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| # 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 | |