Iteration 1 - DATA_ENGINEER
Sequence: 2
Timestamp: 2025-07-28 00:23:04

Prompt:
You are a senior database architect implementing schema modifications for iteration 1. Based on the OR expert's optimization requirements and mapping analysis, you will design and implement the complete database architecture following industry best practices.

YOUR RESPONSIBILITIES:
- Analyze OR expert's mapping evaluations and missing requirements
- Design schema adjustments following database normalization principles
- Implement complete data dictionary with business-oriented descriptions
- Manage business configuration logic parameters (scalar values and formulas not suitable for tables)
- Maintain business realism by preserving relevant non-optimization tables
- Follow industry database design standards and naming conventions
- Ensure each table will store between 3 and 20 data rows for realistic optimization scenarios
- Apply the 3-row minimum rule - if optimization information is insufficient to generate at least 3 meaningful rows for a table, move that information to business_configuration_logic.json instead.


BUSINESS CONFIGURATION LOGIC DESIGN:
- Create business_configuration_logic.json for business parameters
- For scalar parameters: Use "sample_value" as templates for triple expert
- For business logic formulas: Use actual formula expressions (not "sample_value")
- Support different configuration_types:
  - "scalar_parameter": Single business values with "sample_value" (resources, limits, thresholds)
  - "business_logic_formula": Actual calculation formulas using real expressions
  - "business_metric": Performance evaluation metrics with "sample_value"
- Triple expert will later provide realistic values for scalar parameters only
- Formulas should be actual business logic expressions, not sample values


CRITICAL: Respond with ONLY a valid JSON object. No explanations, no markdown, no extra text.

OR EXPERT ANALYSIS (iteration 1):
{
  "database_id": "wine_1",
  "iteration": 0,
  "business_context": "A winery wants to maximize its revenue from wine sales while considering production capacity and market demand constraints.",
  "optimization_problem_description": "The winery needs to decide how many cases of each wine to produce to maximize revenue, given constraints on production capacity, market demand, and available grape types.",
  "optimization_formulation": {
    "objective": "maximize total_revenue = sum(Price[i] * Cases[i]) for all wines i",
    "decision_variables": "Cases[i] for each wine i, representing the number of cases to produce (integer)",
    "constraints": [
      "sum(Cases[i]) <= total_production_capacity",
      "Cases[i] <= market_demand[i] for all wines i",
      "Cases[i] >= 0 for all wines i"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "Price[i]": {
        "currently_mapped_to": "wine.Price",
        "mapping_adequacy": "good",
        "description": "Price per case of wine i"
      }
    },
    "constraint_bounds": {
      "total_production_capacity": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Total production capacity of the winery"
      },
      "market_demand[i]": {
        "currently_mapped_to": "missing",
        "mapping_adequacy": "missing",
        "description": "Market demand for wine i"
      }
    },
    "decision_variables": {
      "Cases[i]": {
        "currently_mapped_to": "wine.Cases",
        "mapping_adequacy": "partial",
        "description": "Number of cases to produce for wine i",
        "variable_type": "integer"
      }
    }
  },
  "missing_optimization_requirements": [
    "Total production capacity of the winery",
    "Market demand for each wine"
  ],
  "iteration_status": {
    "complete": false,
    "confidence": "medium",
    "next_focus": "Gather missing data on production capacity and market demand to refine the optimization model"
  }
}





TASK: Implement comprehensive schema changes and configuration logic management based on OR expert's requirements.

JSON STRUCTURE REQUIRED:

{
  "database_id": "wine_1",
  "iteration": 1,
  "implementation_summary": "Summary of schema changes and configuration logic updates based on OR expert mapping analysis",
  
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "List specific gaps identified from OR expert's mapping_adequacy assessments"
    ],
    "missing_data_requirements": [
      "List missing optimization data requirements from OR expert"
    ],
    "business_configuration_logic_needs": [
      "Scalar parameters and formulas better suited for configuration than tables"
    ]
  },
  
  "schema_adjustment_decisions": {
    "tables_to_delete": [
      {
        "table_name": "table_name",
        "reason": "business justification for removal (optimization irrelevant vs business irrelevant)"
      }
    ],
    "tables_to_create": [
      {
        "table_name": "table_name", 
        "purpose": "optimization role (decision_variables/objective_coefficients/constraint_bounds/business_data)",
        "business_meaning": "what this table represents in business context"
      }
    ],
    "tables_to_modify": [
      {
        "table_name": "existing_table",
        "changes": "specific modifications needed",
        "reason": "why these changes address OR expert's mapping gaps"
      }
    ]
  },
  
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "parameter_name": {
        "sample_value": "sample_parameter_value",
        "data_type": "INTEGER/FLOAT/STRING/BOOLEAN",
        "business_meaning": "what this parameter represents in business context",
        "optimization_role": "how this parameter is used in optimization model",
        "configuration_type": "scalar_parameter"
      },
      "formula_name": {
        "formula_expression": "actual_business_formula_expression",
        "data_type": "STRING",
        "business_meaning": "what this formula represents in business context",
        "optimization_role": "how this formula is used in optimization model",
        "configuration_type": "business_logic_formula"
      }
    },
    "updates_rationale": "why these parameters are better in configuration logic than tables"
  },
  
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "coefficient_name[indices]": "table.column OR business_configuration_logic.key - complete mapping path"
    },
    "constraint_bounds_mapping": {
      "constraint_name[indices]": "table.column OR business_configuration_logic.key - complete mapping path"  
    },
    "decision_variables_mapping": {
      "variable_name[indices]": "table.column OR business_configuration_logic.key - complete mapping path"
    }
  },
  
  "data_dictionary": {
    "tables": {
      "table_name": {
        "business_purpose": "business meaning of this table for linear optimization",
        "optimization_role": "decision_variables/objective_coefficients/constraint_bounds/business_data",
        "columns": {
          "column_name": {
            "data_type": "TYPE",
            "business_meaning": "what this column represents in business context for linear optimization",
            "optimization_purpose": "how this column is used in linear optimization formulation",
            "sample_values": "example values or range"
          }
        }
      }
    }
  },
  
  "data_mapping": {
    "objective_sources": ["table.column pairs for linear objective function"],
    "constraint_sources": ["table.column pairs for linear constraints"],
    "sample_data_rows": {"table1": 3, "table2": 5}
  },
  
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}

Response:
{
  "database_id": "wine_1",
  "iteration": 1,
  "implementation_summary": "Schema changes include adding tables for missing data requirements and updating existing tables to address mapping gaps. Configuration logic updated for scalar parameters and formulas.",
  "or_requirements_analysis": {
    "mapping_gaps_identified": [
      "total_production_capacity not mapped",
      "market_demand[i] not mapped"
    ],
    "missing_data_requirements": [
      "Total production capacity of the winery",
      "Market demand for each wine"
    ],
    "business_configuration_logic_needs": [
      "total_production_capacity as scalar parameter",
      "market_demand[i] as scalar parameter"
    ]
  },
  "schema_adjustment_decisions": {
    "tables_to_delete": [],
    "tables_to_create": [
      {
        "table_name": "market_demand",
        "purpose": "constraint_bounds",
        "business_meaning": "Represents the market demand for each type of wine"
      }
    ],
    "tables_to_modify": [
      {
        "table_name": "wine",
        "changes": "Add column for Cases",
        "reason": "To fully map decision variables for optimization"
      }
    ]
  },
  "business_configuration_logic_updates": {
    "configuration_parameters": {
      "total_production_capacity": {
        "sample_value": "10000",
        "data_type": "INTEGER",
        "business_meaning": "Total production capacity of the winery",
        "optimization_role": "Used as a constraint in the optimization model",
        "configuration_type": "scalar_parameter"
      }
    },
    "updates_rationale": "Parameters like total production capacity are better suited for configuration logic due to their scalar nature and infrequent changes."
  },
  "implemented_optimization_mapping": {
    "objective_coefficients_mapping": {
      "Price[i]": "wine.Price"
    },
    "constraint_bounds_mapping": {
      "total_production_capacity": "business_configuration_logic.total_production_capacity",
      "market_demand[i]": "market_demand.demand"
    },
    "decision_variables_mapping": {
      "Cases[i]": "wine.Cases"
    }
  },
  "data_dictionary": {
    "tables": {
      "wine": {
        "business_purpose": "Stores information about different wines",
        "optimization_role": "decision_variables",
        "columns": {
          "Price": {
            "data_type": "FLOAT",
            "business_meaning": "Price per case of wine",
            "optimization_purpose": "Objective coefficient in revenue maximization",
            "sample_values": "10.0, 15.0, 20.0"
          },
          "Cases": {
            "data_type": "INTEGER",
            "business_meaning": "Number of cases to produce",
            "optimization_purpose": "Decision variable in optimization",
            "sample_values": "100, 200, 300"
          }
        }
      },
      "market_demand": {
        "business_purpose": "Stores market demand for each wine",
        "optimization_role": "constraint_bounds",
        "columns": {
          "demand": {
            "data_type": "INTEGER",
            "business_meaning": "Market demand for wine",
            "optimization_purpose": "Constraint bound in optimization",
            "sample_values": "500, 1000, 1500"
          }
        }
      }
    }
  },
  "data_mapping": {
    "objective_sources": [
      "wine.Price"
    ],
    "constraint_sources": [
      "market_demand.demand",
      "business_configuration_logic.total_production_capacity"
    ],
    "sample_data_rows": {
      "wine": 3,
      "market_demand": 3
    }
  },
  "validation": {
    "schema_complete": true,
    "data_consistent": true,
    "math_traceable": true,
    "business_aligned": true,
    "follows_db_standards": true
  }
}
