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.

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"]
+}