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Virtual Engineer

AI-assisted chiller troubleshooting demo powered by MongoDB Atlas. A field engineer describes a symptom; an LLM agent retrieves operational facts and knowledge through MCP tools, with live visibility into MongoDB retrieval patterns in the demo UI.

What it demonstrates

  • Deterministic grounding — asset resolution, alarms, telemetry, service history
  • Probabilistic retrieval — Atlas Vector Search and hybrid case search
  • Real-time transparency — query patterns, pipelines, and latency in the UI
  • Two demo paradigms — Evidence Board (technical) and Field Chat with X-ray (business)

Architecture

Demo UI (React)  →  LLM Agent (SSE)  →  MCP Server  →  Atlas
                                              ├── Database (operational data)
                                              ├── Vector Search (knowledge, cases)
                                              └── Atlas Search (case notes)

See docs/architecture.md for details.

Prerequisites

  • Node.js 18+
  • MongoDB Atlas cluster
  • OpenAI or Anthropic API key (for chat)

Quick start

cp .env.example .env   # add MONGODB_URI, OPENAI_API_KEY

npm install
npm run seed:drop      # load sample data into virtual_engineer database

# Terminal 1 — backend (MCP + chat API)
npm run mcp:dev

# Terminal 2 — demo UI
cd frontend && npm install && npm run dev

Open http://localhost:5173.

Demo scenarios

Chiller Alarm Story
CH-ATL-003 A1.01 Hero — repeat motor temperature fault
CH-DAL-002 207 High condenser pressure / cooling tower
CH-PHX-005 Co.A1 Compressor board communication loss
CH-ATL-001 Stable unit (negative control)

Starter prompts are on the Overview tab. See scripts/data/README.md for the full scenario matrix.

Scripts

Command Purpose
npm run mcp:dev Start MCP server + chat API (:3100)
npm run seed:drop Reseed Atlas with sample data
npm run test:connectivity Smoke tests (server must be running)
cd frontend && npm run dev Demo UI dev server (:5173)

Documentation

Doc Purpose
docs/runbook.md Operations, env vars, troubleshooting
docs/test-plan.md Test commands and dry-run scenarios
docs/indexes.md Atlas Search / Vector Search index definitions
docs/phase-status.md Build phase tracker

License

MIT

Requirements

Product and design rationale: Virtual Engineer High-Level Requirements and Design Logic.md

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AI-assisted chiller troubleshooting demo powered by MongoDB Atlas, MCP tools, and an LLM agent with real-time retrieval transparency.

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