backend/workflow_executor.py

@@ -281,13 +281,21 @@ async def execute_node(
             prompt_template = await load_prompt_template(node.prompt_slug, context)
             logger.debug(f"Node {node.id}: Loaded prompt '{node.prompt_slug}'")
 
-            # 2. Parse question_augmentations
+            # 2. Parse question_augmentations (Hybrid Model)
             questions = []
             if node.question_augmentations:
-                # Convert list of dicts to JSONB-like format for parser
+                # Node-specific questions (override base prompt questions)
                 questions_jsonb = [q.model_dump() if hasattr(q, 'model_dump') else q for q in node.question_augmentations]
                 questions = parse_question_augmentations_from_jsonb(questions_jsonb)
-                logger.debug(f"Node {node.id}: {len(questions)} question augmentations")
+                logger.debug(f"Node {node.id}: {len(questions)} node-specific questions")
+            else:
+                # Fallback: Load questions from base prompt (Hybrid Model)
+                base_questions = await load_prompt_questions(node.prompt_slug)
+                if base_questions:
+                    questions = parse_question_augmentations_from_jsonb(base_questions)
+                    logger.debug(f"Node {node.id}: {len(questions)} questions from base prompt '{node.prompt_slug}'")
+                else:
+                    logger.debug(f"Node {node.id}: No questions (neither node-specific nor base prompt)")
 
             # 3. Augment Prompt
             if questions:
@@ -295,8 +303,10 @@ async def execute_node(
                     base_prompt=prompt_template,
                     questions=questions
                 )
+                logger.debug(f"Node {node.id}: Augmented prompt with {len(questions)} questions")
             else:
                 augmented_prompt = prompt_template
+                logger.debug(f"Node {node.id}: No augmentation (no questions)")
 
             # 4. LLM Call
             logger.debug(f"Node {node.id}: Calling LLM")
@@ -312,16 +322,17 @@ async def execute_node(
             # 6. Normalize Signals
             normalized_signals = []
             if parsed["decision_signals"]:
-                # Hybrid Model: Node-spezifische Questions überschreiben Catalog
+                # Hybrid Model: Questions (node-specific or base prompt) override Catalog
                 node_catalog = catalog.copy()
-                if node.question_augmentations:
+                if questions:
-                    for q in node.question_augmentations:
+                    for q in questions:
                         q_dict = q.model_dump() if hasattr(q, 'model_dump') else q
                         node_catalog[q_dict['type']] = {
                             "answer_spectrum": q_dict['answer_spectrum'],
-                            "normalization_rules": None  # Node-Questions haben keine Synonyme
+                            "normalization_rules": None  # Questions haben keine Synonyme
                         }
-                        logger.debug(f"Node {node.id}: Override catalog for '{q_dict['type']}' with node-specific spectrum")
+                        source = "node-specific" if node.question_augmentations else "base prompt"
+                        logger.debug(f"Node {node.id}: Override catalog for '{q_dict['type']}' with {source} spectrum")
 
                 normalized_signals = normalize_all_signals(
                     decision_signals=parsed["decision_signals"],
@@ -816,6 +827,52 @@ async def load_prompt_template(prompt_slug: str, context: Dict[str, Any]) -> str
     return resolved
 
 
+async def load_prompt_questions(prompt_slug: str) -> List[Dict]:
+    """
+    Lädt Fragen aus einem Basis-Prompt (Hybrid Model - Fallback).
+
+    Wenn ein Analysis Node KEINE node-spezifischen Fragen hat,
+    werden die Fragen aus dem referenzierten Basis-Prompt geladen.
+
+    Args:
+        prompt_slug: Slug des Prompts (z.B. "pipeline_body")
+
+    Returns:
+        Liste von Question-Dicts im format:
+        [
+            {
+                "id": "q1",
+                "type": "relevanz",
+                "question": "Ist eine vertiefte Analyse relevant?",
+                "answer_spectrum": ["ja", "nein", "unklar"]
+            },
+            ...
+        ]
+
+    Beispiel:
+        >>> questions = await load_prompt_questions("pipeline_body")
+        >>> len(questions) > 0
+        True
+    """
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute(
+            "SELECT questions FROM ai_prompts WHERE slug = %s AND active = true",
+            (prompt_slug,)
+        )
+        row = cur.fetchone()
+        if not row or not row.get('questions'):
+            return []
+
+        questions = row['questions']
+        # PostgreSQL JSONB wird automatisch zu Python list/dict konvertiert
+        if isinstance(questions, list):
+            return questions
+        else:
+            logger.warning(f"Unexpected questions format for {prompt_slug}: {type(questions)}")
+            return []
+
+
 def aggregate_results(node_states: List[NodeExecutionState]) -> Dict[str, Any]:
     """
     Aggregiert Ergebnisse aller Knoten.