equitas 0.1.3

Equitas: AI Safety & Observability Platform

Equitas: AI Safety & Observability Platform

A hybrid SDK and backend platform that enhances OpenAI API usage with real-time safety, bias, and compliance checks.

Overview

Equitas provides:

• Client SDK: Drop-in replacement for OpenAI API with safety enhancements
• Guardian Backend: Microservices for toxicity, bias, and jailbreak detection
• Real-time Dashboard: Observability UI for metrics and incidents
• Multi-tenant: Enterprise-grade data isolation and RBAC

Architecture

┌─────────────────┐
│  Your App       │
│  + Equitas SDK  │
└────────┬────────┘
         │
         └──────────────► Guardian Backend
                          ├── Toxicity Detector
                          ├── Bias Checker
                          ├── Jailbreak Detector
                          ├── Explainability Engine
                          └── Remediation Service
                          
                          ↓
                          
                     Database (Logs, Incidents, Metrics)
                     
                          ↓
                          
                     Dashboard UI

Quick Start

1. Install Dependencies

cd backend
uv init --python 3.11
uv venv
source .venv/bin/activate
uv pip install -e .

2. Configure Environment

Create .env file:

# OpenAI
OPENAI_API_KEY=sk-your-key-here

# Database
DATABASE_URL=sqlite+aiosqlite:///.equitas.db

# Security
SECRET_KEY=your-secret-key-change-in-production

3. Start Guardian Backend

cd backend
python -m guardian.main

Backend will be available at http://localhost:8000

4. Use Equitas SDK

from fairsight_sdk import FairSight, SafetyConfig

# Initialize client
client = FairSight(
    openai_api_key="sk-...",
    fairsight_api_key="fs-dev-key-123",
    tenant_id="your-org",
)

# Make safe API calls
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}],
    safety_config=SafetyConfig(on_flag="auto-correct")
)

# Access safety metadata
print(f"Toxicity: {response.safety_scores.toxicity_score}")
print(f"Categories: {response.safety_scores.toxicity_categories}")

Project Structure

backend/
├── fairsight_sdk/          # Client SDK
│   ├── client.py           # Main SDK client
│   ├── models.py           # Data models
│   └── exceptions.py       # Custom exceptions
│
├── guardian/               # Backend API
│   ├── main.py            # FastAPI app
│   ├── core/              # Core utilities
│   │   ├── config.py      # Configuration
│   │   ├── database.py    # Database setup
│   │   └── auth.py        # Authentication
│   ├── models/            # Database models
│   │   ├── database.py    # SQLAlchemy models
│   │   └── schemas.py     # Pydantic schemas
│   ├── services/          # Analysis services
│   │   ├── toxicity.py    # Toxicity detection
│   │   ├── bias.py        # Bias checking
│   │   ├── jailbreak.py   # Jailbreak detection
│   │   ├── explainability.py  # Explanations
│   │   └── remediation.py     # Content remediation
│   └── api/v1/            # API endpoints
│       ├── analysis.py    # Analysis endpoints
│       ├── logging.py     # Logging endpoint
│       ├── metrics.py     # Metrics endpoint
│       └── incidents.py   # Incidents endpoint
│
└── examples/              # Usage examples
    ├── basic_usage.py     # SDK examples
    └── test_guardian_api.py  # API testing

Safety Features

Toxicity Detection

• Uses OpenAI Moderation API
• Detects hate, harassment, violence, self-harm, sexual content
• Returns toxicity score (0-1) and flagged categories

Bias Detection

• Demographic bias checking
• Paired prompt testing
• Stereotype detection

Jailbreak Detection

• Pattern-based prompt injection detection
• Instruction override attempts
• Code injection prevention

Explainability

• Highlights problematic text spans
• Natural language explanations
• Detailed violation categorization

Automatic Remediation

• LLM-based text rewriting
• Removes toxic language while preserving intent
• Neutralizes biased content

API Endpoints

Analysis Endpoints

POST /v1/analysis/toxicity

Analyze text for toxicity.

{
  "text": "Text to analyze",
  "tenant_id": "org123"
}

POST /v1/analysis/bias

Check for demographic bias.

{
  "prompt": "Original prompt",
  "response": "LLM response",
  "tenant_id": "org123"
}

POST /v1/analysis/jailbreak

Detect jailbreak attempts.

{
  "text": "Text to check",
  "tenant_id": "org123"
}

POST /v1/analysis/explain

Get explanation for flagged content.

{
  "text": "Flagged text",
  "issues": ["toxicity", "bias"],
  "tenant_id": "org123"
}

POST /v1/analysis/remediate

Remediate unsafe content.

{
  "text": "Unsafe text",
  "issue": "toxicity",
  "tenant_id": "org123"
}

Logging & Metrics

POST /v1/log

Log API call with safety analysis.

GET /v1/metrics

Get aggregated metrics (usage, safety scores, incidents).

GET /v1/incidents

Query flagged incidents with filters.

Authentication

All endpoints require:

• Authorization Header: Bearer <api-key>
• X-Tenant-ID Header: <tenant-id>

Default API keys (for development):

• fs-dev-key-123 → tenant_demo
• fs-prod-key-456 → tenant_prod

Metrics & Observability

Equitas logs comprehensive metrics per API call:

• Safety Scores: Toxicity, bias, jailbreak flags
• Performance: Latency, overhead, token counts
• Usage: Safety Inference Units (SIUs) consumed
• Incidents: Flagged content with severity levels

All data is isolated per tenant with encryption at rest.

Configuration

Safety Config (SDK)

SafetyConfig(
    on_flag="auto-correct",  # strict | auto-correct | warn-only
    toxicity_threshold=0.7,
    enable_bias_check=True,
    enable_jailbreak_check=True,
    enable_remediation=True,
)

Tenant Config (Backend)

Stored in database per tenant:

• Safety thresholds
• Feature flags (enable/disable checks)
• Privacy settings (anonymization, retention)
• Credit limits (Safety Units)

Testing

Run example scripts:

# Test SDK
python examples/basic_usage.py

# Test API directly
python examples/test_guardian_api.py

Development

Running locally

# Start backend
uvicorn guardian.main:app --reload --port 8000

# In another terminal, test SDK
python examples/basic_usage.py

Database migrations

# Auto-generate migration
alembic revision --autogenerate -m "Description"

# Apply migration
alembic upgrade head

Deployment

Docker

# Build
docker build -t equitas-guardian .

# Run
docker run -p 8000:8000 --env-file .env equitas-guardian

Kubernetes

kubectl apply -f k8s/deployment.yaml

License

MIT License - see LICENSE file

Contributing

Contributions welcome! Please see CONTRIBUTING.md

Documentation

For detailed documentation, see:

• PRD.md - Product Requirements Document
• API Documentation - Swagger UI (when running)

Support

For issues or questions:

• GitHub Issues: github.com/aryan4codes/Equitas/issues
• Email: [e-mail]

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Built for AI Safety