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Developer Documentation
Integrate Mizaan's scoring engine into your workflow with a simple REST API. Provider-agnostic, feedback-aware, and built for scale.
Quick Start
Python
from mizaan import ScoringEngine
from mizaan.providers import OllamaProvider
# Initialize with local LLM — zero cloud dependency
engine = ScoringEngine(
provider=OllamaProvider(model="mistral"),
rubric="sales-performance-v2"
)
# Score a work item
result = engine.score(
item="90-min discovery call with Fortune 500 CFO",
context={"type": "sales_call", "duration": 90}
)
print(result.score) # 8.5
print(result.reasoning) # "Extended engagement with..."
print(result.confidence) # 0.87
print(result.dimensions) # {"impact": 9, "effort": 8, ...}API Reference
POST
/api/v1/scoreScore a work item with feedback-aware context
Request Body
{
"work_item_id": "wi_001",
"work_item_type": "sales_call",
"description": "90-minute discovery call with Fortune 500 CFO...",
"context": { "duration": 90, "outcome": "follow_up_scheduled" }
}Response
{
"record_id": "fb_001",
"score": 8.5,
"reasoning": "Extended engagement with C-suite decision-maker...",
"confidence": 0.87,
"dimensions": { "impact": 9, "effort": 8, "quality": 9 },
"auditor_decision": "APPROVED",
"similar_cases_used": 3
}POST
/api/v1/feedbackSubmit human feedback on a scoring result
Request Body
{
"record_id": "fb_001",
"decision": "OVERRIDDEN",
"adjusted_score": 9.0,
"reasoning": "Fortune 500 CFO calls are extremely valuable..."
}Response
{
"status": "saved",
"record_id": "fb_001"
}GET
/api/v1/patternsGet detected override patterns for rubric improvement
Response
{
"patterns": [
{
"pattern": "executive_meetings_underscored",
"description": "AI consistently scores C-suite meetings 2+ points below human expectation",
"occurrences": 12,
"suggested_adjustment": "Increase weight for seniority_of_contact dimension"
}
]
}Built With
Python
FastAPI
LangChain
PostgreSQL
pgvector
Redis
Docker
Ollama