0fe6821a89
feat(frontend): add batch processing and review features
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- Add batch processing API and mode
- Add deep read mode for full-text analysis
- Add document selector and switcher components
- Add review page with editorial and methodology assessment
- Add capacity indicator and usage info modal
- Add custom hooks for batch tasks and chat modes
- Update layouts and routing
- Add TypeScript types for chat features
2025-11-16 15:43:39 +08:00
AI Clinical Dev Team
84bf1c86ab
feat: Day 14-17 - Frontend Chat Interface completed
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Frontend:
- Create MessageList component with streaming animation
- Create MessageInput component with @knowledge base support
- Create ModelSelector component (DeepSeek/Qwen/Gemini)
- Implement conversationApi with SSE streaming
- Update AgentChatPage integrate all components
- Add Markdown rendering (react-markdown + remark-gfm)
- Add code highlighting (react-syntax-highlighter)
- Add vite-env.d.ts for environment variables
Features:
- Real-time streaming output with cursor animation
- Markdown and code block rendering
- Model switching (DeepSeek-V3, Qwen3-72B, Gemini Pro)
- @Knowledge base selector (UI ready)
- Auto-scroll to bottom
- Shift+Enter for new line, Enter to send
- Beautiful message bubble design
Build: Frontend build successfully (7.94s, 1.9MB)
New Files:
- components/chat/MessageList.tsx (170 lines)
- components/chat/MessageList.css (150 lines)
- components/chat/MessageInput.tsx (145 lines)
- components/chat/MessageInput.css (60 lines)
- components/chat/ModelSelector.tsx (110 lines)
- components/chat/ModelSelector.css (35 lines)
- api/conversationApi.ts (170 lines)
- src/vite-env.d.ts (9 lines)
Total: ~850 lines of new code
2025-10-10 20:52:30 +08:00
AI Clinical Dev Team
8afff23995
docs: Day 12-13 completion summary and milestone update
2025-10-10 20:33:18 +08:00
AI Clinical Dev Team
864a0b1906
feat: Day 10-11 - Agent Configuration System completed
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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
2025-10-10 20:13:08 +08:00
AI Clinical Dev Team
f7a500bc79
feat(frontend): Day 6 - frontend basic architecture completed
2025-10-10 17:22:37 +08:00
