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Introduce user_id-scoped persistence and history retrieval so health analysis runs are stored and queryable through API endpoints.
…eflect Implement diet recommendation APIs with staged Nutritionist/Coach/Habit outputs, reflect feedback loop, and tool-backed planning for executable meal suggestions.
…runs Add reusable observability view builders to summarize run timelines, errors, and trace metadata for diet and health analysis inspection endpoints.
- 新增 rag 模块：embedding、Milvus 存储、统一 retrieve、索引写入 - RAG 配置与独立 embedding 环境变量；Milvus 不可用时回退 SQL 记忆 - 饮食/体检 Agent 与流水线接入检索摘要；输出 rag_debug 供 observability - 新增 reindex_milvus 脚本；更新依赖 Made-with: Cursor
- API：以 today_food_log_text 为核心；精简饮食请求上下文字段 - 流水线：LLM 食物解析与营养汇总；RAG 查询纳入日志摘要；多 Agent 与 Pydantic schema 对齐 - RAG：retriever 构建 query 时拼接饮食相关键 - 前端：Health 式信息架构与样式；开发者模式；饮食历史与反馈交互优化 - chore：.gitignore 忽略记忆评测产物与评测脚本路径 Made-with: Cursor
- 重写 README：快速开始、目录、API 表、Milvus 可选与常见问题；修正 uvicorn 入口说明 - 新增 backend/.env.example 供复制为 .env - 页眉主标题「健康助手」、副标题「档案分析与饮食建议」；副标题小字号与浅灰样式；文档标题同步 Made-with: Cursor
- README：界面概览前置；功能概览与架构要点；记忆与 RAG 简写；目录说明 - 前端资源：新增 report / diet / reflect 截图供 README 展示 Made-with: Cursor
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项目更新
项目名称: HealthRecordAgent
作者: @Shawnxyxy
项目类型: 生活服务 / 健康管理 / 多智能体应用
项目更新简介
在第一版“健康报告分析”的基础上进行了优化：
1.新增了饮食分析助手，可以分析饮食摄入状况，并结合用户设定的目标（如减脂、增肌）给出对应的饮食建议
2.新增记忆系统与RAG，用户的健康报告分析结果和饮食记录都入库并长期保存，同时设计 SQLite + Milvus，支持规则降级保证可用性；支撑 Agent 召回历史报告/饮食方案/执行反馈。
3.优化前端UI界面，类苹果健康的风格，简洁优雅。
演示效果