sdsconv 0.1.7

Python bindings for sdsconv — SDS ↔ MHLW standard JSON converter

sdsconv

Python-first, Rust-powered toolkit for converting Safety Data Sheets to Japan MHLW standard JSON — with schema validation, GHS/CAS checks, and corpus-scale quality evaluation.

日本語 | 中文

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Install

pip install sdsconv                   # Python bindings
pip install "sdsconv[analysis]"       # + causasv quality analysis
cargo install sdsconv                 # CLI / GUI binary

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Quick Start — Python

import sdsconv

# Extract raw text (no LLM)
text = sdsconv.extract_text("sample.pdf")

# Convert from URL
data, report = sdsconv.to_json_url_with_report(
    "https://example.com/sds.pdf", lang="ja",
)

# Convert SDS document → MHLW standard JSON
data, report = sdsconv.to_json_with_report(
    "sample.pdf",
    lang="ja",
    strict_mhlw=True,
)

# Validate and get structured findings
findings = sdsconv.validate(data, strict_mhlw=True)

print(f"Sections populated: {len(report['populated_sections'])}")
print(f"Findings: {len(findings)} ({sum(1 for f in findings if f['level']=='HIGH')} HIGH)")

# Save MHLW JSON
sdsconv.write_json(data, "output.json")

Corpus-scale evaluation (no manual review needed):

from sdsconv.eval import eval_corpus

df = eval_corpus(
    input_dir="data/sds_raw",
    output_dir="runs/eval_001",
    jobs=8,
)
print(df[["filename", "overall_score", "grade", "high_count"]].head(20))

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Examples

MHLW official sample SDS — allyl chloride (塩化アリル):

export ANTHROPIC_API_KEY=sk-ant-...
python examples/mhlw_allyl_chloride/convert.py

See examples/mhlw_allyl_chloride/ for expected.json, expected_report.json, and source attribution.

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Why sdsconv

• MHLW-native: Converts directly to the Japanese Ministry of Health, Labour and Welfare SDS data exchange format v1.0 (SDS_Schema_v1.0.json), validated against the official schema.
• Evidence-based extraction: Uses LLM to map free-form SDS text to ~200 nested schema fields. Source-text cross-checks detect hallucinations at the field level.
• Corpus-scale quality evaluation: eval_corpus processes hundreds of SDS documents and outputs per-rule failure counts, section scores, and causasv_features.csv for root-cause analysis — without any human review.
• No lock-in: Supports Anthropic Claude, OpenAI GPT, Google Gemini, Mistral, Groq, Cohere, and any OpenAI-compatible local endpoint. Bring your own model.
• Rust core: Extraction, schema validation, GHS/CAS checks, and DOCX/HTML generation run in native code. Thin Python bindings on top.

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MHLW Compliance

sdsconv targets the MHLW SDS data exchange format v1.0 published 2025-03-31.

Rule	Behaviour
Schema validation	Validates against SDS_Schema_v1.0.json
Empty-field removal	Removes "", null, [], {} per §3.3
AdditionalInfo	Content outside the official schema is written to AdditionalInfo.FullText
--strict-mhlw	Exits 1 (CLI) / raises ValueError (Python) if any HIGH or CRIT finding
CRIT/HIGH/MED findings	Structured validation report with rule ID, severity, path, message

Validation rules include: GHS H/P-code validity (GHS Rev.10), CAS format and check-digit, Section 2 GHS completeness (H-codes ↔ pictograms ↔ signal word), Section 3 component row alignment (name/CAS/concentration), UN number completeness, concentration range bounds, duplicate code detection, and more.

Quality baseline (30-file random sample, seed=42):

CRIT=0 · avg score 89.6 · top issues: S2-HAZARD-NO-PICTOGRAM, S15-ZHCN-NO-GB, S14-NO-SHIPPING-NAME

Full rule catalogue → docs/quality-check.md

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Corpus Evaluation

Run without human review:

from sdsconv.eval import eval_corpus

df = eval_corpus("data/sds_raw", "runs/eval_001", jobs=8)

Outputs per file:

File	Contents
generated/<stem>.json	MHLW standard JSON
reports/<stem>.json	ConversionReport (language, populated sections, warnings)
findings/<stem>.json	Structured validation findings
summary.csv	Per-file scores and grades
failures_by_rule.csv	Rule frequency and affected file counts

Root-cause analysis with causasv:

from sdsconv.causasv_bridge import print_ranking
print_ranking("runs/eval_001/causasv_features.csv")

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CLI

# PDF/DOCX/XLSX/HTML/URL → MHLW JSON
sdsconv to-json --input input.pdf --output output.json --lang ja

# With correction pass and PubChem enrichment
sdsconv to-json --input input.pdf --output output.json --correct --enrich

# JSON → Word document (16 JIS Z 7253 sections)
sdsconv to-docx --input output.json --output result.docx --lang ja

# JSON → HTML (printable, A4, inline CSS)
sdsconv to-html --input output.json --output result.html --lang ja

# Validate with strict MHLW mode
sdsconv validate --input output.json --strict-mhlw

# Batch: process a directory
sdsconv to-json --input-dir data/ --output-dir out/ --jobs 8

# Corpus evaluation
sdsconv eval-corpus --input-dir data/sds_raw --output-dir runs/eval_001 --jobs 8

Full CLI reference → sdsconv/README.md

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REST API

# Start server (binds to 127.0.0.1:3000 by default)
SDS_SERVER_TOKEN=secret sdsconv-server

# Convert a PDF
curl -X POST http://localhost:3000/api/to-json \
  -H "Authorization: Bearer secret" \
  -F "file=@input.pdf"

Endpoints: POST /api/to-json · POST /api/to-docx · POST /api/to-html · POST /api/validate · GET /api/health

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GUI

Run sdsconv without arguments to open the graphical interface:

sdsconv

Five tabs: Convert · Generate · Validate · Extract Text · Settings

Download the desktop app: macOS · Windows · brew install --cask sdsconv

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Supported Inputs, Languages, and Backends

Input formats: PDF (text, CID/Shift-JIS, scanned) · DOCX · XLSX · TXT · HTML · URL

Source languages: ja (JIS Z 7253) · en (GHS/OSHA HazCom) · zh-cn (GB/T 16483) · zh-tw (CNS 15030)

LLM backends: Anthropic Claude · OpenAI GPT · Google Gemini · Mistral · Groq · Cohere · Local (any OpenAI-compatible endpoint)

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For Developers

Rust library:

[dependencies]
sdsconv-core = "0.3"

See sdsconv_core/README.md for the Rust API.

Crates: sdsconv · sdsconv-core

Python package: sdsconv on PyPI — pip install sdsconv

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Security & Privacy

• Cloud LLM caution: When using a cloud LLM backend, SDS document text is sent to the API provider. Avoid sending confidential or trade-secret SDS documents to cloud APIs.
• Local operation: Use --backend local with any OpenAI-compatible endpoint (e.g. Ollama, LM Studio) for fully offline operation. No data leaves your machine.
• Raw SDS corpus: Add corpus/raw/ and data/sds_raw/ to .gitignore. Only corpus/manifest.jsonl (URLs + sha256 hashes) is safe to commit.
• REST server: Bearer token authentication with timing-safe comparison, SSRF protection (full IPv6 coverage), redirect-disabled HTTP client, 50 MB upload cap.

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Comparison

→ docs/comparison.md

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References

• MHLW — SDS Standard Data Exchange Format (official) (Japanese)
• SDS Data Exchange Format Developer Manual (PDF) (Japanese)
• Quality Check Rule Catalogue — 53 rules by section (日本語 / 中文)
• CHANGELOG

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License

MIT OR Apache-2.0 — at your option.