Production-grade Python SDK for Zora AI Email fraud/phishing detection APIs
Project description
zora-ai-email-sdk
Production-grade async Python SDK for the Zora AI Email Fraud Detection API.
Features
- Analyse raw emails (sender + subject + body) for phishing / fraud
- Optional LLM-powered explanation
- Rich result dataclass:
risk_score,fraud_type,confidence, sub-scores, similarity, LLM fields - Automatic retries with exponential back-off
- Async-first (
httpx) withasync with/aclose()support
Installation
pip install -e packages/email_package # editable local install
Quick Start
import asyncio
from zora_ai_email import ZoraAIEmailClient, EmailAnalyzer
async def main():
async with ZoraAIEmailClient(api_key="zora_...") as client:
analyzer = EmailAnalyzer(client)
result = await analyzer.analyze(
sender="offers@totally-real-bank.xyz",
subject="Urgent: Verify your account",
body="Click here immediately to avoid suspension: http://evil.xyz/login",
with_llm_explanation=True,
)
print(result.risk_score, result.fraud_type, result.llm_explanation)
asyncio.run(main())
API Reference
ZoraAIEmailClient
| Parameter | Type | Default | Description |
|---|---|---|---|
api_key |
str |
required | Your Zora AI API key |
base_url |
str |
http://localhost:8000 |
API base URL |
timeout |
float |
30.0 |
Request timeout in seconds |
max_retries |
int |
3 |
Number of retry attempts |
backoff_factor |
float |
0.5 |
Exponential back-off base |
EmailAnalyzer.analyze(sender, subject, body, *, with_llm_explanation)
Returns an EmailAnalysisResult dataclass.
EmailAnalysisResult fields
| Field | Type | Description |
|---|---|---|
request_id |
str | None |
UUID of the stored analysis |
message_id |
str |
Echo of the message_id |
sender |
str |
Sender address |
subject |
str |
Email subject |
body |
str |
Body preview (truncated by server) |
risk_score |
float |
0–1 overall threat score |
fraud_type |
str |
e.g. phishing, safe |
confidence |
float |
Model confidence 0–1 |
sub_scores |
dict |
nlp_score, similarity_score, stylometry_score |
llm_enhanced |
bool |
Whether LLM explanation was generated |
llm_explanation |
str | None |
Human-readable explanation |
llm_label |
str | None |
LLM verdict label |
llm_confidence |
float | None |
LLM confidence |
similarity |
dict |
Raw similarity search result |
nlp_prediction |
dict |
Raw NLP model prediction |
raw_response |
dict |
Full API response |
Project details
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