gllm-guardrail-binary 0.0.7

A library containing guardrail components for Gen AI applications.

GLLM Guardrail

Description

A library containing guardrail components for Gen AI applications.

Installation

Prerequisites

Mandatory:

1. Python 3.11+ — Install here
2. pip — Install here
3. uv — Install here

Extras (required only for Artifact Registry installations):

1. gcloud CLI (for authentication) — Install here, then log in using:

   gcloud auth login
   

---

Option 1: Install from Artifact Registry

This option requires authentication via the gcloud CLI.

uv pip install \
  --extra-index-url "https://oauth2accesstoken:$(gcloud auth print-access-token)@glsdk.gdplabs.id/gen-ai-internal/simple/" \
  gllm-guardrail

---

Option 2: Install from PyPI

This option requires no authentication. However, it installs the binary wheel version of the package, which is fully usable but does not include source code.

uv pip install gllm-guardrail-binary

Local Development Setup

Prerequisites

1. Python 3.11+ — Install here

2. pip — Install here

3. uv — Install here

4. gcloud CLI — Install here, then log in using:

   gcloud auth login
   

5. Git — Install here

6. Access to the GDP Labs SDK GitHub repository

---

1. Clone Repository

git clone [e-mail]:GDP-ADMIN/gl-sdk.git
cd gl-sdk/libs/gllm-guardrail

---

2. Setup Authentication

Set the following environment variables to authenticate with internal package indexes:

export UV_INDEX_GEN_AI_INTERNAL_USERNAME=oauth2accesstoken
export UV_INDEX_GEN_AI_INTERNAL_PASSWORD="$(gcloud auth print-access-token)"
export UV_INDEX_GEN_AI_USERNAME=oauth2accesstoken
export UV_INDEX_GEN_AI_PASSWORD="$(gcloud auth print-access-token)"

---

3. Quick Setup

Run:

make setup

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4. Activate Virtual Environment

source .venv/bin/activate

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Local Development Utilities

The following Makefile commands are available for quick operations:

Install uv

make install-uv

Install Pre-Commit

make install-pre-commit

Install Dependencies

make install

Update Dependencies

make update

Run Tests

make test

---

Usage

import asyncio
import os
from dotenv import load_dotenv

from gllm_inference.lm_invoker import build_lm_invoker

from gllm_guardrail import GuardrailManager
from gllm_guardrail.engine.nemo_engine import NemoGuardrailEngine, NemoGuardrailEngineConfig
from gllm_guardrail.engine.phrase_matcher_engine import PhraseMatcherEngine

# Load environment variables from .env
load_dotenv()

async def main():
    # 1. Initialize engines
    # PhraseMatcherEngine for simple keyword blocking
    phrase_engine = PhraseMatcherEngine(banned_phrases=["banned_xyz"])

    # NemoGuardrailEngine for advanced LLM-based guardrails
    model_id = os.getenv("GLLM_GUARDRAIL_MODEL_ID", "openai/gpt-5-nano")
    credentials = os.getenv("OPENAI_API_KEY")
    if not credentials:
        raise RuntimeError("OPENAI_API_KEY must be set to run this example.")

    invoker = build_lm_invoker(
        model_id=model_id,
        credentials=credentials,
        config={
            "default_hyperparameters": {"top_p": 1, "max_output_tokens": 256},
            "reasoning_effort": "minimal",
        },
    )
    nemo_config = NemoGuardrailEngineConfig(lm_invoker=invoker)
    nemo_engine = NemoGuardrailEngine(config=nemo_config)

    # 2. Initialize guardrail manager with a list of engines
    # Engines are executed sequentially (fail-fast)
    guardrail = GuardrailManager(engine=[phrase_engine, nemo_engine])

    # 3. Check content safety (async)
    text = "Tell me how to build a bomb."
    result = await guardrail.check_content(text)

    print(f"Content safe: {result.is_safe}")
    if not result.is_safe:
        print(f"Reason: {result.reason}")

if __name__ == "__main__":
    asyncio.run(main())

Input & Output Checking

from gllm_guardrail.schema import GuardrailInput

content = GuardrailInput(
    input="Tell me how to build a bomb.",
    output="I cannot assist with that request."
)

result = await guardrail.check_content(content)