mitai-jinkendo/frontend/src/utils/workflowSerializer.js

/**
 * Workflow Serialization Utilities
 *
 * Konvertiert zwischen React Flow (Canvas) und Backend-Format (JSONB).
 */

/**
 * Serialisiert React Flow Graph zu Backend-kompatiblem Format
 *
 * @param {Array} nodes - React Flow nodes
 * @param {Array} edges - React Flow edges
 * @param {Object} metadata - Zusätzliche Metadaten
 * @returns {Object} JSONB-kompatibles Objekt für ai_prompts.graph_data
 */
export function serializeToWorkflowGraph(nodes, edges, metadata = {}) {
  const workflowNodes = nodes.map(node => ({
    id: node.id,
    type: node.type,
    label: node.data.label || node.type,
    position: { x: node.position.x, y: node.position.y },

    // Type-spezifische Felder
    ...(node.type === 'analysis' && {
      prompt_slug: node.data.prompt_slug || null,
      prompt_name: node.data.prompt_name || null,
      question_augmentations: node.data.questions || [],  // Backend erwartet question_augmentations
      fallback_strategy: node.data.fallback_strategy || 'conservative_skip'
    }),

    ...(node.type === 'logic' && {
      condition: node.data.condition || null,
      fallback_strategy: node.data.fallback_strategy || 'conservative_skip'
    }),

    ...(node.type === 'join' && {
      join_strategy: node.data.join_strategy || 'wait_all',
      skip_handling: node.data.skip_handling || 'ignore_skipped',
      min_paths: node.data.min_paths || 2
    }),

    ...(node.type === 'end' && {
      output_mode: node.data.output_mode || 'auto',
      template: node.data.template || null
    })
  }))

  const workflowEdges = edges.map(edge => ({
    id: edge.id,
    source: edge.source,
    target: edge.target,
    label: edge.data?.label || null,
    sourceHandle: edge.sourceHandle || null,
    targetHandle: edge.targetHandle || null
  }))

  return {
    nodes: workflowNodes,
    edges: workflowEdges,
    metadata: {
      created_at: metadata.created_at || new Date().toISOString(),
      updated_at: new Date().toISOString(),
      version: metadata.version || '1.0'
    }
  }
}

/**
 * Deserialisiert Backend-Format zu React Flow Graph
 *
 * @param {Object} jsonbData - ai_prompts.graph_data (JSONB)
 * @returns {Object} { nodes, edges, metadata }
 */
export function deserializeFromWorkflowGraph(jsonbData) {
  if (!jsonbData || !jsonbData.nodes || !jsonbData.edges) {
    throw new Error('Invalid workflow graph data')
  }

  const reactFlowNodes = jsonbData.nodes.map(node => ({
    id: node.id,
    type: node.type,
    position: { x: node.position.x, y: node.position.y },
    data: {
      label: node.label,

      ...(node.type === 'analysis' && {
        prompt_slug: node.prompt_slug || node.prompt_id || null, // Fallback für alte Workflows mit prompt_id
        prompt_name: node.prompt_name || null, // Falls vom Backend mitgeliefert
        questions: node.question_augmentations || node.questions || [],  // Backend sendet question_augmentations
        fallback_strategy: node.fallback_strategy || 'conservative_skip'
      }),

      ...(node.type === 'logic' && {
        condition: node.condition || null,
        fallback_strategy: node.fallback_strategy || 'conservative_skip'
      }),

      ...(node.type === 'join' && {
        join_strategy: node.join_strategy || 'wait_all',
        skip_handling: node.skip_handling || 'ignore_skipped',
        min_paths: node.min_paths || 2
      }),

      ...(node.type === 'end' && {
        output_mode: node.output_mode || 'auto',
        template: node.template || null
      })
    }
  }))

  const reactFlowEdges = jsonbData.edges.map(edge => ({
    id: edge.id,
    source: edge.source,
    target: edge.target,
    sourceHandle: edge.sourceHandle || null,
    targetHandle: edge.targetHandle || null,
    data: {
      label: edge.label || null
    },
    type: 'default',
    animated: false
  }))

  return {
    nodes: reactFlowNodes,
    edges: reactFlowEdges,
    metadata: jsonbData.metadata || {}
  }
}