gheim 0.1.7

Detect PII in text, substitute it with stable sentinels (<PERSON_1>, <EMAIL_2>, ...), send the redacted text to any LLM, and restore the originals on the way back, including in streamed responses. The package is framework-agnostic and ships a drop-in openai client wrapper for zero-effort integration.

See the monorepo README for the cross-language overview and architecture.

Install

uv add gheim                          # core: pairs with a RemoteDetector or GHEIM_API_KEY
uv add "gheim[local]"                 # + torch and transformers for on-device detection
uv add "gheim[openai]"                # + drop-in OpenAI client
uv add "gheim[local,openai]"          # both

Model choice

LocalDetector runs a token-classification model in process. The package's default model is joelbarmettler/gheim-ch-560m — a 560M xlm-roberta-large fine-tune optimised for Swiss-market PII (test strict F1 0.910, char F1 0.946 on Swiss text, see MODEL_CARD.md). Any HuggingFace token-classification model that emits the same 33-class BIOES schema can be substituted via the model_id constructor arg.

from gheim import LocalDetector

# Default — Swiss-tuned, 560M, Apache 2.0:
det = LocalDetector()

# Stronger cross-domain transfer (research, non-commercial):
det = LocalDetector(model_id="joelbarmettler/gheim-ch-560m-research")

# Alternative for English or general use:
det = LocalDetector(model_id="openai/privacy-filter")

Drop-in OpenAI client

from gheim.openai import OpenAI

client = OpenAI()  # same constructor args as openai.OpenAI
r = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hi, my name is Joel"}],
)
# r.choices[0].message.content contains "Joel".
# OpenAI only ever saw "<PERSON_1>".

Custom endpoint or key (e.g. OpenRouter, local vLLM):

client = OpenAI(api_key="sk-or-...", base_url="https://openrouter.ai/api/v1")

Streaming:

stream = client.chat.completions.create(..., stream=True)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

Async:

from gheim.openai import AsyncOpenAI
client = AsyncOpenAI()
r = await client.chat.completions.create(...)

Per-call overrides:

from gheim import Session
session = Session()  # reuse across calls for multi-turn coherent sentinels
r = client.chat.completions.create(
    model="gpt-4o",
    messages=[...],
    gheim_session=session,    # or gheim_detector=...
)

Framework-agnostic

from gheim import Session, LocalDetector, anonymize_text, deanonymize_text

session = Session(detector=LocalDetector())  # gheim-ch-560m by default
clean = anonymize_text("Hi, my name is Joel", session)
# ... call any LLM with clean ...
final = deanonymize_text(response_text, session)

Streaming deanonymizer:

from gheim import deanonymize_stream
for chunk in deanonymize_stream(my_chunk_iterator, session):
    print(chunk, end="", flush=True)

Chat-message helpers:

from gheim import anonymize_messages

redacted = anonymize_messages(messages, session)  # preserves role, name, tool_call_id

Wrapped endpoints

The drop-in OpenAI / AsyncOpenAI clients automatically protect every text-carrying endpoint: chat.completions, responses, completions (legacy), embeddings, moderations, audio.speech, audio.transcriptions, audio.translations, images.generate, images.edit. Tool-call arguments and SSE delta chunks are restored on the way back. See the monorepo README for the full coverage matrix and the embeddings caveat.

Strict mode

gheim_strict=True (default) raises RuntimeError if you call an unwrapped endpoint (beta.assistants, batches, files, uploads, fine_tuning, vector_stores). The error message names client.raw.<path> as the documented escape hatch.

client = OpenAI(gheim_strict=False)  # downgrade to one-time warnings
client.raw.beta.assistants.create(...)  # always works regardless of strict mode

Detector backends

import torch
from gheim import LocalDetector, RemoteDetector, default_detector

# Local inference. Weights download to the HF cache on first use.
# `model_id` defaults to "joelbarmettler/gheim-ch-560m"; pass
# `dtype=torch.bfloat16` for half-precision GPU inference.
det = LocalDetector(device="auto", dtype=torch.bfloat16)

# Remote inference against your own gheim-server or api.gheim.ch.
det = RemoteDetector(base_url="http://your-host:8080", api_key="...")

# default_detector() picks remote if GHEIM_API_KEY is set, else local.
det = default_detector()

Composite detector (recommended for production)

For categories where structure is verifiable by checksum (CH-IBAN, AHV, VAT-CHE, credit cards, common token formats) the package ships a regex catalogue under gheim.detectors.composite that pairs with the model detector. The composite detector applies regex first, masks matched spans, then runs the model on the remainder. This pushes effective recall on account_number, private_phone, and private_url close to 1.0 with high precision; the underlying ML model handles person names, addresses, and dates.

Consulting

gheim is built and maintained by Souverana, a Swiss AI consulting business helping companies build secure, privacy-first AI systems — on-premise LLM deployments, PII/data-protection engineering, and Swiss-market NLP. If you need help integrating gheim or building compliant AI, get in touch at souverana.ch.

License

Apache 2.0. Bundled model weights are inherited from the upstream license of the model you select.