psyduck 0.1.0

Agent-safe PDF extraction SDK with structured output and quality reports.

Psyduck

Psyduck is a small Python SDK for turning PDFs into agent-ready structured documents.

It is intentionally SDK-only: import it from Python, run Psyduck().process(...), and inspect the returned document, profile, quality report, and exported artifacts.

Install

pip install psyduck

Development install:

pip install -e ".[dev]"

Python API

from psyduck import Psyduck

duck = Psyduck()
result = duck.process("report.pdf", goal="rag", mode="balanced", return_content=True)

if result.quality.needs_review:
    for warning in result.quality.warnings:
        print(warning.code, warning.message)

for block in result.document.blocks:
    print(block.page, block.text)

Output Directory

output/report-<timestamp>-<id>/
  run.json
  profile.json
  document.md
  document.json
  quality.json
  tables/
  assets/
  pages/

SDK Contract

• Default extraction uses PyMuPDF.
• process() always writes Markdown, JSON, profile, quality, and run metadata.
• return_content=False keeps large document content out of the immediate result.
• load_result(output_dir) reloads a previous SDK run.
• Custom extractors can be supplied through extractor_registry and requested with process(..., extractors=[...]).
• tables="force" and ocr="force" report needs_table_adapter / needs_ocr_adapter warnings unless a caller-provided extractor handles that work.

Custom Extractors

from psyduck import Psyduck
from psyduck.extractors.base import ExtractorOutput
from psyduck.extractors.pymupdf import PyMuPDFExtractor


class MyExtractor:
    def extract(self, file_path, pages=None):
        return ExtractorOutput(source="my_extractor")


duck = Psyduck(
    extractor_registry={
        "pymupdf": PyMuPDFExtractor,
        "my_extractor": lambda: MyExtractor(),
    }
)

result = duck.process("report.pdf", extractors=["pymupdf", "my_extractor"])

Agent Policy

1. Always call process() before answering questions about PDF contents.
2. Check result.quality before using extracted content.
3. Use Markdown for summaries and JSON for page-aware answers.
4. Do not claim complete extraction when needs_review is true.