Pdf parse qa

pdf-parse-qa

A structured way to A/B test PDF parsers — VLMs, OCRs, layout APIs, anything that takes a PDF page and returns text + bounding boxes. You bring the parsers (HuggingFace models, vendor APIs, local libs), the skill brings the validator UI, the data contract, and adapter examples.

Why this exists

Picking a PDF parser is a benchmark problem disguised as a procurement problem. Vendor demos pick the easy pages. Per-block accuracy varies wildly by label (a parser may nail Title and Section-header but butcher Table or Picture). A summary "F1 score" hides that.

This skill gives you the per-block, per-label, source-anchored audit that vendor benchmarks don't.

Workflow

┌─────────────┐    ┌──────────────────┐    ┌─────────────────┐    ┌──────────────┐
│ your PDF    │ →  │ your parser      │ →  │ blocks.json     │ →  │ validator UI │
│ (CORS host) │    │ (one of adapters)│    │ (this contract) │    │ (vote ↑↓123) │
└─────────────┘    └──────────────────┘    └─────────────────┘    └──────────────┘
                                                                          ↓
                                                                  verdicts.json
                                                                  → spreadsheet / Langfuse

1. Host the PDF somewhere CORS-open from embed.okrapdf.com (GitHub raw, R2, S3 with CORS, gist).
2. Run a parser adapter (adapters/*.py) to produce blocks.json matching the data contract below. Host it the same way.
3. Open the validator with both URLs as query params:

   https://embed.okrapdf.com/e/af8cc13f3d270c4b32f9245a?pdfUrl=<YOUR_PDF>&dataUrl=<YOUR_BLOCKS>&docTitle=Apple%2010-K%20(reducto)
   

4. Vote: click a row or use ↑↓ to navigate, 1/2/3 to score pass/partial/fail, 0 clears. Bias toward pass; mark only deviations. Bbox cross-highlights on the canvas as you go.
5. Copy verdicts as JSON when done. Paste into a sheet keyed by (parser, label) → pass-rate. That's the bake-off currency.

Data contract

Adapters MUST emit JSON in this shape. Coordinates are 0–1 normalized (origin = top-left), so they survive any PDF render scale.

{
  "pages": [
    {
      "page_number": 1,
      "blocks": [
        {
          "label": "Title",
          "value": "Form 10-K",
          "bbox": { "x": 0.10, "y": 0.05, "w": 0.80, "h": 0.03 }
        },
        {
          "label": "Table",
          "value": "Title of each class | Name of each exchange ...",
          "bbox": { "x": 0.08, "y": 0.32, "w": 0.84, "h": 0.18 }
        }
      ]
    },
    { "page_number": 2, "blocks": [ ... ] }
  ]
}

Required: pages[].page_number, pages[].blocks[].bbox.{x,y,w,h}. Recommended: pages[].blocks[].label, pages[].blocks[].value.

A starter file lives at examples/sample-blocks.json — sanity-test the widget before writing an adapter.

Validator URL

https://embed.okrapdf.com/e/af8cc13f3d270c4b32f9245a
  ?pdfUrl=<CORS-open PDF URL>
  &dataUrl=<CORS-open blocks.json URL>
  &docTitle=<display title, URL-encoded>
  &scale=1.5   (optional, default 1.5 — pdf.js render scale)

Verdicts persist in sessionStorage keyed by dataUrl, so refreshing the tab won't lose your work.

Adapters

Reference scripts in adapters/. Each one: takes a PDF file path, writes blocks.json in the contract above. ~30–80 lines. Add your own — PRs welcome.

File	Parser	Auth	Notes
adapters/gemini-flash.py	Google Gemini 3 Flash (Thinking)	GEMINI_API_KEY	Uses the ParseBench prompt; native [y_min, x_min, y_max, x_max] bbox order normalized to {x,y,w,h}
adapters/qwen2-vl-hf.py	HuggingFace Qwen/Qwen2.5-VL-7B-Instruct	none (local)	Local GPU; ~8GB VRAM @ fp16. Latest open-weights VL model.
adapters/reducto.py	Reducto /parse	REDUCTO_API_KEY	One of the strongest commercial table extractors as of 2026.
adapters/llamaparse.py	LlamaIndex LlamaParse (Premium / Agentic modes)	LLAMA_CLOUD_API_KEY	Stalwart; agentic mode handles charts.
adapters/azure-di.py	Azure Document Intelligence (prebuilt-layout)	AZURE_DI_ENDPOINT, AZURE_DI_KEY	Enterprise default; strong layout, weaker on free-form OCR.
adapters/okra.py	OkraPDF facets (any of 14: gemini-3-flash-minimal, reducto-parse, llamaparse-premium, mistral-ocr, mineru, unstructured, chandra-ocr, ...)	OKRA_API_KEY	One adapter, 14 parsers — useful if you already have an OkraPDF account.

Adapters worth adding (PRs welcome)

• Mistral OCR — mistral-ocr-latest is fast and cheap; good Mistral-vs-Gemini cost angle
• AWS Textract — analyze_document with FORMS|TABLES|LAYOUT
• Google Document AI — layout parser processor
• MinerU — local Python, strong on academic layout
• Docling — IBM open-source, fast on scientific PDFs
• Unstructured — unstructured.partition.pdf with hi_res
• Chandra OCR — emerging open-source OCR

Bake-off recipe

Same PDF, N parsers. Each adapter writes blocks-<parser>.json. Host all of them. Open one validator tab per parser. Score each. Aggregate by (parser, label) → pass-rate. That's a buyer-grade comparison your CFO can read.

# Same source PDF, four parsers in parallel
PDF=https://raw.githubusercontent.com/you/repo/main/test.pdf

python adapters/gemini-flash.py test.pdf > blocks-gemini.json
python adapters/reducto.py     test.pdf > blocks-reducto.json
python adapters/llamaparse.py  test.pdf > blocks-llamaparse.json
python adapters/qwen2-vl-hf.py test.pdf > blocks-qwen25vl.json

# Upload to gist / R2 / S3 (CORS open) — then:
for P in gemini reducto llamaparse qwen25vl; do
  echo "https://embed.okrapdf.com/e/af8cc13f3d270c4b32f9245a?pdfUrl=$PDF&dataUrl=https://.../blocks-$P.json&docTitle=$P"
done

Open each URL in a tab, score every page, copy verdicts, paste into:

parser	Title pass	Table pass	Picture pass	Section-header pass	overall
gemini	19/20	8/12	4/4	22/22	53/58 (91%)
reducto	18/20	11/12	3/4	22/22	54/58 (93%)
...

Verdict export shape

Copy verdicts as JSON → clipboard:

{
  "source": { "dataUrl": "https://.../blocks-reducto.json", "pdfUrl": "https://.../test.pdf" },
  "docTitle": "Apple 10-K (reducto)",
  "verdicts": [
    { "page": 1, "blockIndex": 0, "verdict": "pass" },
    { "page": 1, "blockIndex": 7, "verdict": "fail" },
    { "page": 2, "blockIndex": 3, "verdict": "partial" }
  ],
  "capturedAt": "2026-05-18T05:40:52Z"
}

When run against an OkraPDF facet (no dataUrl), source becomes { "docId": "...", "facet": "..." } instead. Same shape, different provenance.

Keyboard

Key	Action
↑ / k	previous block
↓ / j	next block
1	mark pass
2	mark partial
3	mark fail
0	clear verdict
Click row	focus block
Click bbox	focus row
Show source	re-scroll + flash the focused block

Hosting tips (CORS-open URLs)

The widget runs in the browser at embed.okrapdf.com and fetches your URLs cross-origin. They must respond with Access-Control-Allow-Origin: * (or include embed.okrapdf.com).

Host	Works out-of-the-box	Notes
GitHub raw (raw.githubusercontent.com)	✅	Use for small JSON + sample PDFs in your repo
GitHub gist (gist.githubusercontent.com/.../raw/...)	✅	Best for one-off blocks.json — gh gist create blocks.json --public
Cloudflare R2 with public bucket + CORS	✅	Best for many large PDFs
AWS S3 with CORS policy AllowedOrigin: *	✅	Add <CORSRule> to bucket
Vercel/Netlify static	✅	Default headers are CORS-open
Local file://	❌	Browser blocks cross-origin file:// — host even short-lived runs via python -m http.server + a tunnel

For private PDFs you don't want to host: see Standalone mode below.

Standalone mode (no embed.okrapdf.com)

The widget is a single HTML file. Clone the repo's parent (this skill ships SKILL.md only; the widget source lives at okrapdf/manual-app under embeds/parse-validator/) and open dist/index.html directly. Same ?pdfUrl=&dataUrl= params work. Use this for confidential PDFs that can't leave your machine.

See also

• OkraPDF facets (okra-curl skill in this repo) — if you want a hosted parser store rather than running 6 SDKs yourself, OkraPDF's /v1/documents/{id}/facets/{name}/run exposes 14 parsers behind one API. Use the adapters/okra.py adapter then.
• Label Studio — heavier, server-installed, supports per-cell table validation. Better when you're building a ground-truth dataset.
• Daloopa — commercial, finance-vertical, similar UX inspired this widget.
• ParseBench (arXiv 2604.08538) — the benchmark this skill operationalizes.

License

MIT. Adapter scripts are reference implementations; PRs welcome at okrapdf/skills.