gllm-guardrail-binary 0.0.6
A library containing guardrail components for Gen AI applications.
Requires Python: <3.14,>=3.11

# GLLM Guardrail

## Description

A library containing guardrail components for Gen AI applications.

## Installation

### Prerequisites

Mandatory:

1. Python 3.11+ — [Install here](https://www.python.org/downloads/)
2. pip — [Install here](https://pip.pypa.io/en/stable/installation/)
3. uv — [Install here](https://docs.astral.sh/uv/getting-started/installation/)

Extras (required only for Artifact Registry installations):

1. gcloud CLI (for authentication) — [Install here](https://cloud.google.com/sdk/docs/install), then log in using:
   ```bash
   gcloud auth login
   ```

---

### Option 1: Install from Artifact Registry

This option requires authentication via the `gcloud` CLI.

```bash
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**.

```bash
uv pip install gllm-guardrail-binary
```

## Local Development Setup

### Prerequisites

1. Python 3.11+ — [Install here](https://www.python.org/downloads/)
2. pip — [Install here](https://pip.pypa.io/en/stable/installation/)
3. uv — [Install here](https://docs.astral.sh/uv/getting-started/installation/)
4. gcloud CLI — [Install here](https://cloud.google.com/sdk/docs/install), then log in using:

   ```bash
   gcloud auth login
   ```

5. Git — [Install here](https://git-scm.com/downloads)
6. Access to the [GDP Labs SDK GitHub repository](https://github.com/GDP-ADMIN/gl-sdk)

---

### 1. Clone Repository

```bash
git clone git@github.com: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:

```bash
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:

```bash
make setup
```

---

### 4. Activate Virtual Environment

```bash
source .venv/bin/activate
```

---

## Local Development Utilities

The following Makefile commands are available for quick operations:

### Install uv

```bash
make install-uv
```

### Install Pre-Commit

```bash
make install-pre-commit
```

### Install Dependencies

```bash
make install
```

### Update Dependencies

```bash
make update
```

### Run Tests

```bash
make test
```

---

## Usage

```python
import asyncio
import os
from dotenv import load_dotenv

from gllm_inference.builder 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

```python
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)
```