4693ebcf83
- 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.
34 lines
702 B
JSON
34 lines
702 B
JSON
{
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"name": "mcp-rag-prompts",
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"version": "1.0.0",
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"description": "MCP Server for RAG-based prompt management with local embeddings",
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"type": "module",
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"main": "src/index.ts",
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"scripts": {
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"start": "tsx src/index.ts",
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"build": "tsc",
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"dev": "tsx watch src/index.ts"
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},
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"keywords": [
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"mcp",
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"rag",
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"embeddings",
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"prompt-management"
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],
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"license": "MIT",
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"dependencies": {
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"@modelcontextprotocol/sdk": "^1.0.0",
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"@xenova/transformers": "^2.17.2",
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"dotenv": "^17.2.3",
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"zod": "^3.23.8"
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},
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"devDependencies": {
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"@types/node": "^20.11.0",
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"tsx": "^4.7.0",
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"typescript": "^5.3.3"
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},
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"engines": {
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"node": ">=18.0.0"
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}
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}
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