flexorch-audit 0.1.0

Zero-dependency PII + quality + noise audit for LLM datasets (TR/EU/US)

flexorch-audit

Zero-dependency PII + quality + noise audit for LLM datasets. Answers one question: is this dataset ready for LLM training?

• PII detection — email, phone (TR + E.164), credit card (Luhn), IP, TCKN, IBAN, SSN, label-prefixed names
• Quality metrics — completeness, average length, duplicate ratio
• Noise metrics — garbage character ratio, encoding health
• Masking — redact / replace / token / hash strategies
• Zero runtime dependencies — pure Python stdlib, Python 3.10+

from flexorch_audit import audit, mask

result = audit(text, locale="tr")
# {
#   "pii": [{"type": "email", "value": "[e-mail]", "start": 8, "end": 23}],
#   "quality": {"completeness": 1.0, "avg_length": 342, "duplicate_ratio": None},
#   "noise": {"garbage_ratio": 0.0, "encoding_ok": True},
# }

clean = mask(text, result["pii"], strategy="redact")
# "Contact: [REDACTED_EMAIL]"

Install

pip install flexorch-audit

Locale support

locale	Active detectors
"tr" (default)	email, iban, credit_card, ip + TCKN, phone_tr, name
"us"	email, iban, credit_card, ip + SSN, E.164 phone
"eu"	email, iban, credit_card, ip + E.164 phone
"all"	All of the above (phone_tr takes precedence over generic phone)

PII types

Type	Description	Locale
email	RFC-5321 address	all
iban	ISO 13616 IBAN (any country)	all
credit_card	16-digit groups, Luhn-validated	all
ip	IPv4 address	all
phone_tr	Turkish mobile (+90/0 prefix + 10 digits)	tr
national_id_tr	TCKN — 11-digit modular arithmetic checksum	tr
name	Label-prefixed name (e.g. "Adı: Ali Yıldız", "Full Name: Jane Doe")	tr
phone	E.164 international phone	us, eu
ssn	US Social Security Number (###-##-####)	us

Masking strategies

Strategy	Example output
redact (default)	[REDACTED_EMAIL]
replace	[e-mail] (realistic synthetic)
token	<PII_EMAIL_1> (unique per type)
hash	[3d4f9a1b2c8e7f0a] (SHA-256 first 16 hex chars)

Quality & noise

duplicate_ratio is null for single-string input. To compute it across a dataset:

texts = [record["text"] for record in dataset]
results = [audit(t) for t in texts]

seen = set()
duplicates = sum(1 for t in texts if t in seen or seen.add(t))
duplicate_ratio = duplicates / len(texts)

Limitations (v0.1)

• Free-standing name detection (without a label prefix) requires NLP/NER — not included.
• duplicate_ratio is per-call; aggregate across your dataset manually (see above).
• IPv6 not detected.
• IBAN format-only check; mod-97 validation not performed.

License

MIT