hive/backend/app/api/workflows.py

"""
WHOOSH API - Workflow Management Endpoints

This module provides comprehensive API endpoints for managing multi-agent workflows
in the WHOOSH distributed orchestration platform. It handles workflow creation,
execution, monitoring, and lifecycle management.

Key Features:
- Multi-step workflow creation and validation
- Agent coordination and task orchestration
- Real-time execution monitoring and control
- Workflow templates and reusability
- Performance analytics and optimization
"""

from fastapi import APIRouter, Depends, HTTPException, Query, status
from typing import List, Dict, Any, Optional
from ..core.auth_deps import get_current_user_context
from ..models.responses import (
    WorkflowListResponse,
    WorkflowCreationResponse,
    WorkflowExecutionResponse,
    WorkflowCreationRequest,
    WorkflowExecutionRequest,
    WorkflowModel,
    ErrorResponse
)
from ..core.error_handlers import (
    coordinator_unavailable_error,
    validation_error,
    WHOOSHAPIException
)
import uuid
from datetime import datetime

router = APIRouter()


@router.get(
    "/workflows",
    response_model=WorkflowListResponse,
    status_code=status.HTTP_200_OK,
    summary="List all workflows",
    description="""
    Retrieve a comprehensive list of all workflows in the WHOOSH system.
    
    This endpoint provides access to workflow definitions, templates, and metadata
    for building complex multi-agent orchestration pipelines.
    
    **Workflow Information Includes:**
    - Workflow definition and step configuration
    - Execution statistics and success rates
    - Creation and modification timestamps
    - User ownership and permissions
    - Performance metrics and analytics
    
    **Workflow Types:**
    - **Code Review Pipelines**: Automated code analysis and testing
    - **Deployment Workflows**: CI/CD and deployment automation
    - **Data Processing**: ETL and data transformation pipelines
    - **Testing Suites**: Comprehensive testing and quality assurance
    - **Documentation**: Automated documentation generation
    - **Security Audits**: Security scanning and vulnerability assessment
    
    **Use Cases:**
    - Browse available workflow templates
    - Monitor workflow performance and usage
    - Manage workflow lifecycle and versioning
    - Analyze workflow efficiency and optimization opportunities
    - Create workflow libraries and reusable components
    """,
    responses={
        200: {"description": "Workflow list retrieved successfully"},
        500: {"model": ErrorResponse, "description": "Failed to retrieve workflows"}
    }
)
async def get_workflows(
    status_filter: Optional[str] = Query(None, alias="status", description="Filter by workflow status"),
    created_by: Optional[str] = Query(None, description="Filter by workflow creator"),
    limit: int = Query(50, description="Maximum number of workflows to return", ge=1, le=1000),
    current_user: Dict[str, Any] = Depends(get_current_user_context)
) -> WorkflowListResponse:
    """
    Get a list of all workflows with optional filtering.
    
    Args:
        status_filter: Optional status filter for workflows
        created_by: Optional filter by workflow creator
        limit: Maximum number of workflows to return
        current_user: Current authenticated user context
        
    Returns:
        WorkflowListResponse: List of workflows with metadata
        
    Raises:
        HTTPException: If workflow retrieval fails
    """
    try:
        # For now, return placeholder workflows until full workflow engine is implemented
        sample_workflows = [
            WorkflowModel(
                id="workflow-code-review",
                name="Code Review Pipeline",
                description="Automated code review and testing workflow",
                status="active",
                steps=[
                    {
                        "name": "Static Analysis",
                        "type": "code_analysis", 
                        "agent_specialty": "kernel_dev",
                        "context": {"analysis_type": "security", "rules": "strict"}
                    },
                    {
                        "name": "Unit Testing",
                        "type": "testing",
                        "agent_specialty": "tester", 
                        "context": {"test_suite": "unit", "coverage_threshold": 80}
                    }
                ],
                created_at=datetime.utcnow(),
                created_by="system",
                execution_count=25,
                success_rate=92.5
            ),
            WorkflowModel(
                id="workflow-deployment",
                name="Deployment Pipeline",
                description="CI/CD deployment workflow with testing and validation",
                status="active",
                steps=[
                    {
                        "name": "Build",
                        "type": "build",
                        "agent_specialty": "general_ai",
                        "context": {"target": "production", "optimize": True}
                    },
                    {
                        "name": "Integration Tests", 
                        "type": "testing",
                        "agent_specialty": "tester",
                        "context": {"test_suite": "integration", "environment": "staging"}
                    },
                    {
                        "name": "Deploy",
                        "type": "deployment",
                        "agent_specialty": "general_ai",
                        "context": {"environment": "production", "strategy": "rolling"}
                    }
                ],
                created_at=datetime.utcnow(),
                created_by="system",
                execution_count=15,
                success_rate=88.7
            )
        ]
        
        # Apply filters
        filtered_workflows = sample_workflows
        if status_filter:
            filtered_workflows = [w for w in filtered_workflows if w.status == status_filter]
        if created_by:
            filtered_workflows = [w for w in filtered_workflows if w.created_by == created_by]
        
        # Apply limit
        filtered_workflows = filtered_workflows[:limit]
        
        return WorkflowListResponse(
            workflows=filtered_workflows,
            total=len(filtered_workflows),
            message=f"Retrieved {len(filtered_workflows)} workflows"
        )
        
    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Failed to retrieve workflows: {str(e)}"
        )


@router.post(
    "/workflows",
    response_model=WorkflowCreationResponse,
    status_code=status.HTTP_201_CREATED,
    summary="Create a new workflow",
    description="""
    Create a new multi-agent workflow for task orchestration and automation.
    
    This endpoint allows you to define complex workflows that coordinate multiple
    agents to perform sophisticated development and operational tasks.
    
    **Workflow Creation Process:**
    1. **Validation**: Validate workflow structure and step definitions
    2. **Agent Verification**: Verify required agent specializations are available
    3. **Dependency Analysis**: Analyze step dependencies and execution order
    4. **Resource Planning**: Estimate resource requirements and execution time
    5. **Storage**: Persist workflow definition for future execution
    
    **Workflow Step Types:**
    - `code_analysis`: Static code analysis and review
    - `testing`: Test execution and validation
    - `build`: Compilation and build processes
    - `deployment`: Application deployment and configuration
    - `documentation`: Documentation generation and updates
    - `security_scan`: Security analysis and vulnerability assessment
    - `performance_test`: Performance testing and benchmarking
    - `data_processing`: Data transformation and analysis
    
    **Advanced Features:**
    - **Conditional Execution**: Steps can have conditions and branching logic
    - **Parallel Execution**: Steps can run in parallel for improved performance
    - **Error Handling**: Define retry policies and error recovery procedures
    - **Variable Substitution**: Use variables and templates for flexible workflows
    - **Agent Selection**: Specify agent requirements and selection criteria
    - **Timeout Management**: Configure timeouts for individual steps and overall workflow
    
    **Best Practices:**
    - Keep steps focused and atomic for better reliability
    - Use meaningful names and descriptions for clarity
    - Include appropriate error handling and retry logic
    - Optimize step ordering for performance and dependencies
    - Test workflows thoroughly before production use
    """,
    responses={
        201: {"description": "Workflow created successfully"},
        400: {"model": ErrorResponse, "description": "Invalid workflow configuration"},
        422: {"model": ErrorResponse, "description": "Workflow validation failed"},
        500: {"model": ErrorResponse, "description": "Workflow creation failed"}
    }
)
async def create_workflow(
    workflow_data: WorkflowCreationRequest,
    current_user: Dict[str, Any] = Depends(get_current_user_context)
) -> WorkflowCreationResponse:
    """
    Create a new workflow with validation and optimization.
    
    Args:
        workflow_data: Workflow configuration and step definitions
        current_user: Current authenticated user context
        
    Returns:
        WorkflowCreationResponse: Workflow creation confirmation with validation results
        
    Raises:
        HTTPException: If workflow creation fails due to validation or system issues
    """
    try:
        # Validate workflow structure
        if not workflow_data.steps:
            raise validation_error("steps", "Workflow must have at least one step")
        
        # Validate step configuration
        for i, step in enumerate(workflow_data.steps):
            if not step.get("name"):
                raise validation_error(f"steps[{i}].name", "Step name is required")
            if not step.get("type"):
                raise validation_error(f"steps[{i}].type", "Step type is required")
        
        # Generate workflow ID
        workflow_id = f"workflow-{uuid.uuid4().hex[:8]}"
        
        # Perform workflow validation
        validation_results = {
            "valid": True,
            "warnings": [],
            "step_count": len(workflow_data.steps),
            "estimated_agents_required": len(set(step.get("agent_specialty", "general_ai") for step in workflow_data.steps)),
            "estimated_duration": workflow_data.timeout or 3600
        }
        
        # Check for potential issues
        if len(workflow_data.steps) > 10:
            validation_results["warnings"].append("Workflow has many steps - consider breaking into smaller workflows")
        
        if workflow_data.timeout and workflow_data.timeout > 7200:  # 2 hours
            validation_results["warnings"].append("Long timeout specified - ensure workflow is optimized")
        
        # TODO: Store workflow in database when workflow engine is fully implemented
        # For now, we simulate successful creation
        
        return WorkflowCreationResponse(
            workflow_id=workflow_id,
            validation_results=validation_results,
            message=f"Workflow '{workflow_data.name}' created successfully with {len(workflow_data.steps)} steps"
        )
        
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Failed to create workflow: {str(e)}"
        )


@router.get(
    "/workflows/{workflow_id}",
    response_model=WorkflowModel,
    status_code=status.HTTP_200_OK,
    summary="Get specific workflow details",
    description="""
    Retrieve comprehensive details about a specific workflow by its ID.
    
    This endpoint provides complete information about a workflow including:
    - Workflow definition and step configuration
    - Execution history and performance metrics
    - Success rates and failure analysis
    - Resource utilization and optimization recommendations
    
    **Detailed Information Includes:**
    - Complete step definitions with agent requirements
    - Execution statistics and performance trends
    - Variable definitions and configuration options
    - Dependencies and prerequisite information
    - User permissions and ownership details
    - Audit trail and modification history
    
    **Use Cases:**
    - Review workflow configuration before execution
    - Analyze workflow performance and success rates
    - Debug workflow issues and failures
    - Copy or modify existing workflows
    - Generate workflow documentation and reports
    """,
    responses={
        200: {"description": "Workflow details retrieved successfully"},
        404: {"model": ErrorResponse, "description": "Workflow not found"},
        500: {"model": ErrorResponse, "description": "Failed to retrieve workflow details"}
    }
)
async def get_workflow(
    workflow_id: str,
    current_user: Dict[str, Any] = Depends(get_current_user_context)
) -> WorkflowModel:
    """
    Get detailed information about a specific workflow.
    
    Args:
        workflow_id: Unique identifier of the workflow to retrieve
        current_user: Current authenticated user context
        
    Returns:
        WorkflowModel: Comprehensive workflow details and configuration
        
    Raises:
        HTTPException: If workflow not found or retrieval fails
    """
    try:
        # For now, return a sample workflow until full implementation
        if workflow_id == "workflow-code-review":
            return WorkflowModel(
                id=workflow_id,
                name="Code Review Pipeline",
                description="Automated code review and testing workflow",
                status="active",
                steps=[
                    {
                        "name": "Static Analysis",
                        "type": "code_analysis",
                        "agent_specialty": "kernel_dev",
                        "context": {"analysis_type": "security", "rules": "strict"},
                        "timeout": 600,
                        "retry_policy": {"max_attempts": 3, "backoff": "exponential"}
                    },
                    {
                        "name": "Unit Testing",
                        "type": "testing",
                        "agent_specialty": "tester",
                        "context": {"test_suite": "unit", "coverage_threshold": 80},
                        "timeout": 1200,
                        "depends_on": ["Static Analysis"]
                    }
                ],
                created_at=datetime.utcnow(),
                created_by="system",
                execution_count=25,
                success_rate=92.5
            )
        
        # Return 404 for unknown workflows
        raise HTTPException(
            status_code=status.HTTP_404_NOT_FOUND,
            detail=f"Workflow with ID '{workflow_id}' not found"
        )
        
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Failed to retrieve workflow: {str(e)}"
        )


@router.post(
    "/workflows/{workflow_id}/execute",
    response_model=WorkflowExecutionResponse,
    status_code=status.HTTP_202_ACCEPTED,
    summary="Execute a workflow",
    description="""
    Execute a workflow with optional input parameters and configuration overrides.
    
    This endpoint starts a new execution of the specified workflow, coordinating
    multiple agents to complete the defined sequence of tasks.
    
    **Execution Process:**
    1. **Validation**: Validate input parameters and workflow readiness
    2. **Resource Allocation**: Reserve required agents and resources
    3. **Step Orchestration**: Execute workflow steps in correct order
    4. **Progress Monitoring**: Track execution progress and status
    5. **Result Collection**: Collect and aggregate step results
    6. **Cleanup**: Release resources and generate execution report
    
    **Execution Features:**
    - **Parallel Processing**: Execute independent steps simultaneously
    - **Error Recovery**: Automatic retry and error handling
    - **Progress Tracking**: Real-time execution status and progress
    - **Resource Management**: Efficient agent allocation and scheduling
    - **Result Aggregation**: Collect and combine step outputs
    - **Audit Logging**: Complete execution audit trail
    
    **Input Parameters:**
    - Workflow variables and configuration overrides
    - Environment-specific settings and credentials
    - Resource constraints and preferences
    - Execution priority and scheduling options
    
    **Monitoring:**
    - Use the executions endpoints to monitor progress
    - Real-time status updates via WebSocket connections
    - Step-by-step progress tracking and logging
    - Performance metrics and resource utilization
    """,
    responses={
        202: {"description": "Workflow execution started successfully"},
        404: {"model": ErrorResponse, "description": "Workflow not found"},
        409: {"model": ErrorResponse, "description": "Workflow cannot be executed (insufficient resources, etc.)"},
        500: {"model": ErrorResponse, "description": "Workflow execution failed to start"}
    }
)
async def execute_workflow(
    workflow_id: str,
    execution_data: WorkflowExecutionRequest,
    current_user: Dict[str, Any] = Depends(get_current_user_context)
) -> WorkflowExecutionResponse:
    """
    Execute a workflow with the specified inputs and configuration.
    
    Args:
        workflow_id: Unique identifier of the workflow to execute
        execution_data: Execution parameters and configuration
        current_user: Current authenticated user context
        
    Returns:
        WorkflowExecutionResponse: Execution confirmation with tracking details
        
    Raises:
        HTTPException: If workflow not found or execution fails to start
    """
    try:
        # Verify workflow exists (placeholder check)
        if workflow_id not in ["workflow-code-review", "workflow-deployment"]:
            raise HTTPException(
                status_code=status.HTTP_404_NOT_FOUND,
                detail=f"Workflow with ID '{workflow_id}' not found"
            )
        
        # Generate execution ID
        execution_id = f"exec-{uuid.uuid4().hex[:8]}"
        
        # Estimate execution duration based on workflow and inputs
        estimated_duration = execution_data.timeout_override or 3600
        
        # TODO: Start actual workflow execution when workflow engine is implemented
        # For now, simulate successful execution start
        
        return WorkflowExecutionResponse(
            execution_id=execution_id,
            workflow_id=workflow_id,
            estimated_duration=estimated_duration,
            message=f"Workflow execution '{execution_id}' started with priority {execution_data.priority}"
        )
        
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Failed to execute workflow: {str(e)}"
        )


@router.delete(
    "/workflows/{workflow_id}",
    status_code=status.HTTP_204_NO_CONTENT,
    summary="Delete a workflow",
    description="""
    Delete a workflow from the system.
    
    This endpoint permanently removes a workflow definition and all associated
    metadata. This action cannot be undone.
    
    **Deletion Process:**
    1. **Validation**: Verify workflow exists and user has permissions
    2. **Active Check**: Ensure no active executions are running
    3. **Cleanup**: Remove workflow definition and associated data
    4. **Audit**: Log deletion event for audit trail
    
    **Safety Measures:**
    - Cannot delete workflows with active executions
    - Requires appropriate user permissions
    - Maintains execution history for completed runs
    - Generates audit log entry for deletion
    
    **Use Cases:**
    - Remove obsolete or unused workflows
    - Clean up test or experimental workflows
    - Maintain workflow library organization
    - Comply with data retention policies
    """,
    responses={
        204: {"description": "Workflow deleted successfully"},
        404: {"model": ErrorResponse, "description": "Workflow not found"},
        409: {"model": ErrorResponse, "description": "Workflow has active executions"},
        403: {"model": ErrorResponse, "description": "Insufficient permissions"},
        500: {"model": ErrorResponse, "description": "Workflow deletion failed"}
    }
)
async def delete_workflow(
    workflow_id: str,
    current_user: Dict[str, Any] = Depends(get_current_user_context)
):
    """
    Delete a workflow permanently.
    
    Args:
        workflow_id: Unique identifier of the workflow to delete
        current_user: Current authenticated user context
        
    Raises:
        HTTPException: If workflow not found, has active executions, or deletion fails
    """
    try:
        # Verify workflow exists
        if workflow_id not in ["workflow-code-review", "workflow-deployment"]:
            raise HTTPException(
                status_code=status.HTTP_404_NOT_FOUND,
                detail=f"Workflow with ID '{workflow_id}' not found"
            )
        
        # TODO: Check for active executions when execution engine is implemented
        # TODO: Verify user permissions for deletion
        # TODO: Perform actual deletion when database is implemented
        
        # For now, simulate successful deletion
        
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Failed to delete workflow: {str(e)}"
        )