Use case · Customer support

DigUp for customer support

Ticket tools search tickets; DigUp searches your local runbooks, screenshots, and call audio with EmbeddingGemma 2.

On-device flowSchematic: DigUp uses EmbeddingGemma 2 for Customer support local search
Original schematic — DigUp indexes local files with EmbeddingGemma 2 (llama.cpp).

Sourced snapshot (2026-10-15)

FactValueSource
ModelEmbeddingGemma 2 · ~740M params · ~865 MB Q8 via llama.cppGoogle AI · DigUp README
Vector space768-d shared multimodal spaceGoogle AI · DigUp README
Mac / Win·Linux0.6.1 · 0.7.0-beta.1GitHub Releases
Stars / forks~3,768 / ~254GitHub API

Pain → DigUp workflow → tip

PainDigUp + EmbeddingGemma 2 workflowTip
Which runbook covers refund edge case?Index support PDFs/mdSemantic + keyword
Call where user described error UIAudio windowsJump near description
Screenshot of rare settings panelImage (+ Mac OCR)Find by panel contents
Agent laptop privacyLocal-first DigUpNo DigUp telemetry of file contents (README)

Numbered checklist

  1. Install DigUp; index Knowledge / Recordings allowed by policy.
  2. Finish EmbeddingGemma 2 download (~865 MB).
  3. Search customer phrases, not only internal IDs.
  4. Verify before pasting into replies.
  5. See zero-files guide if synced knowledge base is cloud-only.

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.

Downloads & installs

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