Update pipeline.py

pipeline.py

@@ -206,28 +206,24 @@ def run_pipeline(query: str) -> str:
         return tailor_chain.run({"response": refusal_text}).strip()
 
 # Initialize chains and vectorstores
-# Initialize chains and vectorstores
-try:
-    classification_chain = get_classification_chain()
-    refusal_chain = get_refusal_chain()
-    tailor_chain = get_tailor_chain()
-    cleaner_chain = get_cleaner_chain()
-
-    wellness_csv = "AIChatbot.csv"
-    brand_csv = "BrandAI.csv"
-    wellness_store_dir = "faiss_wellness_store"
-    brand_store_dir = "faiss_brand_store"
-
-    wellness_vectorstore = build_or_load_vectorstore(wellness_csv, wellness_store_dir)
-    brand_vectorstore = build_or_load_vectorstore(brand_csv, brand_store_dir)
-
-    gemini_llm = LiteLLMModel(model_id="gemini/gemini-pro", api_key=os.environ.get("GEMINI_API_KEY"))
-    wellness_rag_chain = build_rag_chain(gemini_llm, wellness_vectorstore)
-    brand_rag_chain = build_rag_chain(gemini_llm, brand_vectorstore)
-
-    print("Pipeline initialized successfully!")
-except Exception as e:
-    print(f"Error initializing pipeline: {str(e)}")
+classification_chain = get_classification_chain()
+refusal_chain = get_refusal_chain()
+tailor_chain = get_tailor_chain()
+cleaner_chain = get_cleaner_chain()
+
+wellness_csv = "AIChatbot.csv"
+brand_csv = "BrandAI.csv"
+wellness_store_dir = "faiss_wellness_store"
+brand_store_dir = "faiss_brand_store"
+
+wellness_vectorstore = build_or_load_vectorstore(wellness_csv, wellness_store_dir)
+brand_vectorstore = build_or_load_vectorstore(brand_csv, brand_store_dir)
+
+gemini_llm = LiteLLMModel(model_id="gemini/gemini-pro", api_key=os.environ.get("GEMINI_API_KEY"))
+wellness_rag_chain = build_rag_chain(gemini_llm, wellness_vectorstore)
+brand_rag_chain = build_rag_chain(gemini_llm, brand_vectorstore)
+
+print("Pipeline initialized successfully!")
 
 
 def run_with_chain(query: str) -> str: