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🛡️ Universal Base KPI Calculator
Enterprise Pattern: Async, fault-tolerant, LLM-guarded, schema-aware
"""
import pandas as pd
import logging
from abc import ABC, abstractmethod
from typing import Dict, Any, Optional, List
from datetime import datetime
import asyncio
import json
from app.schemas.org_schema import OrgSchema
from app.service.llm_service import get_llm_service
logger = logging.getLogger(__name__)
class BaseKPICalculator(ABC):
"""
🏛️ Enterprise Base Class
- Async-ready
- LLM-guarded (won't crash if LLM not loaded)
- Schema-aware with dynamic mapping
- Comprehensive error handling
"""
def __init__(self, org_id: str, df: pd.DataFrame, source_id: Optional[str] = None, entity_type: str = "SALES"):
"""
✅ Universal constructor - all parameters optional except org_id and df
Args:
org_id: Organization ID (required)
df: DataFrame to analyze (required)
source_id: Optional source identifier for tracking
entity_type: Entity type from Redis (e.g., "SALES", "INVENTORY")
"""
if not org_id or df.empty:
raise ValueError("org_id and non-empty df required")
self.org_id = org_id
self.source_id = source_id
self.df = df.copy() # Defensive copy to prevent mutation
self.entity_type = entity_type # ✅ Store entity_type
# ✅ FIXED: Pass entity_type to OrgSchema
self.schema = OrgSchema(org_id=org_id, entity_type=entity_type)
self.llm = get_llm_service()
self.computed_at = datetime.utcnow()
self._cache: Dict[str, Any] = {} # In-memory cache for this run
logger.info(f"[KPI] 📊 {self.__class__.__name__} initialized for {org_id}/{entity_type} ({len(df)} rows)")
@abstractmethod
async def compute_all(self) -> Dict[str, Any]:
"""
🎯 Main entry point - **MUST BE ASYNC** for LLM calls
Returns:
Complete KPI dictionary with metadata
"""
pass
def _safe_calc(
self,
semantic_field: str,
operation: str,
default: Any = 0.0,
fallback_field: Optional[str] = None
) -> Any:
"""
🔒 **Enterprise-safe calculation** with multiple fallback strategies
Args:
semantic_field: Semantic field name (e.g., "total")
operation: pandas operation ("sum", "mean", "nunique", etc.)
default: Default value if calculation fails
fallback_field: Secondary field to try if primary fails
Returns:
Scalar result or default
"""
try:
# Primary field resolution
actual_col = self.schema.get_column(semantic_field)
if actual_col and actual_col in self.df.columns:
series = self.df[actual_col]
# Handle different operation types
if operation == "nunique":
return int(series.nunique())
elif operation == "count":
return int(series.count())
elif operation == "sum":
return float(series.sum())
elif operation == "mean":
return float(series.mean())
elif operation == "max":
return float(series.max())
elif operation == "min":
return float(series.min())
elif operation == "std":
return float(series.std())
else:
logger.warning(f"[KPI] Unknown operation: {operation}")
return default
# Fallback field if provided
if fallback_field and fallback_field in self.df.columns:
logger.info(f"[KPI] Fallback to {fallback_field} for {semantic_field}")
return getattr(self.df[fallback_field], operation, lambda: default)()
logger.warning(f"[KPI] Field '{semantic_field}' not found, returning default: {default}")
return default
except Exception as e:
logger.error(f"[KPI] Calculation failed for '{semantic_field}.{operation}': {e}")
return default
def _cache_value(self, key: str, value: Any, ttl: int = 3600):
"""
💾 Cache value in Redis for cross-worker sharing
Args:
key: Cache key (will be prefixed with org_id)
value: Value to cache (must be JSON-serializable)
ttl: Time-to-live in seconds
"""
try:
from app.core.event_hub import event_hub
cache_key = f"kpi_cache:{self.org_id}:{key}"
event_hub.setex(cache_key, ttl, json.dumps(value))
except Exception as e:
logger.warning(f"[KPI] Cache write failed: {e}")
def _get_cached_value(self, key: str, default: Any = None) -> Any:
"""
📖 Retrieve cached value from Redis
Args:
key: Cache key (without prefix)
default: Default value if cache miss
Returns:
Cached value or default
"""
try:
from app.core.event_hub import event_hub
cache_key = f"kpi_cache:{self.org_id}:{key}"
data = event_hub.get_key(cache_key)
if data:
return json.loads(data)
return default
except Exception as e:
logger.warning(f"[KPI] Cache read failed: {e}")
return default
def _calculate_growth(self, current: float, previous: float) -> float:
"""
📈 Safe growth calculation with divide-by-zero protection
Args:
current: Current period value
previous: Previous period value
Returns:
Growth percentage or 0.0 if invalid
"""
try:
if previous and previous > 0:
return float((current - previous) / previous * 100)
return 0.0
except Exception:
return 0.0
async def _llm_generate_safe(self, prompt: str, max_tokens: int = 50) -> Optional[str]:
"""
🤖 **LLM-guarded generation** - won't crash if LLM not ready
Args:
prompt: Prompt for LLM
max_tokens: Max tokens to generate
Returns:
Generated text or None if LLM unavailable
"""
try:
if not self.llm.is_ready():
logger.warning("[KPI] LLM not ready, skipping AI tier")
return None
return await asyncio.to_thread(
self.llm.generate,
prompt,
max_tokens=max_tokens
)
except Exception as e:
logger.warning(f"[KPI] LLM generation failed: {e}")
return None
def _validate_data_quality(self) -> List[Dict[str, Any]]:
"""
🔍 **Enterprise data quality check**
Returns:
List of quality issues with severity levels
"""
issues = []
# Check for missing timestamps
if 'timestamp' in self.df.columns:
missing_ts = self.df['timestamp'].isna().sum()
if missing_ts > 0:
issues.append({
"field": "timestamp",
"issue": "missing_values",
"count": int(missing_ts),
"severity": "high" if missing_ts > len(self.df) * 0.1 else "medium"
})
# Check for negative totals
if 'total' in self.df.columns:
negative_sales = (self.df['total'] < 0).sum()
if negative_sales > 0:
issues.append({
"field": "total",
"issue": "negative_values",
"count": int(negative_sales),
"severity": "medium"
})
return issues |