pdf-extract

name

pdf-extract

description

Extract structured information from PDF files (invoices, receipts, contracts, reports) using a local vision LLM via Ollama. Fully offline. Use this skill whenever the user asks to read, parse, extract, or analyze content from one or more PDF files — especially when they mention invoices, receipts, bills, or any scanned/image-heavy documents. Also trigger when the user wants to summarize, compare, or tabulate data across multiple PDFs.

PDF Extract (Offline, Bun + Ollama Vision)

Extracts text and structured data from PDFs by converting pages to images and sending them to a local Ollama vision model. Zero npm dependencies — uses pdftoppm (poppler) and Bun builtins.

Prerequisites

• Bun runtime
• poppler installed (pdftoppm must be on PATH)
  • Arch/Manjaro: sudo pacman -S poppler
  • Debian/Ubuntu: sudo apt install poppler-utils
  • macOS: brew install poppler
• Ollama running locally with a vision-capable model pulled:
  • ollama pull gemma4:26b (recommended) or ollama pull qwen3-vl:32b

Usage

The extraction script lives at scripts/extract-pdf.ts relative to this skill's directory.

# Single PDF — returns extracted text/data to stdout as JSON
bun run <skill-dir>/scripts/extract-pdf.ts invoice.pdf

# Multiple PDFs
bun run <skill-dir>/scripts/extract-pdf.ts receipt1.pdf receipt2.pdf

# Custom prompt (override default extraction prompt)
EXTRACT_PROMPT="List all line items with amounts" \
  bun run <skill-dir>/scripts/extract-pdf.ts invoice.pdf

# Choose model (default: gemma4:26b)
OLLAMA_MODEL="qwen3-vl:32b" \
  bun run <skill-dir>/scripts/extract-pdf.ts invoice.pdf

# Custom Ollama endpoint (default: http://localhost:11434/v1)
OLLAMA_BASE_URL="http://192.168.1.50:11434/v1" \
  bun run <skill-dir>/scripts/extract-pdf.ts invoice.pdf

# DPI for rasterization (default: 200, increase for tiny text)
EXTRACT_DPI="300" \
  bun run <skill-dir>/scripts/extract-pdf.ts invoice.pdf

Output Format

The script writes JSON to stdout — one object per PDF. Progress logs go to stderr.

[
  {
    "file": "invoice.pdf",
    "pages": [
      { "page": 1, "content": "..." },
      { "page": 2, "content": "..." }
    ]
  }
]

Pipe or capture as needed:

# Save to file
bun run <skill-dir>/scripts/extract-pdf.ts *.pdf > results.json

# Pipe into jq
bun run <skill-dir>/scripts/extract-pdf.ts invoice.pdf | jq '.[0].pages[0].content'

How It Works

1. For each PDF, runs pdftoppm -png -r <DPI> to rasterize pages into a temp directory.
2. Reads each PNG as base64.
3. Sends each image to the Ollama OpenAI-compatible vision endpoint (/v1/chat/completions).
4. Strips Gemma 4 thinking-channel tokens from output (transparent for other models).
5. Collects responses and outputs structured JSON.
6. Cleans up temp images.

Integration Tips for the Agent

When the user asks to extract data from PDFs:

1. Run the script with the PDF path(s).
2. Capture the JSON output.
3. Post-process as needed (summarize, compare, tabulate, convert to CSV, etc.).

For structured extraction (e.g., "get invoice number, date, total, line items"), set EXTRACT_PROMPT to a task-specific instruction — the default prompt is general-purpose.