ThermIQ — Industrial Knowledge Intelligence

AI-powered knowledge platform for thermal power plants. Quantifies knowledge gaps as ₹ crore operational risk.

Live: https://therm-iq.vercel.app (mirror: https://ghostunamused.github.io/thermIQ)

21-second intro (media/thermiq-intro.mp4), made with the /brag Claude Code plugin.

Architecture

• Frontend: static single-page app in docs/ (served by Vercel and GitHub Pages)
• Backend: Vercel Functions (api/*.js)
• Vector DB: Qdrant Cloud (collection thermiq_chunks)
• Embeddings: Jina AI (jina-embeddings-v3)
• Generation: Gemini 2.5 Flash, with NVIDIA NIM and OpenRouter fallbacks
• Structured data: Firebase Firestore
• Knowledge graph: Neo4j Aura
• Pipelines: GitHub Actions (daily CEA outage fetch, gap scans, Drive ingest, Neo4j keep-alive)

Local setup (fresh clone)

git clone https://github.com/GhostUnamused/thermIQ.git
cd thermIQ
cp .env.example .env            # fill in keys (same values as the Vercel dashboard)
npm install                     # deps for api/*.js
python -m venv .venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r scripts/requirements.txt

Preview the frontend: python -m http.server 5173 --directory docs (it calls the live Vercel backend). To run the API locally too: npx vercel dev (after npx vercel link).

Deploys are automatic: pushing to main redeploys Vercel (frontend + API) and GitHub Pages. GitHub Actions secrets must mirror the keys listed in .env.example.

Claude Code plugins

The repo pins the brag and frontend-design plugins in .claude/settings.json. To install them for your user account on a new machine: powershell -ExecutionPolicy Bypass -File scripts\install_claude_plugins.ps1 (Windows) or sh scripts/install_claude_plugins.sh (macOS/Linux).

Common tasks

Ingest benchmark documents (CEA standards — the yardstick):

python scripts/ingest_documents.py <pdf_path> guideline "CEA Tech Spec 500MW" <url>

For scanned/image PDFs use scripts/ingest_ocr.py instead.

Compute knowledge gaps (single source of truth for risk_scores):

python scripts/detect_gaps.py [--client NAME]

Fetch CEA outages manually: python scripts/fetch_cea_outage.py

Risk formula

risk_score_cr = criticality_score (1-5) × consequence_cr (₹ Cr) × exposure_score (0-1)

• criticality: 1-5, sourced to CEA outage frequency + CERC regulations
• consequence_cr: avg revenue impact from CEA outage records (₹5.0/kWh, LBNL/Ember 2024)
• exposure: 1 − best client-corpus match score