Update app.py

app.py

@@ -68,17 +68,15 @@ def load_db():
     return vectordb
 
 # Initialize langchain LLM chain
-def initialize_llmchain(vector_db):
-    llm = HuggingFaceHub(repo_id = llm_model,
-                        model_kwargs={"temperature": temperature, 
-                                      "max_new_tokens": max_tokens, 
-                                      "top_k": top_k, 
-                                      "load_in_8bit": True})
-    
-    memory = ConversationBufferMemory(memory_key="chat_history", output_key='answer', return_messages=True)
-    retriever=vector_db.as_retriever()
-
-    #return qa_chain
+llm = HuggingFaceHub(repo_id = llm_model,
+                    model_kwargs={"temperature": temperature, 
+                                  "max_new_tokens": max_tokens, 
+                                  "top_k": top_k, 
+                                  "load_in_8bit": True})
+retriever=vector_db.as_retriever()
+memory = ConversationBufferMemory(memory_key="chat_history", output_key='answer', return_messages=True)
+qa_chain = ConversationalRetrievalChain.from_llm(llm_model,retriever=retriever,chain_type="stuff", 
+                                                 memory=memory,return_source_documents=True,verbose=False,)
 
 
 
@@ -155,9 +153,7 @@ def conversation(qa_chain, message, history):
 #document = os.listdir(list_file_obj)
 vector_db, collection_name = initialize_database(list_file_obj)
 #qa_chain = 
-initialize_llmchain(vector_db)
-qa_chain = ConversationalRetrievalChain.from_llm(llm,retriever=retriever,chain_type="stuff", 
-                                                 memory=memory,return_source_documents=True,verbose=False,)
+
 print('qa chain and vector_db done')
 
 def demo():