Atomizer/optimization_engine/future/workflow_decomposer.py

"""
Workflow Decomposer

Breaks complex user requests into atomic workflow steps that can be matched
against existing codebase capabilities.

IMPROVED VERSION: Handles multi-objective optimization, constraints, and complex requests.

Author: Atomizer Development Team
Version: 0.2.0 (Phase 2.5 - Improved)
Last Updated: 2025-01-16
"""

import re
from typing import List, Dict, Any, Set
from dataclasses import dataclass


@dataclass
class WorkflowStep:
    """Represents a single atomic step in a workflow."""
    action: str
    domain: str
    params: Dict[str, Any]
    priority: int = 0


class WorkflowDecomposer:
    """Breaks complex requests into atomic workflow steps."""

    def __init__(self):
        # Extended result type mapping
        self.result_types = {
            'displacement': 'displacement',
            'deformation': 'displacement',
            'stress': 'stress',
            'von mises': 'stress',
            'strain': 'strain',
            'modal': 'modal',
            'mode': 'modal',
            'eigenvalue': 'modal',
            'frequency': 'modal',
            'temperature': 'temperature',
            'thermal': 'temperature',
            'reaction': 'reaction_force',
            'reaction force': 'reaction_force',
            'nodal reaction': 'reaction_force',
            'force': 'reaction_force',
            'mass': 'mass',
            'weight': 'mass',
            'volume': 'volume'
        }

        # Solver type mapping
        self.solver_types = {
            'sol101': 'SOL101',
            'sol 101': 'SOL101',
            'static': 'SOL101',
            'sol103': 'SOL103',
            'sol 103': 'SOL103',
            'modal': 'SOL103',
            'sol106': 'SOL106',
            'sol 106': 'SOL106',
            'nonlinear': 'SOL106',
            'sol105': 'SOL105',
            'buckling': 'SOL105'
        }

    def decompose(self, user_request: str) -> List[WorkflowStep]:
        """
        Break user request into atomic workflow steps.

        Handles:
        - Multi-objective optimization
        - Constraints
        - Multiple result extractions
        - Custom expressions
        - Parameter filtering
        """
        steps = []
        request_lower = user_request.lower()

        # Check if this is an optimization request
        is_optimization = self._is_optimization_request(request_lower)

        if is_optimization:
            steps = self._decompose_optimization_workflow(user_request, request_lower)
        else:
            steps = self._decompose_simple_workflow(user_request, request_lower)

        # Sort by priority
        steps.sort(key=lambda s: s.priority)

        return steps

    def _is_optimization_request(self, text: str) -> bool:
        """Check if request involves optimization."""
        optimization_keywords = [
            'optimize', 'optimiz', 'minimize', 'minimiz', 'maximize', 'maximiz',
            'optuna', 'genetic', 'iteration', 'vary', 'varying'
        ]
        return any(kw in text for kw in optimization_keywords)

    def _decompose_optimization_workflow(self, request: str, request_lower: str) -> List[WorkflowStep]:
        """Decompose an optimization request into workflow steps."""
        steps = []
        priority = 1

        # 1. Identify and filter parameters
        param_filter = self._extract_parameter_filter(request, request_lower)
        if param_filter:
            steps.append(WorkflowStep(
                action='identify_parameters',
                domain='geometry',
                params={'filter': param_filter},
                priority=priority
            ))
            priority += 1

        # 2. Update parameters (this happens in the optimization loop)
        steps.append(WorkflowStep(
            action='update_parameters',
            domain='geometry',
            params={'source': 'optimization_algorithm'},
            priority=priority
        ))
        priority += 1

        # 3. Run simulation
        solver = self._extract_solver_type(request_lower)
        if solver:
            steps.append(WorkflowStep(
                action='run_analysis',
                domain='simulation',
                params={'solver': solver},
                priority=priority
            ))
            priority += 1

        # 4. Extract ALL result types mentioned (multi-objective!)
        result_extractions = self._extract_all_results(request, request_lower)
        for result_info in result_extractions:
            # If result has custom_expression (e.g., mass from .prt expression),
            # it's a geometry operation, not result_extraction (OP2 file)
            if 'custom_expression' in result_info:
                steps.append(WorkflowStep(
                    action='read_expression',
                    domain='geometry',
                    params=result_info,
                    priority=priority
                ))
            else:
                steps.append(WorkflowStep(
                    action='extract_result',
                    domain='result_extraction',
                    params=result_info,
                    priority=priority
                ))
            priority += 1

        # 5. Handle constraints
        constraints = self._extract_constraints(request, request_lower)
        if constraints:
            steps.append(WorkflowStep(
                action='apply_constraints',
                domain='optimization',
                params={'constraints': constraints},
                priority=priority
            ))
            priority += 1

        # 6. Optimize (multi-objective if multiple objectives detected)
        objectives = self._extract_objectives(request, request_lower)
        algorithm = self._extract_algorithm(request_lower)

        steps.append(WorkflowStep(
            action='optimize',
            domain='optimization',
            params={
                'objectives': objectives,
                'algorithm': algorithm,
                'multi_objective': len(objectives) > 1
            },
            priority=priority
        ))

        return steps

    def _decompose_simple_workflow(self, request: str, request_lower: str) -> List[WorkflowStep]:
        """Decompose a non-optimization request."""
        steps = []

        # Check for material creation
        if 'material' in request_lower and ('create' in request_lower or 'generate' in request_lower):
            steps.append(WorkflowStep(
                action='create_material',
                domain='materials',
                params={}
            ))

        # Check for simulation run
        solver = self._extract_solver_type(request_lower)
        if solver:
            steps.append(WorkflowStep(
                action='run_analysis',
                domain='simulation',
                params={'solver': solver}
            ))

        # Check for result extraction
        result_extractions = self._extract_all_results(request, request_lower)
        for result_info in result_extractions:
            # If result has custom_expression (e.g., mass from .prt expression),
            # it's a geometry operation, not result_extraction (OP2 file)
            if 'custom_expression' in result_info:
                steps.append(WorkflowStep(
                    action='read_expression',
                    domain='geometry',
                    params=result_info
                ))
            else:
                steps.append(WorkflowStep(
                    action='extract_result',
                    domain='result_extraction',
                    params=result_info
                ))

        return steps

    def _extract_parameter_filter(self, request: str, request_lower: str) -> str:
        """Extract parameter filter from text."""
        # Look for specific suffixes/prefixes
        if '_opt' in request_lower or ' opt ' in request_lower:
            return '_opt'
        if 'v_' in request_lower:
            return 'v_'
        if '_var' in request_lower:
            return '_var'
        if 'design variable' in request_lower or 'design parameter' in request_lower:
            return 'design_variables'
        if 'all parameter' in request_lower or 'all expression' in request_lower:
            return 'all'

        # Default to none if not specified
        return ''

    def _extract_solver_type(self, text: str) -> str:
        """Extract solver type from text."""
        for keyword, solver in self.solver_types.items():
            if keyword in text:
                return solver
        return ''

    def _extract_all_results(self, request: str, request_lower: str) -> List[Dict[str, Any]]:
        """
        Extract ALL result types mentioned in the request.
        Handles multiple objectives and constraints.
        """
        result_extractions = []

        # Find all result types mentioned
        found_types = set()
        for keyword, result_type in self.result_types.items():
            if keyword in request_lower:
                found_types.add(result_type)

        # For each result type, extract details
        for result_type in found_types:
            result_info = {
                'result_type': result_type
            }

            # Extract subcase information
            subcase = self._extract_subcase(request, request_lower)
            if subcase:
                result_info['subcase'] = subcase

            # Extract direction (for reaction forces, displacements)
            if result_type in ['reaction_force', 'displacement']:
                direction = self._extract_direction(request, request_lower)
                if direction:
                    result_info['direction'] = direction

            # Extract metric (min, max, specific location)
            metric = self._extract_metric_for_type(request, request_lower, result_type)
            if metric:
                result_info['metric'] = metric

            # Extract custom expression (for mass, etc.)
            if result_type == 'mass':
                custom_expr = self._extract_custom_expression(request, request_lower, 'mass')
                if custom_expr:
                    result_info['custom_expression'] = custom_expr

            result_extractions.append(result_info)

        return result_extractions

    def _extract_subcase(self, request: str, request_lower: str) -> str:
        """Extract subcase information (solution X subcase Y)."""
        # Look for patterns like "solution 1 subcase 3"
        match = re.search(r'solution\s+(\d+)\s+subcase\s+(\d+)', request_lower)
        if match:
            return f"solution_{match.group(1)}_subcase_{match.group(2)}"

        # Look for just "subcase X"
        match = re.search(r'subcase\s+(\d+)', request_lower)
        if match:
            return f"subcase_{match.group(1)}"

        return ''

    def _extract_direction(self, request: str, request_lower: str) -> str:
        """Extract direction (X, Y, Z) for vectorial results."""
        # Look for explicit direction mentions
        if re.search(r'\bin\s+[xyz]\b', request_lower):
            match = re.search(r'in\s+([xyz])\b', request_lower)
            if match:
                return match.group(1).upper()

        # Look for "Y direction" pattern
        if re.search(r'[xyz]\s+direction', request_lower):
            match = re.search(r'([xyz])\s+direction', request_lower)
            if match:
                return match.group(1).upper()

        return ''

    def _extract_metric_for_type(self, request: str, request_lower: str, result_type: str) -> str:
        """Extract metric (min, max, average) for specific result type."""
        # Check for explicit min/max keywords near the result type
        if 'max' in request_lower or 'maximum' in request_lower:
            return f'max_{result_type}'
        if 'min' in request_lower or 'minimum' in request_lower:
            return f'min_{result_type}'
        if 'average' in request_lower or 'mean' in request_lower:
            return f'avg_{result_type}'

        # Default to max for most result types
        return f'max_{result_type}'

    def _extract_custom_expression(self, request: str, request_lower: str, expr_type: str) -> str:
        """Extract custom expression names (e.g., mass_of_only_this_part)."""
        if expr_type == 'mass':
            # Look for custom mass expressions
            match = re.search(r'mass[_\w]*(?:of|for)[_\w]*', request_lower)
            if match:
                return match.group(0).replace(' ', '_')

            # Look for explicit expression names
            if 'expression' in request_lower:
                match = re.search(r'expression\s+(\w+)', request_lower)
                if match:
                    return match.group(1)

        return ''

    def _extract_constraints(self, request: str, request_lower: str) -> List[Dict[str, Any]]:
        """
        Extract constraints from the request.
        Examples: "maintain stress under 100 MPa", "keep displacement < 5mm"
        """
        constraints = []

        # Pattern 1: "maintain X under/below Y"
        maintain_pattern = r'maintain\s+(\w+)\s+(?:under|below|less than|<)\s+([\d.]+)\s*(\w+)?'
        for match in re.finditer(maintain_pattern, request_lower):
            result_type = self.result_types.get(match.group(1), match.group(1))
            value = float(match.group(2))
            unit = match.group(3) if match.group(3) else ''

            constraints.append({
                'type': 'upper_bound',
                'result_type': result_type,
                'value': value,
                'unit': unit
            })

        # Pattern 2: "stress < 100 MPa" or "stress < 100MPa"
        comparison_pattern = r'(\w+)\s*(<|>|<=|>=)\s*([\d.]+)\s*(\w+)?'
        for match in re.finditer(comparison_pattern, request_lower):
            result_type = self.result_types.get(match.group(1), match.group(1))
            operator = match.group(2)
            value = float(match.group(3))
            unit = match.group(4) if match.group(4) else ''

            constraint_type = 'upper_bound' if operator in ['<', '<='] else 'lower_bound'

            constraints.append({
                'type': constraint_type,
                'result_type': result_type,
                'operator': operator,
                'value': value,
                'unit': unit
            })

        return constraints

    def _extract_objectives(self, request: str, request_lower: str) -> List[Dict[str, str]]:
        """
        Extract optimization objectives.
        Can be multiple for multi-objective optimization.
        """
        objectives = []

        # Find all "minimize X" or "maximize X" patterns
        minimize_pattern = r'minimi[zs]e\s+(\w+(?:\s+\w+)*?)(?:\s+(?:and|but|with|using|varying|to)|\.|\,|$)'
        for match in re.finditer(minimize_pattern, request_lower):
            objective_text = match.group(1).strip()
            result_type = self._map_to_result_type(objective_text)
            objectives.append({
                'type': 'minimize',
                'target': result_type if result_type else objective_text
            })

        maximize_pattern = r'maximi[zs]e\s+(\w+(?:\s+\w+)*?)(?:\s+(?:and|but|with|using|varying|to)|\.|\,|$)'
        for match in re.finditer(maximize_pattern, request_lower):
            objective_text = match.group(1).strip()
            result_type = self._map_to_result_type(objective_text)
            objectives.append({
                'type': 'maximize',
                'target': result_type if result_type else objective_text
            })

        # If no explicit minimize/maximize but mentions optimization
        if not objectives and ('optimize' in request_lower or 'optim' in request_lower):
            # Try to infer from context
            for keyword, result_type in self.result_types.items():
                if keyword in request_lower:
                    # Assume minimize for stress, strain, displacement
                    # Assume maximize for modal frequencies
                    obj_type = 'maximize' if result_type == 'modal' else 'minimize'
                    objectives.append({
                        'type': obj_type,
                        'target': result_type
                    })

        return objectives if objectives else [{'type': 'minimize', 'target': 'unknown'}]

    def _map_to_result_type(self, text: str) -> str:
        """Map objective text to result type."""
        text_lower = text.lower().strip()
        for keyword, result_type in self.result_types.items():
            if keyword in text_lower:
                return result_type
        return text  # Return as-is if no mapping found

    def _extract_algorithm(self, text: str) -> str:
        """Extract optimization algorithm."""
        if 'optuna' in text:
            return 'optuna'
        if 'genetic' in text or 'ga' in text:
            return 'genetic_algorithm'
        if 'gradient' in text:
            return 'gradient_based'
        if 'pso' in text or 'particle swarm' in text:
            return 'pso'
        return 'optuna'  # Default

    def get_workflow_summary(self, steps: List[WorkflowStep]) -> str:
        """Get human-readable summary of workflow."""
        if not steps:
            return "No workflow steps identified"

        lines = ["Workflow Steps Identified:", "=" * 60, ""]

        for i, step in enumerate(steps, 1):
            lines.append(f"{i}. {step.action.replace('_', ' ').title()}")
            lines.append(f"   Domain: {step.domain}")
            if step.params:
                lines.append(f"   Parameters:")
                for key, value in step.params.items():
                    if isinstance(value, list) and value:
                        lines.append(f"     {key}:")
                        for item in value[:3]:  # Show first 3 items
                            lines.append(f"       - {item}")
                        if len(value) > 3:
                            lines.append(f"       ... and {len(value) - 3} more")
                    else:
                        lines.append(f"     {key}: {value}")
            lines.append("")

        return "\n".join(lines)


def main():
    """Test the improved workflow decomposer."""
    decomposer = WorkflowDecomposer()

    # Test case 1: Complex multi-objective with constraints
    test_request_1 = """update a geometry (.prt) with all expressions that have a _opt suffix to make the mass minimized. But the mass is not directly the total mass used, its the value under the part expression mass_of_only_this_part which is the calculation of 1of the body mass of my part, the one that I want to minimize.

the objective is to minimize mass but maintain stress of the solution 1 subcase 3 under 100Mpa. And also, as a second objective in my objective function, I want to minimize nodal reaction force in y of the same subcase."""

    print("Test 1: Complex Multi-Objective Optimization with Constraints")
    print("=" * 80)
    print(f"Request: {test_request_1[:100]}...")
    print()

    steps_1 = decomposer.decompose(test_request_1)
    print(decomposer.get_workflow_summary(steps_1))

    print("\nDetailed Analysis:")
    print("-" * 80)
    for i, step in enumerate(steps_1, 1):
        print(f"{i}. Action: {step.action}")
        print(f"   Domain: {step.domain}")
        print(f"   Params: {step.params}")
        print()

    # Test case 2: Simple strain optimization
    test_request_2 = "minimize strain using SOL101 and optuna varying v_ parameters"

    print("\n" + "=" * 80)
    print("Test 2: Simple Strain Optimization")
    print("=" * 80)
    print(f"Request: {test_request_2}")
    print()

    steps_2 = decomposer.decompose(test_request_2)
    print(decomposer.get_workflow_summary(steps_2))


if __name__ == '__main__':
    main()