refinement to handle NoneType error in phase 2

tools.py

@@ -442,9 +442,9 @@ def label_topics_with_llm(run_key: str = "abstract") -> str:
     # Batch call all models
     # Truncate to 500 chars per sentence to fit within Groq's 12000 TPM limit
     topics_json = json.dumps(list(map(lambda s: {"id": s["topic_id"], "sents": [sent[:500] + "..." for sent in s["nearest_sentences"][:2]]}, top)), indent=2)
-    res_a = chain_a.invoke({"topics_json": topics_json, "n": len(top)})
-    res_b = chain_b.invoke({"topics_json": topics_json, "n": len(top)})
-    res_c = chain_c.invoke({"topics_json": topics_json, "n": len(top)})
+    res_a = chain_a.invoke({"topics_json": topics_json, "n": len(top)}) or []
+    res_b = chain_b.invoke({"topics_json": topics_json, "n": len(top)}) or []
+    res_c = chain_c.invoke({"topics_json": topics_json, "n": len(top)}) or []
 
     idx_a = {str(item["topic_id"]): item for item in res_a}
     idx_b = {str(item["topic_id"]): item for item in res_b}
@@ -515,9 +515,9 @@ def consolidate_into_themes(run_key: str = "abstract", theme_map: str = "") -> s
     chain_c = prompt | llm_c | parser
 
     summary = json.dumps(list(map(lambda t: {"id": t["topic_id"], "lbl": t["label"]}, labelled)), indent=2)
-    raw_a = chain_a.invoke({"topics_json": summary})
-    raw_b = chain_b.invoke({"topics_json": summary})
-    raw_c = chain_c.invoke({"topics_json": summary})
+    raw_a = chain_a.invoke({"topics_json": summary}) or []
+    raw_b = chain_b.invoke({"topics_json": summary}) or []
+    raw_c = chain_c.invoke({"topics_json": summary}) or []
 
     # Simple comparison of first 2 themes generated
     l_a = ", ".join(map(lambda x: x["theme_name"], raw_a[:2]))
@@ -1087,9 +1087,9 @@ def run_ai_council(run_key: str = "abstract") -> str:
         "n":           len(top),
     }
 
-    results_a = chain_a.invoke(input_data)
-    results_b = chain_b.invoke(input_data)
-    results_c = chain_c.invoke(input_data)
+    results_a = chain_a.invoke(input_data) or []
+    results_b = chain_b.invoke(input_data) or []
+    results_c = chain_c.invoke(input_data) or []
 
     idx_a = {str(r["cluster_id"]): r for r in results_a}
     idx_b = {str(r["cluster_id"]): r for r in results_b}