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Skillspixelrag

pixelrag

Data & analytics

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Updated 10-29 04:36

About this skill

PixelRAG operator

Instructions

  1. Check the source pin. From jeo-skills root use python3 .agent-skills/pixelrag/scripts/preflight.py --repo /absolute/upstream --json; from the installed skill root use scripts/preflight.py. No --repo is inventory only. Hashes cover selected source files, not Git HEAD, the full tree, package installation, model downloads, or runtime. Read pinned source notes.
  2. Choose local or hosted retrieval explicitly. PixelRAG renders visual documents/pages to image tiles, embeds visual content, builds a search index, and can serve queries. The upstream README also documents a hosted API and a prebuilt multi-terabyte index download. Do not switch to either external surface by implication; confirm data transfer, provider, cost, account, and destination.
  3. Map the local pipeline. Separate pixelshot capture from pixelrag stages (chunk, embed, build-index, index, serve). Inspect pixelrag.yaml, source connector, embedding model/device, output/index directory, and server bind before proceeding. PixelRAG is multimodal visual retrieval, not a knowledge graph/ontology.
  4. Get approval before effects. Ask before installing extras/native packages; downloading model weights or datasets; capturing private/third-party URLs; sending content or queries to hosted APIs or paid providers; writing/rebuilding/deleting tiles, embeddings, indexes or target data; or starting a service/public bind. Do not put credentials in CLI arguments or reports.
  5. Verify by stage. After approval, inspect pixelrag <stage> --help, run a small authorized sample, verify rendered tiles and their source mapping, index output, and query results. Preflight is offline provenance only; it is not a successful render, model load, index build, API deployment, or search test.

Examples

# Provenance check only; absolute, non-symlink checkout.
python3 .agent-skills/pixelrag/scripts/preflight.py --repo /work/PixelRAG --json
Only after package/model/source/index approval:

pip install 'pixelrag[index]'

Create and inspect pixelrag.yaml before running the pipeline.

pixelrag index build pixelrag serve --index-dir ./my_index --port 30001


## Best practices

- The pinned `pyproject.toml` requires Python `>=3.12`, although one README example says 3.10+; follow package metadata and test the exact environment.
- README describes Linux CUDA and macOS Apple Silicon/MPS paths with `device: auto`; CPU fallback and optional extras have different cost/performance. PDF rendering needs the PDF extra and Poppler. Confirm hardware and system packages before choosing a pipeline.
- `pixelrag[index]` pulls index/embed dependencies. Model weights may be large. The documented prebuilt index's base subset is about 217 GB; do not download it unless explicitly approved and storage is checked.
- `pixelrag serve` starts a service. Inspect actual bind defaults and restrict to loopback unless public exposure is explicitly approved and secured.
- Keep benchmark or hosted-corpus results scoped to their exact dataset/model/hardware; do not claim them for a user's corpus.

## References

- [Pinned upstream pipeline, dependencies, and limitations](references/upstream.md)
- [Pin, license, and selected-file hashes](references/source.json)
- [Authored routing scenarios](evals/evals.json)
- Helper contract: `python3 .agent-skills/pixelrag/scripts/preflight.py --help`; helper supports Python 3.10+ on POSIX Linux/macOS/WSL2 and fails closed when secure directory traversal is unavailable, including unsupported native Windows.