Update app.py

app.py

@@ -1,8 +1,8 @@
 """
-Question Answering with Retrieval QA and LangChain Language Models featuring Chroma Vector Stores
-This script uses the LangChain Language Model API to answer questions using Retrieval QA and Chroma Vector Stores.
+Question Answering with Retrieval QA and LangChain Language Models featuring FAISS vector stores.
+This script uses the LangChain Language Model API to answer questions using Retrieval QA 
+and FAISS vector stores. It also uses the OpenAI API to generate responses.
 """
-
 import os
 import streamlit as st
 from dotenv import load_dotenv
@@ -104,10 +104,6 @@ def get_conversation_chain(vectorstore):
         A conversational retrieval chain for generating responses.
 
     """
-    # llm = HuggingFaceHub(
-    #     repo_id="mistralai/Mistral-7B-Instruct-v0.1",
-    #     model_kwargs={"temperature": 0.5, "max_length": 512},
-    # )
     llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
 
     memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
@@ -143,6 +139,7 @@ def handle_userinput(user_question):
 
 
 def main():
+    """Put it all together"""
     st.set_page_config(
         page_title="Chat with a Bot that tries to answer questions about multiple PDFs",
         page_icon=":books:",