Multi-Agent Context Processor for Operational Technology (OT) data.

Connects to industrial data sources (MQTT, OPC UA, Modbus), filters signals, embeds context into vector stores, and enables lightweight LLM-based Q&A — all using modular agents and Redis pub/sub.

• 🛰️ Plug in MQTT, OPC UA, or Modbus sources
• 🔁 Agents run asynchronously (filtering, vectorizing, answering)
• 📦 Vector DB per zone using FAISS
• 🧠 Q&A using MiniLM (via sentence-transformers)
• 🌐 FastAPI dashboard to monitor messages and triggers
• 🧩 Modular, extensible, and modern Python packaging (pyproject.toml)

• Open browser: http://localhost:8000

🔌 Configuring Data Sources

Edit mcpai/configs/data_sources.yml:

🧱 Architecture

ingest_agent.py – connects to all OT sources dynamically

filter_agent.py – identifies meaningful anomalies (e.g., "high temp")

vector_agent.py – builds FAISS DB per zone

llm_agent.py – answers based on vector context

coordinator.py – starts everything

dashboard.py – FastAPI UI

redis_pubsub.py – pub/sub messaging layer

📄 License

MIT

🤝 Contributing

PRs and issues welcome. Designed to be extensible for edge computing and OT AI use cases.