Update pipeline.py

pipeline.py

@@ -148,22 +148,21 @@ def run_with_chain_context(inputs: Dict[str, Any]) -> Dict[str, str]:
         return {"answer": final_refusal.strip()}
 
     if classification == "Wellness":
-        rag_result = wellness_rag_chain.invoke({"query": user_query, "chat_history": chat_history})
+        rag_result = wellness_rag_chain.invoke({
+            "query": user_query,
+            "chat_history": chat_history  # Pass history here
+        })
         csv_answer = rag_result["result"].strip()
-        if not csv_answer:
-            web_answer = do_web_search(user_query)
-        else:
-            lower_ans = csv_answer.lower()
-            if any(phrase in lower_ans for phrase in ["i do not know", "not sure", "no context", "cannot answer"]):
-                web_answer = do_web_search(user_query)
-            else:
-                web_answer = ""
+        web_answer = do_web_search(user_query) if not csv_answer else ""
         final_merged = cleaner_chain.merge(kb=csv_answer, web=web_answer)
         final_answer = tailor_chain.run({"response": final_merged}).strip()
         return {"answer": final_answer}
 
     if classification == "Brand":
-        rag_result = brand_rag_chain.invoke({"query": user_query, "chat_history": chat_history})
+        rag_result = brand_rag_chain.invoke({
+            "query": user_query,
+            "chat_history": chat_history  # Pass history here
+        })
         csv_answer = rag_result["result"].strip()
         final_merged = cleaner_chain.merge(kb=csv_answer, web="")
         final_answer = tailor_chain.run({"response": final_merged}).strip()
@@ -172,7 +171,6 @@ def run_with_chain_context(inputs: Dict[str, Any]) -> Dict[str, str]:
     refusal_text = refusal_chain.run({})
     final_refusal = tailor_chain.run({"response": refusal_text}).strip()
     return {"answer": final_refusal}
-
 ###############################################################################
 # 7) Build a "Runnable" wrapper so .with_listeners() works
 ###############################################################################