gibs 0.3.1

Python SDK for the Gibs multi-regulation compliance API

Gibs Python SDK

Official Python SDK for the Gibs multi-regulation compliance API. Classify AI system risk levels, check regulatory obligations, and get answers to compliance questions -- all with source citations to EU legislation (AI Act, GDPR, DORA).

Installation

pip install gibs

Quick Start

from gibs import GibsClient

client = GibsClient(api_key="gbs_live_xxx")

# Classify an AI system's risk level
result = client.classify("Facial recognition system for airport security")
print(result.risk_level)   # "high"
print(result.confidence)   # 0.92
print(result.reasoning)    # Detailed explanation with article references
print(result.sources)      # [SourceCitation(article_id="Article 6", ...)]

# Ask a compliance question (auto-detects regulation)
answer = client.check("What are the transparency obligations for chatbots?")
print(answer.answer)
print(answer.confidence_score)  # 0.87
print(answer.sources)           # [SourceCitation(article_id="Article 50", ...)]

# Get structured (machine-readable) response
answer = client.check(
    "What are the risk management obligations for high-risk AI?",
    response_format="structured",
)
print(answer.structured.summary)       # Direct 1-2 sentence answer
print(answer.structured.requirements)  # ["Implement risk management per Article 9", ...]
print(answer.structured.articles_cited)  # ["Article 9", "Article 6", ...]
print(answer.confidence_score)         # 0.89

# DORA: ICT resilience for financial entities
dora = client.check("What are the ICT incident reporting timelines under DORA?")
print(dora.answer)         # Timeline with Article 19 citations

Async Support

from gibs import AsyncGibsClient

client = AsyncGibsClient(api_key="gbs_live_xxx")

result = await client.classify("CV screening tool for recruitment")
answer = await client.check("Does GDPR apply to employee monitoring?")

# Use as async context manager
async with AsyncGibsClient(api_key="gbs_live_xxx") as client:
    result = await client.classify("...")

Authentication

Get your API key at gibs.dev. Pass it directly or set the GIBS_API_KEY environment variable:

export GIBS_API_KEY=gbs_live_xxx

# Reads from GIBS_API_KEY automatically
client = GibsClient()

API Reference

client.classify(description, **kwargs)

Classify an AI system's risk level under EU regulation.

result = client.classify(
    "Emotion detection system for job interviews",
    data_types=["biometric", "behavioral"],
    decision_scope="employment",
    sector="recruitment",
    jurisdiction="EU",
)

print(result.risk_level)      # "prohibited" | "high" | "limited" | "minimal"
print(result.confidence)      # 0.0 - 1.0
print(result.reasoning)       # Detailed explanation
print(result.obligations)     # List of compliance obligations
print(result.sources)         # Source citations to legislation
print(result.corpus_version)  # Version of the legal corpus used

client.check(question, **kwargs)

Ask a compliance question and get an answer with citations. The API automatically detects which regulation(s) the question targets (AI Act, GDPR, DORA) and routes to the correct corpus.

answer = client.check(
    "What data protection impact assessment is required for high-risk AI?",
    system_context={"type": "recommendation engine", "sector": "healthcare"},
)

print(answer.answer)            # Detailed answer with [Article X] citations
print(answer.confidence)        # "high" | "medium" | "low"
print(answer.confidence_score)  # 0.89 (numeric, 0.0–1.0)
print(answer.sources)           # Source citations
print(answer.should_abstain)    # True if out of scope
print(answer.corpus_version)    # Corpus version used

Structured response mode

Pass response_format="structured" to get machine-readable parsed sections alongside the full answer:

answer = client.check(
    "What are the transparency requirements for chatbots?",
    response_format="structured",
)

print(answer.structured.summary)         # Direct 1-2 sentence answer
print(answer.structured.legal_basis)     # Legal basis with article references
print(answer.structured.requirements)    # ["Disclose AI interaction per Article 50(1)", ...]
print(answer.structured.timeline)        # ["Applies from 2 August 2026"]
print(answer.structured.articles_cited)  # ["Article 50", "Article 52"]

Confidence score

Every /check response includes a numeric confidence_score (0.0–1.0) for programmatic decisions:

Score	Meaning
0.8–1.0	High confidence — well-sourced, specific answer
0.5–0.8	Medium — partial sources or hedged language
0.0–0.5	Low — limited sources, verify with legal counsel

if answer.confidence_score >= 0.7:
    apply_compliance_rules(answer.structured.requirements)
else:
    flag_for_human_review(answer)

client.health()

Check API health status.

health = client.health()
print(health.status)       # "healthy"
print(health.components)   # {"qdrant": "ok", "postgres": "ok", ...}

Account Management

# List API keys
keys = client.list_keys()

# Create a new key
new_key = client.create_key("CI Pipeline")
print(new_key.api_key)  # Full key shown once

# Revoke a key
client.delete_key(key_id=2)

Error Handling

from gibs import GibsClient, GibsAuthError, GibsRateLimitError, GibsAPIError

client = GibsClient(api_key="gbs_live_xxx")

try:
    result = client.classify("...")
except GibsAuthError:
    print("Invalid API key")
except GibsRateLimitError as e:
    print(f"Rate limited. Retry after {e.retry_after}s")
except GibsAPIError as e:
    print(f"API error {e.status_code}: {e.message}")

The SDK automatically retries on rate limits and transient network errors with exponential backoff (3 retries by default).

Configuration

client = GibsClient(
    api_key="gbs_live_xxx",          # Required (or set GIBS_API_KEY env var)
    base_url="https://api.gibs.dev", # Default
    timeout=120.0,                    # Seconds (default: 120)
    max_retries=3,                    # Retry count (default: 3)
)

Requirements

• Python 3.10+
• httpx
• pydantic v2

Links

• Documentation
• API Reference
• Website