| --- |
| 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](https://huggingface.co/nvidia/nemotron-colembed-vl-4b-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. |
|
|
| ## Space setup |
|
|
| - **Persistent storage** must be enabled (embeddings + PDFs live under |
| `/data/library`). Budget roughly 5–12 MB per page of float16 token |
| embeddings; a 300-page manual is ~2–3.5 GB. |
| - Optional env vars: `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 |
|
|
