Capstone 04 — Multimodal Document QA (Vision-First PDF, Tables, Charts)

> The 2026 document-QA frontier moved away from OCR-then-text and toward vision-first late interaction. ColPali, ColQwen2.5, and ColQwen3-omni treat each PDF page as an image, embed it with multi-vector late interaction, and let the query attend to patches directly. On financial 10-Ks, scientific papers, and handwritten notes this pattern beats OCR-first by a large margin. Build the pipeline end to end on 10k pages and publish the side-by-side against OCR-then-text.

Type: Capstone

Languages: Python (pipeline), TypeScript (viewer UI)

Prerequisites: Phase 4 (computer vision), Phase 5 (NLP), Phase 7 (transformers), Phase 11 (LLM engineering), Phase 12 (multimodal), Phase 17 (infrastructure)

Phases exercised: P4 · P5 · P7 · P11 · P12 · P17

Time: 30 hours

Problem

Enterprises sit on PDFs that OCR pipelines mangle: scanned 10-Ks with rotated tables, scientific papers dense with equations, charts that only make sense as images, handwritten annotations. Treating these as text-first means losing half the signal. The 2026 answer is late-interaction multi-vector retrieval on raw page images. ColPali (Illuin Tech) introduced it; ColQwen2.5-v0.2 and ColQwen3-omni pushed accuracy. On ViDoRe v3, vision-first retrieval scores above OCR-then-text by meaningful margins — and the gap widens on charts, tables, and handwriting.

The trade-off is storage and latency. A ColQwen embedding is ~2048 patch vectors per page, not a single 1024-dim vector. Raw storage balloons. DocPruner (2026) brings 50% pruning without measurable accuracy loss. You will index 10k pages, measure ViDoRe v3 nDCG@5, serve answers under 2s, and compare directly against an OCR-then-text baseline.

Concept

Late interaction means every query token scores against every patch token, and the maximum score per query token is summed. You get fine-grained matching without needing a single pooled vector. A multi-vector index (Vespa, Qdrant multi-vector, or AstraDB) stores the per-patch embeddings and runs MaxSim at retrieval time.

The answerer is a vision-language model that takes the query plus the top-k retrieved pages as images and writes an answer with evidence regions (bounding boxes or page references). Qwen3-VL-30B, Gemini 2.5 Pro, and InternVL3 are the 2026 frontier choices. For equations and scientific notation, an OCR fallback (Nougat, dots.ocr) is spliced in as an optional text channel.

Evaluation is a two-dimensional matrix. One axis: content type (plain text paragraphs, dense tables, bar/line charts, handwritten notes, equations). Other axis: retrieval approach (vision-first late interaction vs OCR-then-text vs hybrid). Each cell gets nDCG@5 and answer accuracy. The report is the deliverable.

Architecture

PDFs -> page renderer (PyMuPDF, 180 DPI)
           |
           v
  ColQwen2.5-v0.2 embed (multi-vector per page, ~2048 patches)
           |
           +------> DocPruner 50% compression
           |
           v
   multi-vector index (Vespa or Qdrant multi-vector)
           |
query ----+----> retrieve top-k pages (MaxSim)
           |
           v
  VLM answerer: Qwen3-VL-30B | Gemini 2.5 Pro | InternVL3
    inputs: query + top-k page images + optional OCR text
           |
           v
  answer with cited page numbers + evidence regions
           |
           v
  Streamlit / Next.js viewer: highlighted boxes on source page

Stack

• Page rendering: PyMuPDF (fitz) at 180 DPI, portrait-normalized
• Late-interaction model: ColQwen2.5-v0.2 or ColQwen3-omni (vidore team on Hugging Face)
• Index: Vespa with multi-vector field, or Qdrant multi-vector, or AstraDB with MaxSim
• Pruning: DocPruner 2026 policy (keep high-variance patches, 50% compression at < 0.5% accuracy loss)
• OCR fallback (equations / dense tables): dots.ocr or Nougat
• VLM answerer: Qwen3-VL-30B self-hosted or Gemini 2.5 Pro hosted; InternVL3 as fallback
• Evaluation: ViDoRe v3 benchmark, M3DocVQA for multi-page reasoning
• Viewer UI: Next.js 15 with canvas overlay for evidence regions

Build It

1. Ingest. Walk a corpus of 10k PDF pages across 10-Ks, scientific papers, and scanned documents. Render each page to a 1536x2048 PNG. Persist {doc_id, page_num, image_path}.

1. Embed. Run ColQwen2.5-v0.2 on each page image. Output shape ~2048 patch embeddings of dim 128. Apply DocPruner to keep the highest-signal half. Write to Vespa multi-vector field or Qdrant multi-vector.

1. Query. For each incoming query, embed with the query tower (token-level embeddings). Run MaxSim against the index: for every query token, take the max dot-product over page patch embeddings, sum. Return top-k pages.

1. Synthesize. Call Qwen3-VL-30B with the query and the top-5 page images. Prompt: "Answer using only the supplied pages. Cite each claim by (doc_id, page) and name the region (figure, table, paragraph)."

1. Evidence regions. Post-process the answer to extract cited regions. If the VLM emits bounding boxes (Qwen3-VL does), render them as overlays in the viewer.

1. OCR fallback. For pages identified as equation-dense (heuristic on image variance), run Nougat or dots.ocr and pass the OCR text as an extra channel alongside the image.

1. Eval. Run ViDoRe v3 (retrieval nDCG@5) and M3DocVQA (multi-page QA accuracy). Also run OCR-then-text pipeline on the same corpus with the same synthesizer. Produce a content-type × approach matrix.

1. UI. Streamlit prototype first; Next.js 15 production viewer with page-by-page evidence-region overlay.

Use It

$ doc-qa ask "what was the 2024 operating margin change for segment EMEA?"
[retrieve]   top-5 pages in 320ms (ColQwen2.5, MaxSim, Vespa)
[synth]      qwen3-vl-30b, 1.4s, cited (form-10k-2024, p. 88) + (..., p. 92)
answer:
  EMEA operating margin moved from 18.2% to 16.8%, a 140bp decline.
  cited: 10-K-2024.pdf p.88 (Table 4, Segment Operating Margin)
         10-K-2024.pdf p.92 (MD&A, Operating Performance)
[viewer]     open with highlighted bounding boxes overlaid on p.88 Table 4

Ship It

outputs/skill-doc-qa.md describes the deliverable: a vision-first multimodal document QA system tuned to a specific corpus and evaluated against an OCR-then-text baseline on ViDoRe v3.

Exercises

1. Measure ColQwen2.5-v0.2 vs ColQwen3-omni on the same corpus. Which pages does one get right and the other miss? Add a "content class" tag to the index to route by type.

1. Prune embeddings aggressively (75%, 90%). Find the compression cliff: the point where ViDoRe nDCG@5 drops below the OCR baseline.

1. Build a hybrid: run OCR-then-text and ColQwen in parallel, fuse with RRF, rerank with a cross-encoder. Does the hybrid beat either alone? Where does it help most?

1. Swap Qwen3-VL-30B for a smaller VLM (Qwen2.5-VL-7B). Measure the accuracy-per-dollar curve.

1. Add handwritten-note support. Render the handwriting corpus, embed with ColQwen, measure retrieval. Compare against a handwriting OCR pipeline.

Key Terms

Further Reading

• ColPali (Illuin Tech) repository — reference late-interaction doc retrieval
• ColPali paper (arXiv:2407.01449) — the foundational method paper
• ColQwen family on Hugging Face — production-ready checkpoints
• M3DocRAG (Adobe) — multi-page multimodal RAG baseline
• Vespa multi-vector tutorial — reference serving stack
• Qdrant multi-vector support — alternate index
• AstraDB multi-vector — alternate managed index
• Nougat OCR — equation-capable OCR fallback