chandra-parser 0.1.0

PDF to Markdown parser using Datalab's Marker OCR API with optional GPT-based figure filtering

Chandra Parser

PDF to Markdown parser using Datalab's Marker OCR API with optional GPT-based figure filtering.

Built by ReSolve Asset Management to make financial research more accessible.

Why We Built This

At ReSolve, we produce extensive research on asset allocation, alternatives, and portfolio construction. We wanted a better way to:

• Convert our PDF research papers into searchable, web-friendly markdown
• Extract clean text for AI/LLM processing and RAG pipelines
• Make our educational content more accessible across devices

We open-sourced this tool so others in the investment community can benefit too.

Try it on our research: Download any paper from Return Stacked ETFs or ReSolve Asset Management and parse it with Chandra.

Features

• High-quality OCR: Uses Datalab's Marker API for accurate PDF text extraction
• Paginated output: Splits documents into individual page files
• Page images: Extracts PDF pages as PNG images at configurable resolution
• Figure filtering: Optionally removes logos, decorations, and non-essential images using GPT
• CLI & library: Use from command line or import as a Python package

Installation

# Using uv (recommended)
uv add chandra-parser

# Using pip
pip install chandra-parser

Quick Start

Command Line

# Parse a PDF
chandra parse document.pdf ./output

# Parse without figure filtering
chandra parse document.pdf ./output --no-filter

# Extract just the page images
chandra images document.pdf ./page_images --width 1024

Python API

from chandra_parser import parse_pdf

# Full pipeline
result = parse_pdf(
    "document.pdf",
    "./output",
    filter_figures=True,  # Requires OPENAI_API_KEY
    extract_images=True,
    image_width=712,
)

# Individual functions
from chandra_parser import extract_page_images, filter_pages

extract_page_images("document.pdf", "./images", width=712)

Configuration

Create a .env file or set environment variables:

# Required: Datalab API key
DATALAB_API_KEY=your_key_here

# Optional: For GPT-based figure filtering
OPENAI_API_KEY=your_key_here

Get your Datalab API key at datalab.to.

Output Structure

output/
├── final.md              # Cleaned, bound markdown (filtered if enabled)
├── output.md             # Raw OCR output
├── response.json         # Full API response
├── images/               # Extracted figures from OCR
├── page_images/          # PDF page renders as PNG
├── pages/                # Individual page markdown files
└── pages_filtered/       # Filtered page markdown (if filtering enabled)

macOS Quick Action (Right-Click to Parse)

Want to parse PDFs with a simple right-click? We have a step-by-step guide to set up a Finder Quick Action — no coding experience required.

View the Quick Action Setup Guide →

Once set up, just right-click any PDF and select "Parse PDF with Chandra". The output folder opens automatically when complete.

API Reference

parse_pdf(file_path, output_dir, **options)

Main entry point for parsing PDFs.

Arguments:

• file_path: Path to the PDF file
• output_dir: Directory for output files
• filter_figures: Use GPT to remove non-essential figures (default: True)
• extract_images: Extract PDF pages as PNG (default: True)
• image_width: Width for page images in pixels (default: 712)
• verbose: Print progress messages (default: True)

Returns: Path to the final markdown file

extract_page_images(pdf_path, output_dir, width=712)

Extract each page of a PDF as a PNG image.

filter_pages(input_dir, output_dir, batch_size=10, model="gpt-5-mini")

Filter non-essential figures from markdown pages using GPT.

About

Chandra Parser is developed and maintained by ReSolve Asset Management, a systematic asset manager specializing in risk parity, managed futures, and alternative investment strategies.

Learn more about our work:

• Return Stacked ETFs - Capital-efficient ETFs that stack return sources
• ReSolve Research - Free research on portfolio construction and alternatives
• Gestalt University - Educational content on YouTube

License

MIT License - see LICENSE for details.