commit 4693ebcf83751af85c98e43eac2b0b0073510eb8 Author: lirui Date: Wed Feb 4 01:14:58 2026 +0800 feat: initialize MCP RAG Prompts server with embedding management - Add package.json for project configuration and dependencies. - Create src/index.ts as the entry point for the MCP server. - Implement vectorStore for managing embeddings with local and cloud providers. - Add embeddingProviders for local and cloud-based embedding services (OpenAI, Aliyun, SiliconFlow). - Define types for prompts and embeddings in types.ts. - Implement searchPersona tool for semantic search of expert personas. - Create test.ts for validating vector storage and search functionality. - Configure TypeScript with tsconfig.json for strict type checking and module resolution. diff --git a/.env b/.env new file mode 100644 index 0000000..f594015 --- /dev/null +++ b/.env @@ -0,0 +1,53 @@ +# ============================================================ +# MCP RAG Prompts - Embedding 配置 +# 复制此文件为 .env 并填入你的配置 +# ============================================================ + +# 选择 Embedding 提供者 +# 可选值: local | openai | aliyun | siliconflow +EMBEDDING_PROVIDER=siliconflow + +# ============================================================ +# 本地模型配置 (provider=local) +# ============================================================ +# 默认使用多语言模型，支持中英文 +# LOCAL_MODEL_NAME=Xenova/paraphrase-multilingual-MiniLM-L12-v2 + +# 其他可选模型: +# LOCAL_MODEL_NAME=Xenova/all-MiniLM-L6-v2 # 英文效果更好，体积更小 +# LOCAL_MODEL_NAME=Xenova/multilingual-e5-small # 多语言 E5 模型 + +# ============================================================ +# OpenAI 配置 (provider=openai) +# ============================================================ +# OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx +# OPENAI_BASE_URL=https://api.openai.com/v1 +# OPENAI_EMBEDDING_MODEL=text-embedding-3-small + +# 可选模型: +# text-embedding-3-small (1536维，便宜) +# text-embedding-3-large (3072维，更精准) +# text-embedding-ada-002 (1536维，旧版) + +# ============================================================ +# 阿里云百炼 DashScope 配置 (provider=aliyun) +# ============================================================ +# DASHSCOPE_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx +# ALIYUN_EMBEDDING_MODEL=text-embedding-v3 + +# 可选模型: +# text-embedding-v3 (最新版，推荐) +# text-embedding-v2 (旧版) +# text-embedding-v1 (更旧) + +# ============================================================ +# SiliconFlow 硅基流动配置 (provider=siliconflow) +# ============================================================ +SILICONFLOW_API_KEY= +SILICONFLOW_EMBEDDING_MODEL=Qwen/Qwen3-Embedding-8B + +# 可选模型 (开源模型，性价比高): +# BAAI/bge-m3 # 多语言，效果很好 +# BAAI/bge-large-zh-v1.5 # 中文专用 +# BAAI/bge-large-en-v1.5 # 英文专用 +# nomic-ai/nomic-embed-text-v1.5 # 多语言 diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..64417e1 --- /dev/null +++ b/.env.example @@ -0,0 +1,53 @@ +# ============================================================ +# MCP RAG Prompts - Embedding 配置 +# 复制此文件为 .env 并填入你的配置 +# ============================================================ + +# 选择 Embedding 提供者 +# 可选值: local | openai | aliyun | siliconflow +EMBEDDING_PROVIDER=siliconflow + +# ============================================================ +# 本地模型配置 (provider=local) +# ============================================================ +# 默认使用多语言模型，支持中英文 +# LOCAL_MODEL_NAME=Xenova/paraphrase-multilingual-MiniLM-L12-v2 + +# 其他可选模型: +# LOCAL_MODEL_NAME=Xenova/all-MiniLM-L6-v2 # 英文效果更好，体积更小 +# LOCAL_MODEL_NAME=Xenova/multilingual-e5-small # 多语言 E5 模型 + +# ============================================================ +# OpenAI 配置 (provider=openai) +# ============================================================ +# OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx +# OPENAI_BASE_URL=https://api.openai.com/v1 +# OPENAI_EMBEDDING_MODEL=text-embedding-3-small + +# 可选模型: +# text-embedding-3-small (1536维，便宜) +# text-embedding-3-large (3072维，更精准) +# text-embedding-ada-002 (1536维，旧版) + +# ============================================================ +# 阿里云百炼 DashScope 配置 (provider=aliyun) +# ============================================================ +# DASHSCOPE_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx +# ALIYUN_EMBEDDING_MODEL=text-embedding-v3 + +# 可选模型: +# text-embedding-v3 (最新版，推荐) +# text-embedding-v2 (旧版) +# text-embedding-v1 (更旧) + +# ============================================================ +# SiliconFlow 硅基流动配置 (provider=siliconflow) +# ============================================================ +SILICONFLOW_API_KEY=sk-fwemdoaytkxelpbjlnohiqvkeqjxxraoduadokrpvtynxoej +SILICONFLOW_EMBEDDING_MODEL=Qwen/Qwen3-Embedding-8B + +# 可选模型 (开源模型，性价比高): +# BAAI/bge-m3 # 多语言，效果很好 +# BAAI/bge-large-zh-v1.5 # 中文专用 +# BAAI/bge-large-en-v1.5 # 英文专用 +# nomic-ai/nomic-embed-text-v1.5 # 多语言 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..40b878d --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +node_modules/ \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..14a0e5e --- /dev/null +++ b/README.md @@ -0,0 +1,187 @@ +# MCP RAG Prompts Server + +基于 MCP 协议的 RAG 提示词管理服务器，支持本地模型和云服务 API 进行语义搜索。 + +## 功能特性 + +- 🔍 **语义搜索**：Hybrid Search（向量语义 + 关键词加权） +- 🔄 **多提供者支持**：本地模型 / OpenAI / 阿里云百炼 / SiliconFlow +- 🚀 **MCP 协议**：标准 MCP Server 实现，可与任何 MCP 客户端集成 +- 💾 **内存缓存**：启动时一次性向量化，搜索速度快 + +## 项目结构 + +``` +mcp-rag-prompts/ +├── package.json +├── tsconfig.json +├── .env.example # 环境变量配置模板 +├── data/ +│ └── prompts.json # Prompt 数据文件 +└── src/ + ├── index.ts # MCP Server 入口 + ├── test.ts # 测试脚本 + └── lib/ + ├── types.ts # 类型定义 + ├── embeddingProviders.ts # Embedding 提供者实现 + └── vectorStore.ts # 向量存储和搜索 +``` + +## 安装 + +```bash +npm install +``` + +## Embedding 提供者配置 + +通过环境变量选择 Embedding 提供者： + +### 方式 1：本地模型（默认） + +无需配置，开箱即用。首次运行会自动下载模型（约 90MB）。 + +```bash +# 默认配置，无需设置 +npm start + +# 或显式指定 +EMBEDDING_PROVIDER=local npm start +``` + +### 方式 2：OpenAI API + +```bash +# Windows PowerShell +$env:EMBEDDING_PROVIDER="openai" +$env:OPENAI_API_KEY="sk-xxxxxxxxxxxxxxxx" +npm start + +# Linux/macOS +EMBEDDING_PROVIDER=openai \ +OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxx \ +npm start +``` + +可选配置： +- `OPENAI_BASE_URL` - 自定义 API 地址（支持代理） +- `OPENAI_EMBEDDING_MODEL` - 模型名称（默认 `text-embedding-3-small`） + +### 方式 3：阿里云百炼 DashScope + +```bash +$env:EMBEDDING_PROVIDER="aliyun" +$env:DASHSCOPE_API_KEY="sk-xxxxxxxxxxxxxxxx" +npm start +``` + +可选配置： +- `ALIYUN_EMBEDDING_MODEL` - 模型名称（默认 `text-embedding-v3`） + +### 方式 4：SiliconFlow 硅基流动 + +```bash +$env:EMBEDDING_PROVIDER="siliconflow" +$env:SILICONFLOW_API_KEY="sk-xxxxxxxxxxxxxxxx" +npm start +``` + +可选配置： +- `SILICONFLOW_EMBEDDING_MODEL` - 模型名称（默认 `BAAI/bge-m3`） + +## 运行 + +```bash +# 启动 MCP Server +npm start + +# 开发模式（热重载） +npm run dev + +# 运行测试 +npx tsx src/test.ts +``` + +## 本地模型网络问题 + +首次运行本地模型时需要从 HuggingFace 下载。如果遇到网络问题： + +```bash +# 设置代理 +$env:HTTPS_PROXY="http://127.0.0.1:7890" + +# 或使用 HuggingFace 镜像（中国大陆） +$env:HF_ENDPOINT="https://hf-mirror.com" +``` + +## MCP Tool + +### `search_expert_persona` + +根据用户问题语义搜索最匹配的专家角色设定。 + +**参数**： +- `query` (string): 用户的原始问题或需求描述 + +**返回示例**： +```json +{ + "success": true, + "matchedExpert": { + "id": "python-expert", + "tags": ["python", "programming"], + "description": "Python 编程专家...", + "similarity": 0.75 + }, + "systemPrompt": "你是一位资深的 Python 编程专家..." +} +``` + +## 配置 MCP 客户端 + +### Claude Desktop + +在 `claude_desktop_config.json` 中添加： + +```json +{ + "mcpServers": { + "rag-prompts": { + "command": "npx", + "args": ["tsx", "C:/path/to/mcp-rag-prompts/src/index.ts"], + "env": { + "EMBEDDING_PROVIDER": "siliconflow", + "SILICONFLOW_API_KEY": "sk-xxxxxxxx" + } + } + } +} +``` + +### 自定义 Prompt 数据 + +编辑 `data/prompts.json` 添加你自己的专家角色： + +```json +[ + { + "id": "unique-id", + "tags": ["tag1", "tag2"], + "description": "用于语义搜索的描述文本（会被向量化）", + "content": "实际的 System Prompt 内容" + } +] +``` + +## 提供者对比 + +| 提供者 | 优点 | 缺点 | +|--------|------|------| +| **local** | 免费、离线可用、隐私安全 | 首次加载慢、需下载模型 | +| **openai** | 效果好、稳定 | 需要付费、需要网络 | +| **aliyun** | 中文效果好、国内访问快 | 需要付费 | +| **siliconflow** | 性价比高、支持多种开源模型 | 需要付费 | + +## License + +MIT diff --git a/data/prompts.json b/data/prompts.json new file mode 100644 index 0000000..9f6540a --- /dev/null +++ b/data/prompts.json @@ -0,0 +1,122 @@ +[ + { + "id": "python-expert", + "tags": ["python", "programming", "backend", "data-science", "django", "flask", "fastapi"], + "description": "Python 编程专家，擅长后端开发、数据科学、机器学习和自动化脚本", + "content": "你是一位资深的 Python 编程专家，拥有 10 年以上的开发经验。你精通：\n\n1. **后端开发**：Flask、FastAPI、Django 框架，RESTful API 设计\n2. **数据科学**：Pandas、NumPy、Matplotlib 数据处理与可视化\n3. **机器学习**：Scikit-learn、TensorFlow、PyTorch 模型训练与部署\n4. **自动化**：脚本编写、任务调度、系统运维自动化\n\n回答问题时请：\n- 提供可运行的代码示例\n- 解释代码背后的原理\n- 指出常见的陷阱和最佳实践\n- 推荐相关的库和工具" + }, + { + "id": "java-expert", + "tags": ["java", "jvm", "spring-boot", "enterprise", "backend", "spring-cloud"], + "description": "Java 架构师，精通 Spring Boot 全家桶、JVM 调优、高并发系统设计和企业级应用开发", + "content": "你是一位拥有 15 年经验的 Java 首席架构师。你精通现代 Java (JDK 17/21) 生态系统：\n\n1. **核心框架**：Spring Boot 3, Spring Cloud, Hibernate/JPA, Mybatis-Plus\n2. **并发编程**：多线程, 线程池, JUC (java.util.concurrent), 虚拟线程 (Virtual Threads)\n3. **底层原理**：JVM 内存模型, GC 调优, 类加载机制\n4. **架构设计**：DDD (领域驱动设计), 微服务架构, 设计模式\n\n回答规范：\n- 代码必须遵循 Google Java Style Guide\n- 优先使用 Stream API 和 Lambda 表达式\n- 涉及数据库操作时，请考虑事务管理 (@Transactional)\n- 解释性能影响（如装箱拆箱、IO开销）" + }, + { + "id": "golang-expert", + "tags": ["go", "golang", "microservices", "cloud-native", "concurrency", "grpc"], + "description": "Go 语言专家，专注于云原生架构、微服务、高性能网络编程和容器技术", + "content": "你是一位追求极致性能的 Go (Golang) 资深工程师，崇尚 'Less is more' 的设计哲学。你擅长：\n\n1. **并发模型**：熟练运用 Goroutines 和 Channels 进行 CSP 风格编程，避免共享内存竞争\n2. **微服务**：gRPC, Protobuf, Gin/Echo 框架, Go-Zero\n3. **云原生**：Kubernetes Operator 开发, Prometheus 监控集成\n4. **标准库**：深入理解 net/http, io, context 等核心库\n\n回答规范：\n- 必须编写 Idiomatic Go (地道的 Go 代码)\n- 严格处理错误 (if err != nil)，不忽略任何 error\n- 避免过度设计，优先使用标准库而非第三方库\n- 解释 Context 的传递和生命周期管理" + }, + { + "id": "javascript-expert", + "tags": ["javascript", "typescript", "nodejs", "frontend", "react", "vue", "npm"], + "description": "JavaScript/TypeScript 全栈专家，精通 React、Vue、Node.js 生态和现代前端工程化", + "content": "你是一位全栈 JavaScript/TypeScript 专家，深耕 Web 开发领域。你精通：\n\n1. **前端框架**：React 18 (Hooks, Server Components), Vue 3 (Composition API), Next.js, Nuxt\n2. **状态管理**：Redux Toolkit, Zustand, Pinia, Jotai\n3. **Node.js**：Express, Fastify, NestJS, Prisma ORM\n4. **工程化**：Webpack, Vite, ESBuild, Turborepo, pnpm workspace\n\n回答规范：\n- 优先使用 TypeScript，提供完整类型定义\n- 遵循函数式编程范式，避免 class 组件\n- 关注性能优化（懒加载、代码分割、memo）\n- 解释异步处理和事件循环机制" + }, + { + "id": "rust-expert", + "tags": ["rust", "systems", "memory-safety", "wasm", "performance"], + "description": "Rust 系统编程专家，专注于内存安全、高性能计算、WebAssembly 和嵌入式开发", + "content": "你是一位 Rust 语言布道者，追求零成本抽象和内存安全。你擅长：\n\n1. **所有权系统**：深入理解 Ownership, Borrowing, Lifetimes\n2. **并发安全**：Send/Sync trait, Arc/Mutex, async/await with Tokio\n3. **系统编程**：FFI 调用, 内联汇编, no_std 嵌入式开发\n4. **WebAssembly**：wasm-bindgen, wasm-pack, 浏览器与服务端 WASM\n\n回答规范：\n- 代码必须通过 cargo clippy 检查\n- 优先使用迭代器和零拷贝操作\n- 解释编译器错误信息和修复方案\n- 说明 unsafe 代码的必要性和安全保证" + }, + { + "id": "cpp-expert", + "tags": ["cpp", "c++", "systems", "game-dev", "embedded", "performance"], + "description": "C++ 底层开发专家，精通现代 C++ 标准、游戏引擎开发、嵌入式系统和高频交易", + "content": "你是一位 C++ 领域的资深专家，精通从 C++11 到 C++23 的现代特性。你擅长：\n\n1. **现代 C++**：智能指针, move 语义, constexpr, concepts, ranges\n2. **内存管理**：手动内存管理, 内存池, 自定义 allocator\n3. **性能优化**：SIMD 指令, cache 友好设计, 编译器优化\n4. **应用领域**：游戏引擎 (Unreal), 高频交易, 嵌入式系统\n\n回答规范：\n- 遵循 C++ Core Guidelines\n- 优先使用 RAII 和值语义\n- 避免裸指针，使用 unique_ptr/shared_ptr\n- 解释未定义行为 (UB) 的风险" + }, + { + "id": "devops-expert", + "tags": ["devops", "docker", "kubernetes", "ci-cd", "jenkins", "github-actions", "terraform"], + "description": "DevOps 工程师，专注于 CI/CD 流水线、容器编排、基础设施即代码和云平台运维", + "content": "你是一位 DevOps 文化的践行者，致力于打通开发与运维的壁垒。你精通：\n\n1. **容器化**：Docker 最佳实践, 多阶段构建, 镜像优化\n2. **编排调度**：Kubernetes 集群管理, Helm Charts, Operator 模式\n3. **CI/CD**：GitHub Actions, GitLab CI, Jenkins Pipeline, ArgoCD\n4. **IaC**：Terraform, Pulumi, Ansible, CloudFormation\n\n回答规范：\n- 提供可复用的配置文件和脚本\n- 遵循 GitOps 原则\n- 考虑安全性（Secret 管理, RBAC）\n- 解释监控和告警策略" + }, + { + "id": "database-expert", + "tags": ["database", "mysql", "postgresql", "mongodb", "redis", "sql", "nosql"], + "description": "数据库专家，精通关系型与 NoSQL 数据库设计、性能调优、高可用架构和数据迁移", + "content": "你是一位数据库领域的资深 DBA 和架构师。你精通：\n\n1. **关系型数据库**：MySQL 8, PostgreSQL 15, 索引优化, 查询调优\n2. **NoSQL**：MongoDB 分片集群, Redis 数据结构与持久化, Elasticsearch\n3. **架构设计**：分库分表, 读写分离, 主从复制, 分布式事务\n4. **数据治理**：备份恢复, 数据迁移, 版本管理 (Flyway/Liquibase)\n\n回答规范：\n- 提供 EXPLAIN 分析和优化建议\n- 考虑 ACID 与 CAP 权衡\n- 给出具体的 SQL 或配置示例\n- 解释锁机制和并发控制" + }, + { + "id": "security-expert", + "tags": ["security", "cybersecurity", "penetration", "encryption", "owasp"], + "description": "网络安全专家，专注于渗透测试、漏洞挖掘、安全架构设计和合规审计", + "content": "你是一位白帽黑客和安全架构师，致力于构建安全可靠的系统。你擅长：\n\n1. **渗透测试**：Web 漏洞 (OWASP Top 10), 网络渗透, 社会工程学\n2. **安全开发**：安全编码规范, 代码审计, SAST/DAST 工具\n3. **密码学**：对称/非对称加密, 数字签名, TLS/SSL, JWT\n4. **合规安全**：等保 2.0, GDPR, SOC 2, ISO 27001\n\n回答规范：\n- 只讨论防御性安全技术\n- 提供漏洞修复的具体代码\n- 解释攻击原理以便更好防御\n- 推荐安全工具和最佳实践" + }, + { + "id": "ai-ml-expert", + "tags": ["ai", "machine-learning", "deep-learning", "llm", "pytorch", "tensorflow", "nlp"], + "description": "AI/机器学习专家，精通深度学习框架、大语言模型、计算机视觉和 MLOps", + "content": "你是一位 AI 研究员和机器学习工程师，站在人工智能的前沿。你精通：\n\n1. **深度学习**：PyTorch, TensorFlow, 神经网络架构设计\n2. **大语言模型**：Transformer 原理, 微调技术 (LoRA, QLoRA), RAG 系统\n3. **计算机视觉**：CNN, 目标检测 (YOLO), 图像分割, 多模态模型\n4. **MLOps**：模型训练流水线, 模型部署 (ONNX, TensorRT), A/B 测试\n\n回答规范：\n- 提供可运行的训练/推理代码\n- 解释算法的数学原理（如需要）\n- 给出超参数调优建议\n- 讨论模型的局限性和改进方向" + }, + { + "id": "frontend-ui-expert", + "tags": ["ui", "ux", "css", "tailwind", "design-system", "accessibility", "responsive"], + "description": "前端 UI/UX 专家，精通 CSS 架构、设计系统、响应式布局和无障碍设计", + "content": "你是一位追求像素级完美的前端 UI 专家。你擅长：\n\n1. **CSS 架构**：Tailwind CSS, CSS Modules, Styled Components, CSS-in-JS\n2. **设计系统**：组件库设计, Design Tokens, Storybook 文档\n3. **响应式设计**：移动优先, 弹性布局, Container Queries\n4. **无障碍 (a11y)**：WCAG 标准, ARIA 属性, 键盘导航, 屏幕阅读器\n\n回答规范：\n- 提供语义化的 HTML 结构\n- CSS 代码需考虑浏览器兼容性\n- 关注性能（减少重排重绘）\n- 确保设计对色盲用户友好" + }, + { + "id": "mobile-expert", + "tags": ["mobile", "ios", "android", "react-native", "flutter", "swift", "kotlin"], + "description": "移动端开发专家，精通 iOS/Android 原生开发和 React Native、Flutter 跨平台方案", + "content": "你是一位全能的移动端开发专家，覆盖原生与跨平台方案。你精通：\n\n1. **iOS 开发**：Swift, SwiftUI, UIKit, Core Data, Combine\n2. **Android 开发**：Kotlin, Jetpack Compose, Room, Coroutines\n3. **跨平台**：React Native (Expo), Flutter (Dart), 性能优化\n4. **发布流程**：App Store/Google Play 审核, CI/CD, 热更新\n\n回答规范：\n- 遵循各平台的设计规范 (HIG/Material Design)\n- 考虑不同设备尺寸的适配\n- 关注启动速度和内存占用\n- 解释原生模块桥接机制" + }, + { + "id": "legal-advisor", + "tags": ["law", "legal", "contract", "compliance", "intellectual-property"], + "description": "法律顾问，专注于合同审查、知识产权、公司法和合规咨询", + "content": "你是一位经验丰富的法律顾问，专注于以下领域：\n\n1. **合同法**：合同起草、审查、谈判和争议解决\n2. **知识产权**：专利、商标、版权保护策略\n3. **公司法**：公司设立、股权架构、并购重组\n4. **合规咨询**：数据隐私、行业监管、风险管理\n\n回答问题时请：\n- 提供清晰的法律分析框架\n- 引用相关法律法规（如适用）\n- 指出潜在的法律风险\n- 建议具体的应对措施\n- 提醒用户在必要时寻求专业律师意见" + }, + { + "id": "creative-writer", + "tags": ["writing", "creative", "storytelling", "copywriting", "content"], + "description": "创意写作专家，擅长故事创作、文案撰写、内容策划和品牌叙事", + "content": "你是一位才华横溢的创意写作专家，擅长：\n\n1. **故事创作**：小说、剧本、短篇故事的构思与写作\n2. **商业文案**：广告文案、品牌故事、营销内容\n3. **内容策划**：社交媒体内容、博客文章、视频脚本\n4. **品牌叙事**：品牌定位、价值主张、情感连接\n\n创作时请：\n- 注重情感共鸣和读者体验\n- 运用修辞手法增强表达力\n- 保持风格一致性和创意新颖性\n- 根据目标受众调整语言风格\n- 提供多个创意方向供选择" + }, + { + "id": "product-manager", + "tags": ["product", "pm", "agile", "scrum", "roadmap", "user-story"], + "description": "产品经理，擅长需求分析、产品规划、敏捷开发流程和用户增长策略", + "content": "你是一位经验丰富的产品经理，具备技术背景和商业洞察力。你擅长：\n\n1. **需求管理**：用户调研, 需求挖掘, PRD 文档, 用户故事\n2. **产品规划**：产品路线图, 优先级排序 (RICE/ICE), OKR 制定\n3. **敏捷实践**：Scrum/Kanban, Sprint 规划, 站会, 复盘\n4. **增长策略**：用户留存, A/B 测试, 北极星指标, AARRR 模型\n\n回答规范：\n- 以用户价值为导向思考问题\n- 用数据驱动决策\n- 平衡技术可行性与商业价值\n- 提供可落地的执行方案" + }, + { + "id": "data-analyst", + "tags": ["data-analysis", "sql", "excel", "tableau", "power-bi", "statistics"], + "description": "数据分析师，精通 SQL 查询、数据可视化、统计分析和商业智能报表", + "content": "你是一位数据驱动的分析专家，善于从数据中发现洞察。你精通：\n\n1. **数据处理**：SQL 高级查询, Python/R 数据清洗, ETL 流程\n2. **可视化**：Tableau, Power BI, ECharts, 仪表盘设计\n3. **统计分析**：描述性统计, 假设检验, 回归分析, 时间序列\n4. **商业分析**：用户分群, 漏斗分析, 归因模型, LTV 计算\n\n回答规范：\n- 提供可执行的 SQL 或代码\n- 解释数据背后的业务含义\n- 给出可视化图表建议\n- 注意数据的统计显著性" + }, + { + "id": "english-teacher", + "tags": ["english", "language", "grammar", "writing", "ielts", "toefl"], + "description": "英语教师，擅长语法讲解、写作指导、口语训练和雅思托福备考", + "content": "你是一位耐心专业的英语教师，拥有丰富的教学经验。你擅长：\n\n1. **语法教学**：从基础到高级语法的系统讲解，易错点分析\n2. **写作指导**：学术写作, 商务邮件, 雅思/托福作文批改\n3. **口语训练**：发音纠正, 地道表达, 场景对话练习\n4. **考试备考**：雅思, 托福, GRE, 四六级备考策略\n\n教学原则：\n- 用简单易懂的方式解释复杂语法\n- 提供大量例句和对比练习\n- 指出中式英语的常见错误\n- 鼓励学生多练习，及时给予反馈" + }, + { + "id": "fitness-coach", + "tags": ["fitness", "workout", "nutrition", "health", "gym", "exercise"], + "description": "健身教练，提供训练计划制定、动作指导、营养建议和体态改善方案", + "content": "你是一位专业的健身教练和营养顾问，帮助人们实现健康目标。你擅长：\n\n1. **训练计划**：力量训练, 有氧运动, HIIT, 分化训练安排\n2. **动作指导**：标准动作讲解, 常见错误纠正, 替代动作推荐\n3. **营养指导**：宏量营养素计算, 增肌/减脂饮食, 补剂建议\n4. **体态改善**：圆肩驼背矫正, 骨盆前倾改善, 拉伸放松\n\n指导原则：\n- 根据个人情况定制方案\n- 安全第一，循序渐进\n- 解释训练背后的原理\n- 提醒热身和恢复的重要性" + }, + { + "id": "finance-advisor", + "tags": ["finance", "investment", "stock", "fund", "financial-planning"], + "description": "财务顾问，提供个人理财规划、投资策略、税务优化和财务分析", + "content": "你是一位专业的财务顾问，帮助个人和家庭实现财务自由。你擅长：\n\n1. **理财规划**：预算管理, 储蓄策略, 应急基金, 保险配置\n2. **投资分析**：股票基本面分析, 基金筛选, 资产配置, 风险评估\n3. **税务优化**：个税筹划, 专项扣除, 税收优惠政策\n4. **财务分析**：财务报表解读, 估值方法, 行业分析\n\n建议原则：\n- 强调风险与收益的平衡\n- 根据风险承受能力给建议\n- 不推荐具体股票，只讲方法论\n- 提醒投资有风险，入市需谨慎" + }, + { + "id": "psychologist", + "tags": ["psychology", "mental-health", "counseling", "emotion", "stress"], + "description": "心理咨询师，提供情绪疏导、压力管理、人际关系和自我成长指导", + "content": "你是一位温暖有同理心的心理咨询师，致力于帮助人们获得心理健康。你擅长：\n\n1. 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"tsx": "^4.7.0", + "typescript": "^5.3.3" + }, + "engines": { + "node": ">=18.0.0" + } +} diff --git a/src/index.ts b/src/index.ts new file mode 100644 index 0000000..f34508f --- /dev/null +++ b/src/index.ts @@ -0,0 +1,124 @@ +/** + * MCP RAG Prompts Server 入口文件 + * 基于 MCP 协议的 RAG 提示词管理服务器 + */ + +// 加载 .env 文件（必须在最前面） +import 'dotenv/config'; + +import { Server } from '@modelcontextprotocol/sdk/server/index.js'; +import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js'; +import { + CallToolRequestSchema, + ListToolsRequestSchema, +} from '@modelcontextprotocol/sdk/types.js'; + +import { vectorStore } from './lib/vectorStore.js'; +import { + TOOL_NAME, + TOOL_DESCRIPTION, + inputJsonSchema, + handleSearchPersona, + type SearchPersonaInput, +} from './tools/searchPersona.js'; + +/** + * 创建并配置 MCP Server + */ +function createServer(): Server { + const server = new Server( + { + name: 'mcp-rag-prompts', + version: '1.0.0', + }, + { + capabilities: { + tools: {}, + }, + } + ); + + /** + * 处理 tools/list 请求 + * 返回服务器提供的所有工具列表 + */ + server.setRequestHandler(ListToolsRequestSchema, async () => { + console.error('[Server] 收到 tools/list 请求'); + + return { + tools: [ + { + name: TOOL_NAME, + description: TOOL_DESCRIPTION, + inputSchema: inputJsonSchema, + }, + ], + }; + }); + + /** + * 处理 tools/call 请求 + * 执行指定的工具并返回结果 + */ + server.setRequestHandler(CallToolRequestSchema, async (request) => { + const { name, arguments: args } = request.params; + + console.error(`[Server] 收到 tools/call 请求: ${name}`); + console.error(`[Server] 参数: ${JSON.stringify(args)}`); + + // 路由到对应的工具处理函数 + switch (name) { + case TOOL_NAME: + return handleSearchPersona(args as SearchPersonaInput); + + default: + return { + content: [ + { + type: 'text' as const, + text: JSON.stringify({ + success: false, + error: `未知工具: ${name}`, + }), + }, + ], + isError: true, + }; + } + }); + + return server; +} + +/** + * 主函数：初始化并启动服务器 + */ +async function main(): Promise { + console.error('='.repeat(50)); + console.error('MCP RAG Prompts Server 启动中...'); + console.error('='.repeat(50)); + + try { + // 1. 初始化向量存储（加载数据并生成 embeddings） + console.error('\n[Main] 步骤 1: 初始化向量存储'); + await vectorStore.initialize(); + + // 2. 创建 MCP Server + console.error('\n[Main] 步骤 2: 创建 MCP Server'); + const server = createServer(); + + // 3. 创建 Stdio Transport 并连接 + console.error('\n[Main] 步骤 3: 启动 Stdio Transport'); + const transport = new StdioServerTransport(); + await server.connect(transport); + + console.error('\n[Main] ✓ MCP Server 已启动，等待客户端连接...'); + console.error('='.repeat(50)); + } catch (error) { + console.error('\n[Main] ✗ 启动失败:', error); + process.exit(1); + } +} + +// 启动服务器 +main(); diff --git a/src/lib/embeddingProviders.ts b/src/lib/embeddingProviders.ts new file mode 100644 index 0000000..4823ebe --- /dev/null +++ b/src/lib/embeddingProviders.ts @@ -0,0 +1,305 @@ +/** + * Embedding 提供者实现 + * 支持本地模型和多种云服务 API + */ + +import type { IEmbeddingProvider, EmbeddingConfig } from './types.js'; + +// ============================================================ +// 本地模型提供者 (使用 @xenova/transformers) +// ============================================================ +export class LocalEmbeddingProvider implements IEmbeddingProvider { + readonly name = 'local'; + private modelName: string; + private extractor: any = null; + + constructor(modelName: string = 'Xenova/paraphrase-multilingual-MiniLM-L12-v2') { + this.modelName = modelName; + } + + async initialize(): Promise { + if (this.extractor) return; + + console.error(`[LocalProvider] 正在加载本地模型: ${this.modelName}`); + // 动态导入，避免在使用 API 时也加载这个大依赖 + const { pipeline } = await import('@xenova/transformers'); + this.extractor = await pipeline('feature-extraction', this.modelName); + console.error(`[LocalProvider] 模型加载完成`); + } + + async embed(text: string): Promise { + if (!this.extractor) { + throw new Error('LocalProvider 尚未初始化'); + } + const output = await this.extractor(text, { + pooling: 'mean', + normalize: true, + }); + return Array.from(output.data as Float32Array); + } + + async embedBatch(texts: string[]): Promise { + // 本地模型逐个处理 + const results: number[][] = []; + for (const text of texts) { + results.push(await this.embed(text)); + } + return results; + } +} + +// ============================================================ +// OpenAI 兼容 API 提供者 +// 支持 OpenAI 官方 API 和兼容接口（如 Azure OpenAI） +// ============================================================ +export class OpenAIEmbeddingProvider implements IEmbeddingProvider { + readonly name = 'openai'; + private apiKey: string; + private baseUrl: string; + private model: string; + + constructor(config: { apiKey: string; baseUrl?: string; model?: string }) { + this.apiKey = config.apiKey; + this.baseUrl = config.baseUrl || 'https://api.openai.com/v1'; + this.model = config.model || 'text-embedding-3-small'; + } + + async initialize(): Promise { + console.error(`[OpenAIProvider] 使用模型: ${this.model}`); + console.error(`[OpenAIProvider] API 地址: ${this.baseUrl}`); + } + + async embed(text: string): Promise { + const response = await fetch(`${this.baseUrl}/embeddings`, { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + 'Authorization': `Bearer ${this.apiKey}`, + }, + body: JSON.stringify({ + input: text, + model: this.model, + }), + }); + + if (!response.ok) { + const error = await response.text(); + throw new Error(`OpenAI API 错误: ${response.status} - ${error}`); + } + + const data = await response.json(); + return data.data[0].embedding; + } + + async embedBatch(texts: string[]): Promise { + // OpenAI 支持批量请求 + const response = await fetch(`${this.baseUrl}/embeddings`, { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + 'Authorization': `Bearer ${this.apiKey}`, + }, + body: JSON.stringify({ + input: texts, + model: this.model, + }), + }); + + if (!response.ok) { + const error = await response.text(); + throw new Error(`OpenAI API 错误: ${response.status} - ${error}`); + } + + const data = await response.json(); + // 按 index 排序确保顺序正确 + return data.data + .sort((a: any, b: any) => a.index - b.index) + .map((item: any) => item.embedding); + } +} + +// ============================================================ +// 阿里云百炼 DashScope API 提供者 +// ============================================================ +export class AliyunEmbeddingProvider implements IEmbeddingProvider { + readonly name = 'aliyun'; + private apiKey: string; + private model: string; + private baseUrl = 'https://dashscope.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding'; + + constructor(config: { apiKey: string; model?: string }) { + this.apiKey = config.apiKey; + this.model = config.model || 'text-embedding-v3'; + } + + async initialize(): Promise { + console.error(`[AliyunProvider] 使用模型: ${this.model}`); + } + + async embed(text: string): Promise { + const response = await fetch(this.baseUrl, { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + 'Authorization': `Bearer ${this.apiKey}`, + }, + body: JSON.stringify({ + model: this.model, + input: { + texts: [text], + }, + parameters: { + text_type: 'query', + }, + }), + }); + + if (!response.ok) { + const error = await response.text(); + throw new Error(`阿里云 API 错误: ${response.status} - ${error}`); + } + + const data = await response.json(); + + if (data.output?.embeddings?.[0]?.embedding) { + return data.output.embeddings[0].embedding; + } + + throw new Error(`阿里云 API 返回格式错误: ${JSON.stringify(data)}`); + } + + async embedBatch(texts: string[]): Promise { + const response = await fetch(this.baseUrl, { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + 'Authorization': `Bearer ${this.apiKey}`, + }, + body: JSON.stringify({ + model: this.model, + input: { + texts: texts, + }, + parameters: { + text_type: 'query', + }, + }), + }); + + if (!response.ok) { + const error = await response.text(); + throw new Error(`阿里云 API 错误: ${response.status} - ${error}`); + } + + const data = await response.json(); + + if (data.output?.embeddings) { + return data.output.embeddings + .sort((a: any, b: any) => a.text_index - b.text_index) + .map((item: any) => item.embedding); + } + + throw new Error(`阿里云 API 返回格式错误: ${JSON.stringify(data)}`); + } +} + +// ============================================================ +// SiliconFlow API 提供者 +// 硅基流动，支持多种开源 Embedding 模型 +// ============================================================ +export class SiliconFlowEmbeddingProvider implements IEmbeddingProvider { + readonly name = 'siliconflow'; + private apiKey: string; + private model: string; + private baseUrl = 'https://api.siliconflow.cn/v1/embeddings'; + + constructor(config: { apiKey: string; model?: string }) { + this.apiKey = config.apiKey; + // 默认使用 BGE-M3，多语言效果好 + this.model = config.model || 'BAAI/bge-m3'; + } + + async initialize(): Promise { + console.error(`[SiliconFlowProvider] 使用模型: ${this.model}`); + } + + async embed(text: string): Promise { + const response = await fetch(this.baseUrl, { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + 'Authorization': `Bearer ${this.apiKey}`, + }, + body: JSON.stringify({ + model: this.model, + input: text, + encoding_format: 'float', + }), + }); + + if (!response.ok) { + const error = await response.text(); + throw new Error(`SiliconFlow API 错误: ${response.status} - ${error}`); + } + + const data = await response.json(); + return data.data[0].embedding; + } + + async embedBatch(texts: string[]): Promise { + // SiliconFlow 支持批量（与 OpenAI 兼容） + const response = await fetch(this.baseUrl, { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + 'Authorization': `Bearer ${this.apiKey}`, + }, + body: JSON.stringify({ + model: this.model, + input: texts, + encoding_format: 'float', + }), + }); + + if (!response.ok) { + const error = await response.text(); + throw new Error(`SiliconFlow API 错误: ${response.status} - ${error}`); + } + + const data = await response.json(); + return data.data + .sort((a: any, b: any) => a.index - b.index) + .map((item: any) => item.embedding); + } +} + +// ============================================================ +// 工厂函数：根据配置创建对应的提供者 +// ============================================================ +export function createEmbeddingProvider(config: EmbeddingConfig): IEmbeddingProvider { + switch (config.provider) { + case 'local': + return new LocalEmbeddingProvider(config.local?.modelName); + + case 'openai': + if (!config.openai?.apiKey) { + throw new Error('OpenAI 配置缺少 apiKey'); + } + return new OpenAIEmbeddingProvider(config.openai); + + case 'aliyun': + if (!config.aliyun?.apiKey) { + throw new Error('阿里云配置缺少 apiKey'); + } + return new AliyunEmbeddingProvider(config.aliyun); + + case 'siliconflow': + if (!config.siliconflow?.apiKey) { + throw new Error('SiliconFlow 配置缺少 apiKey'); + } + return new SiliconFlowEmbeddingProvider(config.siliconflow); + + default: + throw new Error(`不支持的 Embedding 提供者: ${config.provider}`); + } +} diff --git a/src/lib/types.ts b/src/lib/types.ts new file mode 100644 index 0000000..e9bebd5 --- /dev/null +++ b/src/lib/types.ts @@ -0,0 +1,84 @@ +/** + * 类型定义文件 + * 定义 Prompt 数据结构和向量存储相关类型 + */ + +/** + * 原始 Prompt 数据结构（从 JSON 文件读取） + */ +export interface PromptData { + id: string; + tags: string[]; + description: string; + content: string; +} + +/** + * 带向量的 Prompt 数据（内存中缓存） + */ +export interface PromptWithEmbedding extends PromptData { + embedding: number[]; +} + +/** + * 搜索结果结构 + */ +export interface SearchResult { + prompt: PromptData; + similarity: number; +} + +/** + * Embedding 提供者类型 + */ +export type EmbeddingProvider = 'local' | 'openai' | 'aliyun' | 'siliconflow'; + +/** + * Embedding 配置接口 + */ +export interface EmbeddingConfig { + /** 提供者类型 */ + provider: EmbeddingProvider; + + /** 本地模型配置 */ + local?: { + modelName: string; + }; + + /** OpenAI 配置 */ + openai?: { + apiKey: string; + baseUrl?: string; // 支持自定义 base URL（如代理） + model?: string; // 默认 text-embedding-3-small + }; + + /** 阿里云百炼配置 */ + aliyun?: { + apiKey: string; + model?: string; // 默认 text-embedding-v3 + }; + + /** SiliconFlow 配置 */ + siliconflow?: { + apiKey: string; + model?: string; // 默认 BAAI/bge-m3 + }; +} + +/** + * Embedding 提供者接口 + * 所有 Embedding 实现都需要遵循此接口 + */ +export interface IEmbeddingProvider { + /** 提供者名称 */ + readonly name: string; + + /** 初始化（如加载模型） */ + initialize(): Promise; + + /** 生成单个文本的向量 */ + embed(text: string): Promise; + + /** 批量生成向量（可选优化） */ + embedBatch?(texts: string[]): Promise; +} diff --git a/src/lib/vectorStore.ts b/src/lib/vectorStore.ts new file mode 100644 index 0000000..aeaf147 --- /dev/null +++ b/src/lib/vectorStore.ts @@ -0,0 +1,232 @@ +/** + * 向量存储模块 (增强版) + * 支持本地模型和云服务 API 切换 + * 核心功能：Hybrid Search (向量语义 + 关键词加权) + */ + +import { readFile } from 'fs/promises'; +import { fileURLToPath } from 'url'; +import { dirname, join } from 'path'; +import type { + PromptData, + PromptWithEmbedding, + SearchResult, + EmbeddingConfig, + IEmbeddingProvider, +} from './types.js'; +import { createEmbeddingProvider } from './embeddingProviders.js'; + +const __filename = fileURLToPath(import.meta.url); +const __dirname = dirname(__filename); + +// 相似度阈值 +const SIMILARITY_THRESHOLD = 0.4; + +// 关键词命中加分 +const KEYWORD_BOOST_SCORE = 0.3; + +/** + * 从环境变量读取配置 + * 优先级：环境变量 > 默认值 + */ +function getConfigFromEnv(): EmbeddingConfig { + const provider = (process.env.EMBEDDING_PROVIDER || 'local') as EmbeddingConfig['provider']; + + const config: EmbeddingConfig = { + provider, + local: { + modelName: process.env.LOCAL_MODEL_NAME || 'Xenova/paraphrase-multilingual-MiniLM-L12-v2', + }, + openai: { + apiKey: process.env.OPENAI_API_KEY || '', + baseUrl: process.env.OPENAI_BASE_URL || 'https://api.openai.com/v1', + model: process.env.OPENAI_EMBEDDING_MODEL || 'text-embedding-3-small', + }, + aliyun: { + apiKey: process.env.DASHSCOPE_API_KEY || '', + model: process.env.ALIYUN_EMBEDDING_MODEL || 'text-embedding-v3', + }, + siliconflow: { + apiKey: process.env.SILICONFLOW_API_KEY || '', + model: process.env.SILICONFLOW_EMBEDDING_MODEL || 'BAAI/bge-m3', + }, + }; + + return config; +} + +/** + * 向量存储类 + */ +export class VectorStore { + private prompts: PromptWithEmbedding[] = []; + private embeddingProvider: IEmbeddingProvider | null = null; + private initialized = false; + private config: EmbeddingConfig; + + constructor(config?: EmbeddingConfig) { + // 使用传入配置或从环境变量读取 + this.config = config || getConfigFromEnv(); + } + + /** + * 计算余弦相似度 + */ + private cosineSimilarity(vecA: number[], vecB: number[]): number { + if (vecA.length !== vecB.length) return 0; + + let dotProduct = 0; + let normA = 0; + let normB = 0; + + for (let i = 0; i < vecA.length; i++) { + dotProduct += vecA[i] * vecB[i]; + normA += vecA[i] * vecA[i]; + normB += vecB[i] * vecB[i]; + } + + const magnitude = Math.sqrt(normA) * Math.sqrt(normB); + return magnitude === 0 ? 0 : dotProduct / magnitude; + } + + /** + * 初始化向量存储 + */ + async initialize(): Promise { + if (this.initialized) return; + + console.error('='.repeat(50)); + console.error('[VectorStore] 开始初始化...'); + console.error(`[VectorStore] Embedding 提供者: ${this.config.provider}`); + + // 创建并初始化 Embedding 提供者 + this.embeddingProvider = createEmbeddingProvider(this.config); + await this.embeddingProvider.initialize(); + + // 读取 Prompt 数据 + const dataPath = join(__dirname, '../../data/prompts.json'); + const rawData = await readFile(dataPath, 'utf-8'); + const promptsData: PromptData[] = JSON.parse(rawData); + + console.error(`[VectorStore] 读取到 ${promptsData.length} 条 Prompt 数据`); + + // 准备待向量化的文本 + const textsToEmbed = promptsData.map( + (prompt) => `${prompt.tags.join(' ')} ${prompt.description}` + ); + + // 生成向量（优先使用批量接口） + let embeddings: number[][]; + + if (this.embeddingProvider.embedBatch) { + console.error('[VectorStore] 使用批量向量化...'); + embeddings = await this.embeddingProvider.embedBatch(textsToEmbed); + } else { + console.error('[VectorStore] 逐个向量化...'); + embeddings = []; + for (const text of textsToEmbed) { + embeddings.push(await this.embeddingProvider.embed(text)); + } + } + + // 组装数据 + for (let i = 0; i < promptsData.length; i++) { + this.prompts.push({ + ...promptsData[i], + embedding: embeddings[i], + }); + } + + this.initialized = true; + console.error(`[VectorStore] ✓ 初始化完成，加载了 ${promptsData.length} 条数据`); + console.error('='.repeat(50)); + } + + /** + * 语义搜索 + */ + async search(query: string): Promise { + if (!this.initialized || !this.embeddingProvider) { + throw new Error('VectorStore 尚未初始化'); + } + + console.error(`[VectorStore] 搜索: "${query}"`); + + // 生成查询向量 + const queryEmbedding = await this.embeddingProvider.embed(query); + const queryLower = query.toLowerCase(); + + // 混合打分 + const results: SearchResult[] = this.prompts.map((prompt) => { + // A. 向量相似度 + const vectorScore = this.cosineSimilarity(queryEmbedding, prompt.embedding); + + // B. 关键词加分 + let boostScore = 0; + if (prompt.tags && prompt.tags.length > 0) { + for (const tag of prompt.tags) { + if (queryLower.includes(tag.toLowerCase())) { + boostScore = KEYWORD_BOOST_SCORE; + break; + } + } + } + + // 最终得分 + const finalScore = Math.min(vectorScore + boostScore, 1.0); + + return { + prompt: { + id: prompt.id, + tags: prompt.tags, + description: prompt.description, + content: prompt.content, + }, + similarity: finalScore, + }; + }); + + // 排序 + results.sort((a, b) => b.similarity - a.similarity); + const bestMatch = results[0]; + + // 调试输出 + console.error(`--- Top 3 候选 ---`); + results.slice(0, 3).forEach((r, i) => { + console.error(`${i + 1}. [${r.prompt.id}] 得分: ${r.similarity.toFixed(4)}`); + }); + + // 阈值检查 + if (bestMatch.similarity < SIMILARITY_THRESHOLD) { + console.error( + `[VectorStore] ✗ 最高分 ${bestMatch.similarity.toFixed(4)} 低于阈值 ${SIMILARITY_THRESHOLD}` + ); + return null; + } + + console.error( + `[VectorStore] ✓ 选中: ${bestMatch.prompt.id} (得分: ${bestMatch.similarity.toFixed(4)})` + ); + return bestMatch; + } + + /** + * 获取所有可用的 Prompt ID + */ + getAvailablePrompts(): string[] { + return this.prompts.map((p) => p.id); + } + + /** + * 获取当前使用的 Embedding 提供者信息 + */ + getProviderInfo(): { provider: string; initialized: boolean } { + return { + provider: this.config.provider, + initialized: this.initialized, + }; + } +} + +// 导出单例（使用环境变量配置） +export const vectorStore = new VectorStore(); diff --git a/src/test.ts b/src/test.ts new file mode 100644 index 0000000..2d919c3 --- /dev/null +++ b/src/test.ts @@ -0,0 +1,184 @@ +/** + * 测试脚本：验证向量存储和搜索功能 + * 包含多场景测试用例 + */ + +// 加载 .env 文件 +import 'dotenv/config'; + +import { vectorStore } from './lib/vectorStore.js'; + +// 定义测试用例结构 +interface TestCase { + query: string; + expectedId: string | null; // null 表示期望无匹配 + category: string; +} + +// 测试用例集 +const testCases: TestCase[] = [ + // ==================== 编程语言类 ==================== + { query: '如何用 Python 写一个爬虫？', expectedId: 'python-expert', category: '编程-Python' }, + { query: '帮我用 FastAPI 搭建一个后端服务', expectedId: 'python-expert', category: '编程-Python' }, + { query: 'Django ORM 怎么做多表关联查询？', expectedId: 'python-expert', category: '编程-Python' }, + + { query: '如何用 Java 实现单例模式？', expectedId: 'java-expert', category: '编程-Java' }, + { query: 'Spring Boot 项目如何配置多数据源？', expectedId: 'java-expert', category: '编程-Java' }, + { query: 'JVM 垃圾回收机制是怎样的？', expectedId: 'java-expert', category: '编程-Java' }, + + { query: '用 Go 写一个高并发的 HTTP 服务', expectedId: 'golang-expert', category: '编程-Go' }, + { query: 'Golang 的 channel 和 goroutine 怎么用？', expectedId: 'golang-expert', category: '编程-Go' }, + { query: '如何用 gRPC 实现微服务通信？', expectedId: 'golang-expert', category: '编程-Go' }, + + { query: 'React Hooks 的最佳实践是什么？', expectedId: 'javascript-expert', category: '编程-JS/TS' }, + { query: 'TypeScript 泛型怎么用？', expectedId: 'javascript-expert', category: '编程-JS/TS' }, + { query: 'Vue 3 的 Composition API 和 Options API 有什么区别？', expectedId: 'javascript-expert', category: '编程-JS/TS' }, + { query: 'Node.js 如何处理大文件上传？', expectedId: 'javascript-expert', category: '编程-JS/TS' }, + + { query: 'Rust 的所有权机制是什么？', expectedId: 'rust-expert', category: '编程-Rust' }, + { query: '如何用 Rust 编写 WebAssembly 模块？', expectedId: 'rust-expert', category: '编程-Rust' }, + + { query: 'C++ 智能指针有哪几种？', expectedId: 'cpp-expert', category: '编程-C++' }, + { query: '如何优化 C++ 程序的内存使用？', expectedId: 'cpp-expert', category: '编程-C++' }, + { query: 'Unreal Engine 游戏开发入门', expectedId: 'cpp-expert', category: '编程-C++' }, + + // ==================== DevOps/运维类 ==================== + { query: 'Docker 镜像怎么优化体积？', expectedId: 'devops-expert', category: 'DevOps' }, + { query: 'Kubernetes 如何实现滚动更新？', expectedId: 'devops-expert', category: 'DevOps' }, + { query: 'GitHub Actions 如何配置 CI/CD？', expectedId: 'devops-expert', category: 'DevOps' }, + { query: 'Terraform 怎么管理云资源？', expectedId: 'devops-expert', category: 'DevOps' }, + + // ==================== 数据库类 ==================== + { query: 'MySQL 索引优化有哪些技巧？', expectedId: 'database-expert', category: '数据库' }, + { query: 'Redis 缓存穿透怎么解决？', expectedId: 'database-expert', category: '数据库' }, + { query: 'MongoDB 如何设计 Schema？', expectedId: 'database-expert', category: '数据库' }, + { query: '如何实现数据库读写分离？', expectedId: 'database-expert', category: '数据库' }, + + // ==================== 安全类 ==================== + { query: 'SQL 注入攻击如何防范？', expectedId: 'security-expert', category: '安全' }, + { query: 'HTTPS 证书怎么配置？', expectedId: 'security-expert', category: '安全' }, + { query: 'JWT Token 安全最佳实践', expectedId: 'security-expert', category: '安全' }, + + // ==================== AI/ML 类 ==================== + { query: '如何用 PyTorch 训练一个图像分类模型？', expectedId: 'ai-ml-expert', category: 'AI/ML' }, + { query: '大语言模型微调有哪些方法？', expectedId: 'ai-ml-expert', category: 'AI/ML' }, + { query: 'RAG 系统怎么搭建？', expectedId: 'ai-ml-expert', category: 'AI/ML' }, + { query: 'YOLO 目标检测怎么用？', expectedId: 'ai-ml-expert', category: 'AI/ML' }, + + // ==================== 前端 UI 类 ==================== + { query: 'Tailwind CSS 怎么自定义主题？', expectedId: 'frontend-ui-expert', category: '前端UI' }, + { query: '如何实现响应式布局？', expectedId: 'frontend-ui-expert', category: '前端UI' }, + { query: '网站无障碍设计要注意什么？', expectedId: 'frontend-ui-expert', category: '前端UI' }, + + // ==================== 移动端类 ==================== + { query: 'SwiftUI 和 UIKit 哪个更好？', expectedId: 'mobile-expert', category: '移动端' }, + { query: 'Flutter 性能优化技巧', expectedId: 'mobile-expert', category: '移动端' }, + { query: 'React Native 如何调用原生模块？', expectedId: 'mobile-expert', category: '移动端' }, + { query: 'Android Kotlin 协程怎么用？', expectedId: 'mobile-expert', category: '移动端' }, + + // ==================== 非技术类 ==================== + { query: '帮我写一份劳动合同', expectedId: 'legal-advisor', category: '法律' }, + { query: '商标注册流程是什么？', expectedId: 'legal-advisor', category: '法律' }, + { query: '公司股权架构怎么设计？', expectedId: 'legal-advisor', category: '法律' }, + + { query: '帮我写一个广告文案', expectedId: 'creative-writer', category: '写作' }, + { query: '如何写一个吸引人的故事开头？', expectedId: 'creative-writer', category: '写作' }, + { query: '品牌故事怎么写才能打动人？', expectedId: 'creative-writer', category: '写作' }, + + { query: '如何做用户调研？', expectedId: 'product-manager', category: '产品' }, + { query: 'PRD 文档怎么写？', expectedId: 'product-manager', category: '产品' }, + { query: 'Scrum 和 Kanban 有什么区别？', expectedId: 'product-manager', category: '产品' }, + + { query: '用户留存率怎么分析？', expectedId: 'data-analyst', category: '数据分析' }, + { query: 'SQL 窗口函数怎么用？', expectedId: 'data-analyst', category: '数据分析' }, + { query: 'Power BI 仪表盘怎么设计？', expectedId: 'data-analyst', category: '数据分析' }, + + { query: '雅思写作怎么拿高分？', expectedId: 'english-teacher', category: '英语' }, + { query: '英语语法中的虚拟语气怎么用？', expectedId: 'english-teacher', category: '英语' }, + { query: '托福口语怎么准备？', expectedId: 'english-teacher', category: '英语' }, + + { query: '制定一个增肌训练计划', expectedId: 'fitness-coach', category: '健身' }, + { query: '减脂期间怎么吃？', expectedId: 'fitness-coach', category: '健身' }, + { query: '深蹲的标准动作是什么？', expectedId: 'fitness-coach', category: '健身' }, + + { query: '基金定投策略有哪些？', expectedId: 'finance-advisor', category: '理财' }, + { query: '个人所得税怎么优化？', expectedId: 'finance-advisor', category: '理财' }, + { query: '如何分析一只股票？', expectedId: 'finance-advisor', category: '理财' }, + + { query: '最近工作压力很大怎么办？', expectedId: 'psychologist', category: '心理' }, + { query: '如何克服社交焦虑？', expectedId: 'psychologist', category: '心理' }, + { query: '和同事关系紧张怎么处理？', expectedId: 'psychologist', category: '心理' }, + + // ==================== 无匹配/边界测试 ==================== + { query: '今天天气怎么样？', expectedId: null, category: '无匹配' }, + { query: '推荐一部好看的电影', expectedId: null, category: '无匹配' }, + { query: '附近有什么好吃的？', expectedId: null, category: '无匹配' }, +]; + +async function runTests() { + console.log('='.repeat(60)); + console.log('MCP RAG Prompts - 综合测试'); + console.log('='.repeat(60)); + + // 初始化 + await vectorStore.initialize(); + + const providerInfo = vectorStore.getProviderInfo(); + console.log(`\n当前 Embedding 提供者: ${providerInfo.provider}`); + console.log(`总测试用例数: ${testCases.length}\n`); + + // 统计 + let passed = 0; + let failed = 0; + const failedCases: { query: string; expected: string | null; actual: string | null }[] = []; + + // 按类别分组显示 + let currentCategory = ''; + + for (const testCase of testCases) { + // 打印类别标题 + if (testCase.category !== currentCategory) { + currentCategory = testCase.category; + console.log(`\n--- ${currentCategory} ---`); + } + + const result = await vectorStore.search(testCase.query); + const actualId = result?.prompt.id ?? null; + const isPass = actualId === testCase.expectedId; + + if (isPass) { + passed++; + console.log(`✓ "${testCase.query.slice(0, 30)}..." → ${actualId ?? '(无匹配)'}`); + } else { + failed++; + failedCases.push({ + query: testCase.query, + expected: testCase.expectedId, + actual: actualId, + }); + console.log(`✗ "${testCase.query.slice(0, 30)}..."`); + console.log(` 期望: ${testCase.expectedId ?? '(无匹配)'}, 实际: ${actualId ?? '(无匹配)'}`); + } + } + + // 打印统计结果 + console.log('\n' + '='.repeat(60)); + console.log('测试结果统计'); + console.log('='.repeat(60)); + console.log(`总计: ${testCases.length} 个用例`); + console.log(`通过: ${passed} (${((passed / testCases.length) * 100).toFixed(1)}%)`); + console.log(`失败: ${failed} (${((failed / testCases.length) * 100).toFixed(1)}%)`); + + if (failedCases.length > 0) { + console.log('\n失败用例详情:'); + failedCases.forEach((fc, i) => { + console.log(`${i + 1}. "${fc.query}"`); + console.log(` 期望: ${fc.expected ?? '(无匹配)'}`); + console.log(` 实际: ${fc.actual ?? '(无匹配)'}`); + }); + } + + console.log('\n测试完成！'); +} + +runTests().catch(console.error); diff --git a/src/tools/searchPersona.ts b/src/tools/searchPersona.ts new file mode 100644 index 0000000..7d8f395 --- /dev/null +++ b/src/tools/searchPersona.ts @@ -0,0 +1,129 @@ +/** + * MCP Tool: search_expert_persona + * 根据用户问题语义搜索最匹配的专家角色设定 + */ + +import { z } from 'zod'; +import { vectorStore } from '../lib/vectorStore.js'; + +/** + * 工具名称 + */ +export const TOOL_NAME = 'search_expert_persona'; + +/** + * 工具描述 + */ +export const TOOL_DESCRIPTION = + '根据用户的具体问题或场景，利用语义搜索找到最匹配的专家角色设定 (System Prompt)。' + + '输入用户的问题或需求描述，返回最适合处理该问题的专家角色 Prompt。'; + +/** + * 参数 Schema（使用 Zod 定义） + */ +export const inputSchema = z.object({ + query: z + .string() + .min(1, '查询内容不能为空') + .describe('用户的原始问题或需求描述，例如："如何用 Python 实现一个 REST API？"'), +}); + +/** + * 参数类型 + */ +export type SearchPersonaInput = z.infer; + +/** + * 将 Zod Schema 转换为 JSON Schema（供 MCP SDK 使用） + */ +export const inputJsonSchema = { + type: 'object' as const, + properties: { + query: { + type: 'string', + description: '用户的原始问题或需求描述，例如："如何用 Python 实现一个 REST API？"', + }, + }, + required: ['query'], +}; + +/** + * 执行搜索的处理函数 + * @param input 经过验证的输入参数 + * @returns MCP 工具响应内容 + */ +export async function handleSearchPersona(input: SearchPersonaInput): Promise<{ + content: Array<{ type: 'text'; text: string }>; + isError?: boolean; +}> { + try { + // 验证输入 + const validatedInput = inputSchema.parse(input); + + // 执行语义搜索 + const result = await vectorStore.search(validatedInput.query); + + if (!result) { + // 没有找到匹配结果 + return { + content: [ + { + type: 'text', + text: JSON.stringify( + { + success: false, + message: '未找到与您问题匹配的专家角色。请尝试更具体地描述您的需求。', + availableExperts: vectorStore.getAvailablePrompts(), + }, + null, + 2 + ), + }, + ], + }; + } + + // 返回匹配结果 + return { + content: [ + { + type: 'text', + text: JSON.stringify( + { + success: true, + matchedExpert: { + id: result.prompt.id, + tags: result.prompt.tags, + description: result.prompt.description, + similarity: Math.round(result.similarity * 100) / 100, + }, + systemPrompt: result.prompt.content, + }, + null, + 2 + ), + }, + ], + }; + } catch (error) { + // 处理错误 + const errorMessage = error instanceof Error ? error.message : '未知错误'; + + return { + content: [ + { + type: 'text', + text: JSON.stringify( + { + success: false, + error: errorMessage, + }, + null, + 2 + ), + }, + ], + isError: true, + }; + } +} diff --git a/tsconfig.json b/tsconfig.json new file mode 100644 index 0000000..c83c533 --- /dev/null +++ b/tsconfig.json @@ -0,0 +1,20 @@ +{ + "compilerOptions": { + "target": "ES2022", + "module": "NodeNext", + "moduleResolution": "NodeNext", + "lib": ["ES2022"], + "outDir": "./dist", + "rootDir": "./src", + "strict": true, + "esModuleInterop": true, + "skipLibCheck": true, + "forceConsistentCasingInFileNames": true, + "resolveJsonModule": true, + "declaration": true, + "declarationMap": true, + "sourceMap": true + }, + "include": ["src/**/*"], + "exclude": ["node_modules", "dist"] +}