Update app.py

app.py

@@ -368,31 +368,20 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
             response = fetch_google_flights()
             return response, extract_addresses(response)
 
-        # Use different retrievers based on the selected model
-        if selected_model == phi_pipe:
-            retriever = phi_retriever
-            # Retrieve context from vector store
-            context = retriever.get_relevant_documents(message)
-            # Construct a prompt with clear formatting instructions
-            prompt = f"""
-            Based on the provided documents, generate a well-formatted response with the following details:
-            {context}
-
-            Please format the output as follows:
-            Name: [Event Name]
-            Location: [Location]
-            Date and Time: [Date and Time]
-            Description: [Event Description]
-
-            Question: {message}
-            """
-        else:
-            retriever = gpt_retriever
-            prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2
+        if retrieval_mode == "VDB":
+            if selected_model == chat_model:
+                # Use GPT-4o with its vector store and template
+                retriever = gpt_retriever
+                prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2
+            elif selected_model == phi_pipe:
+                # Use Phi-3.5 with its vector store and always use template2
+                retriever = phi_retriever
+                prompt_template = QA_CHAIN_PROMPT_2
+
+            # Retrieve context and construct the prompt
             context = retriever.get_relevant_documents(message)
             prompt = prompt_template.format(context=context, question=message)
 
-        if retrieval_mode == "VDB":
             if selected_model == chat_model:
                 # Use GPT-4o with Langchain
                 qa_chain = RetrievalQA.from_chain_type(
@@ -425,7 +414,6 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
         return "Sorry, I encountered an error while processing your request.", []
 
 
-
 # def bot(history, choice, tts_choice, retrieval_mode):
 #     if not history:
 #         return history