DigUp for DevOps & SRE — Runbooks, Graphs & Incident Recordings

Download DigUp for Mac EmbeddingGemma 2 All roles

On-device flow Schematic: DigUp uses EmbeddingGemma 2 for DevOps and SRE local search

Original schematic — DigUp indexes local files with EmbeddingGemma 2 (llama.cpp).

Sourced snapshot (2026-10-15)

Fact	Value	Source
Model	EmbeddingGemma 2 · ~740M params · ~865 MB Q8 via llama.cpp	Google AI · DigUp README
Vector space	768-d shared multimodal space	Google AI · DigUp README
Mac / Win·Linux	0.6.1 · 0.7.0-beta.1	GitHub Releases
Stars / forks	~3,768 / ~254	GitHub API

Pain → DigUp workflow → tip

Pain	DigUp + EmbeddingGemma 2 workflow	Tip
Postmortem PDF section on rollback	PDF semantic search	Open at section
Grafana screenshot from last SEV	Image embeddings (+ OCR on Mac)	Search by panel text/look
Bridge recording timestamp	Audio/video windows	Jump near decision
IaC snippet	code: opt-in	Open at line

Numbered checklist

Install DigUp on the admin workstation you control.
Index Incidents / Runbooks / MetricsExports (local).
Complete EmbeddingGemma 2 (~865 MB); Use Files… if HF blocked (#25).
Enable code search for Terraform/Helm repos.
Prefer overnight indexing; verify GPU on Win/Linux via speed.log.

Why EmbeddingGemma 2 matters here

Google’s EmbeddingGemma 2 (~740M, unified 768-d multimodal space) is what DigUp runs via llama.cpp (~865 MB Q8 package). One query can rank images, PDF pages, audio windows, video frames, and optional code — see the local multimodal guide and model page. digup.run is not Google-official.