向量化搜索系统

MCP Server → MCP Client → Embedding Service → Vector Storage → REST API
     ↓            ↓              ↓              ↓            ↓
Customer Data → Descriptions → Embeddings → ChromaDB → Search Results

# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key_here

# ChromaDB Configuration  
CHROMA_HOST=localhost
CHROMA_PORT=8001
CHROMA_COLLECTION_NAME=mcp_customers

# MCP Server Configuration
MCP_SERVER_URL=http://localhost:8080/mcp
MCP_TENANT_ID=development

# API Configuration
API_PORT=3000

# Process customers from your MCP server
npm run vectorize

# Or use the main vectorizer class
node src/examples/process-customers.js

npm start

# Search via API
curl -X POST http://localhost:3000/search \
  -H "Content-Type: application/json" \
  -d '{"query": "high-value wine club member", "limit": 5}'

# Get customer by ID
curl http://localhost:3000/customers/customer-id-here

GET /health

POST /search
Content-Type: application/json

{
  "query": "customer description or search terms",
  "limit": 10,
  "filters": {
    "isActiveClubMember": true
  }
}

GET /customers/:id

# Run with mock data
npm run test:mock

# Run integration tests
npm test

mcp-vectorizer/
├── src/
│   ├── api/           # REST API endpoints
│   ├── mcp/           # MCP client and communication
│   ├── vectorizer/    # Embedding generation
│   ├── storage/       # ChromaDB integration
│   └── examples/      # Usage examples
├── config/            # Configuration files
├── docs/             # Documentation
└── package.json