porosdata-designer 0.1.3

Structured delivery toolkit: MinerU content_list to per-doc layout (content/structure/assets under one doc_id directory).

PorosData-Designer

PorosData-Designer converts MinerU-generated document parses into a unified per-document layout under {output_root}/{doc_id}/: structure-aware training text (*.content.json / *.content.txt), a datamining view (*.structure.json), and multimodal delivery (*.assets.index.json and images/).

It is designed for scientific data processing and structure-aware preparation centered on paragraphs, formulas, chemical expressions, and figure assets.

What it does

• Builds a structure-aware full-text view from *_content_list.json.
• Maps document sections, formulas, chemical expressions, and asset references into *.structure.json.
• Extracts image/caption/mention relationships and ships copied assets plus Markdown cards under images/.

Install

pip install porosdata-designer

After install, use import designer in Python and the designer CLI command. The PyPI distribution name is porosdata-designer.

Python requirement: >=3.8.

Quick start (CLI)

1. --input_dir: a directory tree containing MinerU outputs (recursive *_content_list.json).
2. --output_dir: where the structured delivery will be written (default is determined by the package config when omitted).

Run the full pipeline:

designer run all --input_dir "path/to/input_dir" --output_dir "path/to/output_dir" --log_dir "path/to/log_dir"

You can also run only one stage:

designer run text --input_dir "path/to/input_dir" --output_dir "path/to/output_dir"
designer run multimodal --input_dir "path/to/input_dir" --output_dir "path/to/output_dir"

Outputs

Each document is written to {output_root}/{doc_id}/:

path/to/output_root/
└── {doc_id}/
    ├── {doc_id}.content.json
    ├── {doc_id}.content.txt
    ├── {doc_id}.structure.json
    ├── {doc_id}.assets.index.json
    └── images/
        ├── fig_1.jpg
        └── fig_1.md

Validation / audit (CLI)

Examples (paths are relative to your local filesystem):

# Audit structured outputs (content/structure/*.json under each doc_id)
designer audit structured --root_dir "path/to/output_root"

# Validate structured outputs
designer validate structured --output_dir "path/to/output_root" --log_dir "path/to/log_dir"

# Validate multimodal index files
designer validate multimodal --output_dir "path/to/output_root" --log_dir "path/to/log_dir"

# Final acceptance validation
designer validate acceptance --output_dir "path/to/output_root" --log_dir "path/to/log_dir"

# Validate against the delivery standard
designer validate delivery --root_dir "path/to/output_root" --log_dir "path/to/log_dir"

Python usage

You can also use the package directly in Python:

from designer import DataMiningMapper, MultimodalInterleaver, TextAggregator

aggregator = TextAggregator()
mapper = DataMiningMapper()
interleaver = MultimodalInterleaver()

Text-side example:

from designer import DataMiningMapper, TextAggregator

content_list = [
    {"type": "text", "text_level": 1, "text": "Abstract", "page_idx": 0},
    {"type": "text", "text": "This work studies a Cu-Zr metallic glass system.", "page_idx": 0},
    {"type": "text", "text_level": 1, "text": "Results and Discussion", "page_idx": 1},
    {"type": "text", "text": "Figure 1 shows the microstructure evolution at 700 K.", "page_idx": 1},
]

aggregator = TextAggregator()
structured_text = aggregator.aggregate(content_list)

mapper = DataMiningMapper()
datamining_view = mapper.map(structured_text, {"doc_id": "demo-0001"})

print(structured_text)
print(datamining_view.pure_text_stream)
print(datamining_view.structured_json["sections"])

Expected outcome:

• structured_text contains Poros tags such as <poros_doc>, <poros_section_*>, and <poros_paragraph>.
• pure_text_stream removes the structure tags while keeping readable text.
• structured_json exposes mined fields such as sections, formulas, chemical_formulas, and asset_refs.