Update app.py

app.py

@@ -105,6 +105,17 @@ phi_pipe = initialize_phi_model()
 gpt_model = initialize_gpt_model()
 
 
+# Existing embeddings and vector store for GPT-4o
+gpt_embeddings = OpenAIEmbeddings(api_key=os.environ['OPENAI_API_KEY'])
+gpt_vectorstore = PineconeVectorStore(index_name="radarfinaldata08192024", embedding=gpt_embeddings)
+gpt_retriever = gpt_vectorstore.as_retriever(search_kwargs={'k': 5})
+
+# New vector store setup for Phi-3.5
+phi_embeddings = OpenAIEmbeddings(api_key=os.environ['OPENAI_API_KEY'])
+phi_vectorstore = PineconeVectorStore(index_name="phivector08252024", embedding=phi_embeddings)
+phi_retriever = phi_vectorstore.as_retriever(search_kwargs={'k': 5})
+
+
 
 # Pinecone setup
 from pinecone import Pinecone
@@ -357,11 +368,12 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
             response = fetch_google_flights()
             return response, extract_addresses(response)
 
-        # Use a simple, direct prompt for Phi-3.5
+        # Use different retrievers based on the selected model
         if selected_model == phi_pipe:
-            prompt = f"Here is the information : , {message}"
+            retriever = phi_retriever
+            prompt = f"Based on the provided documents, {message}"
         else:
-            # Use the existing prompt templates for GPT-4o
+            retriever = gpt_retriever
             prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2
             context = retriever.get_relevant_documents(message)
             prompt = prompt_template.format(context=context, question=message)
@@ -379,8 +391,10 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
                 return response['result'], extract_addresses(response['result'])
 
             elif selected_model == phi_pipe:
-                # Use Phi-3.5 directly with the formatted prompt
-                response = selected_model(prompt, **{
+                # Use Phi-3.5 directly with the formatted prompt and specific vector store
+                context = retriever.get_relevant_documents(message)
+                full_prompt = f"{context}\n\n{prompt}"
+                response = selected_model(full_prompt, **{
                     "max_new_tokens": 300,
                     "return_full_text": False,
                     "temperature": 0.5,
@@ -400,7 +414,6 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
 
 
 
-
 # def bot(history, choice, tts_choice, retrieval_mode):
 #     if not history:
 #         return history