stella-anonymize-core

Python bindings for the stella anonymization Rust core.

Install

Prebuilt wheels are published to PyPI (this activates with the next release). Wheels ship the bundled native pipeline packages, so no monorepo checkout is needed:

uv add stella-anonymize-core
# or: pip install stella-anonymize-core

Wheels target Python 3.11+ (abi3) on manylinux x64/aarch64, macOS x64/arm64, and Windows x64. Only wheels are published; there is no source distribution. The build.rs step needs the monorepo's generated .stlanonpkg native pipeline packages, so a source build cannot be self-contained. To build from a checkout instead, run bun run build first so those packages exist, then:

uv add ./crates/anonymize-py

Usage

Prepare or load the anonymizer once, then reuse it for documents.

import stella_anonymize as anonymize

languages = anonymize.available_default_native_pipeline_languages()
prepared = anonymize.preload_default_native_pipeline(
    language="en" if "en" in languages else None
)
result = prepared.redact_text(text, redact_string="***")

print(result.redaction.redacted_text)

Reverse replacement placeholders with the returned redaction map (a mapping of placeholder -> original, a sequence of RedactionEntry, or (placeholder, original) pairs; entries apply in order):

restored = anonymize.deanonymise(
    result.redaction.redacted_text,
    result.redaction.redaction_map,
)

For related documents, create an explicit in-memory session from the prepared anonymizer. Repeated normalized entities reuse their placeholders within that session:

session = prepared.create_redaction_session("opaque_case_1")
first = session.redact_text(first_document)
second = session.redact_text(second_document)
restored_text = session.restore_text(first.redaction.redacted_text)

restore_text() restores complete known placeholders in one non-cascading pass. Other session namespaces remain unchanged; unknown placeholders owned by the session fail closed. Lifecycle sessions also require the caller-supplied observed_at_epoch_seconds argument.

session.to_plaintext_json() supports deterministic in-memory transfer between runtime instances. Its output contains original personal data in plaintext: do not log it or persist it without an application-owned protection layer. Restore validated transfer state with prepared.restore_redaction_session(json_state).

For persistence, use the authenticated binary archive API with a caller-owned 32-byte key. Restoring requires the expected session identity so an archive cannot be substituted across records:

archive = session.to_encrypted_archive(application_key)
restored = prepared.restore_encrypted_redaction_session(
    archive,
    application_key,
    session.session_id(),
)

Generate, store, rotate, and authorize access to the key outside the SDK. The archive contains personal data as ciphertext; do not log the archive or key. Lifecycle sessions use to_encrypted_archive_at() and require observed_at_epoch_seconds when restored.

Sessions can carry explicit lifecycle bounds. The engine never reads the system clock; supply the UTC epoch-second observation time for each lifecycle-aware operation:

session = prepared.create_redaction_session_with_lifecycle(
    "opaque_case_2",
    created_at_epoch_seconds=1_800_000_000,
    expires_at_epoch_seconds=1_800_086_400,
)
result = session.redact_text_at(
    document,
    observed_at_epoch_seconds=1_800_000_100,
)
metadata = session.inspect(1_800_000_100)  # contains no entity values
deletion = session.delete()

Expiry is fail-closed at its exact boundary. delete() performs logical deletion: it clears the session mappings and prevents future use, but does not revoke earlier exported copies or claim physical erasure of process memory.

Caller-produced detections use Python character indexes and enter the same resolution and redaction pipeline as built-in detections:

result = prepared.redact_text_with_caller_detections(
    "😀Alice signed.",
    [{"start": 1, "end": 6, "label": "person", "score": 0.95,
      "provider_id": "example-ner", "detection_id": "person-1"}],
)

Pass {"organization": "keep"} as the operators argument to preserve detected organizations while processing other labels normally. Kept entities remain in the result and operator map, but create no reversible mapping entry.

Use a tagged mask configuration to replace a number of visible Unicode grapheme clusters from the start or end:

operators = {
    "email address": {
        "type": "mask",
        "masking_character": "*",
        "characters_to_mask": 6,
        "direction": "start",
    }
}

provider_id and detection_id are required 1–128 byte ASCII identifiers: they start with an alphanumeric character and otherwise contain only alphanumerics, ., _, :, or -. Do not encode personal data in them. Retained entities preserve both IDs; redact_text_with_caller_detections_diagnostics_json() reports audit-safe external input and retained counts without matched text.

Regional codes use the exact package when present and otherwise fall back to the base language package, so en-US can use the shipped en artifact.

For caller-owned configs, prepare package bytes before serving documents and load them at runtime:

import stella_anonymize as anonymize

package_bytes = anonymize.prepare_search_package(config_json)
prepared = anonymize.load_prepared_package(package_bytes)
prepared.warm_lazy_regex()
result = prepared.redact_text(text, redact_string="***")

get_default_native_pipeline() defers lazy regex warmup by default so the first call only pays for regexes the document actually touches. Use preload_default_native_pipeline() or pass warmup="lazy-regex" when startup can absorb that cost before serving documents. Top-level redact_text() and redact_text_json() are available for one-off calls, but they prepare from config on each invocation. Use load_prepared_package() or load_prepared_package_file() for repeated document processing.

API

• prepare_search_package(config_json | config_bytes | config_mapping, compressed=True) -> bytes
• load_prepared_package(package_bytes) -> PreparedAnonymizer
• load_prepared_package_file(package_path) -> PreparedAnonymizer
• available_default_native_pipeline_languages() -> tuple[str, ...]
• read_default_native_pipeline_package_file(language=None) -> bytes
• get_default_native_pipeline(language=None, package_path=None, warmup="none") -> PreparedAnonymizer
• preload_default_native_pipeline(language=None, package_path=None) -> PreparedAnonymizer
• PreparedAnonymizer.warm_lazy_regex()
• PreparedAnonymizer.warm_lazy_regex_diagnostics_json()
• PreparedAnonymizer.create_redaction_session(session_id) -> PreparedRedactionSession
• PreparedAnonymizer.create_redaction_session_with_lifecycle(...) -> PreparedRedactionSession
• PreparedAnonymizer.restore_redaction_session(plaintext_json) -> PreparedRedactionSession
• PreparedRedactionSession.restore_text(full_text, observed_at_epoch_seconds=None) -> str
• deanonymise(redacted_text, redaction_map) -> str
• PreparedAnonymizer.redact_text(text, operators=None, redact_string=None)
• PreparedAnonymizer.redact_text_json(text, operators=None, redact_string=None)
• PreparedAnonymizer.diagnostics_json(text, operators=None, redact_string=None)

PreparedSearch is an alias for PreparedAnonymizer.