def chat_completion(user_input):
 # Generate embeddings from the user input
 user_embeddings = generate_embeddings(openai_client, user_input)
 # Query the chat history cache first to see if this question has been asked before
 cache_results = cache_search(vectors=user_embeddings , num_results=1)
 
 if len(cache_results) > 0:
 return cache_results[0]['completion'], True
 else:
 # Perform vector search 
 search_results = get_similar_docs(openai_client, dbsource, user_input, 3)
 print("Getting Chat History\n")
 # Chat history
 chat_history = get_chat_history(1)
 
 # Generate the completion
 print("Generating completions \n")
 completions_results = generatecompletionede(user_input, search_results, chat_history)
 
 # Cache the response
 cacheresponse(user_input, user_embeddings, completions_results)
 
 print("\n")
 # Return the generated LLM completion
 return completions_results['choices'][0]['message']['content'], False