feat: Day 10-11 - Agent Configuration System completed
Backend: - Create agents.yaml config file with 12 agents definition - Create Prompt templates for topic-evaluation agent - Implement agentService.ts for loading and managing agent configs - Create agentController.ts with CRUD operations - Create agent routes (GET /agents, /agents/:id, etc.) - Register agent routes in main server Frontend: - Create agentApi.ts service module - Update AgentChatPage to dynamically load agent config from API - Add loading state and error handling - Display agent details (description, category, model) Build: Both frontend and backend build successfully
This commit is contained in:
AI Clinical Dev Team
309
backend/config/agents.yaml
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309
backend/config/agents.yaml
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@@ -0,0 +1,309 @@
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# AI临床研究平台 - 智能体配置文件
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# 版本: 1.0
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# 更新日期: 2025-10-10
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agents:
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# ==================== 选题阶段 ====================
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- id: topic-evaluation
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name: 选题评价智能体
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nameEn: Topic Evaluation Agent
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description: 从创新性、临床价值、科学性和可行性等维度评估研究选题
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category: 选题阶段
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icon: 🎯
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enabled: true
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systemPromptFile: topic_evaluation_system.txt
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userPromptTemplateFile: topic_evaluation_user.txt
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models:
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deepseek-v3:
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temperature: 0.4
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maxTokens: 2000
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topP: 0.9
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qwen3-72b:
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temperature: 0.5
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maxTokens: 2000
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topP: 0.9
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ragEnabled: true
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requiresProject: true
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outputFormat: structured
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tags:
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- 选题
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- 评估
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- 创新性
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- id: scientific-question
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name: 科学问题梳理智能体
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nameEn: Scientific Question Agent
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description: 将模糊的研究想法提炼成清晰、具体、可验证的科学问题
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category: 选题阶段
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icon: 🔬
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enabled: false
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systemPromptFile: scientific_question_system.txt
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userPromptTemplateFile: scientific_question_user.txt
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models:
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deepseek-v3:
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temperature: 0.5
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maxTokens: 1500
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qwen3-72b:
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temperature: 0.6
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maxTokens: 1500
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ragEnabled: true
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requiresProject: true
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outputFormat: structured
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tags:
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- 科学问题
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- PICOS
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- id: picos-construction
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name: PICOS构建智能体
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nameEn: PICOS Construction Agent
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description: 按照PICOS原则结构化定义临床研究的核心要素
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category: 选题阶段
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icon: 📋
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enabled: false
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systemPromptFile: picos_construction_system.txt
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userPromptTemplateFile: picos_construction_user.txt
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models:
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deepseek-v3:
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temperature: 0.3
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maxTokens: 1500
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qwen3-72b:
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temperature: 0.4
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maxTokens: 1500
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ragEnabled: true
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requiresProject: true
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outputFormat: structured
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tags:
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- PICOS
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- 研究设计
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# ==================== 研究设计阶段 ====================
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- id: observation-design
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name: 观察指标设计智能体
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nameEn: Observation Design Agent
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description: 推荐合适的主要、次要及安全性观察指标集
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category: 研究设计阶段
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icon: 📊
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enabled: false
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systemPromptFile: observation_design_system.txt
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userPromptTemplateFile: observation_design_user.txt
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models:
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deepseek-v3:
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temperature: 0.4
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maxTokens: 2000
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qwen3-72b:
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temperature: 0.5
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maxTokens: 2000
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ragEnabled: true
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requiresProject: true
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outputFormat: structured
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tags:
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- 观察指标
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- 终点事件
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- id: crf-development
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name: CRF制定智能体
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nameEn: CRF Development Agent
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description: 自动生成结构化、符合规范的病例报告表（CRF）框架
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category: 研究设计阶段
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icon: 📝
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enabled: false
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systemPromptFile: crf_development_system.txt
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userPromptTemplateFile: crf_development_user.txt
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models:
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deepseek-v3:
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temperature: 0.3
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maxTokens: 3000
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qwen3-72b:
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temperature: 0.4
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maxTokens: 3000
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ragEnabled: true
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requiresProject: true
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outputFormat: document
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tags:
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- CRF
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- 病例报告表
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- id: sample-size
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name: 样本量计算智能体
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nameEn: Sample Size Calculation Agent
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description: 根据研究参数提供科学合理的样本量估算结果
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category: 研究设计阶段
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icon: 🔢
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enabled: false
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systemPromptFile: sample_size_system.txt
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userPromptTemplateFile: sample_size_user.txt
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models:
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deepseek-v3:
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temperature: 0.2
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maxTokens: 1500
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qwen3-72b:
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temperature: 0.3
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maxTokens: 1500
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ragEnabled: false
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requiresProject: true
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outputFormat: structured
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tags:
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- 样本量
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- 统计学
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- id: protocol-writing
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name: 临床研究方案撰写智能体
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nameEn: Protocol Writing Agent
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description: 自动生成结构完整的临床研究设计方案
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category: 研究设计阶段
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icon: 📄
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enabled: false
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systemPromptFile: protocol_writing_system.txt
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userPromptTemplateFile: protocol_writing_user.txt
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models:
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deepseek-v3:
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temperature: 0.5
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maxTokens: 4000
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qwen3-72b:
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temperature: 0.6
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maxTokens: 4000
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ragEnabled: true
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requiresProject: true
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outputFormat: document
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tags:
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- 研究方案
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- 文档生成
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# ==================== 论文撰写阶段 ====================
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- id: paper-polishing
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name: 论文润色智能体
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nameEn: Paper Polishing Agent
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description: 提供专业级的语言润色，修正语法、拼写和表达方式
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category: 论文撰写阶段
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icon: ✨
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enabled: false
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systemPromptFile: paper_polishing_system.txt
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userPromptTemplateFile: paper_polishing_user.txt
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models:
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deepseek-v3:
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temperature: 0.4
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maxTokens: 3000
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qwen3-72b:
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temperature: 0.5
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maxTokens: 3000
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ragEnabled: false
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requiresProject: false
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outputFormat: text
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tags:
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- 润色
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- 语言优化
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- id: paper-translation
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name: 论文翻译智能体
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nameEn: Paper Translation Agent
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description: 提供专业、精准的中英互译服务
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category: 论文撰写阶段
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icon: 🌐
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enabled: false
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systemPromptFile: paper_translation_system.txt
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userPromptTemplateFile: paper_translation_user.txt
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models:
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deepseek-v3:
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temperature: 0.3
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maxTokens: 3000
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qwen3-72b:
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temperature: 0.4
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maxTokens: 3000
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ragEnabled: false
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requiresProject: false
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outputFormat: text
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tags:
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- 翻译
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- 中英互译
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- id: methodology-review
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name: 方法学评审智能体
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nameEn: Methodology Review Agent
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description: 对研究方案或论文进行全面的方法学评审
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category: 论文撰写阶段
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icon: 🔍
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enabled: false
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systemPromptFile: methodology_review_system.txt
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userPromptTemplateFile: methodology_review_user.txt
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models:
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deepseek-v3:
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temperature: 0.5
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maxTokens: 2500
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qwen3-72b:
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temperature: 0.6
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maxTokens: 2500
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ragEnabled: true
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requiresProject: false
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outputFormat: structured
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tags:
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- 方法学评审
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- 质量控制
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- id: journal-methodology-review
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name: 期刊方法学评审智能体
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nameEn: Journal Methodology Review Agent
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description: 模拟期刊审稿人视角，进行方法学挑战
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category: 论文撰写阶段
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icon: 📑
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enabled: false
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systemPromptFile: journal_methodology_review_system.txt
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userPromptTemplateFile: journal_methodology_review_user.txt
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models:
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deepseek-v3:
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temperature: 0.6
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maxTokens: 2500
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qwen3-72b:
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temperature: 0.7
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maxTokens: 2500
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ragEnabled: true
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requiresProject: false
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outputFormat: structured
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tags:
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- 期刊审稿
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- 方法学挑战
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- id: journal-guidelines-review
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name: 期刊稿约评审智能体
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nameEn: Journal Guidelines Review Agent
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description: 检查文章格式、字数、参考文献规范等是否符合投稿要求
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category: 论文撰写阶段
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icon: ✅
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enabled: false
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systemPromptFile: journal_guidelines_review_system.txt
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userPromptTemplateFile: journal_guidelines_review_user.txt
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models:
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deepseek-v3:
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temperature: 0.3
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maxTokens: 2000
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qwen3-72b:
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temperature: 0.4
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maxTokens: 2000
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ragEnabled: false
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requiresProject: false
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outputFormat: structured
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tags:
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- 期刊投稿
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- 格式检查
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# 配置说明:
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# - id: 智能体唯一标识符
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# - name: 显示名称
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# - nameEn: 英文名称
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# - description: 功能描述
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# - category: 所属阶段
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# - icon: 显示图标
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# - enabled: 是否启用（true/false）
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# - systemPromptFile: 系统Prompt文件名
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# - userPromptTemplateFile: 用户Prompt模板文件名
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# - models: 支持的模型及参数配置
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# - temperature: 温度参数（0-1）
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# - maxTokens: 最大token数
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# - topP: Top-p采样参数
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# - ragEnabled: 是否支持知识库检索
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# - requiresProject: 是否需要项目上下文
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# - outputFormat: 输出格式（text/structured/document）
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# - tags: 标签列表
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8
backend/package-lock.json
generated
8
backend/package-lock.json
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@@ -17,6 +17,7 @@
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"prisma": "^6.17.0"
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},
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"devDependencies": {
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"@types/js-yaml": "^4.0.9",
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"@types/node": "^24.7.1",
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"nodemon": "^3.1.10",
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"pino-pretty": "^13.1.1",
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@@ -784,6 +785,13 @@
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"dev": true,
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"license": "MIT"
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},
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"node_modules/@types/js-yaml": {
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"version": "4.0.9",
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"resolved": "https://registry.npmmirror.com/@types/js-yaml/-/js-yaml-4.0.9.tgz",
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"integrity": "sha512-k4MGaQl5TGo/iipqb2UDG2UwjXziSWkh0uysQelTlJpX1qGlpUZYm8PnO4DxG1qBomtJUdYJ6qR6xdIah10JLg==",
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"dev": true,
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"license": "MIT"
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},
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"node_modules/@types/node": {
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"version": "24.7.1",
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"resolved": "https://registry.npmmirror.com/@types/node/-/node-24.7.1.tgz",
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@@ -30,6 +30,7 @@
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"prisma": "^6.17.0"
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},
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"devDependencies": {
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"@types/js-yaml": "^4.0.9",
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"@types/node": "^24.7.1",
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"nodemon": "^3.1.10",
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"pino-pretty": "^13.1.1",
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119
backend/prompts/topic_evaluation_system.txt
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119
backend/prompts/topic_evaluation_system.txt
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你是一位经验丰富的临床研究专家，擅长评估研究选题的质量和可行性。你的任务是从多个维度对研究选题进行专业、客观的评价，并给出建设性的改进建议。
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## 你的职责
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1. **全面评估**：从创新性、临床价值、科学性和可行性四个维度评价研究选题
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2. **指出问题**：客观指出选题存在的不足和潜在风险
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3. **提供建议**：给出具体的改进方向和优化思路
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4. **评分量化**：对每个维度给出1-10分的评分，并说明理由
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## 评价维度
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### 1. 创新性（Innovation）
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- 研究问题是否新颖？是否填补了现有知识的空白？
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- 研究方法或角度是否具有创新性？
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- 与已有研究相比有哪些突破？
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- 评分标准：
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* 9-10分：开创性研究，填补重大空白
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* 7-8分：显著创新，有新的发现或方法
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* 5-6分：一定创新性，但突破有限
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* 3-4分：创新性较弱，主要是验证性研究
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* 1-2分：缺乏创新，重复已有研究
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### 2. 临床价值（Clinical Value）
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- 研究结果对临床实践有何指导意义？
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- 能否改善患者预后或生活质量？
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- 是否解决了临床中的实际问题？
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- 潜在的临床应用前景如何？
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- 评分标准：
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* 9-10分：重大临床价值，可能改变临床实践
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* 7-8分：明确临床价值，有较大应用潜力
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* 5-6分：一定临床价值，但应用范围有限
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||||
* 3-4分：临床价值不明显
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* 1-2分：临床价值很低或无价值
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### 3. 科学性（Scientific Rigor）
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- 研究假设是否合理？理论基础是否扎实？
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- 研究设计是否科学？方法学是否严谨？
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- 样本量是否合理？统计方法是否恰当？
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- 是否考虑了混杂因素和偏倚控制？
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||||
- 评分标准：
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||||
* 9-10分：研究设计严谨，方法学无懈可击
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* 7-8分：研究设计合理，方法学较为严谨
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* 5-6分：研究设计基本合理，但存在一些缺陷
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||||
* 3-4分：研究设计有明显缺陷，方法学有问题
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* 1-2分：研究设计不科学，方法学严重缺陷
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||||
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### 4. 可行性（Feasibility）
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- 研究所需资源（时间、经费、设备、人力）是否可获得？
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- 样本招募是否困难？数据收集是否可行？
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||||
- 伦理审查是否容易通过？
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||||
- 研究周期是否合理？
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- 是否存在重大技术或操作障碍？
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- 评分标准：
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* 9-10分：可行性非常高，资源充足，易于实施
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* 7-8分：可行性较高，资源基本可获得
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||||
* 5-6分：可行性一般，存在一些困难但可克服
|
||||
* 3-4分：可行性较低，存在较多障碍
|
||||
* 1-2分：可行性很低，难以实施
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## 评价流程
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||||
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||||
1. **理解选题**：仔细阅读用户提供的研究选题描述，包括研究背景、目的、方法等
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||||
2. **维度评分**：对每个维度进行1-10分的评分，并详细说明理由
|
||||
3. **综合评价**：给出选题的总体评价和优先级建议
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||||
4. **改进建议**：针对不足之处，给出3-5条具体的改进建议
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||||
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||||
## 输出格式
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||||
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||||
你的评价应该包含以下结构：
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||||
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||||
### 📊 评分总览
|
||||
- 创新性：X/10分
|
||||
- 临床价值：X/10分
|
||||
- 科学性：X/10分
|
||||
- 可行性：X/10分
|
||||
- **综合得分：XX/40分**
|
||||
|
||||
### 🎯 详细评价
|
||||
|
||||
#### 1. 创新性评价（X/10分）
|
||||
[详细说明为什么给出这个分数，指出创新点或不足]
|
||||
|
||||
#### 2. 临床价值评价（X/10分）
|
||||
[详细说明临床价值体现在哪里，或为什么价值有限]
|
||||
|
||||
#### 3. 科学性评价（X/10分）
|
||||
[分析研究设计和方法学的优缺点]
|
||||
|
||||
#### 4. 可行性评价（X/10分）
|
||||
[分析实施难度和资源需求]
|
||||
|
||||
### 💡 改进建议
|
||||
|
||||
1. **建议一**：[具体的改进方向]
|
||||
2. **建议二**：[具体的改进方向]
|
||||
3. **建议三**：[具体的改进方向]
|
||||
[如有必要，可以提供更多建议]
|
||||
|
||||
### ✅ 总体评价
|
||||
|
||||
[用1-2段话总结选题的整体质量，给出是否建议推进的明确意见，以及优先级建议（高/中/低）]
|
||||
|
||||
## 注意事项
|
||||
|
||||
- 评价要**客观公正**，既要肯定优点，也要指出不足
|
||||
- 评分要**有理有据**，不能主观臆断
|
||||
- 建议要**具体可行**，不能空泛
|
||||
- 语气要**专业友好**，鼓励研究者改进
|
||||
- 如果用户提供的信息不足，要明确指出需要补充哪些信息
|
||||
- 如果选题存在严重缺陷，要明确指出，但也要给出挽救方案（如果有）
|
||||
|
||||
## 特殊情况处理
|
||||
|
||||
- 如果用户提供的选题描述过于简略，要求其补充关键信息（研究目的、方法、预期结果等）
|
||||
- 如果选题涉及伦理敏感问题，要特别提醒伦理审查的注意事项
|
||||
- 如果选题超出你的专业范围（如纯基础研究），要说明你的评价可能有局限性
|
||||
|
||||
记住：你的评价将直接影响研究者的决策，因此要认真负责、专业严谨。
|
||||
|
||||
15
backend/prompts/topic_evaluation_user.txt
Normal file
15
backend/prompts/topic_evaluation_user.txt
Normal file
@@ -0,0 +1,15 @@
|
||||
请对以下研究选题进行评价：
|
||||
|
||||
## 项目背景
|
||||
{{projectBackground}}
|
||||
|
||||
## 研究选题
|
||||
{{userInput}}
|
||||
|
||||
{{#if knowledgeBaseContext}}
|
||||
## 参考文献（来自知识库）
|
||||
{{knowledgeBaseContext}}
|
||||
{{/if}}
|
||||
|
||||
请根据创新性、临床价值、科学性和可行性四个维度，对上述选题进行全面评价，并给出改进建议。
|
||||
|
||||
215
backend/src/controllers/agentController.ts
Normal file
215
backend/src/controllers/agentController.ts
Normal file
@@ -0,0 +1,215 @@
|
||||
import { FastifyRequest, FastifyReply } from 'fastify';
|
||||
import { agentService } from '../services/agentService.js';
|
||||
|
||||
interface AgentParams {
|
||||
id: string;
|
||||
}
|
||||
|
||||
interface RenderPromptBody {
|
||||
projectBackground?: string;
|
||||
userInput: string;
|
||||
knowledgeBaseContext?: string;
|
||||
}
|
||||
|
||||
class AgentController {
|
||||
// 获取所有智能体列表
|
||||
async getAllAgents(request: FastifyRequest, reply: FastifyReply) {
|
||||
try {
|
||||
const agents = agentService.getAllAgents();
|
||||
|
||||
return reply.code(200).send({
|
||||
success: true,
|
||||
data: agents,
|
||||
});
|
||||
} catch (error) {
|
||||
request.log.error(error);
|
||||
return reply.code(500).send({
|
||||
success: false,
|
||||
message: '获取智能体列表失败',
|
||||
error: error instanceof Error ? error.message : 'Unknown error',
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// 获取启用的智能体列表
|
||||
async getEnabledAgents(request: FastifyRequest, reply: FastifyReply) {
|
||||
try {
|
||||
const agents = agentService.getEnabledAgents();
|
||||
|
||||
return reply.code(200).send({
|
||||
success: true,
|
||||
data: agents,
|
||||
});
|
||||
} catch (error) {
|
||||
request.log.error(error);
|
||||
return reply.code(500).send({
|
||||
success: false,
|
||||
message: '获取智能体列表失败',
|
||||
error: error instanceof Error ? error.message : 'Unknown error',
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// 获取单个智能体详情
|
||||
async getAgentById(
|
||||
request: FastifyRequest<{ Params: AgentParams }>,
|
||||
reply: FastifyReply
|
||||
) {
|
||||
try {
|
||||
const { id } = request.params;
|
||||
const agent = agentService.getAgentById(id);
|
||||
|
||||
if (!agent) {
|
||||
return reply.code(404).send({
|
||||
success: false,
|
||||
message: '智能体不存在',
|
||||
});
|
||||
}
|
||||
|
||||
return reply.code(200).send({
|
||||
success: true,
|
||||
data: agent,
|
||||
});
|
||||
} catch (error) {
|
||||
request.log.error(error);
|
||||
return reply.code(500).send({
|
||||
success: false,
|
||||
message: '获取智能体详情失败',
|
||||
error: error instanceof Error ? error.message : 'Unknown error',
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// 获取智能体的系统Prompt
|
||||
async getSystemPrompt(
|
||||
request: FastifyRequest<{ Params: AgentParams }>,
|
||||
reply: FastifyReply
|
||||
) {
|
||||
try {
|
||||
const { id } = request.params;
|
||||
|
||||
if (!agentService.agentExists(id)) {
|
||||
return reply.code(404).send({
|
||||
success: false,
|
||||
message: '智能体不存在',
|
||||
});
|
||||
}
|
||||
|
||||
const systemPrompt = agentService.getSystemPrompt(id);
|
||||
|
||||
return reply.code(200).send({
|
||||
success: true,
|
||||
data: {
|
||||
agentId: id,
|
||||
systemPrompt,
|
||||
},
|
||||
});
|
||||
} catch (error) {
|
||||
request.log.error(error);
|
||||
return reply.code(500).send({
|
||||
success: false,
|
||||
message: '获取系统Prompt失败',
|
||||
error: error instanceof Error ? error.message : 'Unknown error',
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// 渲染用户Prompt（用于预览或调试）
|
||||
async renderPrompt(
|
||||
request: FastifyRequest<{ Params: AgentParams }>,
|
||||
reply: FastifyReply
|
||||
) {
|
||||
try {
|
||||
const { id } = request.params;
|
||||
const body = request.body as RenderPromptBody;
|
||||
|
||||
if (!agentService.agentExists(id)) {
|
||||
return reply.code(404).send({
|
||||
success: false,
|
||||
message: '智能体不存在',
|
||||
});
|
||||
}
|
||||
|
||||
if (!body.userInput) {
|
||||
return reply.code(400).send({
|
||||
success: false,
|
||||
message: 'userInput为必填项',
|
||||
});
|
||||
}
|
||||
|
||||
const renderedPrompt = agentService.renderUserPrompt(id, {
|
||||
projectBackground: body.projectBackground,
|
||||
userInput: body.userInput,
|
||||
knowledgeBaseContext: body.knowledgeBaseContext,
|
||||
});
|
||||
|
||||
return reply.code(200).send({
|
||||
success: true,
|
||||
data: {
|
||||
agentId: id,
|
||||
renderedPrompt,
|
||||
},
|
||||
});
|
||||
} catch (error) {
|
||||
request.log.error(error);
|
||||
return reply.code(500).send({
|
||||
success: false,
|
||||
message: '渲染Prompt失败',
|
||||
error: error instanceof Error ? error.message : 'Unknown error',
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// 根据分类获取智能体
|
||||
async getAgentsByCategory(
|
||||
request: FastifyRequest<{ Querystring: { category: string } }>,
|
||||
reply: FastifyReply
|
||||
) {
|
||||
try {
|
||||
const { category } = request.query;
|
||||
|
||||
if (!category) {
|
||||
return reply.code(400).send({
|
||||
success: false,
|
||||
message: 'category参数为必填项',
|
||||
});
|
||||
}
|
||||
|
||||
const agents = agentService.getAgentsByCategory(category);
|
||||
|
||||
return reply.code(200).send({
|
||||
success: true,
|
||||
data: agents,
|
||||
});
|
||||
} catch (error) {
|
||||
request.log.error(error);
|
||||
return reply.code(500).send({
|
||||
success: false,
|
||||
message: '获取智能体列表失败',
|
||||
error: error instanceof Error ? error.message : 'Unknown error',
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// 重新加载配置（管理员功能）
|
||||
async reloadConfig(request: FastifyRequest, reply: FastifyReply) {
|
||||
try {
|
||||
agentService.reloadConfig();
|
||||
|
||||
return reply.code(200).send({
|
||||
success: true,
|
||||
message: '智能体配置已重新加载',
|
||||
});
|
||||
} catch (error) {
|
||||
request.log.error(error);
|
||||
return reply.code(500).send({
|
||||
success: false,
|
||||
message: '重新加载配置失败',
|
||||
error: error instanceof Error ? error.message : 'Unknown error',
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
export const agentController = new AgentController();
|
||||
|
||||
@@ -3,6 +3,7 @@ import cors from '@fastify/cors';
|
||||
import { config } from './config/env.js';
|
||||
import { testDatabaseConnection, prisma } from './config/database.js';
|
||||
import { projectRoutes } from './routes/projects.js';
|
||||
import { agentRoutes } from './routes/agents.js';
|
||||
|
||||
const fastify = Fastify({
|
||||
logger: {
|
||||
@@ -55,6 +56,9 @@ fastify.get('/api/v1', async () => {
|
||||
// 注册项目管理路由
|
||||
await fastify.register(projectRoutes, { prefix: '/api/v1' });
|
||||
|
||||
// 注册智能体管理路由
|
||||
await fastify.register(agentRoutes, { prefix: '/api/v1' });
|
||||
|
||||
// 启动服务器
|
||||
const start = async () => {
|
||||
try {
|
||||
|
||||
56
backend/src/routes/agents.ts
Normal file
56
backend/src/routes/agents.ts
Normal file
@@ -0,0 +1,56 @@
|
||||
import { FastifyInstance, FastifyRequest, FastifyReply } from 'fastify';
|
||||
import { agentController } from '../controllers/agentController.js';
|
||||
|
||||
interface AgentParams {
|
||||
id: string;
|
||||
}
|
||||
|
||||
export async function agentRoutes(fastify: FastifyInstance) {
|
||||
// 获取所有智能体列表
|
||||
fastify.get('/agents', async (request: FastifyRequest, reply: FastifyReply) => {
|
||||
return agentController.getAllAgents(request, reply);
|
||||
});
|
||||
|
||||
// 获取启用的智能体列表
|
||||
fastify.get('/agents/enabled', async (request: FastifyRequest, reply: FastifyReply) => {
|
||||
return agentController.getEnabledAgents(request, reply);
|
||||
});
|
||||
|
||||
// 根据分类获取智能体
|
||||
fastify.get<{ Querystring: { category: string } }>(
|
||||
'/agents/by-category',
|
||||
async (request: FastifyRequest<{ Querystring: { category: string } }>, reply: FastifyReply) => {
|
||||
return agentController.getAgentsByCategory(request, reply);
|
||||
}
|
||||
);
|
||||
|
||||
// 获取单个智能体详情
|
||||
fastify.get<{ Params: AgentParams }>(
|
||||
'/agents/:id',
|
||||
async (request: FastifyRequest<{ Params: AgentParams }>, reply: FastifyReply) => {
|
||||
return agentController.getAgentById(request, reply);
|
||||
}
|
||||
);
|
||||
|
||||
// 获取智能体的系统Prompt
|
||||
fastify.get<{ Params: AgentParams }>(
|
||||
'/agents/:id/system-prompt',
|
||||
async (request: FastifyRequest<{ Params: AgentParams }>, reply: FastifyReply) => {
|
||||
return agentController.getSystemPrompt(request, reply);
|
||||
}
|
||||
);
|
||||
|
||||
// 渲染用户Prompt（预览）
|
||||
fastify.post<{ Params: AgentParams }>(
|
||||
'/agents/:id/render-prompt',
|
||||
async (request: FastifyRequest<{ Params: AgentParams }>, reply: FastifyReply) => {
|
||||
return agentController.renderPrompt(request, reply);
|
||||
}
|
||||
);
|
||||
|
||||
// 重新加载配置（管理员功能）
|
||||
fastify.post('/agents/reload-config', async (request: FastifyRequest, reply: FastifyReply) => {
|
||||
return agentController.reloadConfig(request, reply);
|
||||
});
|
||||
}
|
||||
|
||||
212
backend/src/services/agentService.ts
Normal file
212
backend/src/services/agentService.ts
Normal file
@@ -0,0 +1,212 @@
|
||||
import fs from 'fs';
|
||||
import path from 'path';
|
||||
import yaml from 'js-yaml';
|
||||
import { fileURLToPath } from 'url';
|
||||
|
||||
const __filename = fileURLToPath(import.meta.url);
|
||||
const __dirname = path.dirname(__filename);
|
||||
|
||||
// 智能体配置接口
|
||||
export interface AgentConfig {
|
||||
id: string;
|
||||
name: string;
|
||||
nameEn: string;
|
||||
description: string;
|
||||
category: string;
|
||||
icon: string;
|
||||
enabled: boolean;
|
||||
systemPromptFile: string;
|
||||
userPromptTemplateFile: string;
|
||||
models: {
|
||||
[modelName: string]: {
|
||||
temperature: number;
|
||||
maxTokens: number;
|
||||
topP?: number;
|
||||
};
|
||||
};
|
||||
ragEnabled: boolean;
|
||||
requiresProject: boolean;
|
||||
outputFormat: 'text' | 'structured' | 'document';
|
||||
tags: string[];
|
||||
}
|
||||
|
||||
// 配置文件根结构
|
||||
interface AgentsConfigFile {
|
||||
agents: AgentConfig[];
|
||||
}
|
||||
|
||||
class AgentService {
|
||||
private agents: Map<string, AgentConfig> = new Map();
|
||||
private prompts: Map<string, string> = new Map();
|
||||
private configPath: string;
|
||||
private promptsPath: string;
|
||||
|
||||
constructor() {
|
||||
// 配置文件路径
|
||||
this.configPath = path.resolve(__dirname, '../../config/agents.yaml');
|
||||
this.promptsPath = path.resolve(__dirname, '../../prompts');
|
||||
|
||||
// 初始化加载配置
|
||||
this.loadAgents();
|
||||
}
|
||||
|
||||
// 加载智能体配置
|
||||
private loadAgents() {
|
||||
try {
|
||||
const fileContents = fs.readFileSync(this.configPath, 'utf8');
|
||||
const config = yaml.load(fileContents) as AgentsConfigFile;
|
||||
|
||||
if (!config || !config.agents) {
|
||||
throw new Error('Invalid agents configuration file');
|
||||
}
|
||||
|
||||
// 存储到Map中
|
||||
config.agents.forEach((agent) => {
|
||||
this.agents.set(agent.id, agent);
|
||||
});
|
||||
|
||||
console.log(`✅ Loaded ${this.agents.size} agent configurations`);
|
||||
} catch (error) {
|
||||
console.error('❌ Failed to load agent configurations:', error);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
// 加载Prompt模板
|
||||
private loadPrompt(filename: string): string {
|
||||
const cacheKey = filename;
|
||||
|
||||
// 检查缓存
|
||||
if (this.prompts.has(cacheKey)) {
|
||||
return this.prompts.get(cacheKey)!;
|
||||
}
|
||||
|
||||
try {
|
||||
const promptPath = path.join(this.promptsPath, filename);
|
||||
const content = fs.readFileSync(promptPath, 'utf8');
|
||||
|
||||
// 缓存到内存
|
||||
this.prompts.set(cacheKey, content);
|
||||
|
||||
return content;
|
||||
} catch (error) {
|
||||
console.error(`❌ Failed to load prompt file: ${filename}`, error);
|
||||
throw new Error(`Prompt file not found: ${filename}`);
|
||||
}
|
||||
}
|
||||
|
||||
// 获取所有智能体列表
|
||||
getAllAgents(): AgentConfig[] {
|
||||
return Array.from(this.agents.values());
|
||||
}
|
||||
|
||||
// 获取启用的智能体列表
|
||||
getEnabledAgents(): AgentConfig[] {
|
||||
return Array.from(this.agents.values()).filter((agent) => agent.enabled);
|
||||
}
|
||||
|
||||
// 根据ID获取智能体配置
|
||||
getAgentById(agentId: string): AgentConfig | null {
|
||||
return this.agents.get(agentId) || null;
|
||||
}
|
||||
|
||||
// 根据分类获取智能体列表
|
||||
getAgentsByCategory(category: string): AgentConfig[] {
|
||||
return Array.from(this.agents.values()).filter(
|
||||
(agent) => agent.category === category
|
||||
);
|
||||
}
|
||||
|
||||
// 获取智能体的系统Prompt
|
||||
getSystemPrompt(agentId: string): string {
|
||||
const agent = this.getAgentById(agentId);
|
||||
if (!agent) {
|
||||
throw new Error(`Agent not found: ${agentId}`);
|
||||
}
|
||||
|
||||
return this.loadPrompt(agent.systemPromptFile);
|
||||
}
|
||||
|
||||
// 获取智能体的用户Prompt模板
|
||||
getUserPromptTemplate(agentId: string): string {
|
||||
const agent = this.getAgentById(agentId);
|
||||
if (!agent) {
|
||||
throw new Error(`Agent not found: ${agentId}`);
|
||||
}
|
||||
|
||||
return this.loadPrompt(agent.userPromptTemplateFile);
|
||||
}
|
||||
|
||||
// 渲染用户Prompt（替换模板变量）
|
||||
renderUserPrompt(
|
||||
agentId: string,
|
||||
variables: {
|
||||
projectBackground?: string;
|
||||
userInput: string;
|
||||
knowledgeBaseContext?: string;
|
||||
}
|
||||
): string {
|
||||
const template = this.getUserPromptTemplate(agentId);
|
||||
|
||||
let rendered = template;
|
||||
|
||||
// 替换变量
|
||||
rendered = rendered.replace(/\{\{projectBackground\}\}/g, variables.projectBackground || '未提供项目背景');
|
||||
rendered = rendered.replace(/\{\{userInput\}\}/g, variables.userInput);
|
||||
|
||||
// 处理条件块（知识库上下文）
|
||||
if (variables.knowledgeBaseContext) {
|
||||
rendered = rendered.replace(
|
||||
/\{\{#if knowledgeBaseContext\}\}([\s\S]*?)\{\{\/if\}\}/g,
|
||||
'$1'
|
||||
);
|
||||
rendered = rendered.replace(/\{\{knowledgeBaseContext\}\}/g, variables.knowledgeBaseContext);
|
||||
} else {
|
||||
// 移除条件块
|
||||
rendered = rendered.replace(
|
||||
/\{\{#if knowledgeBaseContext\}\}[\s\S]*?\{\{\/if\}\}/g,
|
||||
''
|
||||
);
|
||||
}
|
||||
|
||||
return rendered.trim();
|
||||
}
|
||||
|
||||
// 检查智能体是否存在
|
||||
agentExists(agentId: string): boolean {
|
||||
return this.agents.has(agentId);
|
||||
}
|
||||
|
||||
// 检查智能体是否启用
|
||||
isAgentEnabled(agentId: string): boolean {
|
||||
const agent = this.getAgentById(agentId);
|
||||
return agent ? agent.enabled : false;
|
||||
}
|
||||
|
||||
// 获取智能体的模型配置
|
||||
getModelConfig(agentId: string, modelName: string) {
|
||||
const agent = this.getAgentById(agentId);
|
||||
if (!agent) {
|
||||
throw new Error(`Agent not found: ${agentId}`);
|
||||
}
|
||||
|
||||
const modelConfig = agent.models[modelName];
|
||||
if (!modelConfig) {
|
||||
throw new Error(`Model ${modelName} not configured for agent ${agentId}`);
|
||||
}
|
||||
|
||||
return modelConfig;
|
||||
}
|
||||
|
||||
// 重新加载配置（热更新）
|
||||
reloadConfig() {
|
||||
this.agents.clear();
|
||||
this.prompts.clear();
|
||||
this.loadAgents();
|
||||
console.log('✅ Agent configurations reloaded');
|
||||
}
|
||||
}
|
||||
|
||||
// 导出单例
|
||||
export const agentService = new AgentService();
|
||||
|
||||
10
frontend/package-lock.json
generated
10
frontend/package-lock.json
generated
@@ -9,8 +9,10 @@
|
||||
"version": "1.0.0",
|
||||
"dependencies": {
|
||||
"@ant-design/icons": "^5.5.2",
|
||||
"@types/js-yaml": "^4.0.9",
|
||||
"antd": "^5.22.5",
|
||||
"axios": "^1.7.9",
|
||||
"js-yaml": "^4.1.0",
|
||||
"react": "^18.3.1",
|
||||
"react-dom": "^18.3.1",
|
||||
"react-router-dom": "^6.28.0"
|
||||
@@ -1788,6 +1790,12 @@
|
||||
"dev": true,
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/@types/js-yaml": {
|
||||
"version": "4.0.9",
|
||||
"resolved": "https://registry.npmmirror.com/@types/js-yaml/-/js-yaml-4.0.9.tgz",
|
||||
"integrity": "sha512-k4MGaQl5TGo/iipqb2UDG2UwjXziSWkh0uysQelTlJpX1qGlpUZYm8PnO4DxG1qBomtJUdYJ6qR6xdIah10JLg==",
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/@types/json-schema": {
|
||||
"version": "7.0.15",
|
||||
"resolved": "https://registry.npmmirror.com/@types/json-schema/-/json-schema-7.0.15.tgz",
|
||||
@@ -2245,7 +2253,6 @@
|
||||
"version": "2.0.1",
|
||||
"resolved": "https://registry.npmmirror.com/argparse/-/argparse-2.0.1.tgz",
|
||||
"integrity": "sha512-8+9WqebbFzpX9OR+Wa6O29asIogeRMzcGtAINdpMHHyAg10f05aSFVBbcEqGf/PXw1EjAZ+q2/bEBg3DvurK3Q==",
|
||||
"dev": true,
|
||||
"license": "Python-2.0"
|
||||
},
|
||||
"node_modules/asynckit": {
|
||||
@@ -3577,7 +3584,6 @@
|
||||
"version": "4.1.0",
|
||||
"resolved": "https://registry.npmmirror.com/js-yaml/-/js-yaml-4.1.0.tgz",
|
||||
"integrity": "sha512-wpxZs9NoxZaJESJGIZTyDEaYpl0FKSA+FB9aJiyemKhMwkxQg63h4T1KJgUGHpTqPDNRcmmYLugrRjJlBtWvRA==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"argparse": "^2.0.1"
|
||||
|
||||
@@ -10,12 +10,14 @@
|
||||
"lint": "eslint . --ext ts,tsx --report-unused-disable-directives --max-warnings 0"
|
||||
},
|
||||
"dependencies": {
|
||||
"react": "^18.3.1",
|
||||
"react-dom": "^18.3.1",
|
||||
"react-router-dom": "^6.28.0",
|
||||
"@ant-design/icons": "^5.5.2",
|
||||
"@types/js-yaml": "^4.0.9",
|
||||
"antd": "^5.22.5",
|
||||
"axios": "^1.7.9",
|
||||
"@ant-design/icons": "^5.5.2"
|
||||
"js-yaml": "^4.1.0",
|
||||
"react": "^18.3.1",
|
||||
"react-dom": "^18.3.1",
|
||||
"react-router-dom": "^6.28.0"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@types/react": "^18.3.18",
|
||||
@@ -33,4 +35,3 @@
|
||||
"vite": "^6.0.7"
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
77
frontend/src/api/agentApi.ts
Normal file
77
frontend/src/api/agentApi.ts
Normal file
@@ -0,0 +1,77 @@
|
||||
import request from './request';
|
||||
|
||||
export interface AgentConfig {
|
||||
id: string;
|
||||
name: string;
|
||||
nameEn: string;
|
||||
description: string;
|
||||
category: string;
|
||||
icon: string;
|
||||
enabled: boolean;
|
||||
systemPromptFile: string;
|
||||
userPromptTemplateFile: string;
|
||||
models: {
|
||||
[modelName: string]: {
|
||||
temperature: number;
|
||||
maxTokens: number;
|
||||
topP?: number;
|
||||
};
|
||||
};
|
||||
ragEnabled: boolean;
|
||||
requiresProject: boolean;
|
||||
outputFormat: 'text' | 'structured' | 'document';
|
||||
tags: string[];
|
||||
}
|
||||
|
||||
export interface ApiResponse<T = any> {
|
||||
success: boolean;
|
||||
data?: T;
|
||||
message?: string;
|
||||
error?: string;
|
||||
}
|
||||
|
||||
export const agentApi = {
|
||||
// 获取所有智能体列表
|
||||
getAll: async (): Promise<ApiResponse<AgentConfig[]>> => {
|
||||
const response = await request.get('/agents');
|
||||
return response.data;
|
||||
},
|
||||
|
||||
// 获取启用的智能体列表
|
||||
getEnabled: async (): Promise<ApiResponse<AgentConfig[]>> => {
|
||||
const response = await request.get('/agents/enabled');
|
||||
return response.data;
|
||||
},
|
||||
|
||||
// 获取单个智能体详情
|
||||
getById: async (id: string): Promise<ApiResponse<AgentConfig>> => {
|
||||
const response = await request.get(`/agents/${id}`);
|
||||
return response.data;
|
||||
},
|
||||
|
||||
// 根据分类获取智能体
|
||||
getByCategory: async (category: string): Promise<ApiResponse<AgentConfig[]>> => {
|
||||
const response = await request.get(`/agents/by-category?category=${encodeURIComponent(category)}`);
|
||||
return response.data;
|
||||
},
|
||||
|
||||
// 获取智能体的系统Prompt
|
||||
getSystemPrompt: async (id: string): Promise<ApiResponse<{ agentId: string; systemPrompt: string }>> => {
|
||||
const response = await request.get(`/agents/${id}/system-prompt`);
|
||||
return response.data;
|
||||
},
|
||||
|
||||
// 渲染用户Prompt
|
||||
renderPrompt: async (
|
||||
id: string,
|
||||
data: {
|
||||
projectBackground?: string;
|
||||
userInput: string;
|
||||
knowledgeBaseContext?: string;
|
||||
}
|
||||
): Promise<ApiResponse<{ agentId: string; renderedPrompt: string }>> => {
|
||||
const response = await request.post(`/agents/${id}/render-prompt`, data);
|
||||
return response.data;
|
||||
},
|
||||
};
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
import { useParams } from 'react-router-dom'
|
||||
import { Card, Typography, Input, Button, Space, Select, Upload, Tag, Alert, Divider } from 'antd'
|
||||
import { useState, useEffect } from 'react'
|
||||
import { Card, Typography, Input, Button, Space, Select, Upload, Tag, Alert, Divider, Spin } from 'antd'
|
||||
import {
|
||||
SendOutlined,
|
||||
PaperClipOutlined,
|
||||
@@ -7,20 +8,57 @@ import {
|
||||
FolderOpenOutlined,
|
||||
SyncOutlined,
|
||||
} from '@ant-design/icons'
|
||||
import { AGENTS } from '../layouts/MainLayout'
|
||||
import { agentApi, type AgentConfig } from '../api/agentApi'
|
||||
import { message } from 'antd'
|
||||
|
||||
const { Title, Paragraph } = Typography
|
||||
const { TextArea } = Input
|
||||
|
||||
const AgentChatPage = () => {
|
||||
const { agentId } = useParams()
|
||||
const agent = AGENTS.find((a) => a.id === agentId)
|
||||
const [agent, setAgent] = useState<AgentConfig | null>(null)
|
||||
const [loading, setLoading] = useState(true)
|
||||
const [error, setError] = useState<string | null>(null)
|
||||
|
||||
if (!agent) {
|
||||
// 加载智能体配置
|
||||
useEffect(() => {
|
||||
const fetchAgent = async () => {
|
||||
if (!agentId) return
|
||||
|
||||
try {
|
||||
setLoading(true)
|
||||
const response = await agentApi.getById(agentId)
|
||||
|
||||
if (response.success && response.data) {
|
||||
setAgent(response.data)
|
||||
} else {
|
||||
setError(response.message || '智能体不存在')
|
||||
}
|
||||
} catch (err) {
|
||||
console.error('Failed to load agent:', err)
|
||||
setError('加载智能体配置失败')
|
||||
message.error('加载智能体配置失败')
|
||||
} finally {
|
||||
setLoading(false)
|
||||
}
|
||||
}
|
||||
|
||||
fetchAgent()
|
||||
}, [agentId])
|
||||
|
||||
if (loading) {
|
||||
return (
|
||||
<div style={{ textAlign: 'center', padding: '100px 0' }}>
|
||||
<Spin size="large" tip="加载智能体配置中..." />
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
if (error || !agent) {
|
||||
return (
|
||||
<Alert
|
||||
message="智能体不存在"
|
||||
description="请从首页选择一个智能体"
|
||||
description={error || "请从首页选择一个智能体"}
|
||||
type="error"
|
||||
showIcon
|
||||
/>
|
||||
@@ -49,7 +87,10 @@ const AgentChatPage = () => {
|
||||
{agent.name}
|
||||
</Title>
|
||||
<Paragraph type="secondary" style={{ marginBottom: 0 }}>
|
||||
当前模型：DeepSeek-V3
|
||||
{agent.description}
|
||||
</Paragraph>
|
||||
<Paragraph type="secondary" style={{ marginBottom: 0, fontSize: 12 }}>
|
||||
当前模型：DeepSeek-V3 | 分类：{agent.category}
|
||||
</Paragraph>
|
||||
</div>
|
||||
</Space>
|
||||
|
||||
Reference in New Issue
Block a user