backend/workflow_executor.py

@@ -603,22 +603,56 @@ def execute_end_node(
                     "status": node_state.status.value if node_state.status else "unknown",
                 }
 
-                # Add normalized signals as {{node_id.signal_X}}
-                if node_state.normalized_signals:
-                    for signal in node_state.normalized_signals:
-                        # Convert NormalizedSignal object to dict if needed
-                        signal_dict = signal.model_dump() if hasattr(signal, 'model_dump') else signal
-                        signal_key = f"signal_{signal_dict['question_type']}"
-                        node_context[signal_key] = signal_dict['normalized_value'] or signal_dict['raw_value']
-
-                # Add question texts as {{node_id.question_X}}
+                # Build question_type → question_id mapping from workflow graph
+                question_id_map = {}
                 if graph:
                     workflow_node = next((n for n in graph.nodes if n.id == node_id), None)
                     if workflow_node and workflow_node.question_augmentations:
                         for q in workflow_node.question_augmentations:
                             q_dict = q.model_dump() if hasattr(q, 'model_dump') else q
-                            question_key = f"question_{q_dict['type']}"
-                            node_context[question_key] = q_dict.get('question', '')
+                            q_type = q_dict.get('type')
+                            q_id = q_dict.get('id')
+                            if q_type and q_id:
+                                # Multiple questions with same type → use ID
+                                if q_type not in question_id_map:
+                                    question_id_map[q_type] = []
+                                question_id_map[q_type].append(q_id)
+
+                # Add normalized signals as {{node_id.signal_ID}} (not type!)
+                if node_state.normalized_signals:
+                    # Track which types we've seen (for duplicate detection)
+                    type_counts = {}
+
+                    for signal in node_state.normalized_signals:
+                        # Convert NormalizedSignal object to dict if needed
+                        signal_dict = signal.model_dump() if hasattr(signal, 'model_dump') else signal
+                        q_type = signal_dict['question_type']
+
+                        # Determine which ID to use for this signal
+                        if q_type in question_id_map:
+                            # Get the Nth ID for this type (handles duplicates)
+                            type_idx = type_counts.get(q_type, 0)
+                            type_counts[q_type] = type_idx + 1
+
+                            if type_idx < len(question_id_map[q_type]):
+                                q_id = question_id_map[q_type][type_idx]
+                                signal_key = f"signal_{q_id}"
+                                node_context[signal_key] = signal_dict['normalized_value'] or signal_dict['raw_value']
+                            else:
+                                logger.warning(f"No question_id found for signal type={q_type} index={type_idx}")
+                        else:
+                            logger.warning(f"No question_id mapping for type={q_type}")
+
+                # Add question texts as {{node_id.question_ID}}
+                if graph:
+                    workflow_node = next((n for n in graph.nodes if n.id == node_id), None)
+                    if workflow_node and workflow_node.question_augmentations:
+                        for q in workflow_node.question_augmentations:
+                            q_dict = q.model_dump() if hasattr(q, 'model_dump') else q
+                            q_id = q_dict.get('id')
+                            if q_id:
+                                question_key = f"question_{q_id}"
+                                node_context[question_key] = q_dict.get('question', '')
 
                 template_context[node_id] = node_context
 

frontend/src/components/workflow/panels/PlaceholderPicker.jsx

@@ -376,21 +376,21 @@ function extractWorkflowPlaceholders(nodes) {
     if (node.type === 'analysis' && node.data.questions && node.data.questions.length > 0) {
       node.data.questions.forEach((q, qIdx) => {
         const questionId = q.id || `q${qIdx + 1}`
-        const questionType = q.type || `q${qIdx + 1}`
+        const questionType = q.type || 'unknown'
         const questionText = q.question || `Frage ${qIdx + 1}`
 
-        // Signal-Platzhalter (Antwort)
+        // Signal-Platzhalter (Antwort) - VERWENDET ID für Eindeutigkeit!
         placeholders.push({
-          placeholder: `{{ ${nodeId}.signal_${questionType} }}`,
-          description: `${nodeLabel} (${questionId}) - Signal ${questionType}: ${questionText.substring(0, 40)}${questionText.length > 40 ? '...' : ''}`,
+          placeholder: `{{ ${nodeId}.signal_${questionId} }}`,
+          description: `${nodeLabel} - ${questionId} (${questionType}): ${questionText.substring(0, 45)}${questionText.length > 45 ? '...' : ''}`,
           icon: '📊',
           category: 'Workflow - Signals'
         })
 
         // Frage-Text-Platzhalter
         placeholders.push({
-          placeholder: `{{ ${nodeId}.question_${questionType} }}`,
-          description: `${nodeLabel} (${questionId}) - Frage ${questionType}: ${questionText.substring(0, 40)}${questionText.length > 40 ? '...' : ''}`,
+          placeholder: `{{ ${nodeId}.question_${questionId} }}`,
+          description: `${nodeLabel} - ${questionId} (${questionType}): ${questionText.substring(0, 45)}${questionText.length > 45 ? '...' : ''}`,
           icon: '❓',
           category: 'Workflow - Questions'
         })