PDF Reader

Use the pdfread.py tool next to this file to read PDFs. Run it either way:

• With uv 0.11+ (preferred): invoke pdfread.py directly — the shebang runs it via uv run, and the first invocation downloads dependencies and may take a minute.
• Without uv: python3 pdfread.py … works on any Python 3.10+ that has pymupdf4llm>=0.0.17 installed (pip install pymupdf4llm).

The search command needs SQLite with FTS5; uv-managed and nearly all modern system Pythons include it, and the script errors clearly if not.

Run pdfread.py --help for full usage. In short:

• pdfread.py info FILE — page count, metadata, outline/TOC. Start here.
• pdfread.py search FILE "terms" — pages mentioning the keywords, BM25-ranked with a snippet per hit. The first search builds a cached index (seconds); later searches are instant. Terms are ANDed, term* matches prefixes, --raw unlocks full FTS5 syntax ("exact phrase", OR, NEAR). Text layer only — scanned pages are invisible to search.
• pdfread.py text FILE --pages 10-25 — markdown text for a 1-based page range. Figures appear as [image: p12-img2 640x480px] placeholders.
• pdfread.py image FILE p12-img2 — write that figure as a PNG and print its path; view it with your file-reading tool.
• pdfread.py images FILE --pages 12 — list figure IDs on pages.
• pdfread.py render FILE 7 — render a whole page (or --rect x0,y0,x1,y1 crop) to a PNG; the fallback for scanned pages and anything the text layer misses.

Preserving context on large PDFs

For pinpoint questions ("where does the contract mention termination fees?"), run search first and read only the hit pages with text --pages — no subagent fan-out needed. The index cache is shared, so subagents can search too without rebuilding.

For broad synthesis, do not dump a whole document into the main conversation. Instead:

1. Run info to get the page count and outline, and partition the document into sensible ranges (by chapter, or ~15–25 pages).
2. Spawn a subagent per range with instructions like: "Run <path>/pdfread.py text <file> --pages 10-25 and answer <the user's question> / summarize in at most N words. If a [image: ...] placeholder looks relevant, extract it with the image command and look at it."
3. Keep only the summaries/answers in the main conversation and synthesize from those. Re-target specific pages yourself only when a summary indicates the answer lives there.

Notes

• There is no OCR: a page with no text layer comes back as just its full-page [image: ...] placeholder (or a "no extractable text" note) — extract or render it and read the image visually.
• Extraction is deterministic: image IDs are stable across runs, so a subagent's reported ID can be extracted later from the main conversation.