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98a466d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | # app/entity_detector.py
import pandas as pd
from typing import Tuple
# Entity-specific canonical schemas
ENTITY_SCHEMAS = {
"sales": {
"indicators": ["timestamp", "total", "amount", "qty", "quantity", "sale_date", "transaction_id"],
"required_matches": 2,
"aliases": {
"timestamp": ["timestamp", "date", "sale_date", "created_at", "transaction_time"],
"product_id": ["sku", "barcode", "plu", "product_id", "item_code"],
"qty": ["qty", "quantity", "units", "pieces", "item_count"],
"total": ["total", "amount", "line_total", "sales_amount", "price"],
"store_id": ["store_id", "branch", "location", "outlet_id", "branch_code"],
}
},
"inventory": {
"indicators": ["stock", "quantity_on_hand", "reorder", "inventory", "current_stock", "warehouse_qty"],
"required_matches": 2,
"aliases": {
"product_id": ["sku", "barcode", "plu", "product_id", "item_code"],
"current_stock": ["stock", "quantity_on_hand", "qty_available", "current_quantity"],
"reorder_point": ["reorder_level", "min_stock", "reorder_point", "threshold"],
"supplier_id": ["supplier", "supplier_id", "vendor", "vendor_code"],
"last_stock_date": ["last_stock_date", "last_receipt", "last_updated"],
}
},
"customer": {
"indicators": ["customer_id", "email", "phone", "customer_name", "client_id", "loyalty_number"],
"required_matches": 2,
"aliases": {
"customer_id": ["customer_id", "client_id", "member_id", "loyalty_number", "phone"],
"full_name": ["customer_name", "full_name", "name", "client_name"],
"email": ["email", "email_address", "e_mail"],
"phone": ["phone", "phone_number", "mobile", "contact"],
}
},
"product": {
"indicators": ["product_name", "product_id", "sku", "category", "price", "cost", "unit_of_measure"],
"required_matches": 2,
"aliases": {
"product_id": ["sku", "barcode", "plu", "product_id", "item_code"],
"product_name": ["product_name", "name", "description", "item_name"],
"category": ["category", "department", "cat", "family", "classification"],
"unit_price": ["price", "unit_price", "selling_price", "retail_price"],
"cost_price": ["cost", "cost_price", "purchase_price", "wholesale_price"],
}
}
}
def detect_entity_type(df: pd.DataFrame) -> Tuple[str, float]:
"""
AUTO-DETECT entity type from DataFrame columns.
Returns: (entity_type, confidence_score)
"""
columns = {str(col).lower().strip() for col in df.columns}
scores = {}
for entity_type, config in ENTITY_SCHEMAS.items():
# Count matches between DataFrame columns and entity indicators
matches = sum(
1 for indicator in config["indicators"]
if any(indicator in col for col in columns)
)
# Calculate confidence (0.0 to 1.0)
confidence = min(matches / config["required_matches"], 1.0)
scores[entity_type] = confidence
# Return best match if confident enough
if scores:
best_entity = max(scores, key=scores.get)
confidence = scores[best_entity]
if confidence > 0.3: # 30% threshold
return best_entity, confidence
# Default to sales if uncertain (most common)
return "sales", 0.0 |