Visual RAG for technical PDFs
Finds the right page in product catalogues and technical PDFs by how the page looks (drawings, tables, dimension callouts), then answers from the page images with a vision model and cites the page.
- 55% → 84%
- top-1 page, visual-only vs hybrid
- 100%
- recall with LLM page reranking
- p = 0.001
- hybrid beats visual 11–0 where they disagree
- Benchmarked 7 retrieval modes on 38 questions whose gold page is verified by string match (114 in the full set), with Wilson intervals and an exact McNemar test for every pair.
- Hybrid search (pixel index + BM25, fused with reciprocal rank fusion) raised top-1 from 55% to 84%. The same benchmark showed that a paid query-expansion stage added nothing, so the default mode skips it.
- Web UI and API with a per-question retrieval mode, page citations and an answerability score.
Thin layer over PixelRAG (StarTrail): retrieval modes, hybrid search, evaluation and UI are mine.
- Python
- FAISS
- BM25 + RRF
- PixelRAG
- Gemini
- Claude