Update app.py

app.py

@@ -14,6 +14,7 @@ from deep_translator import GoogleTranslator
 import pandas as pd
 from langchain_groq import ChatGroq
 from openai import OpenAI
+from langchain.chat_models import ChatOpenAI
 # set this key as an environment variable
 os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets['Key2']
 ###########################################################################################
@@ -52,23 +53,20 @@ def get_vectorstore(text_chunks : list) -> FAISS:
     return vectorstore
 
 
-def get_conversation_chain(vectorstore: FAISS) -> ConversationalRetrievalChain:
-    client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio")
-    llm = client.chat.completions.create(
-        model="lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF",
-        messages=[
-            {"role": "system", "content": "Always answer in rhymes."},
-            {"role": "user", "content": "Introduce yourself."}
-        ],
-        temperature=0.5,
-    )
+def get_conversation_chain(vectorstore):
+    llm = ChatOpenAI(temperature=0.2)
+    # llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.2, "max_length":512})
     memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
     conversation_chain = ConversationalRetrievalChain.from_llm(
-        llm=llm, retriever=vectorstore.as_retriever(), memory=memory
+        llm=llm,
+        retriever=vectorstore.as_retriever(),
+        memory=memory,
+        # retriever_kwargs={"k": 1},
     )
     return conversation_chain
 
 
+
 def handle_userinput(user_question:str):
     response = st.session_state.conversation({"question": user_question})
     st.session_state.chat_history = response["chat_history"]