lens-qda

Python utilities for LENS, a local-first qualitative data analysis (QDA) desktop application.

This package bundles the same PDF text-extraction pipeline that the LENS desktop app uses to ingest PDF documents, exposing it as a small CLI so it can also be used directly from Python or from shell scripts.

Install

pip install lens-qda

Requires Python 3.8+ and the prebuilt wheels for pdfplumber and its dependencies (cryptography, pillow, pdfminer.six, ...) on PyPI; no compiler is needed on supported platforms.

CLI usage

# Print plain text extracted from a PDF (one paragraph per page):
lens-qda extract path/to/paper.pdf

# Emit the same JSON envelope the LENS desktop sidecar produces:
lens-qda extract paper.pdf --json

# Save the extracted text to a file:
lens-qda extract paper.pdf -o paper.txt

# Tune pdfplumber's tolerances (defaults match the sidecar):
lens-qda extract paper.pdf --x-tolerance 3 --y-tolerance 3

The --json schema matches the contract the LENS Tauri sidecar already implements:

{ "success": true, "text": "...all pages, joined by blank lines..." }

On failure:

{ "success": false, "error": "<exception message>" }

(the process exits with status 1 in that case).

Programmatic usage

from pathlib import Path
import json, subprocess

result = subprocess.run(
    ["lens-qda", "extract", "paper.pdf", "--json"],
    capture_output=True, text=True, check=True,
)
envelope = json.loads(result.stdout)
assert envelope["success"], envelope["error"]
corpus = envelope["text"]

License

MIT — same as the parent LENS project.