Aegis — Event-Driven Disaster Intelligence Pipeline

Ingests live public hazard feeds (wildfires, earthquakes, severe weather), enriches each event with an agentic LLM workflow, scores geospatial risk, and serves a RAG query layer so an analyst can ask "what's escalating near these assets right now?"

Ingestion is event-driven — feeds fire updates that trigger processing — not naive polling, with change-detection and dedup so the same event isn't reprocessed or double-counted.

Why it exists: disaster response needs fresh, deduplicated, enriched, location-aware signal. This pipeline mirrors that end to end on real open data.

Architecture

flowchart LR
    subgraph Sources
      F[NASA FIRMS<br/>active fires]
      Q[USGS<br/>earthquakes]
      W[NOAA / NWS<br/>alerts]
    end
    F & Q & W --> ING[Event-driven ingestors<br/>change-detection + dedup]
    ING --> N[Normalize to<br/>HazardEvent schema]
    N --> EN[Agentic enrichment<br/>LangChain: geocode, classify,<br/>summarize, severity]
    EN --> GEO[Geospatial risk scoring<br/>haversine / H3, asset proximity]
    GEO --> ST[(Store: events +<br/>FAISS vector index)]
    ST --> RAG[RAG query API<br/>'what's escalating near X?']
    AF[Airflow] -. schedules backfills & sweeps .-> ING

Loading

What makes it different

• Event-driven, not polling — change-detection on feed state; only new/changed events flow.
• Agentic enrichment — an LLM workflow geocodes, classifies hazard type, estimates severity, and writes an analyst-ready summary, with validation guards.
• Geospatial-first — proximity-to-asset risk scoring, not just a table of rows.
• RAG over live events — natural-language situational queries with citations.

Tech stack

Python 3.11 · LangChain (agentic enrichment) · FAISS · GeoPandas / H3 · Airflow · real APIs: NASA FIRMS, USGS, NOAA/NWS · Docker

Status

🚧 Built in public, in phases — see ROADMAP.md. Each phase ships tested code + a design note in docs/.

Quickstart

pip install -e ".[dev]"
pytest

License

MIT © Venkateswarlu Nagineni