# A local scrubber for text you're about to send to an LLM

> Source: <https://github.com/Sushmey/Redact/tree/main>
> Published: 2026-08-29 17:29:10+00:00

**A local scrubber for text you're about to send to an LLM.**

Before you paste a log file, a résumé, or a support thread into an LLM, this
tool strips out the sensitive parts first — names, emails, phone numbers,
SSNs, card numbers, addresses, API keys, database passwords — and hands back a
clean copy. It runs entirely on your machine with no LLM involved: a stack of
regexes and checksums catches the structured stuff, a small NER model handles
the fuzzier things like people and companies, and a merge layer reconciles
them when they disagree. You can either blank each value out as `<PERSON>`

or
swap in stable placeholders like `PERSON_001`

so the text still reads
coherently, with the original values kept in a separate file that never leaves
your machine. Most of the work went into *not* over-redacting — teaching it
that "Django" in a skills list is a framework, not a person.

For how the detection actually works, see [PIPELINE.md](/Sushmey/Redact/blob/main/PIPELINE.md).

```
python -m venv redact_venv
source redact_venv/bin/activate
pip install -r requirements.txt
python -m spacy download en_core_web_sm
```

The GLiNER model (`gliner_multi_pii-v1`

, ~1.1 GB) downloads from Hugging Face
on first run and is cached.

```
source env.sh          # thread-safety env vars — see the comments in the file

python -m redact notes.txt                 # -> notes.redacted.txt
python -m redact notes.txt -o clean.txt     # choose the output path
python -m redact notes.txt --pseudonymize   # -> notes.redacted.txt + notes.redacted.txt.map.json
```

`--pseudonymize`

also writes `notes.redacted.txt.map.json`

(label → original
value). That file is sensitive — keep it local, never send it anywhere.

As a library:

``` python
from redact import redact_text

sanitized, counts, mapping = redact_text(text, language="en", pseudonymize=False)
# counts  -> {"EMAIL_ADDRESS": 3, "PERSON": 5, ...}
# mapping -> {} unless pseudonymize=True
python run_tests.py
```

12 fixtures in `tests/`

(logs, résumés, chat, source code, medical, financial,
and adversarial "same shape, different meaning" cases). Each checks both
directions: sensitive values must be gone, ordinary values must survive
unchanged.