diff --git a/docs/knowledge-graph/README.md b/docs/knowledge-graph/README.md new file mode 100644 index 0000000..f3e88e0 --- /dev/null +++ b/docs/knowledge-graph/README.md @@ -0,0 +1,223 @@ +# DataMate 知识图谱实现方案 + +## 📋 项目概述 + +DataMate 知识图谱旨在构建企业级数据处理平台的知识网络，通过图结构揭示数据资产之间的关系，支持智能推荐、影响分析、血缘追踪等高级功能。 + +## 🎯 核心目标 + +1. **数据血缘追踪**：追踪数据从源到目标的完整流转路径 +2. **影响分析**：评估数据变更对下游任务的影响范围 +3. **智能推荐**：基于历史使用模式推荐相关数据集和工作流 +4. **知识发现**：挖掘隐藏的数据关系和模式 + +## 🏗️ 技术架构 + +### 技术栈 + +``` +存储层：MySQL (元数据) + Neo4j (图结构) + Milvus (向量) +后端：Spring Boot (kg-service) + FastAPI (kg-ingestion) +前端：React + AntV G6 +抽取：LangChain LLMGraphTransformer +``` + +### 架构设计 + +``` +┌─────────────────────────────────────────────────┐ +│ 前端层 │ +│ React + AntV G6 (图谱可视化 + 编辑) │ +└─────────────────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────────────────┐ +│ 服务层 │ +│ kg-service (Spring Boot) │ +│ - 图查询 API │ +│ - 权限过滤 │ +│ - 缓存层 (Redis) │ +│ │ +│ rag-query-service (增强) │ +│ - 混合检索 (Milvus + Neo4j) │ +│ - GraphRAG │ +└─────────────────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────────────────┐ +│ 摄入层 │ +│ kg-ingestion (FastAPI) │ +│ - LangChain LLMGraphTransformer │ +│ - 实体对齐 │ +│ - 关系生成 │ +└─────────────────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────────────────┐ +│ 存储层 │ +│ MySQL + Neo4j + Milvus │ +└─────────────────────────────────────────────────┘ +``` + +## 📊 数据模型 + +> 详细定义参见 [实体文档](./schema/entities.md) 和 [关系文档](./schema/relationships.md) + +### 核心实体（8 类） + +- **Dataset**：数据集 +- **Field**：字段 +- **LabelTask**：标注任务 +- **Workflow**：工作流 +- **Job**：作业 +- **User**：用户 +- **Org**：组织 +- **KnowledgeSet**：知识集 + +### 核心关系（10 类） + +- **HAS_FIELD**：Dataset → Field，数据集包含字段 +- **DERIVED_FROM**：Dataset → Dataset，数据集血缘派生 +- **USES_DATASET**：Job/LabelTask/Workflow → Dataset，使用数据集 +- **PRODUCES**：Job → Dataset，作业产出数据集 +- **ASSIGNED_TO**：LabelTask/Job → User，任务分配给用户 +- **BELONGS_TO**：User/Dataset → Org，组织归属 +- **TRIGGERS**：Workflow → Job，工作流触发作业 +- **DEPENDS_ON**：Job → Job，作业执行依赖 +- **IMPACTS**：Field → Field，字段级影响 +- **SOURCED_FROM**：KnowledgeSet → Dataset，知识溯源 + +### 节点公共属性 + +```json +{ + "id": "UUID，全局唯一标识符", + "name": "实体名称", + "type": "实体类型（Dataset / Field / LabelTask 等）", + "description": "实体描述", + "graph_id": "所属图谱 ID（多租户隔离）", + "source_id": "来源记录 ID", + "source_type": "来源类型：SYNC / EXTRACTION / MANUAL", + "confidence": "置信度 0.0-1.0", + "created_at": "创建时间" +} +``` + +### 边公共属性 + +```json +{ + "id": "UUID，关系唯一标识符", + "relation_type": "语义关系类型", + "graph_id": "所属图谱 ID", + "weight": "关系权重 0.0-1.0", + "confidence": "置信度 0.0-1.0", + "source_id": "来源记录 ID", + "properties_json": "扩展属性 JSON", + "created_at": "创建时间" +} +``` + +## 🚀 实施路线图 + +### 第 0 阶段：基础设施（1周）✅ 已完成 + +- ✅ 搭建 Neo4j（docker-compose） +- ✅ 更新 Makefile +- ✅ 创建 knowledge-graph-service（Spring Boot） +- ✅ 创建 kg_extraction 模块（Python） +- ✅ 代码审查和修复（3 轮审查，2 轮修复） + +**成果**： +- Neo4j 配置：`deployment/docker/neo4j/docker-compose.yml` +- Java 服务：`backend/services/knowledge-graph-service/`（11 个文件） +- Python 模块：`runtime/datamate-python/app/module/kg_extraction/`（3 个文件） +- Makefile 命令：`neo4j-up`, `neo4j-down`, `neo4j-logs`, `neo4j-shell` + +### 第 1 阶段：MVP（2-3周）⏳ 进行中 + +**目标**：实现基础的图谱构建和查询功能 + +**任务**： +1. 实现 Python 抽取器的 FastAPI 接口 + - 创建 `/api/kg/extract` 端点 + - 支持文本输入，输出节点和边 + - 集成到 FastAPI 路由 + +2. 实现 Java 服务的关系（Relation）功能 + - 补充 Relation 的 Repository/Service/Controller + - 实现关系的 CRUD 操作 + - 支持关系查询和遍历 + +3. 定义核心实体和关系模型 + - 确定 5-8 类核心实体 + - 定义实体之间的关系 + - 设计 Schema 版本管理 + +4. 实现基础的图谱构建流程 + - 从 MySQL 同步元数据到 Neo4j + - 实现增量更新机制 + - 支持手动触发构建 + +**验收标准**： +- ✅ 能够从文本抽取实体和关系 +- ✅ 能够存储到 Neo4j +- ✅ 能够查询和遍历图谱 +- ✅ 支持基础的权限控制 + +### 第 2 阶段：GraphRAG 融合（3-4周） + +**目标**：将知识图谱与现有 RAG 系统深度融合 + +**任务**： +1. 在 rag-query-service 中增加"混合检索"模式 +2. 查询时同时检索 Milvus（向量）+ Neo4j（图结构） +3. 将 2-hop 子图的三元组文本化后作为 Context 喂给 LLM +4. 实现 GraphRAG 的评估和优化 + +**验收标准**： +- ✅ 混合检索性能优于单一检索 +- ✅ 支持可配置的检索策略 +- ✅ 有完整的评估指标 + +### 第 3 阶段：可视化与优化（4-6周） + +**目标**：提供友好的图谱可视化和编辑功能 + +**任务**： +1. 前端图谱浏览器（React + AntV G6） +2. Human-in-the-loop 编辑功能 +3. 性能优化（索引、缓存、离线计算） +4. 监控和运维（Prometheus + Grafana） + +**验收标准**： +- ✅ 支持大规模图谱可视化（10000+ 节点） +- ✅ 支持实时编辑和反馈 +- ✅ 查询响应时间 < 1s + +## 🔑 核心原则 + +1. **先做"窄而深"的场景**：不追求"大而全本体"，先聚焦 2-3 个高价值场景 +2. **最终一致性**：MySQL 为主库，Neo4j 为专用存储，通过对账机制保证一致性 +3. **双重防御**：Controller 格式校验 + Service 业务校验 +4. **权限隔离**：所有操作都在正确的 graph_id 范围内 +5. **性能优先**：限制遍历深度、使用缓存、离线计算 + +## 📚 相关文档 + +- [架构设计](./architecture.md) +- [数据模型 - 实体定义](./schema/entities.md) +- [数据模型 - 关系定义](./schema/relationships.md) +- [数据模型 - ER 图](./schema/er-diagram.md) +- [实施计划](./implementation.md) +- [Gemini 分析结果](./analysis/gemini.md) +- [Codex 分析结果](./analysis/codex.md) +- [Claude 分析结果](./analysis/claude.md) + +## 🔗 快速链接 + +- Neo4j Browser: http://localhost:7474 +- Bolt URI: bolt://localhost:7687 +- 默认密码: datamate123（生产环境请修改） + +## 📝 更新日志 + +- 2026-02-17：完成基础设施搭建（第 0 阶段） +- 2026-02-17：创建项目文档 diff --git a/docs/knowledge-graph/analysis/claude.md b/docs/knowledge-graph/analysis/claude.md new file mode 100644 index 0000000..88b9b73 --- /dev/null +++ b/docs/knowledge-graph/analysis/claude.md @@ -0,0 +1,289 @@ +# Claude 知识图谱分析结果 + +## 分析时间 +2026-02-17 + +## 核心建议 + +### 1. 技术选型 + +**图数据库**：Neo4j（社区版或企业版） + +**存储架构**：MySQL + Neo4j 双存储 +- **MySQL**：元数据主库，保持现有业务逻辑 +- **Neo4j**：图结构专用存储，支持复杂查询 + +**同步策略**：最终一致性 + 对账机制 + +### 2. 架构设计（复用现有基础设施） + +**核心原则**： +- 复用现有的服务架构 +- 最小化对现有系统的影响 +- 渐进式集成 + +**集成方式**： +``` +现有服务 → MySQL（主库） + ↓ 同步 + Neo4j（图库） + ↓ 查询 + kg-service（新服务） +``` + +### 3. 数据建模（Schema 先行 + 版本管理） + +#### Schema 设计原则 +1. **先行设计**：明确定义实体和关系 +2. **版本管理**：支持 Schema 演进 +3. **向后兼容**：新版本兼容旧版本 +4. **文档化**：详细记录每个版本的变更 + +#### 实体属性设计 +```json +{ + "id": "UUID", + "name": "名称", + "type": "类型", + "description": "描述", + "tenant_id": "租户ID", + "schema_version": "1.0", + "created_at": "创建时间", + "updated_at": "更新时间" +} +``` + +#### 关系属性设计 +```json +{ + "source": "源节点ID", + "target": "目标节点ID", + "type": "关系类型", + "confidence": "置信度（0-1）", + "source": "来源（manual/auto）", + "valid_from": "生效时间", + "valid_to": "失效时间" +} +``` + +### 4. 实施路线图（4 阶段） + +#### 第 0 阶段：基础设施（1周）✅ +- 搭建 Neo4j +- 创建基础服务 +- 定义 Schema + +#### 第 1 阶段：核心功能（2-3周） +- 实现同步机制 +- 实现基础查询 +- 集成到现有系统 + +#### 第 2 阶段：高级功能（3-4周） +- 实现 GraphRAG +- 实现可视化 +- 性能优化 + +#### 第 3 阶段：持续优化 +- 扩展功能 +- 优化性能 +- 提升体验 + +### 5. 挑战解决方案 + +#### 数据一致性 +**问题**：MySQL 和 Neo4j 数据可能不一致 + +**解决方案**： +- **最终一致性**：允许短暂的不一致 +- **对账机制**：定期对比并修复 +- **事件驱动**：通过事件同步变更 + +**实现**： +```java +@Scheduled(cron = "0 0 2 * * *") // 每天凌晨 2 点 +public void reconcile() { + // 1. 查询 MySQL 中的所有实体 + List datasets = datasetRepository.findAll(); + + // 2. 查询 Neo4j 中的所有实体 + List graphEntities = graphEntityRepository.findAll(); + + // 3. 对比并找出差异 + List diffs = compare(datasets, graphEntities); + + // 4. 修复差异 + for (Diff diff : diffs) { + if (diff.getType() == DiffType.MISSING_IN_NEO4J) { + syncToNeo4j(diff.getEntity()); + } else if (diff.getType() == DiffType.OUTDATED_IN_NEO4J) { + updateNeo4j(diff.getEntity()); + } + } + + // 5. 记录日志 + log.info("Reconciliation completed: {} diffs fixed", diffs.size()); +} +``` + +#### 性能优化 +**问题**：大规模图谱查询性能下降 + +**解决方案**： +- **索引策略**：在高频字段上创建索引 +- **限制遍历深度**：最大 3 跳 +- **Redis 缓存**：缓存热点数据 +- **离线计算**：预计算常用子图 + +**索引创建**： +```cypher +// 实体 ID 索引 +CREATE INDEX entity_id IF NOT EXISTS FOR (n:Entity) ON (n.id); + +// 租户 ID 索引 +CREATE INDEX entity_tenant_id IF NOT EXISTS FOR (n:Entity) ON (n.tenant_id); + +// 复合索引 +CREATE INDEX entity_id_graph_id IF NOT EXISTS +FOR (n:Entity) ON (n.id, n.graph_id); +``` + +#### 前端可视化 +**问题**：大规模图谱难以可视化 + +**解决方案**： +- **分层加载**：先加载核心节点，再加载周边 +- **子图裁剪**：只显示相关子图 +- **WebGL 渲染**：使用 WebGL 提升性能 +- **虚拟滚动**：只渲染可见区域 + +**推荐库**： +- Cytoscape.js（功能丰富） +- AntV G6（国产，文档友好） +- vis.js（简单易用） + +### 6. 最佳实践 + +#### 开发实践 +1. **API 规范一致**：遵循 RESTful 规范 +2. **复用现有模式**：使用现有的 DTO、ErrorCode +3. **事件驱动解耦**：通过事件同步变更 +4. **Cypher 注入防护**：使用参数化查询 + +#### 运维实践 +1. **Neo4j 备份**：每天全量备份 +2. **监控告警**：Prometheus + Grafana +3. **性能调优**：定期分析慢查询 +4. **容量规划**：根据数据增长预测资源需求 + +#### 部署实践 +1. **Docker 部署**：使用 docker-compose +2. **Kubernetes 扩展**：使用 Helm Chart +3. **灰度发布**：先在小范围验证 +4. **回滚机制**：支持快速回滚 + +### 7. 代码实现细节 + +#### 双重防御示例 +```java +// Controller 层：格式校验 +@GetMapping("/{graphId}/entities/{entityId}") +public GraphEntity getEntity( + @PathVariable @Pattern(regexp = UUID_REGEX, message = "graphId 格式无效") + String graphId, + @PathVariable @Pattern(regexp = UUID_REGEX, message = "entityId 格式无效") + String entityId +) { + return entityService.getEntity(graphId, entityId); +} + +// Service 层：业务校验 +public GraphEntity getEntity(String graphId, String entityId) { + // 1. 校验 graphId 格式 + validateGraphId(graphId); + + // 2. 查询实体（同时校验 graphId 和 entityId） + return entityRepository.findByIdAndGraphId(entityId, graphId) + .orElseThrow(() -> BusinessException.of( + KnowledgeGraphErrorCode.ENTITY_NOT_FOUND + )); +} + +// Repository 层：数据访问 +@Query("MATCH (n:Entity {id: $id, graph_id: $graphId}) RETURN n") +Optional findByIdAndGraphId( + @Param("id") String id, + @Param("graphId") String graphId +); +``` + +#### 查询限流示例 +```java +public List getNeighbors( + String graphId, + String entityId, + int depth, + int limit +) { + // Clamp 参数到配置的最大值 + int actualDepth = Math.min(depth, properties.getMaxDepth()); + int actualLimit = Math.min(limit, properties.getMaxNodesPerQuery()); + + // 查询 + return entityRepository.findNeighbors( + graphId, entityId, actualDepth, actualLimit + ); +} +``` + +### 8. 建议的下一步 + +**立即行动**： +1. 实现 Relation 的完整功能 +2. 实现 MySQL → Neo4j 同步 +3. 补充单元测试 + +**短期目标**（1-2周）： +1. 完成 MVP 功能 +2. 集成到现有系统 +3. 进行性能测试 + +**中期目标**（1-2月）： +1. 实现 GraphRAG +2. 实现可视化 +3. 上线第一个场景 + +## 与其他工具的对比 + +| 维度 | Claude | Codex | Gemini | +|------|--------|-------|--------| +| **技术选型** | Neo4j | Neo4j/JanusGraph | Neo4j | +| **架构重点** | 复用现有基础设施 | 3个新模块 | GraphRAG 融合 | +| **数据建模** | Schema先行+版本管理 | 10类实体+6类关系 | 灵活Schema+embedding | +| **实现路径** | 4阶段 | 4阶段（0-3） | 3阶段（MVP优先） | +| **独特优势** | 深度集成现有系统 | 详细的领域模型 | LangChain+RAG融合 | + +## 关键洞察 + +1. **深度集成**：Claude 强调复用现有基础设施，最小化影响 +2. **最终一致性**：提出了实用的数据同步和对账方案 +3. **详细的代码示例**：提供了可直接使用的代码片段 +4. **运维实践**：关注生产环境的监控、备份、部署 + +## 建议采纳度 + +**强烈推荐**： +- ✅ MySQL + Neo4j 双存储架构 +- ✅ 最终一致性 + 对账机制 +- ✅ 双重防御（Controller + Service） +- ✅ 查询限流 +- ✅ 运维实践（备份、监控） + +**可选**： +- ⚠️ 事件驱动同步（可以先用定时任务） + +## 相关文档 + +- [总体方案](../README.md) +- [架构设计](../architecture.md) +- [Gemini 分析结果](./gemini.md) +- [Codex 分析结果](./codex.md) diff --git a/docs/knowledge-graph/analysis/codex.md b/docs/knowledge-graph/analysis/codex.md new file mode 100644 index 0000000..cd3129c --- /dev/null +++ b/docs/knowledge-graph/analysis/codex.md @@ -0,0 +1,201 @@ +# Codex 知识图谱分析结果 + +## 分析时间 +2026-02-17 + +## 核心建议 + +### 1. 技术选型 + +**图数据库**： +- **首选**：Neo4j（成熟稳定，社区活跃） +- **备选**：JanusGraph（分布式场景） + +**理由**： +- Neo4j 的 Cypher 查询语言简洁强大 +- Spring Data Neo4j 集成良好 +- 丰富的图算法库 +- 适合中小规模图谱（< 1000万节点） + +### 2. 架构设计（3 个新模块） + +#### kg-ingestion (FastAPI) +**职责**：知识抽取和预处理 +- 文本 → 实体 + 关系 +- 实体对齐和消歧 +- 置信度评分 + +#### kg-service (Spring Boot) +**职责**：图谱查询和管理 +- 图查询 API +- 权限控制 +- 缓存管理 + +#### kg-ui (React) +**职责**：图谱可视化 +- AntV G6 可视化 +- 交互式查询 +- 编辑功能 + +### 3. 数据建模（10 类实体 + 6 类关系） + +#### 核心实体（10 类） +1. **Dataset**：数据集 +2. **Field**：字段 +3. **LabelTask**：标注任务 +4. **Workflow**：工作流 +5. **Job**：作业 +6. **Rule**：规则 +7. **User**：用户 +8. **Org**：组织 +9. **Model**：模型 +10. **Issue**：问题 + +#### 核心关系（6 类） +1. **HAS_FIELD**：数据集包含字段 +2. **TRIGGERS**：触发关系 +3. **USES_RULE**：使用规则 +4. **ASSIGNED_TO**：分配给 +5. **PRODUCED_BY**：产生于 +6. **IMPACTS**：影响 + +### 4. 实施路线图（4 阶段） + +#### 第 0 阶段：场景确定（1-2周） +- 确定 2 个高价值场景 +- 定义核心实体和关系 +- 设计 Schema + +#### 第 1 阶段：PoC（2-4周） +- 搭建基础设施 +- 实现基础抽取 +- 验证技术可行性 + +#### 第 2 阶段：生产化（4-8周） +- 完善功能 +- 性能优化 +- 集成到现有系统 + +#### 第 3 阶段：持续优化 +- 扩展实体和关系 +- 优化算法 +- 提升用户体验 + +### 5. 潜在挑战 + +#### 数据质量 +**问题**：元数据不完整或不准确 + +**解决方案**： +- 数据清洗和标准化 +- 人工审核机制 +- 置信度评分 + +#### 性能瓶颈 +**问题**：大规模图谱查询性能下降 + +**解决方案**： +- 索引优化 +- 查询限流 +- 缓存热点数据 +- 离线计算 + +#### 多租户隔离 +**问题**：不同租户的数据需要隔离 + +**解决方案**： +- 所有节点包含 tenant_id +- 查询时自动过滤 +- 权限控制 + +### 6. 最佳实践 + +#### Schema 设计 +- **先行设计**：明确定义实体和关系 +- **版本管理**：支持 Schema 演进 +- **文档化**：详细记录每个实体和关系 + +#### 查询优化 +- **限制深度**：最大 3 跳 +- **限制数量**：最大 1000 个节点 +- **使用索引**：在高频字段上创建索引 +- **缓存结果**：缓存热点查询 + +#### 安全性 +- **参数化查询**：防止 Cypher 注入 +- **权限控制**：基于角色的访问控制 +- **审计日志**：记录所有操作 + +### 7. 代码审查发现的问题 + +#### P0 - 严重问题 +1. **主应用未声明依赖**：已修复 +2. **Neo4j 凭据硬编码**：已修复 +3. **graphId 参数未校验**：已修复 + +#### P1 - 重要问题 +4. **异常处理不规范**：已修复 +5. **查询未限流**：已修复 +6. **异常码体系未对齐**：已修复 + +#### P2 - 中等问题 +7. **关系建模未打通**：待实现 +8. **列表接口缺分页**：待实现 +9. **Python 模块未接入路由**：待实现 +10. **密钥处理不规范**：待实现 + +#### P3 - 次要问题 +11. **Neo4j 镜像浮动 tag**：待修复 +12. **测试覆盖为空**：待补充 + +### 8. 建议的下一步 + +**立即行动**： +1. 补充 P2 问题（关系功能、分页、Python 路由） +2. 定义核心实体和关系模型 +3. 实现 MySQL → Neo4j 同步 + +**短期目标**（1-2周）： +1. 完成 MVP 功能 +2. 补充单元测试 +3. 进行性能测试 + +**中期目标**（1-2月）： +1. 集成到现有系统 +2. 实现 GraphRAG +3. 上线第一个场景 + +## 与其他工具的对比 + +| 维度 | Codex | Gemini | Claude | +|------|-------|--------|--------| +| **技术选型** | Neo4j/JanusGraph | Neo4j | Neo4j | +| **架构重点** | 3个新模块 | GraphRAG 融合 | 复用现有基础设施 | +| **数据建模** | 10类实体+6类关系 | 灵活Schema+embedding | Schema先行+版本管理 | +| **实现路径** | 4阶段（0-3） | 3阶段（MVP优先） | 4阶段 | +| **独特优势** | 详细的领域模型 | LangChain+RAG融合 | 深度集成现有系统 | + +## 关键洞察 + +1. **详细的领域模型**：Codex 提供了最详细的实体和关系定义 +2. **严格的代码审查**：发现了 12 个问题，确保代码质量 +3. **实用的最佳实践**：提供了具体的优化建议 +4. **分阶段实施**：强调先做 PoC，验证可行性 + +## 建议采纳度 + +**强烈推荐**： +- ✅ 10 类实体 + 6 类关系的数据模型 +- ✅ 代码审查发现的问题修复 +- ✅ 最佳实践（查询优化、安全性） +- ✅ 4 阶段实施路线 + +**可选**： +- ⚠️ JanusGraph（如果需要分布式） + +## 相关文档 + +- [总体方案](../README.md) +- [架构设计](../architecture.md) +- [Gemini 分析结果](./gemini.md) +- [Claude 分析结果](./claude.md) diff --git a/docs/knowledge-graph/analysis/gemini.md b/docs/knowledge-graph/analysis/gemini.md new file mode 100644 index 0000000..a3f6c44 --- /dev/null +++ b/docs/knowledge-graph/analysis/gemini.md @@ -0,0 +1,154 @@ +# Gemini 知识图谱分析结果 + +## 分析时间 +2026-02-17 + +## 核心建议 + +### 1. GraphRAG 融合方案（独特贡献） + +**创新点**：将知识图谱与现有 RAG 系统深度融合 + +**实现方案**： +- 在 `rag-query-service` 中增加"混合检索"模式 +- 查询时同时检索 Milvus（向量）+ Neo4j（图结构） +- 将 2-hop 子图的三元组文本化后作为 Context 喂给 LLM + +**优势**： +- 充分利用现有的 Milvus 向量检索能力 +- 结合向量相似度和图结构关系 +- 提供更丰富的上下文信息 + +### 2. LangChain 集成方案 + +**技术路径**： +- 利用 LangChain 的 `LLMGraphTransformer` 实现自动抽取 +- 在 `runtime/datamate-python` 中实现 +- API: `POST /graph/extract`，输入文本，输出节点和边 + +**实现细节**： +```python +from langchain_experimental.graph_transformers import LLMGraphTransformer + +transformer = LLMGraphTransformer( + llm=llm, + allowed_nodes=["Dataset", "Field", "Workflow"], + allowed_relationships=["HAS_FIELD", "USES"] +) + +graph_documents = transformer.convert_to_graph_documents([document]) +``` + +### 3. 数据建模增强 + +**核心元模型**： +- **Entity**：增加 `embedding` 字段（节点的向量表示） +- **Document**：新增节点类型，用于溯源 +- **关系**：`(Entity)-[MENTIONED_IN]->(Document)` + +**优势**： +- 支持向量检索与图检索的混合 +- 方便溯源，追踪实体来源 +- 提升检索准确性 + +### 4. 实施路线图（3 阶段） + +#### 第一阶段：基础设施与基础抽取 (MVP) +1. 环境搭建：在 `deployment/docker/` 下新建 neo4j 目录 +2. Python 抽取器：利用 LangChain 的 LLMGraphTransformer +3. 简单存储：直接存入 Neo4j + +#### 第二阶段：图谱服务与 RAG 融合 +1. Java 服务：创建 `knowledge-graph-service` +2. GraphRAG：在 `rag-query-service` 中增加"混合检索"模式 + - 查询时同时检索 Milvus 和 Neo4j（2-hop 子图） + - 将三元组文本化后作为 Context 喂给 LLM + +#### 第三阶段：可视化与高级功能 +1. 前端可视化：知识图谱浏览器 +2. 图谱编辑：Human-in-the-loop 修正 + +### 5. 潜在挑战与应对 + +#### 实体歧义 +**问题**：同名实体可能指代不同对象 + +**解决方案**： +- 实体对齐步骤 +- 利用 LLM 或向量相似度合并 +- 人工审核机制 + +#### 信息过载（Super Nodes） +**问题**：某些节点连接过多，查询性能下降 + +**解决方案**： +- 限制跳数（最大 3 跳） +- 限制最大边数（最大 1000 条） +- 分页返回结果 + +#### 幻觉与错误抽取 +**问题**：LLM 可能产生不存在的实体或关系 + +**解决方案**： +- 置信度评分 +- 人工审核 +- 对比多个模型的结果 + +### 6. 首要行动 + +**基础设施搭建**： +1. 在 `deployment/docker/` 下创建 neo4j 目录 +2. 编写 docker-compose.yml +3. 更新 Makefile 支持 Neo4j 的启动 + +**示例配置**： +```yaml +version: '3.8' +services: + neo4j: + image: neo4j:latest + ports: + - "7474:7474" + - "7687:7687" + environment: + - NEO4J_AUTH=neo4j/datamate123 + volumes: + - neo4j_data:/data +volumes: + neo4j_data: +``` + +## 与其他工具的对比 + +| 维度 | Gemini | Codex | Claude | +|------|--------|-------|--------| +| **技术选型** | Neo4j | Neo4j/JanusGraph | Neo4j | +| **架构重点** | GraphRAG 融合 | 3个新模块 | 复用现有基础设施 | +| **数据建模** | 灵活Schema+embedding | 10类实体+6类关系 | Schema先行+版本管理 | +| **实现路径** | 3阶段（MVP优先） | 4阶段（0-3） | 4阶段 | +| **独特优势** | LangChain+RAG融合 | 详细的领域模型 | 深度集成现有系统 | + +## 关键洞察 + +1. **GraphRAG 是核心创新**：Gemini 提出的混合检索方案特别适合 DataMate 现有的 RAG 架构 +2. **LangChain 简化开发**：利用现成的 LLMGraphTransformer 可以快速实现抽取功能 +3. **向量 + 图结构**：embedding 字段的引入使得向量检索和图检索可以无缝结合 +4. **MVP 优先**：强调先做基础设施，再逐步扩展功能 + +## 建议采纳度 + +**强烈推荐**： +- ✅ GraphRAG 融合方案 +- ✅ LangChain 集成 +- ✅ embedding 字段 +- ✅ Document 节点 + +**可选**： +- ⚠️ 3 阶段实施路线（可与其他工具的 4 阶段结合） + +## 相关文档 + +- [总体方案](../README.md) +- [架构设计](../architecture.md) +- [Codex 分析结果](./codex.md) +- [Claude 分析结果](./claude.md) diff --git a/docs/knowledge-graph/architecture.md b/docs/knowledge-graph/architecture.md new file mode 100644 index 0000000..c3d149a --- /dev/null +++ b/docs/knowledge-graph/architecture.md @@ -0,0 +1,397 @@ +# DataMate 知识图谱架构设计 + +## 🏗️ 整体架构 + +### 分层架构 + +``` +┌─────────────────────────────────────────────────────────────┐ +│ 前端层 (Frontend) │ +│ ┌─────────────────────────────────────────────────────┐ │ +│ │ React + AntV G6 │ │ +│ │ - 图谱可视化（分层加载、子图裁剪） │ │ +│ │ - 图谱编辑（Human-in-the-loop） │ │ +│ │ - 查询界面（Cypher 查询构建器） │ │ +│ └─────────────────────────────────────────────────────┘ │ +└─────────────────────────────────────────────────────────────┘ + ↓ HTTP/REST +┌─────────────────────────────────────────────────────────────┐ +│ 服务层 (Service) │ +│ ┌──────────────────────┐ ┌──────────────────────────┐ │ +│ │ kg-service │ │ rag-query-service │ │ +│ │ (Spring Boot) │ │ (FastAPI) │ │ +│ │ │ │ │ │ +│ │ - 图查询 API │ │ - 混合检索 │ │ +│ │ - 权限过滤 │ │ - GraphRAG │ │ +│ │ - 缓存层 (Redis) │ │ - 向量检索 + 图检索 │ │ +│ └──────────────────────┘ └──────────────────────────┘ │ +└─────────────────────────────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────────────────────────────┐ +│ 摄入层 (Ingestion) │ +│ ┌─────────────────────────────────────────────────────┐ │ +│ │ kg-ingestion (FastAPI) │ │ +│ │ - LangChain LLMGraphTransformer │ │ +│ │ - 实体对齐（向量相似度 + LLM） │ │ +│ │ - 关系生成（规则 + LLM） │ │ +│ │ - 置信度评分 │ │ +│ └─────────────────────────────────────────────────────┘ │ +└─────────────────────────────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────────────────────────────┐ +│ 存储层 (Storage) │ +│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ +│ │ MySQL │ │ Neo4j │ │ Milvus │ │ +│ │ (元数据) │ │ (图结构) │ │ (向量) │ │ +│ └──────────┘ └──────────┘ └──────────┘ │ +└─────────────────────────────────────────────────────────────┘ +``` + +## 🔧 技术选型 + +### 图数据库：Neo4j + +**选择理由**： +- ✅ 成熟稳定，社区活跃 +- ✅ Cypher 查询语言简洁强大 +- ✅ Spring Data Neo4j 集成良好 +- ✅ 支持 ACID 事务 +- ✅ 丰富的图算法库 + +**版本**：Neo4j 社区版（生产环境可升级企业版） + +**配置**： +- 端口：7474 (HTTP), 7687 (Bolt) +- 内存：heap 512MB, page cache 512MB（可根据数据量调整） +- 持久化：Docker volume + +### 后端框架 + +#### knowledge-graph-service (Spring Boot) + +**职责**： +- 图谱查询 API +- 权限控制和租户隔离 +- 缓存管理 +- 与其他服务集成 + +**技术栈**： +- Spring Boot 3.x +- Spring Data Neo4j +- Spring Security（权限控制） +- Redis（缓存） + +**DDD 分层**： +``` +com.datamate.knowledgegraph/ +├── application/ # 应用服务层 +│ └── GraphEntityService.java +├── domain/ # 领域层 +│ ├── model/ +│ │ ├── GraphEntity.java +│ │ └── GraphRelation.java +│ └── repository/ +│ └── GraphEntityRepository.java +├── infrastructure/ # 基础设施层 +│ ├── neo4j/ +│ │ └── KnowledgeGraphProperties.java +│ └── exception/ +│ └── KnowledgeGraphErrorCode.java +└── interfaces/ # 接口层 + ├── rest/ + │ └── GraphEntityController.java + └── dto/ + ├── CreateEntityRequest.java + ├── UpdateEntityRequest.java + └── CreateRelationRequest.java +``` + +#### kg-ingestion (FastAPI) + +**职责**： +- 知识抽取（文本 → 实体 + 关系） +- 实体对齐和消歧 +- 关系生成和验证 +- 置信度评分 + +**技术栈**： +- FastAPI +- LangChain +- LangChain LLMGraphTransformer +- Pydantic（数据验证） + +**模块结构**： +``` +kg_extraction/ +├── __init__.py +├── models.py # 数据模型 +├── extractor.py # 抽取器 +└── aligner.py # 实体对齐（待实现） +``` + +### 前端框架 + +**技术栈**： +- React 18 +- AntV G6（图可视化） +- TypeScript +- Ant Design（UI 组件） + +**核心功能**： +- 图谱可视化（支持 10000+ 节点） +- 交互式查询构建器 +- 实时编辑和反馈 +- 导出和分享 + +## 🔐 安全设计 + +### 多租户隔离 + +**策略**： +- 所有实体和关系都包含 `graph_id` 属性 +- 查询时自动添加 `graph_id` 过滤条件 +- Neo4j 索引包含 `graph_id` + +**实现**： +```cypher +// 创建索引 +CREATE INDEX entity_graph_id IF NOT EXISTS FOR (n:Entity) ON (n.graph_id); + +// 查询时自动过滤 +MATCH (n:Entity {graph_id: $graphId}) +WHERE n.id = $entityId +RETURN n; +``` + +### 权限控制 + +**graphId 双重防御**： +1. **Controller 层**：`@Pattern(regexp = UUID_REGEX)` 格式校验 +2. **Service 层**：`validateGraphId()` 业务校验 + +**实现**： +```java +// Controller 层 +@GetMapping("/{graphId}/entities/{entityId}") +public GraphEntity getEntity( + @PathVariable @Pattern(regexp = UUID_REGEX) String graphId, + @PathVariable @Pattern(regexp = UUID_REGEX) String entityId +) { + return entityService.getEntity(graphId, entityId); +} + +// Service 层 +public GraphEntity getEntity(String graphId, String entityId) { + validateGraphId(graphId); + return entityRepository.findByIdAndGraphId(entityId, graphId) + .orElseThrow(() -> BusinessException.of( + KnowledgeGraphErrorCode.ENTITY_NOT_FOUND + )); +} +``` + +### Cypher 注入防护 + +**策略**： +- 使用参数化查询 +- 禁止拼接 Cypher 字符串 +- 输入验证和转义 + +**示例**： +```java +// ✅ 正确：参数化查询 +@Query("MATCH (n:Entity {id: $id, graph_id: $graphId}) RETURN n") +Optional findByIdAndGraphId( + @Param("id") String id, + @Param("graphId") String graphId +); + +// ❌ 错误：字符串拼接 +String cypher = "MATCH (n:Entity {id: '" + id + "'}) RETURN n"; +``` + +## 📊 数据同步策略 + +### MySQL → Neo4j 同步 + +**策略**：最终一致性 + 对账机制 + +**同步方式**： +1. **实时同步**：通过 CDC（Change Data Capture）捕获 MySQL 变更 +2. **批量同步**：定时任务（每小时/每天）全量同步 +3. **手动同步**：提供 API 触发同步 + +**对账机制**： +- 每天凌晨对比 MySQL 和 Neo4j 的数据 +- 发现不一致时记录日志并告警 +- 提供修复工具 + +**实现**： +```java +@Scheduled(cron = "0 0 * * * *") // 每小时 +public void syncFromMySQL() { + // 1. 查询 MySQL 中的变更 + List changedDatasets = datasetRepository + .findByUpdatedAtAfter(lastSyncTime); + + // 2. 转换为图实体 + List entities = changedDatasets.stream() + .map(this::toGraphEntity) + .collect(Collectors.toList()); + + // 3. 批量写入 Neo4j + graphEntityRepository.saveAll(entities); + + // 4. 更新同步时间 + lastSyncTime = Instant.now(); +} +``` + +## ⚡ 性能优化 + +### 查询优化 + +**策略**： +1. **限制遍历深度**：最大 3 跳 +2. **限制返回节点数**：最大 1000 个 +3. **使用索引**：在高频查询字段上创建索引 +4. **缓存热点数据**：使用 Redis 缓存 + +**实现**： +```java +public List getNeighbors( + String graphId, + String entityId, + int depth, + int limit +) { + // Clamp 参数 + int actualDepth = Math.min(depth, properties.getMaxDepth()); + int actualLimit = Math.min(limit, properties.getMaxNodesPerQuery()); + + // 查询 + return entityRepository.findNeighbors( + graphId, entityId, actualDepth, actualLimit + ); +} +``` + +### 索引策略 + +**必需索引**： +```cypher +// 实体 ID 索引 +CREATE INDEX entity_id IF NOT EXISTS FOR (n:Entity) ON (n.id); + +// 图 ID 索引 +CREATE INDEX entity_graph_id IF NOT EXISTS FOR (n:Entity) ON (n.graph_id); + +// 复合索引 +CREATE INDEX entity_id_graph_id IF NOT EXISTS +FOR (n:Entity) ON (n.id, n.graph_id); +``` + +### 缓存策略 + +**缓存层次**： +1. **L1 缓存**：Spring Cache（本地缓存） +2. **L2 缓存**：Redis（分布式缓存） +3. **L3 缓存**：Neo4j 内置缓存 + +**缓存内容**： +- 热点实体（访问频率 > 100/小时） +- 常用子图（2-hop 邻居） +- 查询结果（TTL 5 分钟） + +## 🔄 GraphRAG 融合 + +### 混合检索架构 + +``` +用户查询 + ↓ +┌─────────────────────────────────────┐ +│ 查询理解和改写 │ +└─────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────┐ +│ 并行检索 │ +│ ┌───────────┐ ┌─────────────┐ │ +│ │ Milvus │ │ Neo4j │ │ +│ │ 向量检索 │ │ 图检索 │ │ +│ │ Top-K │ │ 2-hop 子图 │ │ +│ └───────────┘ └─────────────┘ │ +└─────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────┐ +│ 结果融合和排序 │ +│ - 向量相似度 × 0.6 │ +│ - 图结构相关性 × 0.4 │ +└─────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────┐ +│ Context 构建 │ +│ - 文档片段（Milvus） │ +│ - 三元组文本化（Neo4j） │ +└─────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────┐ +│ LLM 生成 │ +└─────────────────────────────────────┘ +``` + +### 三元组文本化 + +**策略**：将图结构转换为自然语言 + +**示例**： +```python +# 图结构 +(Dataset:用户行为数据)-[HAS_FIELD]->(Field:user_id) +(Dataset:用户行为数据)-[USED_BY]->(Workflow:用户画像构建) + +# 文本化 +""" +数据集"用户行为数据"包含字段"user_id"。 +数据集"用户行为数据"被工作流"用户画像构建"使用。 +""" +``` + +## 📈 监控和运维 + +### 监控指标 + +**Neo4j 指标**： +- 节点数量 +- 关系数量 +- 查询响应时间 +- 内存使用率 +- 磁盘使用率 + +**服务指标**： +- API 响应时间 +- 错误率 +- 吞吐量 +- 缓存命中率 + +**工具**： +- Prometheus（指标采集） +- Grafana（可视化） +- Neo4j Metrics（Neo4j 专用指标） + +### 备份策略 + +**Neo4j 备份**： +- 每天凌晨全量备份 +- 保留最近 7 天的备份 +- 备份到对象存储（S3/OSS） + +**恢复测试**： +- 每月进行一次恢复演练 +- 验证备份的完整性和可用性 + +## 🔗 相关文档 + +- [总体方案](./README.md) +- [实施计划](./implementation.md) +- [AI 分析结果](./analysis/) diff --git a/docs/knowledge-graph/implementation.md b/docs/knowledge-graph/implementation.md new file mode 100644 index 0000000..869f352 --- /dev/null +++ b/docs/knowledge-graph/implementation.md @@ -0,0 +1,395 @@ +# DataMate 知识图谱实施计划 + +## 📅 总体时间线 + +``` +第 0 阶段：基础设施（1周） ✅ 已完成 +第 1 阶段：MVP（2-3周） ⏳ 进行中 +第 2 阶段：GraphRAG 融合（3-4周） ⏳ 待开始 +第 3 阶段：可视化与优化（4-6周） ⏳ 待开始 +``` + +**总计**：10-14 周 + +--- + +## ✅ 第 0 阶段：基础设施（已完成） + +### 目标 +搭建知识图谱的基础设施，包括 Neo4j、Java 服务、Python 模块。 + +### 已完成任务 + +#### 1. Neo4j Docker Compose 配置 +- ✅ 创建 `deployment/docker/neo4j/docker-compose.yml` +- ✅ 配置 Neo4j 社区版 +- ✅ 端口：7474 (HTTP), 7687 (Bolt) +- ✅ 数据持久化：Docker volume +- ✅ 环境变量化密码 + +#### 2. Makefile 更新 +- ✅ 添加 `neo4j-up`：启动 Neo4j +- ✅ 添加 `neo4j-down`：停止 Neo4j +- ✅ 添加 `neo4j-logs`：查看日志 +- ✅ 添加 `neo4j-shell`：进入 Cypher Shell + +#### 3. knowledge-graph-service（Spring Boot） +- ✅ 创建完整的 DDD 分层架构 +- ✅ 实现 GraphEntity 的 CRUD +- ✅ 实现 graphId 双重防御 +- ✅ 实现查询限流 +- ✅ 统一异常处理体系 + +**文件清单**（11 个 Java 文件）： +- `KnowledgeGraphServiceConfiguration.java` +- `GraphEntityService.java` +- `GraphEntity.java`, `GraphRelation.java` +- `GraphEntityRepository.java` +- `KnowledgeGraphErrorCode.java` +- `KnowledgeGraphProperties.java` +- `GraphEntityController.java` +- `CreateEntityRequest.java`, `UpdateEntityRequest.java`, `CreateRelationRequest.java` +- `application-knowledgegraph.yml` + +#### 4. kg_extraction 模块（Python） +- ✅ 创建 `KnowledgeGraphExtractor` 类 +- ✅ 集成 LangChain LLMGraphTransformer +- ✅ 支持异步/同步/批量抽取 +- ✅ 支持 schema-guided 模式 +- ✅ 兼容 OpenAI 及自部署模型 + +**文件清单**（3 个 Python 文件）： +- `__init__.py` +- `models.py`：Pydantic 数据模型 +- `extractor.py`：抽取器实现 + +#### 5. 代码审查和修复 +- ✅ 3 轮 Codex 审查 +- ✅ 2 轮 Claude 修复 +- ✅ 所有 P0 和 P1 问题已解决 +- ✅ 编译通过，无阻塞性问题 + +### 成果 +- Commit: `5a553dd` +- 文件变更：22 个文件，1007 行新增 +- 分支：`lsf` + +--- + +## ⏳ 第 1 阶段：MVP（2-3周） + +### 目标 +实现基础的图谱构建和查询功能，支持 2-3 个高价值场景。 + +### 任务列表 + +#### 任务 1.1：实现 Python 抽取器的 FastAPI 接口（3天） + +**子任务**： +1. 创建 `kg_extraction/interface.py` + - 定义 FastAPI 路由 + - 实现 `/api/kg/extract` 端点 + - 支持文本输入，输出节点和边 + +2. 集成到 FastAPI 主路由 + - 在 `app/module/__init__.py` 中注册路由 + - 添加 API 文档 + +3. 实现配置管理 + - 从环境变量读取 API Key + - 使用 `SecretStr` 保护敏感信息 + +4. 编写单元测试 + - 测试抽取功能 + - 测试错误处理 + +**验收标准**： +- ✅ 能够通过 API 调用抽取功能 +- ✅ 返回结构化的节点和边 +- ✅ 有完整的 API 文档 +- ✅ 单元测试覆盖率 > 80% + +#### 任务 1.2：实现 Java 服务的关系（Relation）功能（3天） + +**子任务**： +1. 补充 `GraphRelationRepository` + - 实现 `findByGraphId` + - 实现 `findBySourceAndTarget` + - 实现 `findByType` + +2. 实现 `GraphRelationService` + - 创建关系 + - 查询关系 + - 更新关系 + - 删除关系 + +3. 实现 `GraphRelationController` + - `POST /{graphId}/relations`：创建关系 + - `GET /{graphId}/relations`：列表查询 + - `GET /{graphId}/relations/{relationId}`：单个查询 + - `PUT /{graphId}/relations/{relationId}`：更新关系 + - `DELETE /{graphId}/relations/{relationId}`：删除关系 + +4. 编写单元测试和集成测试 + +**验收标准**： +- ✅ 关系的 CRUD 功能完整 +- ✅ 支持按类型、源节点、目标节点查询 +- ✅ 有完整的权限控制 +- ✅ 测试覆盖率 > 80% + +#### 任务 1.3：定义核心实体和关系模型（2天） + +**子任务**： +1. 确定 5-8 类核心实体 + - 分析 DataMate 现有数据模型 + - 选择高价值实体 + - 定义实体属性 + +2. 定义实体之间的关系 + - 分析业务流程 + - 定义关系类型 + - 定义关系属性 + +3. 设计 Schema 版本管理 + - 定义 Schema 版本号 + - 实现 Schema 迁移机制 + - 记录 Schema 变更历史 + +4. 创建文档 + - 实体和关系清单 + - 属性说明 + - 示例数据 + +**验收标准**： +- ✅ 有清晰的实体和关系定义 +- ✅ 有完整的文档 +- ✅ 有示例数据 + +**建议的核心实体**（5-8 类）： +1. **Dataset**：数据集 +2. **Field**：字段 +3. **LabelTask**：标注任务 +4. **Workflow**：工作流 +5. **Job**：作业 +6. **User**：用户 +7. **Model**：模型（可选） +8. **Rule**：规则（可选） + +**建议的核心关系**： +1. **HAS_FIELD**：数据集包含字段 +2. **TRIGGERS**：触发关系（Workflow → Job） +3. **USES_DATASET**：使用数据集（Workflow → Dataset） +4. **ASSIGNED_TO**：分配给（LabelTask → User） +5. **PRODUCED_BY**：产生于（Dataset → Job） +6. **DEPENDS_ON**：依赖于（Job → Job） + +#### 任务 1.4：实现基础的图谱构建流程（5天） + +**子任务**： +1. 实现 MySQL → Neo4j 同步 + - 创建 `GraphSyncService` + - 实现全量同步 + - 实现增量同步 + - 实现对账机制 + +2. 实现手动触发构建 + - 创建 `/api/kg/sync` 端点 + - 支持按实体类型同步 + - 支持按时间范围同步 + +3. 实现同步监控 + - 记录同步日志 + - 统计同步数据量 + - 监控同步耗时 + +4. 编写集成测试 + - 测试全量同步 + - 测试增量同步 + - 测试对账机制 + +**验收标准**： +- ✅ 能够从 MySQL 同步元数据到 Neo4j +- ✅ 支持增量更新 +- ✅ 有完整的监控和日志 +- ✅ 集成测试通过 + +#### 任务 1.5：实现基础查询功能（2天） + +**子任务**： +1. 实现邻居查询 + - 支持 N 跳邻居查询 + - 支持按关系类型过滤 + - 支持分页 + +2. 实现路径查询 + - 最短路径 + - 所有路径（限制最大数量） + +3. 实现子图查询 + - 按条件筛选子图 + - 支持导出 + +4. 编写单元测试 + +**验收标准**： +- ✅ 查询功能完整 +- ✅ 性能满足要求（< 1s） +- ✅ 测试覆盖率 > 80% + +### 里程碑 + +**M1.1**（第 1 周结束）： +- ✅ Python 抽取器 API 完成 +- ✅ Java 关系功能完成 + +**M1.2**（第 2 周结束）： +- ✅ 核心实体和关系模型定义完成 +- ✅ 图谱构建流程完成 + +**M1.3**（第 3 周结束）： +- ✅ 基础查询功能完成 +- ✅ 集成测试通过 +- ✅ MVP 演示 + +### 验收标准 + +1. **功能完整性**： + - ✅ 能够从文本抽取实体和关系 + - ✅ 能够存储到 Neo4j + - ✅ 能够查询和遍历图谱 + - ✅ 支持基础的权限控制 + +2. **性能指标**： + - ✅ 抽取响应时间 < 5s + - ✅ 查询响应时间 < 1s + - ✅ 同步吞吐量 > 1000 实体/分钟 + +3. **质量指标**： + - ✅ 单元测试覆盖率 > 80% + - ✅ 集成测试通过 + - ✅ 代码审查通过 + +--- + +## ⏳ 第 2 阶段：GraphRAG 融合（3-4周） + +### 目标 +将知识图谱与现有 RAG 系统深度融合，提升检索和生成质量。 + +### 任务列表 + +#### 任务 2.1：实现混合检索（2周） + +**子任务**： +1. 在 `rag-query-service` 中增加图检索模块 +2. 实现 Milvus + Neo4j 并行检索 +3. 实现结果融合和排序 +4. 实现三元组文本化 + +#### 任务 2.2：实现 GraphRAG（1周） + +**子任务**： +1. 设计 GraphRAG 流程 +2. 实现 Context 构建 +3. 实现 LLM 生成 +4. 优化 Prompt + +#### 任务 2.3：评估和优化（1周） + +**子任务**： +1. 设计评估指标 +2. 收集测试数据 +3. 进行 A/B 测试 +4. 优化检索策略 + +### 验收标准 +- ✅ 混合检索性能优于单一检索 +- ✅ 支持可配置的检索策略 +- ✅ 有完整的评估指标 + +--- + +## ⏳ 第 3 阶段：可视化与优化（4-6周） + +### 目标 +提供友好的图谱可视化和编辑功能，优化性能和运维。 + +### 任务列表 + +#### 任务 3.1：前端图谱浏览器（2周） + +**子任务**： +1. 搭建 React + AntV G6 项目 +2. 实现图谱可视化 +3. 实现交互功能（缩放、拖拽、搜索） +4. 实现查询构建器 + +#### 任务 3.2：Human-in-the-loop 编辑（1周） + +**子任务**： +1. 实现实体编辑 +2. 实现关系编辑 +3. 实现批量操作 +4. 实现审核流程 + +#### 任务 3.3：性能优化（1周） + +**子任务**： +1. 优化索引策略 +2. 实现缓存机制 +3. 实现离线计算 +4. 优化查询语句 + +#### 任务 3.4：监控和运维（1周） + +**子任务**： +1. 集成 Prometheus + Grafana +2. 实现备份和恢复 +3. 编写运维文档 +4. 进行压力测试 + +### 验收标准 +- ✅ 支持大规模图谱可视化（10000+ 节点） +- ✅ 支持实时编辑和反馈 +- ✅ 查询响应时间 < 1s +- ✅ 有完整的监控和告警 + +--- + +## 📊 资源需求 + +### 人力资源 +- **后端开发**：1-2 人 +- **前端开发**：1 人 +- **算法工程师**：1 人（兼职） +- **测试工程师**：1 人（兼职） + +### 基础设施 +- **Neo4j**：4 核 8GB 内存（开发环境） +- **MySQL**：现有资源 +- **Milvus**：现有资源 +- **Redis**：现有资源 + +### 外部依赖 +- **LLM API**：OpenAI 或自部署模型 +- **对象存储**：备份使用 + +--- + +## 🎯 关键里程碑 + +| 里程碑 | 时间 | 交付物 | +|--------|------|--------| +| M0 | 第 1 周 | 基础设施搭建完成 ✅ | +| M1 | 第 4 周 | MVP 完成，支持基础图谱构建和查询 | +| M2 | 第 8 周 | GraphRAG 融合完成，检索质量提升 | +| M3 | 第 12 周 | 可视化和优化完成，系统上线 | + +--- + +## 🔗 相关文档 + +- [总体方案](./README.md) +- [架构设计](./architecture.md) +- [AI 分析结果](./analysis/) diff --git a/docs/knowledge-graph/schema/entities.md b/docs/knowledge-graph/schema/entities.md new file mode 100644 index 0000000..962ca62 --- /dev/null +++ b/docs/knowledge-graph/schema/entities.md @@ -0,0 +1,329 @@ +# DataMate 知识图谱 - 核心实体定义 + +> Schema 版本：1.0.0 +> 更新日期：2026-02-17 + +## 概述 + +DataMate 知识图谱定义了 **8 类核心实体**，覆盖数据资产管理、任务追踪、组织归属和知识管理四大领域。 + +所有实体在 Neo4j 中统一使用 `Entity` 标签，通过 `type` 属性区分语义类型。每个实体都包含以下公共属性： + +| 公共属性 | 类型 | 必填 | 说明 | +|---------|------|------|------| +| `id` | String (UUID) | 是 | 全局唯一标识符 | +| `name` | String | 是 | 实体名称 | +| `type` | String | 是 | 实体类型（见下文各类型定义） | +| `description` | String | 否 | 实体描述 | +| `graph_id` | String (UUID) | 是 | 所属图谱 ID，用于多租户隔离 | +| `source_id` | String | 否 | 来源记录 ID（MySQL 主键或外部系统 ID） | +| `source_type` | String | 否 | 来源类型：`SYNC`（MySQL 同步）、`EXTRACTION`（LLM 抽取）、`MANUAL`（人工创建） | +| `confidence` | Double | 否 | 置信度 0.0-1.0（同步数据默认 1.0，抽取数据由模型评分） | +| `properties_json` | String (JSON) | 否 | 类型特有 properties 的 JSON 序列化，各类型的 properties 定义见下文 | +| `created_at` | LocalDateTime | 是 | 创建时间 | + +--- + +## 1. Dataset（数据集） + +数据集是 DataMate 的核心资产，代表一组结构化或非结构化数据的集合。 + +**对应代码模型**：`data-management-service` 的 `Dataset.java` + +### properties（properties_json 字段） + +| property | 类型 | 必填 | 约束 | 说明 | +|----------|------|------|------|------| +| `dataset_type` | String | 是 | `IMAGE` / `TEXT` / `QA` / `MULTIMODAL` / `OTHER` | 数据集类型 | +| `status` | String | 是 | `DRAFT` / `ACTIVE` / `ARCHIVED` | 数据集状态 | +| `category` | String | 否 | 最长 50 字符 | 业务分类 | +| `format` | String | 否 | — | 数据格式（如 CSV、JSON、DICOM） | +| `record_count` | Long | 否 | >= 0 | 记录/文件数量 | +| `size_bytes` | Long | 否 | >= 0 | 数据集大小（字节） | +| `version` | Integer | 否 | >= 1 | 版本号 | +| `tags` | List\ | 否 | — | 标签列表 | + +### Cypher 示例 + +```cypher +// 创建 Dataset 实体（类型 properties 序列化到 properties_json） +CREATE (d:Entity { + id: 'a1b2c3d4-...', + name: '用户行为日志-v2', + type: 'Dataset', + description: '2025年Q4用户行为埋点数据', + graph_id: $graphId, + source_id: '12345', + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"dataset_type":"TEXT","status":"ACTIVE","category":"用户行为","format":"JSON","record_count":1500000,"size_bytes":2147483648,"version":2,"tags":["behavior","production"]}', + created_at: datetime() +}) +``` + +--- + +## 2. Field（字段） + +字段代表数据集中的列或属性元数据，是数据血缘分析和影响评估的基础单元。 + +**对应代码模型**：从 `DatasetFile` 的 schema 元数据中提取 + +### properties（properties_json 字段） + +| property | 类型 | 必填 | 约束 | 说明 | +|----------|------|------|------|------| +| `data_type` | String | 是 | — | 数据类型（如 STRING、INT、FLOAT、DATETIME、JSON） | +| `nullable` | Boolean | 否 | — | 是否允许空值 | +| `is_primary_key` | Boolean | 否 | — | 是否为主键 | +| `default_value` | String | 否 | — | 默认值 | +| `sample_values` | List\ | 否 | 最多 5 个 | 示例值 | +| `statistics` | String | 否 | JSON 格式 | 字段统计信息（null 率、唯一值数等） | + +### Cypher 示例 + +```cypher +CREATE (f:Entity { + id: 'f1e2d3c4-...', + name: 'user_id', + type: 'Field', + description: '用户唯一标识符', + graph_id: $graphId, + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"data_type":"STRING","nullable":false,"is_primary_key":true,"sample_values":["U001","U002","U003"]}', + created_at: datetime() +}) +``` + +--- + +## 3. LabelTask（标注任务） + +标注任务代表一次数据标注活动，包括人工标注和自动标注。 + +**对应代码模型**：`data-annotation-service` 的 `LabelingProject`、`AutoAnnotationTask`；`task-coordination-service` 的 `TaskMeta` + +### properties（properties_json 字段） + +| property | 类型 | 必填 | 约束 | 说明 | +|----------|------|------|------|------| +| `task_mode` | String | 是 | `MANUAL` / `AUTO` / `HYBRID` | 标注模式 | +| `data_type` | String | 否 | `image` / `text` / `audio` / `video` / `pdf` 等 | 标注数据类型 | +| `labeling_type` | String | 否 | — | 标注类型（如 NER、目标检测、情感分析） | +| `status` | String | 是 | `PENDING` / `IN_PROGRESS` / `COMPLETED` / `FAILED` / `STOPPED` | 任务状态 | +| `progress` | Double | 否 | 0.0-100.0 | 完成进度百分比 | +| `template_name` | String | 否 | — | 使用的标注模板名称 | + +### Cypher 示例 + +```cypher +CREATE (t:Entity { + id: 'e5f6a7b8-...', + name: '医学图像病灶标注-批次3', + type: 'LabelTask', + description: 'CT影像中肺结节目标检测标注', + graph_id: $graphId, + source_id: '67890', + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"task_mode":"HYBRID","data_type":"image","labeling_type":"object_detection","status":"IN_PROGRESS","progress":45.5,"template_name":"医学目标检测"}', + created_at: datetime() +}) +``` + +--- + +## 4. Workflow（工作流） + +工作流代表一组数据处理步骤的编排定义，涵盖数据清洗、数据合成、数据评估等处理管道。 + +**对应代码模型**：`data-cleaning-service` 的 `CleaningTemplate`；`data-collection-service` 的 `CollectionTemplate`；算子编排 `Operator` + +### properties（properties_json 字段） + +| property | 类型 | 必填 | 约束 | 说明 | +|----------|------|------|------|------| +| `workflow_type` | String | 是 | `CLEANING` / `SYNTHESIS` / `EVALUATION` / `COLLECTION` / `CUSTOM` | 工作流类型 | +| `status` | String | 否 | `DRAFT` / `ACTIVE` / `DEPRECATED` | 工作流状态 | +| `version` | String | 否 | — | 版本号 | +| `operator_count` | Integer | 否 | >= 0 | 包含的算子数量 | +| `schedule` | String | 否 | Cron 表达式 | 调度表达式（用于定时工作流） | + +### Cypher 示例 + +```cypher +CREATE (w:Entity { + id: 'c9d0e1f2-...', + name: '文本去重清洗管道', + type: 'Workflow', + description: '基于SimHash的文本去重 + 格式标准化 + 质量过滤', + graph_id: $graphId, + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"workflow_type":"CLEANING","status":"ACTIVE","version":"2.1","operator_count":3}', + created_at: datetime() +}) +``` + +--- + +## 5. Job（作业） + +作业代表一次具体的任务执行实例，是工作流的运行时实体，记录输入输出和执行状态。 + +**对应代码模型**：`CleaningTask`、`DataSynthInstance`、`EvaluationTask`、`CollectionTask`、`TaskExecution` + +### properties（properties_json 字段） + +| property | 类型 | 必填 | 约束 | 说明 | +|----------|------|------|------|------| +| `job_type` | String | 是 | `CLEANING` / `SYNTHESIS` / `EVALUATION` / `COLLECTION` / `ANNOTATION` | 作业类型 | +| `status` | String | 是 | `PENDING` / `RUNNING` / `COMPLETED` / `FAILED` / `STOPPED` / `CANCELLED` | 执行状态 | +| `started_at` | String | 否 | ISO 8601 | 开始时间 | +| `completed_at` | String | 否 | ISO 8601 | 完成时间 | +| `duration_seconds` | Long | 否 | >= 0 | 执行耗时（秒） | +| `input_count` | Long | 否 | >= 0 | 输入记录/文件数 | +| `output_count` | Long | 否 | >= 0 | 输出记录/文件数 | +| `error_message` | String | 否 | — | 错误信息（失败时） | + +### Cypher 示例 + +```cypher +CREATE (j:Entity { + id: 'd3e4f5a6-...', + name: '清洗作业-20260215-001', + type: 'Job', + description: '用户行为日志去重清洗', + graph_id: $graphId, + source_id: '54321', + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"job_type":"CLEANING","status":"COMPLETED","started_at":"2026-02-15T10:00:00","completed_at":"2026-02-15T10:35:00","duration_seconds":2100,"input_count":1500000,"output_count":1380000}', + created_at: datetime() +}) +``` + +--- + +## 6. User（用户） + +用户代表 DataMate 平台的操作人员，用于追踪数据资产的责任人和任务的执行者。 + +**对应代码模型**：`User.java`（`user` 表） + +### properties（properties_json 字段） + +| property | 类型 | 必填 | 约束 | 说明 | +|----------|------|------|------|------| +| `username` | String | 是 | 唯一 | 登录用户名 | +| `email` | String | 否 | — | 邮箱地址 | +| `role` | String | 否 | `ADMIN` / `USER` | 角色 | +| `enabled` | Boolean | 否 | — | 是否启用 | + +### Cypher 示例 + +```cypher +CREATE (u:Entity { + id: 'b7c8d9e0-...', + name: '张三', + type: 'User', + graph_id: $graphId, + source_id: '1001', + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"username":"zhangsan","email":"zhangsan@example.com","role":"USER","enabled":true}', + created_at: datetime() +}) +``` + +--- + +## 7. Org（组织） + +组织代表企业内部的团队或部门，用于数据资产的归属管理和权限隔离。 + +**对应代码模型**：从 `User.organization` 字段聚合派生 + +### properties（properties_json 字段） + +| property | 类型 | 必填 | 约束 | 说明 | +|----------|------|------|------|------| +| `org_code` | String | 否 | 唯一 | 组织编码 | +| `parent_org_id` | String | 否 | UUID | 上级组织 ID | +| `level` | Integer | 否 | >= 1 | 组织层级 | +| `member_count` | Integer | 否 | >= 0 | 成员数量 | + +### Cypher 示例 + +```cypher +CREATE (o:Entity { + id: 'a0b1c2d3-...', + name: '数据工程部', + type: 'Org', + description: '负责数据采集、清洗和标注', + graph_id: $graphId, + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"org_code":"DE","level":2,"member_count":15}', + created_at: datetime() +}) +``` + +--- + +## 8. KnowledgeSet（知识集） + +知识集代表经过整理和验证的知识资产集合，是 RAG 检索和知识问答的基础。 + +**对应代码模型**：`KnowledgeSet.java`（`knowledge_set` 表） + +### properties（properties_json 字段） + +| property | 类型 | 必填 | 约束 | 说明 | +|----------|------|------|------|------| +| `status` | String | 是 | `DRAFT` / `PUBLISHED` / `ARCHIVED` / `DEPRECATED` | 知识集状态 | +| `domain` | String | 否 | — | 知识领域 | +| `business_line` | String | 否 | — | 业务线 | +| `sensitivity` | String | 否 | `PUBLIC` / `INTERNAL` / `CONFIDENTIAL` / `SECRET` | 敏感级别 | +| `item_count` | Integer | 否 | >= 0 | 包含的知识条目数 | +| `valid_from` | String | 否 | ISO 8601 | 有效期开始 | +| `valid_to` | String | 否 | ISO 8601 | 有效期结束 | + +### Cypher 示例 + +```cypher +CREATE (k:Entity { + id: 'f4e5d6c7-...', + name: '医学影像标注规范知识库', + type: 'KnowledgeSet', + description: 'CT/MRI影像标注标准和常见病灶特征知识', + graph_id: $graphId, + source_id: '777', + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"status":"PUBLISHED","domain":"医学影像","business_line":"AI辅助诊断","sensitivity":"INTERNAL","item_count":320,"valid_from":"2026-01-01T00:00:00","valid_to":"2027-01-01T00:00:00"}', + created_at: datetime() +}) +``` + +--- + +## 实体类型汇总 + +| 实体类型 | Neo4j type 值 | 核心用途 | 来源 | +|---------|--------------|---------|------| +| Dataset | `Dataset` | 数据资产管理、血缘追踪 | MySQL 同步 | +| Field | `Field` | 字段级血缘、影响分析 | MySQL 同步 / Schema 解析 | +| LabelTask | `LabelTask` | 标注任务追踪、人员管理 | MySQL 同步 | +| Workflow | `Workflow` | 流程编排、复用管理 | MySQL 同步 | +| Job | `Job` | 执行追踪、输入输出血缘 | MySQL 同步 | +| User | `User` | 责任人追踪、权限管理 | MySQL 同步 | +| Org | `Org` | 组织归属、资产隔离 | MySQL 同步 / 派生 | +| KnowledgeSet | `KnowledgeSet` | 知识资产管理、RAG 检索 | MySQL 同步 | + +## 扩展说明 + +- **自定义实体类型**：除上述 8 类核心实体外，用户可通过 LLM 抽取或手动创建自定义实体类型。自定义实体使用相同的 `Entity` 标签和公共属性结构，`type` 字段可为任意字符串。 +- **属性存储**：类型特有 properties 存储在 `properties_json` 字段中（JSON 序列化），不直接作为 Neo4j 节点属性。这保证了 schema 的灵活性，同时通过 `type` 字段实现类型区分。 +- **索引策略**：`id`、`graph_id`、`type`、`name` 字段建立 Neo4j 索引，`properties_json` 中的 properties 不建立索引。如果某个 property 需要高频查询，应提升为节点顶层属性并建立索引。 diff --git a/docs/knowledge-graph/schema/er-diagram.md b/docs/knowledge-graph/schema/er-diagram.md new file mode 100644 index 0000000..12d0646 --- /dev/null +++ b/docs/knowledge-graph/schema/er-diagram.md @@ -0,0 +1,298 @@ +# DataMate 知识图谱 - 实体关系图 + +> Schema 版本：1.0.0 +> 更新日期：2026-02-17 + +## 核心实体关系总览 + +```mermaid +graph LR + %% 实体定义 + Dataset["Dataset
数据集"] + Field["Field
字段"] + LabelTask["LabelTask
标注任务"] + Workflow["Workflow
工作流"] + Job["Job
作业"] + User["User
用户"] + Org["Org
组织"] + KnowledgeSet["KnowledgeSet
知识集"] + + %% 关系连接 + Dataset -->|HAS_FIELD| Field + Dataset -->|DERIVED_FROM| Dataset + Dataset -->|BELONGS_TO| Org + + Job -->|USES_DATASET| Dataset + Job -->|PRODUCES| Dataset + Job -->|DEPENDS_ON| Job + + Workflow -->|TRIGGERS| Job + Workflow -->|USES_DATASET| Dataset + + LabelTask -->|USES_DATASET| Dataset + LabelTask -->|ASSIGNED_TO| User + + User -->|BELONGS_TO| Org + + Field -->|IMPACTS| Field + + KnowledgeSet -->|SOURCED_FROM| Dataset + + %% 样式 + classDef dataAsset fill:#4A90D9,stroke:#2C5F8A,color:#fff,stroke-width:2px + classDef task fill:#7B68EE,stroke:#5A4CB5,color:#fff,stroke-width:2px + classDef actor fill:#50C878,stroke:#3A9B5B,color:#fff,stroke-width:2px + classDef knowledge fill:#FFB347,stroke:#CC8F39,color:#fff,stroke-width:2px + + class Dataset,Field dataAsset + class LabelTask,Workflow,Job task + class User,Org actor + class KnowledgeSet knowledge +``` + +## 分领域视图 + +### 数据血缘视图 + +展示数据集之间的派生关系和字段级血缘。 + +```mermaid +graph TB + subgraph 源数据层 + DS_RAW["Dataset
原始数据集"] + F1["Field: user_id"] + F2["Field: event_type"] + F3["Field: timestamp"] + end + + subgraph 处理层 + JOB_CLEAN["Job
清洗作业"] + JOB_SYNTH["Job
合成作业"] + end + + subgraph 产出数据层 + DS_CLEAN["Dataset
清洗后数据集"] + DS_SYNTH["Dataset
合成数据集"] + F1_CLEAN["Field: user_id"] + F4["Field: user_segment"] + end + + DS_RAW -->|HAS_FIELD| F1 + DS_RAW -->|HAS_FIELD| F2 + DS_RAW -->|HAS_FIELD| F3 + + JOB_CLEAN -->|USES_DATASET| DS_RAW + JOB_CLEAN -->|PRODUCES| DS_CLEAN + JOB_SYNTH -->|USES_DATASET| DS_CLEAN + JOB_SYNTH -->|PRODUCES| DS_SYNTH + + DS_CLEAN -->|DERIVED_FROM| DS_RAW + DS_SYNTH -->|DERIVED_FROM| DS_CLEAN + + DS_CLEAN -->|HAS_FIELD| F1_CLEAN + DS_SYNTH -->|HAS_FIELD| F4 + + F1 -->|IMPACTS| F1_CLEAN + F1_CLEAN -->|IMPACTS| F4 + + classDef source fill:#E8F4FD,stroke:#4A90D9,color:#333 + classDef process fill:#F3E8FF,stroke:#7B68EE,color:#333 + classDef output fill:#E8FFF0,stroke:#50C878,color:#333 + + class DS_RAW,F1,F2,F3 source + class JOB_CLEAN,JOB_SYNTH process + class DS_CLEAN,DS_SYNTH,F1_CLEAN,F4 output +``` + +### 任务编排视图 + +展示工作流、作业和任务之间的编排关系。 + +```mermaid +graph LR + subgraph 工作流定义 + WF_CLEAN["Workflow
清洗管道"] + WF_EVAL["Workflow
评估管道"] + end + + subgraph 作业执行 + JOB1["Job
清洗作业 #1"] + JOB2["Job
清洗作业 #2"] + JOB3["Job
评估作业"] + end + + subgraph 标注任务 + LT1["LabelTask
人工标注"] + LT2["LabelTask
自动标注"] + end + + subgraph 人员 + U1["User
张三"] + U2["User
李四"] + end + + WF_CLEAN -->|TRIGGERS| JOB1 + WF_CLEAN -->|TRIGGERS| JOB2 + WF_EVAL -->|TRIGGERS| JOB3 + + JOB2 -->|DEPENDS_ON| JOB1 + JOB3 -->|DEPENDS_ON| JOB2 + + LT1 -->|ASSIGNED_TO| U1 + LT2 -->|ASSIGNED_TO| U2 + + classDef wf fill:#7B68EE,stroke:#5A4CB5,color:#fff + classDef job fill:#9B8FFF,stroke:#7B68EE,color:#fff + classDef task fill:#B8A9FF,stroke:#9B8FFF,color:#fff + classDef user fill:#50C878,stroke:#3A9B5B,color:#fff + + class WF_CLEAN,WF_EVAL wf + class JOB1,JOB2,JOB3 job + class LT1,LT2 task + class U1,U2 user +``` + +### 组织归属视图 + +展示用户、数据集与组织的归属关系。 + +```mermaid +graph TB + subgraph 组织 + ORG1["Org
数据工程部"] + ORG2["Org
AI研发部"] + end + + subgraph 人员 + U1["User: 张三"] + U2["User: 李四"] + U3["User: 王五"] + end + + subgraph 数据资产 + DS1["Dataset: 用户行为日志"] + DS2["Dataset: 医学影像集"] + DS3["Dataset: 训练数据集"] + end + + U1 -->|BELONGS_TO| ORG1 + U2 -->|BELONGS_TO| ORG1 + U3 -->|BELONGS_TO| ORG2 + + DS1 -->|BELONGS_TO| ORG1 + DS2 -->|BELONGS_TO| ORG2 + DS3 -->|BELONGS_TO| ORG2 + + classDef org fill:#FFB347,stroke:#CC8F39,color:#fff + classDef user fill:#50C878,stroke:#3A9B5B,color:#fff + classDef data fill:#4A90D9,stroke:#2C5F8A,color:#fff + + class ORG1,ORG2 org + class U1,U2,U3 user + class DS1,DS2,DS3 data +``` + +### 知识溯源视图 + +展示知识集与数据集的溯源关系。 + +```mermaid +graph LR + subgraph 数据源 + DS1["Dataset
用户行为日志"] + DS2["Dataset
产品文档"] + end + + subgraph 知识资产 + KS1["KnowledgeSet
用户行为知识库"] + end + + subgraph 标注 + LT["LabelTask
知识标注"] + end + + KS1 -->|SOURCED_FROM| DS1 + KS1 -->|SOURCED_FROM| DS2 + LT -->|USES_DATASET| DS1 + + classDef data fill:#4A90D9,stroke:#2C5F8A,color:#fff + classDef knowledge fill:#FFB347,stroke:#CC8F39,color:#fff + classDef task fill:#7B68EE,stroke:#5A4CB5,color:#fff + + class DS1,DS2 data + class KS1 knowledge + class LT task +``` + +## 综合示例：完整数据流 + +展示从原始数据到知识资产的完整处理链路。 + +```mermaid +graph TB + %% 组织和人员 + ORG["Org: 数据工程部"] + USER["User: 张三"] + + %% 数据资产 + DS_RAW["Dataset: 原始日志"] + DS_CLEAN["Dataset: 清洗数据"] + F_UID_RAW["Field: user_id (原始)"] + F_UID_CLEAN["Field: user_id (清洗)"] + + %% 处理流程 + WF["Workflow: 清洗管道"] + JOB["Job: 清洗作业"] + LT["LabelTask: 情感标注"] + + %% 知识 + KS["KnowledgeSet: 行为知识库"] + + %% 组织归属 + USER -->|BELONGS_TO| ORG + DS_RAW -->|BELONGS_TO| ORG + + %% 数据结构 + DS_RAW -->|HAS_FIELD| F_UID_RAW + DS_CLEAN -->|HAS_FIELD| F_UID_CLEAN + + %% 处理链路 + WF -->|TRIGGERS| JOB + JOB -->|USES_DATASET| DS_RAW + JOB -->|PRODUCES| DS_CLEAN + DS_CLEAN -->|DERIVED_FROM| DS_RAW + + %% 字段血缘 + F_UID_RAW -->|IMPACTS| F_UID_CLEAN + + %% 任务分配 + LT -->|USES_DATASET| DS_CLEAN + LT -->|ASSIGNED_TO| USER + + %% 知识溯源 + KS -->|SOURCED_FROM| DS_CLEAN + + %% 样式 + classDef org fill:#FFB347,stroke:#CC8F39,color:#fff,stroke-width:2px + classDef user fill:#50C878,stroke:#3A9B5B,color:#fff,stroke-width:2px + classDef data fill:#4A90D9,stroke:#2C5F8A,color:#fff,stroke-width:2px + classDef field fill:#87CEEB,stroke:#4A90D9,color:#333,stroke-width:1px + classDef process fill:#7B68EE,stroke:#5A4CB5,color:#fff,stroke-width:2px + classDef knowledge fill:#FF6B6B,stroke:#CC5555,color:#fff,stroke-width:2px + + class ORG org + class USER user + class DS_RAW,DS_CLEAN data + class F_UID_RAW,F_UID_CLEAN field + class WF,JOB,LT process + class KS knowledge +``` + +## 图例 + +| 颜色 | 分类 | 包含实体 | +|------|------|---------| +| 蓝色 | 数据资产 | Dataset, Field | +| 紫色 | 任务/流程 | Workflow, Job, LabelTask | +| 绿色 | 人员 | User, Org | +| 橙色/红色 | 知识 | KnowledgeSet | diff --git a/docs/knowledge-graph/schema/relationships.md b/docs/knowledge-graph/schema/relationships.md new file mode 100644 index 0000000..6c2ac46 --- /dev/null +++ b/docs/knowledge-graph/schema/relationships.md @@ -0,0 +1,585 @@ +# DataMate 知识图谱 - 核心关系定义 + +> Schema 版本：1.0.0 +> 更新日期：2026-02-17 + +## 概述 + +DataMate 知识图谱定义了 **10 类核心关系**，覆盖数据血缘、任务编排、组织归属和知识溯源四大场景。 + +所有关系在 Neo4j 中统一使用 `RELATED_TO` 关系类型，通过 `relation_type` 属性区分语义类型。每个关系都包含以下公共属性： + +| 公共属性 | 类型 | 必填 | 说明 | +|---------|------|------|------| +| `id` | String (UUID) | 是 | 关系唯一标识符 | +| `relation_type` | String | 是 | 语义关系类型（见下文各类型定义） | +| `graph_id` | String (UUID) | 是 | 所属图谱 ID | +| `weight` | Double | 否 | 关系权重 0.0-1.0（默认 1.0） | +| `confidence` | Double | 否 | 置信度 0.0-1.0（同步数据默认 1.0，抽取数据由模型评分） | +| `source_id` | String | 否 | 来源记录 ID | +| `properties_json` | String | 否 | 扩展属性 JSON | +| `created_at` | LocalDateTime | 是 | 创建时间 | + +### 关系方向约定 + +所有关系均为有向关系。方向表示语义上的"主动方 → 被动方"关系： +- `(A)-[:RELATED_TO {relation_type: 'HAS_FIELD'}]->(B)` 表示 A 拥有 B +- 查询时应注意方向，反向查询需要使用 `<-[]-` 语法 + +--- + +## 1. HAS_FIELD（包含字段） + +**方向**：`Dataset → Field` + +表示数据集包含某个字段/列。这是数据血缘分析的基础关系，支撑字段级影响评估。 + +### 关系属性 + +| 属性 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `ordinal` | Integer | 否 | 字段在数据集中的位置（从 0 开始） | +| `required` | Boolean | 否 | 是否为必填字段 | + +### 约束 + +- 源实体类型必须为 `Dataset` +- 目标实体类型必须为 `Field` +- 同一 Dataset → Field 对不应重复 + +### Cypher 示例 + +```cypher +// 创建 HAS_FIELD 关系 +MATCH (d:Entity {id: $datasetId, graph_id: $graphId}) +MATCH (f:Entity {id: $fieldId, graph_id: $graphId}) +CREATE (d)-[r:RELATED_TO { + id: randomUUID(), + relation_type: 'HAS_FIELD', + graph_id: $graphId, + weight: 1.0, + confidence: 1.0, + source_id: '', + properties_json: '{"ordinal": 0, "required": true}', + created_at: datetime() +}]->(f) + +// 查询数据集的所有字段 +MATCH (d:Entity {id: $datasetId, graph_id: $graphId}) + -[r:RELATED_TO {relation_type: 'HAS_FIELD', graph_id: $graphId}]-> + (f:Entity {graph_id: $graphId}) +RETURN f ORDER BY r.properties_json +``` + +### 业务场景 + +- 查看数据集包含哪些字段 +- 字段搜索：找到包含 `user_id` 字段的所有数据集 +- Schema 对比：比较两个数据集的字段差异 + +--- + +## 2. DERIVED_FROM（派生自） + +**方向**：`Dataset → Dataset` + +表示数据集之间的血缘关系：目标数据集是源数据集经过某种处理后派生出来的。涵盖数据清洗、数据合成、版本迭代等场景。 + +### 关系属性 + +| 属性 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `derivation_type` | String | 是 | 派生类型：`CLEANING`（清洗）/ `SYNTHESIS`（合成）/ `SPLIT`（拆分）/ `MERGE`（合并）/ `VERSION`（版本迭代） | +| `job_id` | String | 否 | 产生该派生关系的作业 ID | +| `transformation` | String | 否 | 转换描述（如"去重 + 格式标准化"） | + +### 约束 + +- 源实体和目标实体类型均为 `Dataset` +- 不允许自引用（源 ≠ 目标） +- 建议检查避免循环依赖 + +### Cypher 示例 + +```cypher +// 创建清洗派生关系 +MATCH (output:Entity {id: $outputDatasetId, graph_id: $graphId}) +MATCH (input:Entity {id: $inputDatasetId, graph_id: $graphId}) +CREATE (output)-[r:RELATED_TO { + id: randomUUID(), + relation_type: 'DERIVED_FROM', + graph_id: $graphId, + weight: 1.0, + confidence: 1.0, + properties_json: '{"derivation_type":"CLEANING","job_id":"d3e4f5a6-...","transformation":"SimHash去重 + 空值过滤"}', + created_at: datetime() +}]->(input) + +// 追踪数据血缘（最多 5 跳） +MATCH path = (d:Entity {id: $datasetId, graph_id: $graphId}) + -[:RELATED_TO *1..5 {relation_type: 'DERIVED_FROM'}]-> + (ancestor:Entity {graph_id: $graphId}) +RETURN path +``` + +### 业务场景 + +- **数据血缘追踪**：追溯数据集的来源链路 +- **影响分析**：当源数据集变更时，哪些下游数据集受影响 +- **版本管理**：查看数据集的版本演进历史 + +--- + +## 3. USES_DATASET（使用数据集） + +**方向**：`Job | LabelTask | Workflow → Dataset` + +表示作业、标注任务或工作流使用某个数据集作为输入。 + +### 关系属性 + +| 属性 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `usage_role` | String | 否 | 使用角色：`INPUT`（输入）/ `REFERENCE`（参考）/ `VALIDATION`（验证） | + +### 约束 + +- 源实体类型为 `Job`、`LabelTask` 或 `Workflow` +- 目标实体类型为 `Dataset` + +### Cypher 示例 + +```cypher +// 创建使用关系 +MATCH (j:Entity {id: $jobId, graph_id: $graphId}) +MATCH (d:Entity {id: $datasetId, graph_id: $graphId}) +CREATE (j)-[r:RELATED_TO { + id: randomUUID(), + relation_type: 'USES_DATASET', + graph_id: $graphId, + weight: 1.0, + confidence: 1.0, + properties_json: '{"usage_role":"INPUT"}', + created_at: datetime() +}]->(d) + +// 查询数据集被哪些作业使用 +MATCH (j:Entity {graph_id: $graphId}) + -[r:RELATED_TO {relation_type: 'USES_DATASET', graph_id: $graphId}]-> + (d:Entity {id: $datasetId, graph_id: $graphId}) +RETURN j +``` + +### 业务场景 + +- 查看数据集的消费者：谁在使用这个数据集 +- 评估数据集的重要程度：被多少任务依赖 +- 任务输入追溯：任务使用了哪些数据集 + +--- + +## 4. PRODUCES（产出） + +**方向**：`Job → Dataset` + +表示作业执行后产出了一个新的数据集。与 `USES_DATASET` 相对，构成完整的输入输出链路。 + +### 关系属性 + +| 属性 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `output_type` | String | 否 | 产出类型：`PRIMARY`（主输出）/ `SECONDARY`（副产物，如日志、统计报告） | + +### 约束 + +- 源实体类型为 `Job` +- 目标实体类型为 `Dataset` +- 一个 Job 可以产出多个 Dataset（如主输出 + 统计报告） + +### Cypher 示例 + +```cypher +// 创建产出关系 +MATCH (j:Entity {id: $jobId, graph_id: $graphId}) +MATCH (d:Entity {id: $outputDatasetId, graph_id: $graphId}) +CREATE (j)-[r:RELATED_TO { + id: randomUUID(), + relation_type: 'PRODUCES', + graph_id: $graphId, + weight: 1.0, + confidence: 1.0, + properties_json: '{"output_type":"PRIMARY"}', + created_at: datetime() +}]->(d) + +// 查看作业的完整输入输出 +MATCH (input:Entity {graph_id: $graphId}) + <-[:RELATED_TO {relation_type: 'USES_DATASET'}]- + (j:Entity {id: $jobId, graph_id: $graphId}) + -[:RELATED_TO {relation_type: 'PRODUCES'}]-> + (output:Entity {graph_id: $graphId}) +RETURN input, j, output +``` + +### 业务场景 + +- **端到端血缘**：结合 `USES_DATASET` 查看 Input → Job → Output 完整链路 +- **产出追踪**：查看作业产出了哪些数据集 +- **成本归因**：将产出数据集的成本归因到执行作业 + +--- + +## 5. ASSIGNED_TO（分配给） + +**方向**：`LabelTask | Job → User` + +表示任务被分配给某个用户执行。 + +### 关系属性 + +| 属性 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `assigned_at` | String | 否 | 分配时间（ISO 8601） | +| `role` | String | 否 | 分配角色：`EXECUTOR`（执行者）/ `REVIEWER`（审核者）/ `OWNER`（负责人） | + +### 约束 + +- 源实体类型为 `LabelTask` 或 `Job` +- 目标实体类型为 `User` +- 同一任务可分配给多个用户（不同角色） + +### Cypher 示例 + +```cypher +// 创建分配关系 +MATCH (t:Entity {id: $taskId, graph_id: $graphId}) +MATCH (u:Entity {id: $userId, graph_id: $graphId}) +CREATE (t)-[r:RELATED_TO { + id: randomUUID(), + relation_type: 'ASSIGNED_TO', + graph_id: $graphId, + weight: 1.0, + confidence: 1.0, + properties_json: '{"assigned_at":"2026-02-15T10:00:00","role":"EXECUTOR"}', + created_at: datetime() +}]->(u) + +// 查询用户的所有待办任务 +MATCH (t:Entity {graph_id: $graphId}) + -[r:RELATED_TO {relation_type: 'ASSIGNED_TO', graph_id: $graphId}]-> + (u:Entity {id: $userId, graph_id: $graphId}) +RETURN t +``` + +### 业务场景 + +- **工作量分析**：查看用户被分配了多少任务 +- **任务追踪**：查看任务的执行者和审核者 +- **人员负载均衡**：分析团队内任务分配情况 + +--- + +## 6. BELONGS_TO（归属于） + +**方向**：`User → Org` 或 `Dataset → Org` + +表示用户属于某个组织，或数据集归属于某个组织。 + +### 关系属性 + +| 属性 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `membership_type` | String | 否 | 归属类型：`PRIMARY`（主归属）/ `SECONDARY`（兼任/共享） | +| `since` | String | 否 | 归属起始时间（ISO 8601） | + +### 约束 + +- 源实体类型为 `User` 或 `Dataset` +- 目标实体类型为 `Org` +- User → Org 通常为 1:1（主归属），但允许兼任 + +### Cypher 示例 + +```cypher +// 用户归属组织 +MATCH (u:Entity {id: $userId, graph_id: $graphId}) +MATCH (o:Entity {id: $orgId, graph_id: $graphId}) +CREATE (u)-[r:RELATED_TO { + id: randomUUID(), + relation_type: 'BELONGS_TO', + graph_id: $graphId, + weight: 1.0, + confidence: 1.0, + properties_json: '{"membership_type":"PRIMARY","since":"2025-03-01T00:00:00"}', + created_at: datetime() +}]->(o) + +// 查询组织下的所有数据资产 +MATCH (d:Entity {type: 'Dataset', graph_id: $graphId}) + -[:RELATED_TO {relation_type: 'BELONGS_TO', graph_id: $graphId}]-> + (o:Entity {id: $orgId, graph_id: $graphId}) +RETURN d +``` + +### 业务场景 + +- **组织资产看板**：查看组织拥有的所有数据集 +- **权限继承**：基于组织关系推导数据访问权限 +- **跨组织协作**：发现共享数据集的组织关系 + +--- + +## 7. TRIGGERS（触发） + +**方向**：`Workflow → Job` + +表示工作流触发了一次作业执行。 + +### 关系属性 + +| 属性 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `trigger_type` | String | 否 | 触发方式：`MANUAL`（手动）/ `SCHEDULED`（定时）/ `EVENT`（事件驱动） | +| `triggered_at` | String | 否 | 触发时间（ISO 8601） | + +### 约束 + +- 源实体类型为 `Workflow` +- 目标实体类型为 `Job` +- 一个 Workflow 可触发多个 Job（每次执行产生一个） + +### Cypher 示例 + +```cypher +// 创建触发关系 +MATCH (w:Entity {id: $workflowId, graph_id: $graphId}) +MATCH (j:Entity {id: $jobId, graph_id: $graphId}) +CREATE (w)-[r:RELATED_TO { + id: randomUUID(), + relation_type: 'TRIGGERS', + graph_id: $graphId, + weight: 1.0, + confidence: 1.0, + properties_json: '{"trigger_type":"SCHEDULED","triggered_at":"2026-02-15T10:00:00"}', + created_at: datetime() +}]->(j) + +// 查询工作流的执行历史 +MATCH (w:Entity {id: $workflowId, graph_id: $graphId}) + -[r:RELATED_TO {relation_type: 'TRIGGERS', graph_id: $graphId}]-> + (j:Entity {graph_id: $graphId}) +RETURN j ORDER BY r.created_at DESC +``` + +### 业务场景 + +- **执行历史**：查看工作流的所有执行记录 +- **故障排查**：定位工作流最近一次失败的作业 +- **运行统计**：统计工作流的执行频率和成功率 + +--- + +## 8. DEPENDS_ON（依赖） + +**方向**：`Job → Job` + +表示作业之间的执行依赖关系：源作业的执行依赖于目标作业的完成。 + +### 关系属性 + +| 属性 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `dependency_type` | String | 否 | 依赖类型：`STRICT`（强依赖，必须成功）/ `SOFT`（弱依赖，失败可继续） | + +### 约束 + +- 源实体和目标实体类型均为 `Job` +- 不允许自引用 +- 不允许循环依赖（应用层校验） + +### Cypher 示例 + +```cypher +// 创建依赖关系 +MATCH (j1:Entity {id: $jobId, graph_id: $graphId}) +MATCH (j2:Entity {id: $dependsOnJobId, graph_id: $graphId}) +CREATE (j1)-[r:RELATED_TO { + id: randomUUID(), + relation_type: 'DEPENDS_ON', + graph_id: $graphId, + weight: 1.0, + confidence: 1.0, + properties_json: '{"dependency_type":"STRICT"}', + created_at: datetime() +}]->(j2) + +// 查询作业的完整依赖链 +MATCH path = (j:Entity {id: $jobId, graph_id: $graphId}) + -[:RELATED_TO *1..10 {relation_type: 'DEPENDS_ON'}]-> + (dep:Entity {graph_id: $graphId}) +RETURN path +``` + +### 业务场景 + +- **DAG 执行调度**：确定作业执行顺序 +- **失败传播分析**：当某个作业失败，哪些下游作业受影响 +- **关键路径分析**：找到最长依赖链，识别瓶颈 + +--- + +## 9. IMPACTS（影响） + +**方向**：`Field → Field` + +表示字段之间的影响关系：源字段的变更会影响目标字段。这是跨数据集的字段级血缘关系。 + +### 关系属性 + +| 属性 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `impact_type` | String | 否 | 影响类型：`DIRECT`（直接映射）/ `TRANSFORM`（转换派生）/ `AGGREGATE`（聚合计算） | +| `transformation_rule` | String | 否 | 转换规则描述（如"UPPER(source.name)"） | +| `job_id` | String | 否 | 建立该影响关系的作业 ID | + +### 约束 + +- 源实体和目标实体类型均为 `Field` +- 通常跨越不同 Dataset（但同 Dataset 内的字段派生也允许） +- 不允许自引用 + +### Cypher 示例 + +```cypher +// 创建字段影响关系 +MATCH (f1:Entity {id: $sourceFieldId, graph_id: $graphId}) +MATCH (f2:Entity {id: $targetFieldId, graph_id: $graphId}) +CREATE (f1)-[r:RELATED_TO { + id: randomUUID(), + relation_type: 'IMPACTS', + graph_id: $graphId, + weight: 0.8, + confidence: 0.9, + properties_json: '{"impact_type":"TRANSFORM","transformation_rule":"TRIM(LOWER(source))","job_id":"d3e4f5a6-..."}', + created_at: datetime() +}]->(f2) + +// 查询字段的影响范围（下游） +MATCH (f:Entity {id: $fieldId, graph_id: $graphId}) + -[:RELATED_TO *1..5 {relation_type: 'IMPACTS'}]-> + (downstream:Entity {graph_id: $graphId}) +RETURN downstream +``` + +### 业务场景 + +- **字段级血缘**：追踪字段从源到目标的完整链路 +- **影响评估**：修改某个字段前，评估下游影响范围 +- **数据质量追溯**：发现下游字段质量问题时，回溯源头 + +--- + +## 10. SOURCED_FROM（来源于） + +**方向**：`KnowledgeSet → Dataset` + +表示知识集的知识内容来源于某个数据集，是知识溯源的基础关系。 + +### 关系属性 + +| 属性 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `extraction_method` | String | 否 | 抽取方式：`LLM`（LLM 抽取）/ `RULE`（规则抽取）/ `MANUAL`（人工整理） | +| `extracted_at` | String | 否 | 抽取时间（ISO 8601） | +| `item_count` | Integer | 否 | 从该数据集抽取的知识条目数 | + +### 约束 + +- 源实体类型为 `KnowledgeSet` +- 目标实体类型为 `Dataset` +- 一个 KnowledgeSet 可来源于多个 Dataset + +### Cypher 示例 + +```cypher +// 创建来源关系 +MATCH (k:Entity {id: $knowledgeSetId, graph_id: $graphId}) +MATCH (d:Entity {id: $datasetId, graph_id: $graphId}) +CREATE (k)-[r:RELATED_TO { + id: randomUUID(), + relation_type: 'SOURCED_FROM', + graph_id: $graphId, + weight: 1.0, + confidence: 0.85, + properties_json: '{"extraction_method":"LLM","extracted_at":"2026-02-10T14:30:00","item_count":120}', + created_at: datetime() +}]->(d) + +// 查询知识集的所有数据来源 +MATCH (k:Entity {id: $knowledgeSetId, graph_id: $graphId}) + -[r:RELATED_TO {relation_type: 'SOURCED_FROM', graph_id: $graphId}]-> + (d:Entity {graph_id: $graphId}) +RETURN d, r.properties_json AS extraction_info +``` + +### 业务场景 + +- **知识溯源**：查看知识集基于哪些数据构建 +- **数据变更通知**：当源数据集更新时，提醒知识集需要刷新 +- **知识覆盖分析**：查看哪些数据集尚未被纳入知识管理 + +--- + +## 关系类型汇总 + +| 关系类型 | 方向 | relation_type 值 | 核心用途 | +|---------|------|-----------------|---------| +| HAS_FIELD | Dataset → Field | `HAS_FIELD` | 数据集字段结构 | +| DERIVED_FROM | Dataset → Dataset | `DERIVED_FROM` | 数据集级血缘 | +| USES_DATASET | Job/LabelTask/Workflow → Dataset | `USES_DATASET` | 输入依赖 | +| PRODUCES | Job → Dataset | `PRODUCES` | 输出产出 | +| ASSIGNED_TO | LabelTask/Job → User | `ASSIGNED_TO` | 任务分配 | +| BELONGS_TO | User/Dataset → Org | `BELONGS_TO` | 组织归属 | +| TRIGGERS | Workflow → Job | `TRIGGERS` | 流程触发 | +| DEPENDS_ON | Job → Job | `DEPENDS_ON` | 作业依赖 | +| IMPACTS | Field → Field | `IMPACTS` | 字段级血缘 | +| SOURCED_FROM | KnowledgeSet → Dataset | `SOURCED_FROM` | 知识溯源 | + +## 典型查询模式 + +### 1. 端到端数据血缘 + +```cypher +// 从最终数据集追溯到原始数据集，经过的所有处理步骤 +MATCH path = (final:Entity {id: $datasetId, graph_id: $graphId}) + -[:RELATED_TO *1..10]-> + (origin:Entity {graph_id: $graphId}) +WHERE ALL(r IN relationships(path) WHERE r.relation_type IN ['DERIVED_FROM', 'USES_DATASET', 'PRODUCES']) +RETURN path +``` + +### 2. 数据集影响分析 + +```cypher +// 查找修改某数据集后，所有受影响的下游实体 +MATCH (d:Entity {id: $datasetId, graph_id: $graphId}) + <-[:RELATED_TO {relation_type: 'USES_DATASET'}]- + (consumer:Entity {graph_id: $graphId}) +RETURN consumer.type AS entity_type, consumer.name AS entity_name, consumer.id AS entity_id +``` + +### 3. 用户工作看板 + +```cypher +// 查询用户相关的所有实体和关系 +MATCH (u:Entity {id: $userId, type: 'User', graph_id: $graphId}) +OPTIONAL MATCH (task:Entity)-[:RELATED_TO {relation_type: 'ASSIGNED_TO'}]->(u) +OPTIONAL MATCH (u)-[:RELATED_TO {relation_type: 'BELONGS_TO'}]->(org:Entity) +RETURN u, collect(DISTINCT task) AS tasks, collect(DISTINCT org) AS orgs +``` + +## 扩展说明 + +- **自定义关系类型**：除上述 10 类核心关系外，用户可通过 LLM 抽取或手动创建自定义关系类型。自定义关系使用相同的 `RELATED_TO` Neo4j 关系类型和公共属性结构，`relation_type` 字段可为任意字符串。 +- **双向关系**：所有关系均为单向。如果需要表达双向关系（如"A 和 B 互相影响"），应创建两条方向相反的关系。 +- **关系去重**：应用层应在创建关系前检查是否已存在相同的（source, target, relation_type）组合，避免重复。 diff --git a/docs/knowledge-graph/schema/schema.cypher b/docs/knowledge-graph/schema/schema.cypher new file mode 100644 index 0000000..d4e25cc --- /dev/null +++ b/docs/knowledge-graph/schema/schema.cypher @@ -0,0 +1,469 @@ +// ============================================================================= +// DataMate 知识图谱 - Neo4j Schema 初始化脚本 +// Schema 版本：1.0.0 +// 更新日期：2026-02-17 +// +// 使用方式： +// 1. 通过 Cypher Shell 执行： +// cat schema.cypher | cypher-shell -u neo4j -p +// 2. 或在 Neo4j Browser 中逐段执行 +// +// 注意： +// - 所有索引和约束使用 IF NOT EXISTS，可重复执行 +// - 约束自动创建对应索引，无需重复创建 +// - 关系属性索引需要 Neo4j Enterprise Edition，社区版使用属性内联匹配 +// ============================================================================= + +// ============================================================================= +// 第 1 部分：节点约束 +// ============================================================================= + +// Entity 节点 ID 唯一性约束（自动创建索引） +CREATE CONSTRAINT entity_id_unique IF NOT EXISTS +FOR (n:Entity) REQUIRE n.id IS UNIQUE; + +// ============================================================================= +// 第 2 部分：节点索引 +// ============================================================================= + +// graph_id 索引 —— 多租户隔离的核心索引，所有查询都会带上 graph_id +CREATE INDEX entity_graph_id IF NOT EXISTS +FOR (n:Entity) ON (n.graph_id); + +// type 索引 —— 按实体类型过滤 +CREATE INDEX entity_type IF NOT EXISTS +FOR (n:Entity) ON (n.type); + +// name 索引 —— 按名称搜索 +CREATE INDEX entity_name IF NOT EXISTS +FOR (n:Entity) ON (n.name); + +// source_id 索引 —— MySQL → Neo4j 同步时按源 ID 查找 +CREATE INDEX entity_source_id IF NOT EXISTS +FOR (n:Entity) ON (n.source_id); + +// 复合索引：(graph_id, type) —— 查询某图谱内指定类型的实体 +CREATE INDEX entity_graph_id_type IF NOT EXISTS +FOR (n:Entity) ON (n.graph_id, n.type); + +// 复合索引：(graph_id, id) —— 精确查找实体（最常用查询路径） +CREATE INDEX entity_graph_id_id IF NOT EXISTS +FOR (n:Entity) ON (n.graph_id, n.id); + +// 复合索引：(graph_id, source_id) —— 同步时按源 ID 查找 +CREATE INDEX entity_graph_id_source_id IF NOT EXISTS +FOR (n:Entity) ON (n.graph_id, n.source_id); + +// created_at 索引 —— 按创建时间排序 +CREATE INDEX entity_created_at IF NOT EXISTS +FOR (n:Entity) ON (n.created_at); + +// ============================================================================= +// 第 3 部分：全文索引（用于模糊搜索） +// ============================================================================= + +// Entity name + description 全文索引 +CREATE FULLTEXT INDEX entity_fulltext IF NOT EXISTS +FOR (n:Entity) ON EACH [n.name, n.description]; + +// ============================================================================= +// 第 3.1 部分：SyncHistory 约束和索引（同步元数据节点） +// ============================================================================= + +// (graph_id, sync_id) 唯一约束 —— 防止 syncId 碰撞产生重复记录 +CREATE CONSTRAINT sync_history_graph_sync_unique IF NOT EXISTS +FOR (h:SyncHistory) REQUIRE (h.graph_id, h.sync_id) IS UNIQUE; + +// (graph_id, started_at) 索引 —— 加速按时间范围查询同步历史 +CREATE INDEX sync_history_graph_started IF NOT EXISTS +FOR (h:SyncHistory) ON (h.graph_id, h.started_at); + +// (graph_id, status, started_at) 索引 —— 加速按状态+时间的过滤查询 +CREATE INDEX sync_history_graph_status_started IF NOT EXISTS +FOR (h:SyncHistory) ON (h.graph_id, h.status, h.started_at); + +// ============================================================================= +// 第 4 部分：关系属性说明 +// ============================================================================= + +// Neo4j 社区版不支持关系属性索引。 +// 所有关系查询通过节点索引定位后，在关系上使用属性内联匹配： +// -[r:RELATED_TO {graph_id: $graphId, relation_type: $type}]-> +// +// 如果使用 Neo4j Enterprise Edition，可取消以下注释创建关系索引： +// +// CREATE INDEX rel_graph_id IF NOT EXISTS +// FOR ()-[r:RELATED_TO]-() ON (r.graph_id); +// +// CREATE INDEX rel_relation_type IF NOT EXISTS +// FOR ()-[r:RELATED_TO]-() ON (r.relation_type); +// +// CREATE INDEX rel_id IF NOT EXISTS +// FOR ()-[r:RELATED_TO]-() ON (r.id); + +// ============================================================================= +// 第 5 部分：示例数据（可选，用于验证 Schema） +// ============================================================================= + +// 以下示例数据使用固定的 graph_id，用于开发和测试环境。 +// 生产环境中 graph_id 由应用层生成和管理。 + +// --- 创建示例组织 --- +CREATE (org:Entity { + id: '00000000-0000-0000-0000-000000000001', + name: '数据工程部', + type: 'Org', + description: '负责数据采集、清洗和标注', + graph_id: '11111111-1111-1111-1111-111111111111', + source_type: 'MANUAL', + confidence: 1.0, + properties_json: '{"org_code":"DE","level":1,"member_count":15}', + created_at: datetime() +}); + +// --- 创建示例用户 --- +CREATE (user:Entity { + id: '00000000-0000-0000-0000-000000000002', + name: '张三', + type: 'User', + graph_id: '11111111-1111-1111-1111-111111111111', + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"username":"zhangsan","email":"zhangsan@example.com","role":"USER","enabled":true}', + created_at: datetime() +}); + +// --- 创建示例数据集（源） --- +CREATE (ds1:Entity { + id: '00000000-0000-0000-0000-000000000010', + name: '用户行为日志-原始', + type: 'Dataset', + description: '原始用户行为埋点数据', + graph_id: '11111111-1111-1111-1111-111111111111', + source_id: '100', + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"dataset_type":"TEXT","status":"ACTIVE","category":"用户行为","format":"JSON","record_count":2000000,"size_bytes":3221225472}', + created_at: datetime() +}); + +// --- 创建示例数据集（清洗后） --- +CREATE (ds2:Entity { + id: '00000000-0000-0000-0000-000000000011', + name: '用户行为日志-清洗后', + type: 'Dataset', + description: '经过去重和格式标准化的用户行为数据', + graph_id: '11111111-1111-1111-1111-111111111111', + source_id: '101', + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"dataset_type":"TEXT","status":"ACTIVE","category":"用户行为","format":"JSON","record_count":1500000,"size_bytes":2147483648,"version":1}', + created_at: datetime() +}); + +// --- 创建示例字段 --- +CREATE (f1:Entity { + id: '00000000-0000-0000-0000-000000000020', + name: 'user_id', + type: 'Field', + description: '用户唯一标识符', + graph_id: '11111111-1111-1111-1111-111111111111', + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"data_type":"STRING","nullable":false,"is_primary_key":true}', + created_at: datetime() +}); + +CREATE (f2:Entity { + id: '00000000-0000-0000-0000-000000000021', + name: 'event_type', + type: 'Field', + description: '事件类型', + graph_id: '11111111-1111-1111-1111-111111111111', + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"data_type":"STRING","nullable":false,"sample_values":["click","view","purchase"]}', + created_at: datetime() +}); + +CREATE (f3:Entity { + id: '00000000-0000-0000-0000-000000000022', + name: 'user_id', + type: 'Field', + description: '用户唯一标识符（清洗后）', + graph_id: '11111111-1111-1111-1111-111111111111', + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"data_type":"STRING","nullable":false,"is_primary_key":true}', + created_at: datetime() +}); + +// --- 创建示例工作流 --- +CREATE (wf:Entity { + id: '00000000-0000-0000-0000-000000000030', + name: '文本去重清洗管道', + type: 'Workflow', + description: 'SimHash去重 + 格式标准化 + 空值过滤', + graph_id: '11111111-1111-1111-1111-111111111111', + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"workflow_type":"CLEANING","status":"ACTIVE","version":"1.0","operator_count":3}', + created_at: datetime() +}); + +// --- 创建示例作业 --- +CREATE (job:Entity { + id: '00000000-0000-0000-0000-000000000040', + name: '清洗作业-20260215-001', + type: 'Job', + description: '用户行为日志去重清洗', + graph_id: '11111111-1111-1111-1111-111111111111', + source_id: '500', + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"job_type":"CLEANING","status":"COMPLETED","started_at":"2026-02-15T10:00:00","completed_at":"2026-02-15T10:35:00","duration_seconds":2100,"input_count":2000000,"output_count":1500000}', + created_at: datetime() +}); + +// --- 创建示例标注任务 --- +CREATE (lt:Entity { + id: '00000000-0000-0000-0000-000000000050', + name: '情感分析标注-批次1', + type: 'LabelTask', + description: '用户评论情感标注（正面/负面/中性）', + graph_id: '11111111-1111-1111-1111-111111111111', + source_id: '600', + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"task_mode":"MANUAL","data_type":"text","labeling_type":"sentiment_analysis","status":"IN_PROGRESS","progress":30.0,"template_name":"情感分析"}', + created_at: datetime() +}); + +// --- 创建示例知识集 --- +CREATE (ks:Entity { + id: '00000000-0000-0000-0000-000000000060', + name: '用户行为分析知识库', + type: 'KnowledgeSet', + description: '从用户行为数据中提取的业务规则和洞察', + graph_id: '11111111-1111-1111-1111-111111111111', + source_type: 'SYNC', + confidence: 1.0, + properties_json: '{"status":"PUBLISHED","domain":"用户行为","business_line":"数据分析","sensitivity":"INTERNAL","item_count":85}', + created_at: datetime() +}); + +// ============================================================================= +// 第 6 部分：示例关系 +// ============================================================================= + +// HAS_FIELD：源数据集 → 字段 +MATCH (ds1:Entity {id: '00000000-0000-0000-0000-000000000010'}) +MATCH (f1:Entity {id: '00000000-0000-0000-0000-000000000020'}) +CREATE (ds1)-[:RELATED_TO { + id: '00000000-0000-0000-0000-100000000001', + relation_type: 'HAS_FIELD', + graph_id: '11111111-1111-1111-1111-111111111111', + weight: 1.0, + confidence: 1.0, + source_id: '', + properties_json: '{"ordinal":0,"required":true}', + created_at: datetime() +}]->(f1); + +MATCH (ds1:Entity {id: '00000000-0000-0000-0000-000000000010'}) +MATCH (f2:Entity {id: '00000000-0000-0000-0000-000000000021'}) +CREATE (ds1)-[:RELATED_TO { + id: '00000000-0000-0000-0000-100000000002', + relation_type: 'HAS_FIELD', + graph_id: '11111111-1111-1111-1111-111111111111', + weight: 1.0, + confidence: 1.0, + source_id: '', + properties_json: '{"ordinal":1,"required":true}', + created_at: datetime() +}]->(f2); + +// HAS_FIELD：清洗后数据集 → 字段 +MATCH (ds2:Entity {id: '00000000-0000-0000-0000-000000000011'}) +MATCH (f3:Entity {id: '00000000-0000-0000-0000-000000000022'}) +CREATE (ds2)-[:RELATED_TO { + id: '00000000-0000-0000-0000-100000000003', + relation_type: 'HAS_FIELD', + graph_id: '11111111-1111-1111-1111-111111111111', + weight: 1.0, + confidence: 1.0, + source_id: '', + properties_json: '{"ordinal":0,"required":true}', + created_at: datetime() +}]->(f3); + +// DERIVED_FROM：清洗后数据集 → 源数据集 +MATCH (ds2:Entity {id: '00000000-0000-0000-0000-000000000011'}) +MATCH (ds1:Entity {id: '00000000-0000-0000-0000-000000000010'}) +CREATE (ds2)-[:RELATED_TO { + id: '00000000-0000-0000-0000-100000000004', + relation_type: 'DERIVED_FROM', + graph_id: '11111111-1111-1111-1111-111111111111', + weight: 1.0, + confidence: 1.0, + source_id: '', + properties_json: '{"derivation_type":"CLEANING","job_id":"00000000-0000-0000-0000-000000000040","transformation":"SimHash去重 + 空值过滤"}', + created_at: datetime() +}]->(ds1); + +// TRIGGERS：工作流 → 作业 +MATCH (wf:Entity {id: '00000000-0000-0000-0000-000000000030'}) +MATCH (job:Entity {id: '00000000-0000-0000-0000-000000000040'}) +CREATE (wf)-[:RELATED_TO { + id: '00000000-0000-0000-0000-100000000005', + relation_type: 'TRIGGERS', + graph_id: '11111111-1111-1111-1111-111111111111', + weight: 1.0, + confidence: 1.0, + source_id: '', + properties_json: '{"trigger_type":"MANUAL","triggered_at":"2026-02-15T10:00:00"}', + created_at: datetime() +}]->(job); + +// USES_DATASET：作业 → 源数据集 +MATCH (job:Entity {id: '00000000-0000-0000-0000-000000000040'}) +MATCH (ds1:Entity {id: '00000000-0000-0000-0000-000000000010'}) +CREATE (job)-[:RELATED_TO { + id: '00000000-0000-0000-0000-100000000006', + relation_type: 'USES_DATASET', + graph_id: '11111111-1111-1111-1111-111111111111', + weight: 1.0, + confidence: 1.0, + source_id: '', + properties_json: '{"usage_role":"INPUT"}', + created_at: datetime() +}]->(ds1); + +// PRODUCES：作业 → 清洗后数据集 +MATCH (job:Entity {id: '00000000-0000-0000-0000-000000000040'}) +MATCH (ds2:Entity {id: '00000000-0000-0000-0000-000000000011'}) +CREATE (job)-[:RELATED_TO { + id: '00000000-0000-0000-0000-100000000007', + relation_type: 'PRODUCES', + graph_id: '11111111-1111-1111-1111-111111111111', + weight: 1.0, + confidence: 1.0, + source_id: '', + properties_json: '{"output_type":"PRIMARY"}', + created_at: datetime() +}]->(ds2); + +// ASSIGNED_TO：标注任务 → 用户 +MATCH (lt:Entity {id: '00000000-0000-0000-0000-000000000050'}) +MATCH (user:Entity {id: '00000000-0000-0000-0000-000000000002'}) +CREATE (lt)-[:RELATED_TO { + id: '00000000-0000-0000-0000-100000000008', + relation_type: 'ASSIGNED_TO', + graph_id: '11111111-1111-1111-1111-111111111111', + weight: 1.0, + confidence: 1.0, + source_id: '', + properties_json: '{"assigned_at":"2026-02-14T09:00:00","role":"EXECUTOR"}', + created_at: datetime() +}]->(user); + +// USES_DATASET：标注任务 → 清洗后数据集 +MATCH (lt:Entity {id: '00000000-0000-0000-0000-000000000050'}) +MATCH (ds2:Entity {id: '00000000-0000-0000-0000-000000000011'}) +CREATE (lt)-[:RELATED_TO { + id: '00000000-0000-0000-0000-100000000009', + relation_type: 'USES_DATASET', + graph_id: '11111111-1111-1111-1111-111111111111', + weight: 1.0, + confidence: 1.0, + source_id: '', + properties_json: '{"usage_role":"INPUT"}', + created_at: datetime() +}]->(ds2); + +// BELONGS_TO：用户 → 组织 +MATCH (user:Entity {id: '00000000-0000-0000-0000-000000000002'}) +MATCH (org:Entity {id: '00000000-0000-0000-0000-000000000001'}) +CREATE (user)-[:RELATED_TO { + id: '00000000-0000-0000-0000-100000000010', + relation_type: 'BELONGS_TO', + graph_id: '11111111-1111-1111-1111-111111111111', + weight: 1.0, + confidence: 1.0, + source_id: '', + properties_json: '{"membership_type":"PRIMARY","since":"2025-03-01T00:00:00"}', + created_at: datetime() +}]->(org); + +// BELONGS_TO：源数据集 → 组织 +MATCH (ds1:Entity {id: '00000000-0000-0000-0000-000000000010'}) +MATCH (org:Entity {id: '00000000-0000-0000-0000-000000000001'}) +CREATE (ds1)-[:RELATED_TO { + id: '00000000-0000-0000-0000-100000000011', + relation_type: 'BELONGS_TO', + graph_id: '11111111-1111-1111-1111-111111111111', + weight: 1.0, + confidence: 1.0, + source_id: '', + properties_json: '{"membership_type":"PRIMARY"}', + created_at: datetime() +}]->(org); + +// IMPACTS：源字段 → 清洗后字段 +MATCH (f1:Entity {id: '00000000-0000-0000-0000-000000000020'}) +MATCH (f3:Entity {id: '00000000-0000-0000-0000-000000000022'}) +CREATE (f1)-[:RELATED_TO { + id: '00000000-0000-0000-0000-100000000012', + relation_type: 'IMPACTS', + graph_id: '11111111-1111-1111-1111-111111111111', + weight: 1.0, + confidence: 0.95, + source_id: '', + properties_json: '{"impact_type":"DIRECT","job_id":"00000000-0000-0000-0000-000000000040"}', + created_at: datetime() +}]->(f3); + +// SOURCED_FROM：知识集 → 清洗后数据集 +MATCH (ks:Entity {id: '00000000-0000-0000-0000-000000000060'}) +MATCH (ds2:Entity {id: '00000000-0000-0000-0000-000000000011'}) +CREATE (ks)-[:RELATED_TO { + id: '00000000-0000-0000-0000-100000000013', + relation_type: 'SOURCED_FROM', + graph_id: '11111111-1111-1111-1111-111111111111', + weight: 1.0, + confidence: 0.85, + source_id: '', + properties_json: '{"extraction_method":"LLM","extracted_at":"2026-02-16T14:30:00","item_count":85}', + created_at: datetime() +}]->(ds2); + +// ============================================================================= +// 第 7 部分：验证查询 +// ============================================================================= + +// 验证节点数量 +// MATCH (n:Entity {graph_id: '11111111-1111-1111-1111-111111111111'}) +// RETURN n.type AS type, count(*) AS count +// ORDER BY count DESC; + +// 验证关系数量 +// MATCH (:Entity {graph_id: '11111111-1111-1111-1111-111111111111'}) +// -[r:RELATED_TO {graph_id: '11111111-1111-1111-1111-111111111111'}]-> +// (:Entity {graph_id: '11111111-1111-1111-1111-111111111111'}) +// RETURN r.relation_type AS type, count(*) AS count +// ORDER BY count DESC; + +// 验证端到端血缘 +// MATCH path = (ds2:Entity {name: '用户行为日志-清洗后'}) +// -[:RELATED_TO *1..5]-> +// (origin:Entity) +// WHERE ALL(r IN relationships(path) WHERE r.graph_id = '11111111-1111-1111-1111-111111111111') +// RETURN path; + +// ============================================================================= +// 第 8 部分：清理示例数据（可选） +// ============================================================================= + +// 如需清理示例数据，执行以下语句： +// MATCH (n:Entity {graph_id: '11111111-1111-1111-1111-111111111111'}) +// DETACH DELETE n; diff --git a/frontend/package-lock.json b/frontend/package-lock.json index 0091f86..49442db 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -8,6 +8,7 @@ "name": "edatamate", "version": "0.0.0", "dependencies": { + "@antv/g6": "^5.0.0", "@reduxjs/toolkit": "^2.11.0", "@xyflow/react": "^12.8.3", "antd": "^5.27.0", @@ -157,6 +158,241 @@ "react": ">=16.9.0" } }, + "node_modules/@antv/algorithm": { + "version": "0.1.26", + "resolved": "https://registry.npmmirror.com/@antv/algorithm/-/algorithm-0.1.26.tgz", + "integrity": 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true, + "dependencies": { + "mime-db": "1.52.0" + }, + "engines": { + "node": ">= 0.6" + } + }, "node_modules/which": { "version": "2.0.2", "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz", @@ -7164,6 +8523,19 @@ "node": ">=0.10.0" } }, + "node_modules/workerize-loader": { + "version": "2.0.2", + "resolved": "https://registry.npmmirror.com/workerize-loader/-/workerize-loader-2.0.2.tgz", + "integrity": "sha512-HoZ6XY4sHWxA2w0WpzgBwUiR3dv1oo7bS+oCwIpb6n54MclQ/7KXdXsVIChTCygyuHtVuGBO1+i3HzTt699UJQ==", + "license": "MIT", + "peer": true, + "dependencies": { + "loader-utils": "^2.0.0" + }, + "peerDependencies": { + "webpack": "*" + } + }, "node_modules/wrappy": { "version": "1.0.2", "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz", diff --git a/frontend/package.json b/frontend/package.json index 5d44f3b..6ba0870 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -20,7 +20,8 @@ "react-dom": "^18.1.1", "react-redux": "^9.2.0", "react-router": "^7.8.0", - "recharts": "2.15.0" + "recharts": "2.15.0", + "@antv/g6": "^5.0.0" }, "devDependencies": { "@eslint/js": "^9.33.0", diff --git a/frontend/src/auth/permissions.ts b/frontend/src/auth/permissions.ts index 3340f88..98675eb 100644 --- a/frontend/src/auth/permissions.ts +++ b/frontend/src/auth/permissions.ts @@ -22,6 +22,8 @@ export const PermissionCodes = { taskCoordinationAssign: "module:task-coordination:assign", contentGenerationUse: "module:content-generation:use", agentUse: "module:agent:use", + knowledgeGraphRead: "module:knowledge-graph:read", + knowledgeGraphWrite: "module:knowledge-graph:write", userManage: "system:user:manage", roleManage: "system:role:manage", permissionManage: "system:permission:manage", @@ -39,6 +41,7 @@ const routePermissionRules: Array<{ prefix: string; permission: string }> = [ { prefix: "/data/orchestration", permission: PermissionCodes.orchestrationRead }, { prefix: "/data/task-coordination", permission: PermissionCodes.taskCoordinationRead }, { prefix: "/data/content-generation", permission: PermissionCodes.contentGenerationUse }, + { prefix: "/data/knowledge-graph", permission: PermissionCodes.knowledgeGraphRead }, { prefix: "/chat", permission: PermissionCodes.agentUse }, ]; diff --git a/frontend/src/pages/KnowledgeGraph/Home/KnowledgeGraphPage.tsx b/frontend/src/pages/KnowledgeGraph/Home/KnowledgeGraphPage.tsx new file mode 100644 index 0000000..edf8056 --- /dev/null +++ b/frontend/src/pages/KnowledgeGraph/Home/KnowledgeGraphPage.tsx @@ -0,0 +1,274 @@ +import { useState, useCallback, useEffect } from "react"; +import { Card, Input, Select, Button, Tag, Space, Empty, Tabs, message } from "antd"; +import { Network, RotateCcw } from "lucide-react"; +import { useSearchParams } from "react-router"; +import GraphCanvas from "../components/GraphCanvas"; +import SearchPanel from "../components/SearchPanel"; +import QueryBuilder from "../components/QueryBuilder"; +import NodeDetail from "../components/NodeDetail"; +import RelationDetail from "../components/RelationDetail"; +import useGraphData from "../hooks/useGraphData"; +import useGraphLayout, { LAYOUT_OPTIONS } from "../hooks/useGraphLayout"; +import { + ENTITY_TYPE_COLORS, + DEFAULT_ENTITY_COLOR, + ENTITY_TYPE_LABELS, +} from "../knowledge-graph.const"; + +const UUID_REGEX = /^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$/i; + +export default function KnowledgeGraphPage() { + const [params, setParams] = useSearchParams(); + const [graphId, setGraphId] = useState(() => params.get("graphId") ?? ""); + const [graphIdInput, setGraphIdInput] = useState(() => params.get("graphId") ?? ""); + + const { + graphData, + loading, + searchResults, + searchLoading, + highlightedNodeIds, + loadInitialData, + expandNode, + searchEntities, + mergePathData, + clearGraph, + clearSearch, + } = useGraphData(); + + const { layoutType, setLayoutType } = useGraphLayout(); + + // Detail panel state + const [selectedNodeId, setSelectedNodeId] = useState(null); + const [selectedEdgeId, setSelectedEdgeId] = useState(null); + const [nodeDetailOpen, setNodeDetailOpen] = useState(false); + const [relationDetailOpen, setRelationDetailOpen] = useState(false); + + // Load graph when graphId changes + useEffect(() => { + if (graphId && UUID_REGEX.test(graphId)) { + clearGraph(); + loadInitialData(graphId); + } + }, [graphId, loadInitialData, clearGraph]); + + const handleLoadGraph = useCallback(() => { + if (!UUID_REGEX.test(graphIdInput)) { + message.warning("请输入有效的图谱 ID（UUID 格式）"); + return; + } + setGraphId(graphIdInput); + setParams({ graphId: graphIdInput }); + }, [graphIdInput, setParams]); + + const handleNodeClick = useCallback((nodeId: string) => { + setSelectedNodeId(nodeId); + setSelectedEdgeId(null); + setNodeDetailOpen(true); + setRelationDetailOpen(false); + }, []); + + const handleEdgeClick = useCallback((edgeId: string) => { + setSelectedEdgeId(edgeId); + setSelectedNodeId(null); + setRelationDetailOpen(true); + setNodeDetailOpen(false); + }, []); + + const handleNodeDoubleClick = useCallback( + (nodeId: string) => { + if (!graphId) return; + expandNode(graphId, nodeId); + }, + [graphId, expandNode] + ); + + const handleCanvasClick = useCallback(() => { + setSelectedNodeId(null); + setSelectedEdgeId(null); + setNodeDetailOpen(false); + setRelationDetailOpen(false); + }, []); + + const handleExpandNode = useCallback( + (entityId: string) => { + if (!graphId) return; + expandNode(graphId, entityId); + }, + [graphId, expandNode] + ); + + const handleEntityNavigate = useCallback( + (entityId: string) => { + setSelectedNodeId(entityId); + setNodeDetailOpen(true); + setRelationDetailOpen(false); + }, + [] + ); + + const handleSearchResultClick = useCallback( + (entityId: string) => { + handleNodeClick(entityId); + // If the entity is not in the current graph, expand it + if (!graphData.nodes.find((n) => n.id === entityId) && graphId) { + expandNode(graphId, entityId); + } + }, + [handleNodeClick, graphData.nodes, graphId, expandNode] + ); + + const handleRelationClick = useCallback((relationId: string) => { + setSelectedEdgeId(relationId); + setRelationDetailOpen(true); + setNodeDetailOpen(false); + }, []); + + const hasGraph = graphId && UUID_REGEX.test(graphId); + const nodeCount = graphData.nodes.length; + const edgeCount = graphData.edges.length; + + // Collect unique entity types in current graph for legend + const entityTypes = [...new Set(graphData.nodes.map((n) => n.data.type))].sort(); + + return ( +
+ {/* Header */} +
+

+ + 知识图谱浏览器 +

+
+ + {/* Graph ID Input + Controls */} +
+ + setGraphIdInput(e.target.value)} + onPressEnter={handleLoadGraph} + allowClear + /> + + + + + + setSourceId(e.target.value)} + allowClear + /> + + setTargetId(e.target.value)} + allowClear + /> + +
+ 最大深度 + setMaxDepth(v ?? 5)} + size="small" + className="flex-1" + /> +
+ + {queryType === "all-paths" && ( +
+ 最大路径数 + setMaxPaths(v ?? 3)} + size="small" + className="flex-1" + /> +
+ )} + +
+ + +
+ + + {pathResults.length > 0 ? ( + ( + +
+
+ 路径 {index + 1}（{path.pathLength} 跳） +
+
+ {path.nodes.map((node, ni) => ( + + {ni > 0 && ( + + {path.edges[ni - 1] + ? RELATION_TYPE_LABELS[path.edges[ni - 1].relationType] ?? + path.edges[ni - 1].relationType + : "→"} + + )} + + {ENTITY_TYPE_LABELS[node.type] ?? node.type} + + {node.name} + + ))} +
+
+ + )} + /> + ) : !loading && sourceId && targetId ? ( + + ) : null} + +
+ ); +} diff --git a/frontend/src/pages/KnowledgeGraph/components/RelationDetail.tsx b/frontend/src/pages/KnowledgeGraph/components/RelationDetail.tsx new file mode 100644 index 0000000..f8c5e78 --- /dev/null +++ b/frontend/src/pages/KnowledgeGraph/components/RelationDetail.tsx @@ -0,0 +1,122 @@ +import { useEffect, useState } from "react"; +import { Drawer, Descriptions, Tag, Spin, Empty, message } from "antd"; +import type { RelationVO } from "../knowledge-graph.model"; +import { + ENTITY_TYPE_LABELS, + ENTITY_TYPE_COLORS, + DEFAULT_ENTITY_COLOR, + RELATION_TYPE_LABELS, +} from "../knowledge-graph.const"; +import * as api from "../knowledge-graph.api"; + +interface RelationDetailProps { + graphId: string; + relationId: string | null; + open: boolean; + onClose: () => void; + onEntityNavigate: (entityId: string) => void; +} + +export default function RelationDetail({ + graphId, + relationId, + open, + onClose, + onEntityNavigate, +}: RelationDetailProps) { + const [relation, setRelation] = useState(null); + const [loading, setLoading] = useState(false); + + useEffect(() => { + if (!relationId || !graphId) { + setRelation(null); + return; + } + if (!open) return; + + setLoading(true); + api + .getRelation(graphId, relationId) + .then((data) => setRelation(data)) + .catch(() => message.error("加载关系详情失败")) + .finally(() => setLoading(false)); + }, [graphId, relationId, open]); + + return ( + + + {relation ? ( +
+ + + + {RELATION_TYPE_LABELS[relation.relationType] ?? relation.relationType} + + + +
+ + {ENTITY_TYPE_LABELS[relation.sourceEntityType] ?? relation.sourceEntityType} + + onEntityNavigate(relation.sourceEntityId)} + > + {relation.sourceEntityName} + +
+ + +
+ + {ENTITY_TYPE_LABELS[relation.targetEntityType] ?? relation.targetEntityType} + + onEntityNavigate(relation.targetEntityId)} + > + {relation.targetEntityName} + +
+ + {relation.weight != null && ( + {relation.weight} + )} + {relation.confidence != null && ( + + {(relation.confidence * 100).toFixed(0)}% + + )} + {relation.createdAt && ( + {relation.createdAt} + )} + + + {relation.properties && Object.keys(relation.properties).length > 0 && ( + <> +

扩展属性

+ + {Object.entries(relation.properties).map(([key, value]) => ( + + {String(value)} + + ))} + + + )} +
+ ) : !loading ? ( + + ) : null} + + + ); +} diff --git a/frontend/src/pages/KnowledgeGraph/components/SearchPanel.tsx b/frontend/src/pages/KnowledgeGraph/components/SearchPanel.tsx new file mode 100644 index 0000000..c4d6489 --- /dev/null +++ b/frontend/src/pages/KnowledgeGraph/components/SearchPanel.tsx @@ -0,0 +1,102 @@ +import { useState, useCallback } from "react"; +import { Input, List, Tag, Select, Empty } from "antd"; +import { Search } from "lucide-react"; +import type { SearchHitVO } from "../knowledge-graph.model"; +import { + ENTITY_TYPES, + ENTITY_TYPE_LABELS, + ENTITY_TYPE_COLORS, + DEFAULT_ENTITY_COLOR, +} from "../knowledge-graph.const"; + +interface SearchPanelProps { + graphId: string; + results: SearchHitVO[]; + loading: boolean; + onSearch: (graphId: string, query: string) => void; + onResultClick: (entityId: string) => void; + onClear: () => void; +} + +export default function SearchPanel({ + graphId, + results, + loading, + onSearch, + onResultClick, + onClear, +}: SearchPanelProps) { + const [query, setQuery] = useState(""); + const [typeFilter, setTypeFilter] = useState(undefined); + + const handleSearch = useCallback( + (value: string) => { + setQuery(value); + if (!value.trim()) { + onClear(); + return; + } + onSearch(graphId, value); + }, + [graphId, onSearch, onClear] + ); + + const filteredResults = typeFilter + ? results.filter((r) => r.type === typeFilter) + : results; + + return ( +
+ setQuery(e.target.value)} + onSearch={handleSearch} + allowClear + onClear={() => { + setQuery(""); + onClear(); + }} + prefix={} + loading={loading} + /> + +