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ee7d7b9 | 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 81 82 83 84 85 86 87 88 | # Graph schema definitions
# UNIVERSAL - Works with ANY data domain (Sales, HR, Healthcare, Education, etc.)
KEYS = {
# ENTITY 1: Main grouping entity (Customer/Department/Region/etc.)
"customer": [
# Sales/Business
"customer", "client", "buyer", "customer_name", "client_name", "buyer_name",
"cust", "user_name", "username", "user", "email", "contact", "company", "account",
# HR/Employee
"department", "dept", "team", "division", "unit", "group", "section", "branch",
# Healthcare
"patient", "doctor", "hospital", "clinic", "facility",
# Education
"student", "teacher", "school", "course", "class", "subject",
# General
"region", "area", "zone", "territory", "location", "city", "country", "state"
],
# ENTITY 2: Item/Product/Role
"product": [
# Sales/Business
"product", "item", "goods", "service", "product_name", "item_name", "sku",
"prod", "description", "details", "category", "type",
# HR/Employee
"role", "position", "job", "job_title", "title", "designation", "level", "grade",
# Healthcare
"treatment", "procedure", "medication", "diagnosis", "specialty",
# Education
"subject", "topic", "module", "program", "major"
],
# ENTITY 3: Individual record identifier
"invoice": [
# Sales/Business
"invoice", "bill", "order", "transaction", "inv", "order_id", "transaction_id",
"invoice_no", "invoice_number", "bill_no", "order_no", "invoice_id", "ref", "reference",
# HR/Employee
"employee", "emp", "employee_id", "emp_id", "staff", "staff_id", "worker", "name",
# Healthcare
"patient_id", "record_id", "case_id", "visit_id",
# Education
"student_id", "enrollment_id", "registration_id",
# General
"id", "record", "entry", "row_id"
],
# DATE
"date": [
"date", "order_date", "invoice_date", "transaction_date", "purchase_date", "sale_date",
"created", "created_at", "dt", "time", "timestamp", "year", "month", "day",
"hire_date", "join_date", "start_date", "end_date", "birth_date", "dob",
"admission_date", "discharge_date", "visit_date"
],
# AMOUNT/VALUE
"amount": [
# Sales/Business
"total", "amount", "price", "value", "revenue", "sales", "total_amount",
"total_price", "grand_total", "sum", "net", "gross", "cost", "subtotal",
"total_value", "sale_amount", "invoice_amount", "charge", "payment",
"amount_eur", "amount_usd", "amount_inr", "eur", "usd", "inr",
# HR/Employee
"salary", "wage", "pay", "compensation", "income", "earnings", "bonus",
"commission", "allowance", "ctc", "package",
# Healthcare
"bill_amount", "treatment_cost", "fee", "charges",
# Education
"score", "marks", "grade", "percentage", "gpa", "cgpa", "tuition", "fees"
]
}
def detect_schema(columns):
"""Detect which columns map to which graph entities"""
out = {}
columns_lower = {col: col.lower().strip() for col in columns}
for col, col_lower in columns_lower.items():
for k, vals in KEYS.items():
# First try exact match
if col_lower in vals:
out[k] = col
break
# Then try partial match (key in column name)
for v in vals:
if v in col_lower:
if k not in out: # Don't override exact matches
out[k] = col
break
print(f"📋 Schema detection: columns={list(columns)}, detected={out}")
return out
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