pdf

Read PDFs on the machine, with no OCR service and no API key. This worker classifies a document in about twenty milliseconds — is this real text, or a photograph of a page? — converts text-based documents to markdown that keeps their headings, lists, links and tables, and reports exactly which pages still need OCR and why. It also exposes the layout underneath: where every run of characters sits, and what the text is inside a given box on a page. Nothing is uploaded, and a long document is capped rather than dumped, so a report does not swallow the context an agent needed for the answer.

It ships a console page too. Drop a PDF in and see exactly what the agent sees: the verdict, the per-page OCR decision, and the extracted markdown.

Install

iii worker add pdf

Quickstart

Classify first. It is cheap, and it decides whether anything else is worth doing: extraction on a scan returns nothing, and without the verdict an empty result is indistinguishable from an empty document.

use iii_sdk::{register_worker, InitOptions};
use iii_sdk::protocol::TriggerRequest;
use serde_json::json;

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    let iii = register_worker("ws://localhost:49134", InitOptions::default());

    let verdict = iii.trigger(TriggerRequest {
        function_id: "pdf::classify".into(),
        payload: json!({ "path": "/tmp/report.pdf" }),
        action: None,
        timeout_ms: Some(30_000),
    }).await?;
    // { "document_type": "text_based", "confidence": 1.0, "page_count": 40,
    //   "pages_needing_ocr": [], "ocr_reasons": [], "elapsed_ms": 18, … }

    let markdown = iii.trigger(TriggerRequest {
        function_id: "pdf::to-markdown".into(),
        payload: json!({ "path": "/tmp/report.pdf", "pages": [1, 2, 3] }),
        action: None,
        timeout_ms: Some(60_000),
    }).await?;
    // { "body": { "text": "# Quarterly Report\n…", "chars": 5693,
    //             "total_chars": 5693, "truncated": false }, … }

    println!("{markdown:#?}");
    Ok(())
}

A document with no path goes in as bytes_base64 instead. An encrypted one takes a password on pdf::classify and pdf::to-markdown.

Reading the verdict

document_type is text_based, scanned, image_based or mixed. The document-level answer is not the whole story: a two-hundred-page report with a scanned cover is not a scanned document, and treating it as one sends the whole thing to an OCR service for the sake of one page. pages_needing_ocr and ocr_reasons carry the per-page decision:

Reason	What it means
scanned	A raster page. It needs a vision model.
no_text	Nothing extractable and nothing to OCR. Often a blank page.
vector_text	Characters drawn as outlines rather than text. Unreadable as characters.
suspected_garbled_text	A text layer that decodes to nonsense. Do not trust it, whatever the document type says.

Response caps

Every text-bearing response is capped and says so. truncated: true with a total_chars far above chars means you are holding a fragment.

The cheap fix is pages, not a bigger cap: conversion cost scales with the document, so narrowing to the pages you need is faster as well as smaller. A four-hundred-page report takes tens of seconds to convert whole and milliseconds a page at a time.

max_chars: 0 lifts the cap entirely. That belongs in a pipeline moving a document to storage, not in a call whose result lands in a conversation.

Reading a box on a page

When a vision model has located a region and you want the real characters rather than its transcription:

{
  "path": "/tmp/invoice.pdf",
  "regions": [{ "page": 1, "boxes": [[320.0, 640.0, 560.0, 700.0]] }],
  "mode": "text"
}

mode: "table" runs table detection over the same box and returns a markdown table instead.

Two conventions worth knowing

Page numbers are 1-indexed everywhere on this surface, in requests and responses.

Coordinates are not uniform, and each response states which it used. pdf::extract-items reports PDF points from the bottom left, the PDF convention. pdf::extract-regions takes boxes in PDF points from the top left, which is what a layout model produces. Getting this wrong is silent: text comes back, just from the wrong end of the page.

Configuration

Configuration lives in the configuration worker under the id pdf and every field hot-reloads. Nothing here needs a restart.

max_input_bytes: 268435456   # largest document accepted, before parsing
max_chars: 40000             # default cap on returned text or markdown
preview_chars: 600           # leading characters shown alongside a capped body
max_items: 5000              # default cap on positioned items in one response
classify_sample_pages: 8     # pages sampled to classify; 0 scans everything
min_text_ops_per_page: 3     # text operators before a page counts as text
text_page_ratio_threshold: 0.6  # share of text pages to call a document text-based

The three detection fields are the ones worth understanding. Sampling is what keeps classification at tens of milliseconds on a four-hundred-page file; it also means the verdict comes from part of the document, which is why every response reports pages_sampled. Raise classify_sample_pages, or set it to 0, when a borderline mixed document needs settling.

Defaults live in src/config.rs.

Called on demand

This worker registers no harness hook and injects nothing into any prompt. A conversation that never touches a document never pays for it, and there is no per-turn cost to having it installed. An agent finds it the ordinary way, through the function registry and skills/SKILL.md; a person finds it through the console page.

What this worker does not do

It does not rasterize pages, so it cannot OCR anything. Scanned and image-based documents get classified and routed, not read. Image content is reported as a placeholder with a real bounding box and no pixels.

It is a parser, not a renderer: it walks the document's content streams and reconstructs the geometry, which is why it is fast and why it needs no service behind it.