Update app.py

app.py

@@ -99,12 +99,20 @@ def initialize_phi_model():
 
 def initialize_gpt_model():
     return ChatOpenAI(api_key=os.environ['OPENAI_API_KEY'], temperature=0, model='gpt-4o')
+
+def initialize_gpt4o_mini_model():
+    return ChatOpenAI(api_key=os.environ['OPENAI_API_KEY'], temperature=0, model='gpt-4o-mini')
+
+
+
     
 
 
 # Initialize all models
 phi_pipe = initialize_phi_model()
 gpt_model = initialize_gpt_model()
+gpt4o_mini_model = initialize_gpt4o_mini_model()
+
 
 
 
@@ -140,7 +148,7 @@ vectorstore = PineconeVectorStore(index_name=index_name, embedding=embeddings)
 retriever = vectorstore.as_retriever(search_kwargs={'k': 5})
 
 chat_model = ChatOpenAI(api_key=os.environ['OPENAI_API_KEY'], temperature=0, model='gpt-4o')
-
+chat_model1 = ChatOpenAI(api_key=os.environ['OPENAI_API_KEY'], temperature=0, model='gpt-4o-mini')
 conversational_memory = ConversationBufferWindowMemory(
     memory_key='chat_history',
     k=10,
@@ -353,7 +361,9 @@ def generate_bot_response(history, choice, retrieval_mode, model_choice):
         return
 
     # Select the model
-    selected_model = chat_model if model_choice == "LM-1" else phi_pipe
+    # selected_model = chat_model if model_choice == "LM-1" else phi_pipe
+    selected_model = chat_model if model_choice == "LM-1" else (chat_model1 if model_choice == "LM-3" else phi_pipe)
+
 
     response, addresses = generate_answer(history[-1][0], choice, retrieval_mode, selected_model)
     history[-1][1] = ""
@@ -425,7 +435,9 @@ def generate_bot_response(history, choice, retrieval_mode, model_choice):
         return
 
     # Select the model
-    selected_model = chat_model if model_choice == "LM-1" else phi_pipe
+    # selected_model = chat_model if model_choice == "LM-1" else phi_pipe
+    selected_model = chat_model if model_choice == "LM-1" else (chat_model1 if model_choice == "LM-3" else phi_pipe)
+
 
     response, addresses = generate_answer(history[-1][0], choice, retrieval_mode, selected_model)
     history[-1][1] = ""
@@ -528,7 +540,28 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
                 response = qa_chain({"query": message})
                 logging.debug(f"LM-1 response: {response}")
                 return response['result'], extract_addresses(response['result'])
+            
+            elif selected_model == chat_model1:
+                logging.debug("Selected model: LM-3")
+                retriever = gpt_retriever
+                context = retriever.get_relevant_documents(message)
+                logging.debug(f"Retrieved context: {context}")
 
+                prompt = prompt_template.format(context=context, question=message)
+                logging.debug(f"Generated prompt: {prompt}")
+
+                qa_chain = RetrievalQA.from_chain_type(
+                    llm=chat_model1,
+                    chain_type="stuff",
+                    retriever=retriever,
+                    chain_type_kwargs={"prompt": prompt_template}
+                )
+                response = qa_chain({"query": message})
+                logging.debug(f"LM-3 response: {response}")
+                return response['result'], extract_addresses(response['result']) 
+
+
+            
             elif selected_model == phi_pipe:
                 logging.debug("Selected model: LM-2")
                 retriever = phi_retriever