docs: 更新进度报告 - NER Demo 测试完成

完成情况：
- DeepSeek API 集成（阿里云百炼平台）
- 异步任务轮询模式（Java + Python）
- 图数据库存储验证（188 实体 + 82 关系）
- Pydantic 序列化修复

下一步计划：
- 数据源管理接口
- 模板系统基础
- 占位符绑定与渲染

entities_8bfd4ed9.csv

@@ -0,0 +1,153 @@
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进度报告.md

@@ -1,10 +1,10 @@
 # 📊 灵越智报 2.0 - 当前进度总结
 
-**整体进度：55%**  |  **报告日期：2026-01-19**
+**整体进度：60%**  |  **报告日期：2026-01-20**
 
 ---
 
-## ✅ 已完成（55%）
+## ✅ 已完成（60%）
 
 ### 基础设施
 - Spring Boot 3.1.5 单体应用架构（lingyue-starter）
@@ -15,7 +15,25 @@
 - 📁 文档管理、解析、认证 → 框架完成
 - 🤖 AI服务、图谱服务 → 数据层完成，RAG 功能已实现
 - 🔍 **RAG 向量化存储** → ✅ 已完成
-- 🏷️ **NER 实体识别服务** → ✅ 已完成
+- 🏷️ **NER 实体识别服务** → ✅ 已完成并测试验证
+
+### 新增功能（2026-01-20）✅ NER Demo 测试完成
+- ✅ **DeepSeek API 集成**
+  - 接入阿里云百炼平台 DeepSeek V3.2 实验版
+  - 支持长文本分块处理（每块 2000 字符）
+  - 完整的错误处理和重试机制
+- ✅ **异步任务轮询模式**
+  - Python 端：`/ner/extract/async` 提交异步任务
+  - Python 端：`/ner/task/{task_id}` 查询任务状态和进度
+  - Java 端：轮询模式（每 3 秒）查询进度，实时日志反馈
+  - 最大等待时间 10 分钟，失败自动回退到同步 API
+- ✅ **图数据库存储验证**
+  - 实体成功存储到 `graph_nodes` 表（测试文档：188 个实体）
+  - 关系成功存储到 `graph_relations` 表（测试文档：82 个关系）
+  - 支持按文档 ID 导出 CSV/JSON 数据
+- ✅ **Pydantic 序列化修复**
+  - 修复 Python 模型 alias 序列化问题（snake_case → camelCase）
+  - 确保 Java 端正确解析关系数据中的 `fromEntityId`、`toEntityId`
 
 ### 新增功能（2026-01-19）
 - ✅ **NER 服务完整实现**
@@ -36,41 +54,145 @@
   - 解析 → RAG → NER → 图数据库完整链路
   - NER 服务配置项（ner.python-service.url 等）
 
-### 新增功能（2026-01-17）
+### 已完成功能（2026-01-17）
 - ✅ **单体应用架构重构** - 统一到 lingyue-starter 模块
-- ✅ **配置文件统一** - 全部使用 .properties 格式，删除 .yml 文件
-- ✅ **MyBatisPlusConfig 统一** - 移除各模块独立配置，集中管理
-- ✅ **配置指南文档** - CONFIG_GUIDE.md（环境变量、配置优先级说明）
-- ✅ **接口测试完成** - 验证核心接口可用性
-  - /actuator/health（健康检查）
-  - /auth/register（用户注册）
-  - /auth/login（用户登录）
-  - /api/v1/parse/upload（文档上传）
-  - /api/rag/stats/{documentId}（分块统计）
-  - /api/rag/chunks/{documentId}（分块列表）
-  - /api/rag/query（RAG 问答）
+- ✅ **配置文件统一** - 全部使用 .properties 格式
+- ✅ **接口测试完成** - 核心接口验证通过
 
 ### 已完成功能（2026-01-16）
-- ✅ pgvector 向量数据库集成（text_chunks, vector_embeddings 表）
-- ✅ 文本分块服务（智能句子边界切分，500字符/块，50字符重叠）
-- ✅ Ollama Embedding 向量化服务（nomic-embed-text 模型）
-- ✅ pgvector 向量相似度检索（HNSW 索引，余弦距离）
-- ✅ DeepSeek API 客户端（Chat Completion）
+- ✅ pgvector 向量数据库集成
+- ✅ 文本分块服务（智能句子边界切分）
+- ✅ Ollama Embedding 向量化服务
 - ✅ RAG 核心服务（索引、检索、问答）
-- ✅ RAG API Controller（/api/rag/*）
-- ✅ 自动索引集成（解析完成后自动建立向量索引）
+
+---
+
+## 🎯 第一阶段完成情况
+
+根据设计文档 **6.2 第一阶段：核心数据流程（2月10日前）**
+
+### Week 1-2 任务完成率：85%
+
+| 任务 | 状态 | 说明 |
+|------|------|------|
+| 文件上传接口 | ✅ | 支持 PDF、Word、图片 |
+| 文件类型识别 | ✅ | MIME 类型检测 |
+| OCR 集成（飞浆OCR） | ✅ | Python 服务调用 |
+| 文本提取（Word文档） | ✅ | Apache POI |
+| TXT 文件存储 | ✅ | SSD 硬盘存储 |
+| 解析任务管理 | ✅ | 异步处理、状态跟踪 |
+| NER 服务集成 | ✅ | DeepSeek API 集成 |
+| 实体提取接口 | ✅ | 完整实体列表输出 |
+| 关系分析接口 | ✅ | 规则模式关系抽取 |
+| 实体存储到图节点表 | ✅ | PostgreSQL 存储验证 |
+| 图节点 CRUD 接口 | ✅ | 完整 CRUD |
+| 图关系 CRUD 接口 | ✅ | 完整 CRUD |
+| 图查询接口 | ✅ | 按文档、按用户查询 |
+
+### Week 3-4 任务待完成：
+
+| 任务 | 状态 | 说明 |
+|------|------|------|
+| 数据源创建接口 | ⏳ | 下一步重点 |
+| 数据源绑定节点 | ⏳ | 下一步重点 |
+| 数据源查询接口 | ⏳ | 下一步重点 |
+| 模板创建接口 | ⏳ | 下一步重点 |
+| 占位符绑定数据源 | ⏳ | 下一步重点 |
+| 模板渲染接口 | ⏳ | 下一步重点 |
+| 端到端流程测试 | ⏳ | 需要完善 |
+
+---
+
+## 📋 下一步计划：数据源与模板基础（Week 3）
+
+**计划周期：2026-01-21 ~ 2026-01-27**
+
+**目标：实现数据源管理和模板系统基础功能**（设计文档 2.4 / 2.6）
+
+### 1. 数据源管理（优先级最高）
+
+根据设计文档 2.4.1：
+> 数据源是图数据库中的节点集合，具有层次关系
+
+**实施任务：**
+- [ ] 数据源创建接口（DataSource 实体，节点绑定）
+- [ ] 数据源绑定节点（节点 ID 数组存储，关联关系维护）
+- [ ] 数据源查询接口（按类型、按文档查询）
+- [ ] 数据源编辑接口（添加/移除节点）
+
+**数据源类型**：
+- 表格数据源：表格单元格作为独立节点
+- 文本数据源：文本片段作为节点
+- 图片数据源：图片作为节点
+
+### 2. 模板系统基础
+
+根据设计文档 2.6.1：
+> 模板是对应输出的占位符结构，占位符绑定到数据源编号
+
+**实施任务：**
+- [ ] 模板创建接口（Template 实体，占位符解析）
+- [ ] 占位符绑定数据源（placeholder_mapping JSONB 存储）
+- [ ] 模板渲染接口（占位符替换，数据源值获取）
+- [ ] 模板复制功能（基于已有模板复制）
+
+### 3. 数据库表设计
+
+根据现有 `supplement_tables.sql`，需要完善：
+
+```sql
+-- 数据源表
+CREATE TABLE IF NOT EXISTS data_sources (
+    id VARCHAR(32) PRIMARY KEY,
+    name VARCHAR(255) NOT NULL,
+    type VARCHAR(50) NOT NULL,  -- table/text/image
+    document_id VARCHAR(32),
+    node_ids JSONB,  -- 绑定的节点 ID 数组
+    metadata JSONB,
+    user_id VARCHAR(32),
+    create_time TIMESTAMP,
+    update_time TIMESTAMP
+);
+
+-- 模板表
+CREATE TABLE IF NOT EXISTS templates (
+    id VARCHAR(32) PRIMARY KEY,
+    name VARCHAR(255) NOT NULL,
+    content TEXT,  -- 模板内容（含占位符）
+    placeholder_mapping JSONB,  -- 占位符到数据源的映射
+    user_id VARCHAR(32),
+    create_time TIMESTAMP,
+    update_time TIMESTAMP
+);
+```
+
+### 4. 接口设计
+
+**数据源 API**：
+- `POST /api/datasource` - 创建数据源
+- `GET /api/datasource/{id}` - 获取数据源
+- `GET /api/datasource/document/{documentId}` - 按文档查询
+- `PUT /api/datasource/{id}/nodes` - 更新绑定节点
+- `DELETE /api/datasource/{id}` - 删除数据源
+
+**模板 API**：
+- `POST /api/template` - 创建模板
+- `GET /api/template/{id}` - 获取模板
+- `PUT /api/template/{id}/mapping` - 更新占位符映射
+- `POST /api/template/{id}/render` - 渲染模板
+- `POST /api/template/{id}/copy` - 复制模板
 
 ---
 
 ## ⚠️ 关键缺失（对照技术预研表）
 
-| 预研项 | 进度 | 表格要求 | 已完成 ✅ | 待实现 ❌ |
-|--------|------|----------|-----------|-----------|
-| **1️⃣ 规则"智能体"设计** | 35% | 报告生成逻辑规则多样（字符逻辑、语义理解、实体关系多层计算） | Graph Service 架构（9个Repository）<br>规则、模板数据模型<br>数据访问层<br>**RAG 问答服务** | 规则DSL定义与解析<br>规则执行引擎<br>多层计算算法 |
-| **2️⃣ 产品定位与功能逻辑** | 40% | 产品交互界面、智能体集群、规则逻辑校验 | 6大后端服务框架<br>Flutter 项目结构<br>路由、主题、基础组件 | 所有前端页面UI<br>智能体集群架构<br>规则校验功能<br>前后端API对接 |
-| **3️⃣ 规则智能体模拟** | 40% | 单规则逻辑树构建、规则测试、API记忆化（知识图谱） | TextStorage（文本存储）<br>GraphNode、GraphRelation<br>ParseTask（任务管理）<br>**文本分块、向量存储**<br>**向量相似度检索** | 规则逻辑树算法<br>单规则校验引擎<br>知识图谱构建算法<br>图谱查询与推理 |
-| **4️⃣ AI模态体型/OCR** | 60% | AI模态体型、OCR、文本分析代码，NSDK集成 | PaddleOCR Client 接口<br>PDF/Word/Excel 文本提取<br>AI Service 框架<br>Element、Annotation 实体<br>**DeepSeek API 客户端**<br>**Ollama Embedding 服务** | AI模态体模型接入<br>NSDK集成<br>NLP文本分析算法<br>OCR后处理优化 |
-| **5️⃣ 前端交互设计** | 15% | AI产品"交互应简单"体验、核心交互功能规划 | Flutter 项目结构<br>路由、主题配置<br>基础组件、业务组件<br>7个页面骨架 | 所有7个核心页面UI<br>页面交互逻辑<br>与后端API对接<br>WebSocket实时通信 |
+| 预研项 | 进度 | 已完成 ✅ | 待实现 ❌ |
+|--------|------|-----------|-----------|
+| **1️⃣ 规则"智能体"设计** | 40% | Graph Service 架构<br>规则、模板数据模型<br>RAG 问答服务<br>**NER 实体提取** | 规则 DSL 定义与解析<br>规则执行引擎<br>多层计算算法 |
+| **2️⃣ 产品定位与功能逻辑** | 40% | 6大后端服务框架<br>Flutter 项目结构<br>基础组件 | 所有前端页面 UI<br>智能体集群架构<br>规则校验功能 |
+| **3️⃣ 规则智能体模拟** | 45% | TextStorage（文本存储）<br>GraphNode、GraphRelation<br>**向量相似度检索**<br>**NER + 关系构建** | 规则逻辑树算法<br>单规则校验引擎<br>知识图谱构建算法 |
+| **4️⃣ AI模态体型/OCR** | 65% | PaddleOCR Client<br>PDF/Word/Excel 提取<br>**DeepSeek API**<br>**Ollama Embedding** | AI多模态模型接入<br>NSDK集成<br>OCR后处理优化 |
+| **5️⃣ 前端交互设计** | 15% | Flutter 项目结构<br>路由、主题配置<br>7个页面骨架 | 所有核心页面 UI<br>与后端 API 对接 |
 
 ---
 
@@ -78,74 +200,55 @@
 
 ```
 backend/
-├── common/                 # 公共模块（AjaxResult、异常处理、工具类）
-├── auth-service/           # 认证服务（JWT、Session、User）
+├── common/                 # 公共模块
+├── auth-service/           # 认证服务
 ├── document-service/       # 文档管理服务
-├── parse-service/          # 解析服务（OCR、PDF/Word/Excel提取、任务中心）
-├── ai-service/             # AI服务（DeepSeek客户端）
-├── graph-service/          # 图谱服务（RAG、向量检索、知识图谱）
-├── notification-service/   # 通知服务（WebSocket）
-├── gateway-service/        # 网关服务（JWT过滤器、CORS）
-├── lingyue-starter/        # 单体应用启动器（统一配置）
-└── sql/                    # 数据库初始化脚本
+├── parse-service/          # 解析服务
+├── ai-service/             # AI服务
+├── graph-service/          # 图谱服务（RAG、NER、图数据库）
+├── notification-service/   # 通知服务
+├── gateway-service/        # 网关服务
+├── lingyue-starter/        # 单体应用启动器
+└── sql/                    # 数据库脚本
+
+python-services/
+└── ner-service/            # NER Python 服务
+    ├── app/
+    │   ├── models/         # 数据模型
+    │   ├── routers/        # API 路由
+    │   └── services/       # 业务服务
+    │       ├── ner_service.py      # NER 调度
+    │       ├── deepseek_service.py # DeepSeek API
+    │       └── relation_service.py # 关系抽取
+    └── requirements.txt
 
 frontend_flutter/
-├── lib/
-│   ├── models/             # 数据模型（6个）
-│   ├── pages/              # 页面（7个）
-│   ├── providers/          # 状态管理（4个）
-│   ├── widgets/            # 组件（8个）
-│   └── theme/              # 主题配置
-└── web/                    # Web 构建产物
+└── lib/                    # Flutter 前端（待开发）
 ```
 
 ---
 
-## 🎯 下一阶段实现说明（对照《灵越智报2.0程序设计详解》）
-
-**计划周期：2026-01-19 ~ 2026-01-23**
-
-**目标：跑通“解析文本 → 向量化 → NER → 关系构建 → 图数据库”的完整链路**（设计文档 2.2.2 / 2.3 / 10.2）
-
-**每日任务计划：**
-- **01-19（周一）✅ 已完成**：NER 服务接口定义与数据结构；Python NER 服务联调准备（输入/输出格式）
-  - ✅ NER DTO 类（NerRequest, NerResponse, EntityInfo, RelationInfo 等）
-  - ✅ Python FastAPI NER 服务骨架
-  - ✅ 实体提取和关系抽取实现（规则模式）
-  - ✅ Java PythonNerClient HTTP 客户端
-  - ✅ NerServiceImpl 业务逻辑
-  - ✅ GraphNodeService 扩展
-  - ✅ TextStorageService 集成（自动触发 NER）
-- **01-20（周二）**：联调测试与优化；完善错误处理
-- **01-21（周三）**：关系抽取优化；增加更多实体类型支持
-- **01-22（周四）**：图节点/关系查询接口优化；性能测试
-- **01-23（周五）**：端到端验证；问题收敛与文档更新
-
-1. **NER 实体提取（优先级最高）**
-   - 设计要求：两轮处理（实体→关系），离线方案优先（设计文档 2.3.1 / 10.2）
-   - 实施路径：
-     - 在 `ai-service` 增加 NER 接口（HTTP 调用 Python NER 服务）
-     - 输出节点列表 + 关系列表
-     - 写入图数据库（`graph_nodes`/`graph_relations`）
-
-2. **关系构建与图结构入库**
-   - 设计要求：常识关系/语义关系/计算关系（设计文档 2.3.2）
-   - 实施路径：
-     - 关系抽取接口（基于 NER 输出）
-     - `graph-service` 提供图节点/关系 CRUD 与按文档查询
-
-3. **数据源管理 & 模板基础**
-   - 设计要求：数据源是图节点集合，可编辑替换（设计文档 2.4 / 2.6）
-   - 实施路径：
-     - `data_sources` 表结构与 CRUD
-     - 模板占位符绑定数据源，基础渲染输出
-
-4. **AI 助手路由与问答链路**
-   - 设计要求：意图识别 → 路由 → RAG/图数据库（设计文档 2.7 / 10.1）
-   - 实施路径：
-     - 路由规则与问答入口
-     - DeepSeek/百炼配置稳定化
-
-5. **前后端联调**
-   - 上传、解析、RAG、任务中心 UI 对接
-   - WebSocket 实时进度推送
+## 🔧 技术栈确认
+
+| 组件 | 技术选型 | 状态 |
+|------|----------|------|
+| 后端框架 | Spring Boot 3.1.5 | ✅ |
+| 数据库 | PostgreSQL + pgvector | ✅ |
+| 缓存 | Redis | ✅ |
+| 消息队列 | RabbitMQ | ✅ |
+| NER 模型 | DeepSeek API（内测）| ✅ |
+| Embedding | Ollama nomic-embed-text | ✅ |
+| OCR | PaddleOCR | ✅ |
+| 图数据库 | PostgreSQL 模拟（待接入 Neo4j）| ⏳ |
+| 前端 | Flutter Web | ⏳ |
+
+---
+
+## 📅 里程碑
+
+- **M1（2月10日）**：第一阶段完成，核心流程跑通
+  - ✅ 文件上传解析
+  - ✅ RAG 向量检索
+  - ✅ NER 实体提取
+  - ⏳ 数据源管理（下一步）
+  - ⏳ 模板基础（下一步）