置离线模式，避免联网检查 os.environ['TRANSFORMERS_OFFLINE'] = '1' os.environ['HF_DATASETS_OFFLINE'] = '1' +os.environ['HF_HUB_OFFLINE'] = '1' # 禁用HuggingFace Hub联网 +os.environ['SENTENCE_TRANSFORMERS_HOME'] = 'data/models' # 设置模型缓存目录 class MemoryService: diff --git a/backend/requirements.txt b/backend/requirements.txt index 11db975..91c0a37 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -19,6 +19,15 @@ anthropic==0.72.0 httpx==0.28.1 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 -sentence-transformers>=2.3.1 \ No newline at end of file + + +# 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 \ No newline at end of file diff --git a/frontend/src/pages/Settings.tsx b/frontend/src/pages/Settings.tsx index 85b319a..4e13690 100644 --- a/frontend/src/pages/Settings.tsx +++ b/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() { } - name="model_name" + name="llm_model" rules={[{ required: true, message: '请输入或选择模型名称' }]} >