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.

package.json

@@ -0,0 +1,33 @@
+{
+  "name": "mcp-rag-prompts",
+  "version": "1.0.0",
+  "description": "MCP Server for RAG-based prompt management with local embeddings",
+  "type": "module",
+  "main": "src/index.ts",
+  "scripts": {
+    "start": "tsx src/index.ts",
+    "build": "tsc",
+    "dev": "tsx watch src/index.ts"
+  },
+  "keywords": [
+    "mcp",
+    "rag",
+    "embeddings",
+    "prompt-management"
+  ],
+  "license": "MIT",
+  "dependencies": {
+    "@modelcontextprotocol/sdk": "^1.0.0",
+    "@xenova/transformers": "^2.17.2",
+    "dotenv": "^17.2.3",
+    "zod": "^3.23.8"
+  },
+  "devDependencies": {
+    "@types/node": "^20.11.0",
+    "tsx": "^4.7.0",
+    "typescript": "^5.3.3"
+  },
+  "engines": {
+    "node": ">=18.0.0"
+  }
+}