README.md

metadata

title: Repair Guy
emoji: 🔧
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 6.16.0
python_version: '3.12'
app_file: app.py
pinned: false
preload_from_hub:
  - nvidia/nemotron-colembed-vl-4b-v2
  - openbmb/MiniCPM-V-4_5
license: mit

Repair Guy — visual RAG over repair manuals

No parsing, no chunking, no figure descriptions: every PDF page is embedded as an image with Nemotron ColEmbed v2 (multi-vector, late interaction). At question time the query is embedded and scored against every page with MaxSim (batched torch matmuls on ZeroGPU, pages streamed from disk via numpy memmap), and the top-K page images are read by MiniCPM-V 4.5 — also on ZeroGPU — to produce a grounded answer. Everything runs inside the Space; no external endpoints.

Two tabs

• Library — large manuals indexed offline (scripts/index_modal.py in the GitHub repo runs it on a Modal GPU; index_local.py if you have a CUDA box) and pushed to the library dataset; the Space syncs it to /data/preindexed at startup.
• Upload your own — indexes on the Space's ZeroGPU, capped at MAX_UPLOAD_PAGES (default 50) to protect quota.

Space setup

• Persistent storage must be enabled (the library sync and uploads live under /data). Budget roughly 5–12 MB per page of float16 token embeddings; a 1000-page manual is ~6–12 GB — size the tier to the library.
• Optional env vars: LIBRARY_DATASET_ID, MAX_UPLOAD_PAGES, COLEMBED_MODEL_ID (defaults to the 4B model), MINICPM_MODEL_ID (defaults to openbmb/MiniCPM-V-4_5), COLEMBED_ATTN (defaults to sdpa; set flash_attention_2 if flash-attn is installed).

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference