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bharatid

Detect, validate and redact Indian identifiers in text. Zero dependencies, pure Python 3.9+. Built for LLM guardrails, log scrubbing and data-loss prevention, where a bare regex for "12 digits" or "5 letters, 4 digits, 1 letter" flags far too much.

Identifier Check Notes
Aadhaar Verhoeff checksum + first digit 2-9 rejects ~90% of random 12-digit numbers
GSTIN Base-36 checksum + valid state code + valid embedded PAN beats the PAN inside it when both match
PAN structure (4th letter = entity type) no public checksum
TAN, IFSC, EPIC (voter ID), passport structure IFSC bank/branch not looked up
Driving licence structure + RTO state code + year 1950-2035
Vehicle plate structure + RTO state code (incl. Bharat series)
UPI ID handle@psp with a known PSP handle ordinary emails are not matched
Mobile 10 digits starting 6-9, optional +91 / 0
PIN code, bank account only with a cue word ("PIN code", "A/c no") bare 6- or 12-digit numbers are ignored

Related project: the PyPI package indic-pii (by a different author) also detects Indian PII, with confidence scores and optional spaCy NER for names. bharatid is a different design: zero dependencies, checksum-first validation (Aadhaar, GSTIN), and extra identifiers (GSTIN, TAN, vehicle plates). Pick whichever fits; they are not drop-in compatible.

"Valid" means well-formed (and checksum-correct where a checksum exists). It does not mean the number was issued to anyone.

Install and use

pip install bharatid
from bharatid import scan, redact, is_valid_aadhaar, is_valid_gstin

text = "Aadhaar 2345 6789 0124, PAN ABCPD1234E, call +91 98765 43210"
for m in scan(text):
    print(m.type, m.text, m.validated)      # validated=True when a checksum confirmed it

redact(text)          # 'Aadhaar [AADHAAR], PAN [PAN], call [MOBILE]'
redact(text, types=["aadhaar"])
is_valid_gstin("27AAPFU0939F1ZV")           # True

CLI: echo "PAN ABCPD1234E" | bharatid scan (exit code 1 when something is found), bharatid redact FILE.

How well does it avoid false alarms?

On 15,140 real, mostly English LLM prompts (public in-the-wild jailbreak dataset, MIT licence), it produced 10 matches in total (8 mobile, 1 voter-ID-shaped, 1 Aadhaar-valid). The data has no PII labels, so this is a false-alarm sanity check, not a precision measurement. Reproduce with scripts/fp_sanity.py.

Limits

  • Format and checksum validation cannot tell a real identifier from a coincidentally valid one.
  • Mobile-number and voter-ID shapes are common in unrelated text.
  • No names, addresses or free-text detection. Pair it with an NER tool if you need those.
  • Not legal advice or a compliance guarantee. Test on your own data.
  • guardkit: offline guardrails for LLM apps (PII, secrets, injection), with its own smaller PII guard and a PyPI release.
  • injection-bench: benchmark harness for prompt-injection detectors.

MIT licensed.

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