update:1.新增统一的JSON清洗和重试方法，避免AI响应json格式错误 2.重构提示词模板命名，优化大纲章节初始化提示词 3.移除布冯冗余代码，提高代码复用性 4.优化系统默认写作风格预设提示词和规则
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xiamuceer
@@ -429,199 +429,6 @@ async def remove_organization_member(
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logger.info(f"移除成员成功：{member_id}")
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return {"message": "成员移除成功", "id": member_id}
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@router.post("/generate", response_model=CharacterResponse, summary="AI生成组织")
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async def generate_organization(
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gen_request: OrganizationGenerateRequest,
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http_request: Request,
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db: AsyncSession = Depends(get_db),
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user_ai_service: AIService = Depends(get_user_ai_service)
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):
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"""
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使用AI生成组织设定
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根据用户输入的信息，结合项目的世界观、主题等背景，
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AI会生成一个完整、详细的组织设定。
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生成内容包括：组织名称、类型、特性、背景、目的、势力等级等
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"""
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# 验证用户权限
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user_id = getattr(http_request.state, 'user_id', None)
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project = await verify_project_access(gen_request.project_id, user_id, db)
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try:
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# 获取已存在的角色和组织列表
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existing_chars_result = await db.execute(
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select(Character)
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.where(Character.project_id == gen_request.project_id)
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.order_by(Character.created_at.desc())
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)
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existing_characters = existing_chars_result.scalars().all()
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# 构建现有角色和组织信息摘要
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existing_info = ""
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character_list = []
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organization_list = []
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if existing_characters:
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for c in existing_characters[:10]: # 最多显示10个
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if c.is_organization:
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organization_list.append(f"- {c.name} [{c.organization_type or '组织'}]")
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else:
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character_list.append(f"- {c.name}（{c.role_type or '未知'}）")
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if character_list:
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existing_info += "\n已有角色：\n" + "\n".join(character_list)
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if organization_list:
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existing_info += "\n\n已有组织：\n" + "\n".join(organization_list)
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# 构建项目上下文信息
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project_context = f"""
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项目信息：
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- 书名：{project.title}
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- 主题：{project.theme or '未设定'}
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- 类型：{project.genre or '未设定'}
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- 时间背景：{project.world_time_period or '未设定'}
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- 地理位置：{project.world_location or '未设定'}
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- 氛围基调：{project.world_atmosphere or '未设定'}
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- 世界规则：{project.world_rules or '未设定'}
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{existing_info}
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"""
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# 构建用户输入信息
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user_input = f"""
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用户要求：
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- 组织名称：{gen_request.name or '请AI生成'}
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- 组织类型：{gen_request.organization_type or '请AI根据世界观决定'}
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- 背景设定：{gen_request.background or '无特殊要求'}
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- 其他要求：{gen_request.requirements or '无'}
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"""
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# 获取自定义提示词模板
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template = await PromptService.get_template("SINGLE_ORGANIZATION_GENERATION", user_id, db)
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# 格式化提示词
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prompt = PromptService.format_prompt(
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template,
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project_context=project_context,
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user_input=user_input
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)
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# 调用AI生成组织
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logger.info(f"🎯 开始为项目 {gen_request.project_id} 生成组织")
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logger.info(f" - 组织名：{gen_request.name or 'AI生成'}")
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logger.info(f" - 组织类型：{gen_request.organization_type or 'AI决定'}")
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logger.info(f" - 背景设定：{gen_request.background or '无'}")
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logger.info(f" - AI提供商：{user_ai_service.api_provider}")
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logger.info(f" - AI模型：{user_ai_service.default_model}")
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logger.info(f" - Prompt长度：{len(prompt)} 字符")
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try:
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ai_response = await user_ai_service.generate_text(prompt=prompt)
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logger.info(f"✅ AI响应接收完成")
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except Exception as ai_error:
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logger.error(f"❌ AI服务调用异常：{str(ai_error)}")
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raise HTTPException(
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status_code=500,
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detail=f"AI服务调用失败：{str(ai_error)}"
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)
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# generate_text返回的是字典，需要提取content字段
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ai_content = ai_response.get("content", "") if isinstance(ai_response, dict) else str(ai_response)
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# 检查AI响应
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if not ai_content or not ai_content.strip():
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logger.error("❌ AI返回了空响应")
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raise HTTPException(
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status_code=500,
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detail="AI服务返回空响应。请检查AI配置和网络连接。"
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)
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logger.info(f"📝 开始清理AI响应，长度：{len(ai_content)} 字符")
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# 清理AI响应
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cleaned_response = ai_content.strip()
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if cleaned_response.startswith("```json"):
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cleaned_response = cleaned_response[7:]
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if cleaned_response.startswith("```"):
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cleaned_response = cleaned_response[3:]
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if cleaned_response.endswith("```"):
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cleaned_response = cleaned_response[:-3]
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cleaned_response = cleaned_response.strip()
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logger.info(f" - 清理后长度：{len(cleaned_response)}")
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# 解析AI响应
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logger.info(f"🔍 开始解析JSON")
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try:
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organization_data = json.loads(cleaned_response)
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logger.info(f"✅ JSON解析成功")
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logger.info(f" - 解析后的字段：{list(organization_data.keys())}")
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except json.JSONDecodeError as e:
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logger.error(f"❌ JSON解析失败：{str(e)}")
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raise HTTPException(
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status_code=500,
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detail=f"AI返回的内容无法解析为JSON。错误：{str(e)}"
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)
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# 创建角色记录（组织也是角色的一种）
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character = Character(
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project_id=gen_request.project_id,
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name=organization_data.get("name", gen_request.name or "未命名组织"),
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is_organization=True,
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role_type="supporting", # 组织通常作为配角
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personality=organization_data.get("personality", ""),
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background=organization_data.get("background", ""),
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appearance=organization_data.get("appearance", ""),
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organization_type=organization_data.get("organization_type"),
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organization_purpose=organization_data.get("organization_purpose"),
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organization_members=json.dumps(
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organization_data.get("organization_members", []),
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ensure_ascii=False
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),
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traits=json.dumps(
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organization_data.get("traits", []),
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ensure_ascii=False
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)
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)
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db.add(character)
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await db.flush()
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logger.info(f"✅ 组织角色创建成功：{character.name} (ID: {character.id})")
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# 自动创建Organization详情记录
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organization = Organization(
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character_id=character.id,
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project_id=gen_request.project_id,
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member_count=0,
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power_level=organization_data.get("power_level", 50),
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location=organization_data.get("location"),
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motto=organization_data.get("motto"),
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color=organization_data.get("color")
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)
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db.add(organization)
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await db.flush()
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logger.info(f"✅ 组织详情创建成功：{character.name} (Org ID: {organization.id})")
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# 记录生成历史
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history = GenerationHistory(
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project_id=gen_request.project_id,
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prompt=prompt,
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generated_content=ai_content,
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model=user_ai_service.default_model
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)
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db.add(history)
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await db.commit()
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await db.refresh(character)
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logger.info(f"🎉 成功为项目 {gen_request.project_id} 生成组织: {character.name}")
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return character
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except Exception as e:
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logger.error(f"生成组织失败: {str(e)}")
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raise HTTPException(status_code=500, detail=f"生成组织失败: {str(e)}")
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@router.post("/generate-stream", summary="AI生成组织（流式）")
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async def generate_organization_stream(
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gen_request: OrganizationGenerateRequest,
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@@ -718,19 +525,14 @@ async def generate_organization_stream(
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yield await SSEResponse.send_progress("解析AI响应...", 60)
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# 清理AI响应
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cleaned_response = ai_content.strip()
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if cleaned_response.startswith("```json"):
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cleaned_response = cleaned_response[7:]
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if cleaned_response.startswith("```"):
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cleaned_response = cleaned_response[3:]
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if cleaned_response.endswith("```"):
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cleaned_response = cleaned_response[:-3]
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cleaned_response = cleaned_response.strip()
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# ✅ 使用统一的 JSON 清洗方法
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try:
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cleaned_response = user_ai_service._clean_json_response(ai_content)
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organization_data = json.loads(cleaned_response)
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logger.info(f"✅ 组织JSON解析成功")
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except json.JSONDecodeError as e:
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logger.error(f"❌ 组织JSON解析失败: {e}")
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logger.error(f" 原始响应预览: {ai_content[:200]}")
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yield await SSEResponse.send_error(f"AI返回的内容无法解析为JSON：{str(e)}")
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return
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