ai-sentinel 0.0.10

Helpful utilities for AI Sentinel toxicity detection

AI Sentinel

AI Sentinel is a Python package designed to help developers integrate toxicity analysis into their applications with ease. It provides a simple, unified interface to leverage powerful AI models for detecting and categorizing harmful content in text.

Key Features

• Advanced Toxicity Detection: Comprehensive toxicity detection and classification.
• Multiple LLM Providers: Designed to support various AI model providers (currently Azure OpenAI, and Gemini, with more models being supported in the future).
• Structured Output: Type-safe responses with Pydantic validation.

Getting Started

Installation

ai-sentinel is avalible on PyPI

pip install ai-sentinel

Usage

AI Sentinel is designed to be straightforward to use. You'll primarily interact with the ToxicityGuard and a client specific to your chosen AI model (e.g., AzureOpenAIClient).

from ai_sentinel import AzureOpenAIClient, ToxicityGuard

# Initialize LLM client
client = AzureOpenAIClient(
    api_key="your-api-key",
    model="gpt-4o-mini",
    api_version="2024-02-01",
    azure_endpoint="https://your-resource.openai.azure.com/"
)

# Create toxicity guard
guard = ToxicityGuard(client)

# Analyze text
result = guard.analyze("This is a normal message")

print(f"Is toxic: {result.is_toxic}")
print(f"Confidence: {result.confidence}")
print(f"Categories: {result.categories}")
print(f"Reason: {result.reason}")
print(f"Severity: {result.score}")

Async Usage

Simply call the async analyze function analyze_async and use await with each API call:

import asyncio
from ai_sentinel import AzureOpenAIClient, ToxicityGuard

client = AzureOpenAIClient(
    api_key="your-api-key",
    model="gpt-4o-mini",
    api_version="2024-02-01",
    azure_endpoint="https://your-resource.openai.azure.com/"
)

async def main() -> None:
    guard = ToxicityGuard(client)
    response = await guard.analyze_async("Text to analyze")

    print(f"Is toxic: {result.is_toxic}")
    print(f"Confidence: {result.confidence}")
    print(f"Categories: {result.categories}")
    print(f"Reason: {result.reason}")
    print(f"Severity: {result.score}")

asyncio.run(main())

Supported LLM API Services

ai-sentinel is model agnostic, with support for the following LLM API services:

Provider	Models	Details
Azure OpenAI	GPT-4, GPT-4o, GPT-3.5-turbo	Industry-leading models
Google Gemini	Gemini 2.0 Flash, Gemini 2.5 Pro	Latest Google technology
Open Source LLMs	Qwen3, GPT-oss, Meta Llama	Uses OpenAI Compatible Server's, so check if the server hosting the LLM implements the OpenAI API specifications for endpoints
Anthropic	To Be Implemented	Will be implemented in the future

Gemini Usage

from ai_sentinel import GeminiClient, ToxicityGuard

# Initialize Gemini client
client = GeminiClient(
    api_key="your-gemini-api-key",
    model="gemini-2.0-flash"
)

# Create and use toxicity guard
guard = ToxicityGuard(client)
result = guard.analyze("Your text here")

Open Source using OpenAI Usage

from ai_sentinel import OpenAIClient, ToxicityGuard

# Initialize an Open Source LLM Client
client = OpenAIClient(
    base_url="http://localhost:8000/v1",
    model="Qwen/Qwen3-8B"
)

# Create and use toxicity guard
guard = ToxicityGuard(client)
result = guard.analyze("Your text here")

Output

In AI Sentinel's ToxicityGuard class, both analyze and analyze_async methods return a ToxicityResult object.

Response Format

class ToxicityResult:
    is_toxic: bool                          # Whether content is toxic
    confidence: float                       # Confidence score that the content is toxic (0.0-1.0)
    categories: List[ToxicityCategories]    # Detected toxicity categories
    reason: str                             # Explanation of the assessment
    score: ToxicityScore                    # Simplified confidence score: "low", "medium", "high"

Example

{
    "is_toxic": True,
    "confidence": 0.90,
    "categories": [
        <ToxicityCategories.THREATS: 'threats'>, 
        <ToxicityCategories.VIOLENCE: 'violence'>
    ],
    "reason": "The phrase 'I will punch you' is a clear and direct threat of physical violence. It expresses an intention to harm another person, categorizing it under threats and violence.",
    "score": <ToxicityScore.HIGH: 'high'>
}

The ToxicityCategories and ToxicityScore enums are available from ai_sentinel.models.

Toxicity Categories

AI Sentinel detects the following toxicity categories:

• Hate Speech: Content attacking individuals/groups based on protected characteristics
• Harassment: Hostile behavior targeting specific individuals
• Threats: Direct or implied threats of violence or harm
• Sexual Content: Inappropriate sexual material
• Self Harm: Content promoting self-injury or suicide
• Violence: Content glorifying or promoting violence
• Bullying: Intimidation or aggressive behavior
• Discrimination: Unfair treatment of specific groups

Configuration

Environment Variables

Tip: Add enviormental variables to a .env file

# Azure OpenAI
AZURE_API_KEY=your-azure-api-key
AZURE_API_VERSION=2024-02-01
AZURE_API_BASE=https://your-resource.openai.azure.com/

# Google Gemini
GEMINI_API_KEY=your-gemini-api-key

Using python-dotenv

from dotenv import load_dotenv
import os

load_dotenv()

client = AzureOpenAIClient(
    api_key=os.getenv("AZURE_API_KEY"),
    model="gpt-4o-mini",
    api_version=os.getenv("AZURE_API_VERSION"),
    azure_endpoint=os.getenv("AZURE_API_BASE")
)

License

This project is licensed under the MIT License - see the LICENSE file for details.

Changelog

v0.0.10: Add company branding and enhanced project metadata
v0.0.9: Add Read the Docs configuration and enhanced documentation
v0.0.8: Update documentation
v0.0.7: Add Open Source LLM integration
v0.0.6: Change authorship
v0.0.2: Adjust authorship and maintainers
v0.0.1: Initial release