rail-score-sdk 2.1.0

Official Python SDK for RAIL Score API - Responsible AI Content Evaluation

RAIL Score Python SDK

Official Python client library for the RAIL Score API — Evaluate AI-generated content across 8 dimensions of Responsible AI.

Features

• Evaluation — Score content across 8 RAIL dimensions in basic (fast) or deep (detailed) mode
• Protected Content — Evaluate against a quality threshold and regenerate improved content
• Compliance — Check against GDPR, CCPA, HIPAA, EU AI Act, India DPDP, India AI Governance
• Explanations — Generate human-readable explanations for scores
• Type-Safe — Full type hints and typed response models
• Error Handling — Granular exception hierarchy for every error scenario

Installation

pip install rail-score-sdk

For development:

pip install rail-score-sdk[dev]

Quick Start

from rail_score_sdk import RailScoreClient

client = RailScoreClient(api_key="your-api-key")

# Evaluate content
result = client.eval(
    content="AI should prioritize human welfare and be transparent.",
    mode="basic",
)

print(f"RAIL Score: {result.rail_score.score}/10")
print(f"Summary: {result.rail_score.summary}")

for dim, score in result.dimension_scores.items():
    print(f"  {dim}: {score.score}/10")

API Reference

Evaluate Content

Score content across all 8 RAIL dimensions. Supports basic (hybrid ML, fast) and deep (LLM-as-Judge, detailed) modes.

# Basic mode — all dimensions
result = client.eval(
    content="Your content here",
    mode="basic",
    domain="general",       # general, healthcare, finance, legal, education, code
    usecase="general",      # general, chatbot, content_generation, summarization, translation, code_generation
)

# Deep mode — specific dimensions with explanations
result = client.eval(
    content="Your content here",
    mode="deep",
    dimensions=["safety", "reliability", "fairness"],
    include_explanations=True,
    include_issues=True,
    include_suggestions=True,
)

# Custom dimension weights (must sum to 100)
result = client.eval(
    content="Your content here",
    weights={
        "fairness": 10, "safety": 25, "reliability": 25,
        "transparency": 10, "privacy": 10, "accountability": 10,
        "inclusivity": 5, "user_impact": 5,
    },
)

Protected Content

Evaluate content against a quality threshold and optionally regenerate:

# Step 1: Evaluate against threshold
eval_result = client.protected_evaluate(
    content="Content to check",
    threshold=7.0,
    mode="basic",
)

if eval_result.improvement_needed:
    print(eval_result.improvement_prompt)

    # Step 2: Regenerate improved content
    regen_result = client.protected_regenerate(
        content="Content to improve",
        issues_to_fix={
            "fairness": {
                "score": 2.0,
                "explanation": "Age-based stereotyping.",
                "issues": ["Age-based stereotyping"],
            }
        },
    )
    print(regen_result.improved_content)

Compliance Check

Evaluate against regulatory frameworks:

# Single framework
result = client.compliance_check(
    content="Our AI processes user data...",
    framework="gdpr",
    context={
        "domain": "e-commerce",
        "data_types": ["browsing_history", "purchase_data"],
        "processing_purpose": "personalized_recommendations",
    },
)

print(f"Score: {result.compliance_score.score}/10 ({result.compliance_score.label})")
print(f"Passed: {result.requirements_passed}/{result.requirements_checked}")

# Multi-framework (up to 5)
result = client.compliance_check(
    content="...",
    frameworks=["gdpr", "ccpa"],
)
print(result.cross_framework_summary.average_score)

# Strict mode (8.5 threshold instead of 7.0)
result = client.compliance_check(content="...", framework="ccpa", strict_mode=True)

Supported frameworks: gdpr, ccpa, hipaa, eu_ai_act, india_dpdp, india_ai_gov

Utility

# Health check
health = client.health()
print(health.status)

# Version info
version = client.version()
print(f"{version.version} ({version.api_version})")

RAIL Dimensions

Content is evaluated across 8 dimensions on a 0–10 scale:

Dimension	Description
Fairness	Equitable treatment across demographic groups. No bias or stereotyping.
Safety	Prevention of harmful, toxic, or unsafe content.
Reliability	Factual accuracy, internal consistency, appropriate calibration.
Transparency	Clear communication of limitations and reasoning process.
Privacy	Protection of personal information and data minimization.
Accountability	Traceable reasoning, stated assumptions, error signals.
Inclusivity	Inclusive language, accessibility, cultural awareness.
User Impact	Positive value delivered at the right detail level and tone.

Score labels: Excellent (9–10), Good (7–8.9), Needs improvement (5–6.9), Poor (3–4.9), Critical (0–2.9)

Authentication

# API key or JWT token
client = RailScoreClient(api_key="rail_xxx...")

# Environment variable
import os
client = RailScoreClient(
    api_key=os.getenv("RAIL_API_KEY"),
    base_url=os.getenv("RAIL_BASE_URL", "https://api.responsibleailabs.ai"),
    timeout=int(os.getenv("RAIL_TIMEOUT", "30")),
)

Error Handling

from rail_score_sdk import (
    RailScoreError,
    AuthenticationError,
    InsufficientCreditsError,
    InsufficientTierError,
    ValidationError,
    ContentTooHarmfulError,
    RateLimitError,
    EvaluationFailedError,
    ServiceUnavailableError,
)

try:
    result = client.eval(content="...")
except AuthenticationError:
    print("Check your API key")
except InsufficientCreditsError as e:
    print(f"Credits: {e.balance} available, {e.required} needed")
except InsufficientTierError:
    print("Upgrade your plan for this feature")
except ValidationError as e:
    print(f"Bad request: {e.message}")
except ContentTooHarmfulError:
    print("Content too harmful to regenerate (avg score < 3.0)")
except RateLimitError:
    print("Rate limited — try again later")
except EvaluationFailedError:
    print("Server error — safe to retry")
except ServiceUnavailableError:
    print("Service temporarily unavailable")
except RailScoreError as e:
    print(f"API error ({e.status_code}): {e.message}")

Examples

See the examples directory:

• basic_usage.py — Basic and deep evaluation
• advanced_features.py — Custom weights, dimension filtering, basic vs deep
• compliance_check.py — GDPR, CCPA, HIPAA, EU AI Act, multi-framework
• regenerate_content.py — Protected content evaluation and regeneration
• error_handling.py — Production error handling patterns
• batch_processing.py — Processing multiple items with retry

Testing

pip install -r requirements-dev.txt

pytest
pytest --cov=rail_score_sdk --cov-report=html

black rail_score_sdk/
mypy rail_score_sdk/

License

MIT License — see LICENSE for details.

Links

• Documentation: https://responsibleailabs.ai/developer/docs
• API Reference: https://responsibleailabs.ai/developers/api-ref
• PyPI: https://pypi.org/project/rail-score-sdk/
• GitHub: https://github.com/RAILethicsHub/rail-score
• Issues: https://github.com/RAILethicsHub/rail-score/issues
• Support: [e-mail]