Use all-mpnet-base-v2 vector embedding for highest performance

app.py

@@ -1,19 +1,15 @@
 import streamlit as st
-
 from langchain.chains import ConversationalRetrievalChain
-from langchain.chat_models import ChatOpenAI
 from langchain.memory import ConversationBufferMemory
 from langchain.memory.chat_message_histories import StreamlitChatMessageHistory
+from langchain_community.chat_models import ChatOpenAI
 
-from document_retriever import configure_retriever
 from calback_handler import PrintRetrievalHandler, StreamHandler
 from chat_profile import ChatProfileRoleEnum
-
-# configs
-LLM_MODEL_NAME = "gpt-3.5-turbo"
+from document_retriever import configure_retriever
 
 st.set_page_config(
-    page_title=":books: InkChatGPT: Chat with Documents",
+    page_title="InkChatGPT: Chat with Documents",
     page_icon="📚",
     initial_sidebar_state="collapsed",
     menu_items={
@@ -36,7 +32,9 @@ with st.container():
     col1, col2 = st.columns([0.3, 0.8])
     with col1:
         st.image(
-            "./assets/app_icon.png", use_column_width="always", output_format="PNG"
+            "./assets/app_icon.png",
+            use_column_width="always",
+            output_format="PNG",
         )
     with col2:
         st.header(":books: InkChatGPT")
@@ -55,6 +53,7 @@ with documents_tab:
         label="Select files",
         type=["pdf", "txt", "docx", "epub"],
         accept_multiple_files=True,
+        disabled=(not openai_api_key),
     )
 
 with chat_tab:
@@ -62,23 +61,28 @@ with chat_tab:
         result_retriever = configure_retriever(uploaded_files)
 
         memory = ConversationBufferMemory(
-            memory_key="chat_history", chat_memory=msgs, return_messages=True
+            memory_key="chat_history",
+            chat_memory=msgs,
+            return_messages=True,
         )
 
         # Setup LLM and QA chain
         llm = ChatOpenAI(
-            model_name=LLM_MODEL_NAME,
+            model_name="gpt-3.5-turbo",
             openai_api_key=openai_api_key,
             temperature=0,
             streaming=True,
         )
 
         chain = ConversationalRetrievalChain.from_llm(
-            llm, retriever=result_retriever, memory=memory, verbose=False
+            llm,
+            retriever=result_retriever,
+            memory=memory,
+            verbose=False,
         )
 
         avatars = {
-            ChatProfileRoleEnum.Human: "user",
+            ChatProfileRoleEnum.HUMAN: "user",
             ChatProfileRoleEnum.AI: "assistant",
         }
 
@@ -89,7 +93,8 @@ if not openai_api_key:
     st.caption("🔑 Add your **OpenAI API key** on the `Settings` to continue.")
 
 if user_query := st.chat_input(
-    placeholder="Ask me anything!", disabled=(not openai_api_key)
+    placeholder="Ask me anything!",
+    disabled=(not openai_api_key),
 ):
     st.chat_message("user").write(user_query)