Update app.py

app.py

@@ -1,11 +1,3 @@
-pip install langchain-groq
-!pip install langchain-groq
-pip show langchain-groq
-# For Python 3.x
-pip3 install langchain-groq
-pip install --user langchain-groq
-sudo pip install langchain-groq
-from langchain_groq import ChatGroq
 import os
 import logging
 from dotenv import load_dotenv
@@ -16,34 +8,8 @@ from langchain_cohere import CohereEmbeddings
 from langchain.vectorstores import FAISS
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
-from langchain_groq import ChatGroq
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
+from langchain_groq import ChatGroq  # Ensure this is installed
 
-def get_conversation_chain(vectorstore):
-    try:
-        # Initialize Groq's Chat model
-        llm = ChatGroq(
-            model="llama2-70b-4096",  # You can use other models like "mixtral-8x7b-32768"
-            temperature=0.5,
-            groq_api_key=groq_api_key
-        )
-        
-        # Set up memory for the conversation
-        memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
-        
-        # Create the conversational retrieval chain
-        conversation_chain = ConversationalRetrievalChain.from_llm(
-            llm=llm,
-            retriever=vectorstore.as_retriever(),
-            memory=memory
-        )
-        
-        logging.info("Conversation chain created successfully.")
-        return conversation_chain
-    except Exception as e:
-        logging.error(f"Error creating conversation chain: {e}")
-        st.error("An error occurred while setting up the conversation chain.")
 # Load environment variables
 load_dotenv()