def api_get_chatbot_response(self,

def api_get_chatbot_response(self, project_id, question, index):
answer = ""
try:
persist_dir = self.config.host.path + f"projects/{project_id}/docs/reports/llama_vectordb"
# Set your OpenAI API key
openai.api_key = ""
index = load_index_from_storage(
StorageContext.from_defaults(persist_dir=persist_dir)
)

qa_prompt = PromptTemplate(
)


class RAGStringQueryEngine(CustomQueryEngine):
"""RAG String Query Engine."""

retriever: BaseRetriever
response_synthesizer: BaseSynthesizer
llm: OpenAI
qa_prompt: PromptTemplate

def custom_query(self, query_str: str):
nodes = self.retriever.retrieve(query_str)

context_str = "\n\n".join([n.node.get_content() for n in nodes])
response = self.llm.complete(
qa_prompt.format(context_str=context_str, query_str=query_str)
)

return str(response)

# configure retriever
retriever = VectorIndexRetriever(
index=index,
similarity_top_k=2,
)

# configure response synthesizer
response_synthesizer = get_response_synthesizer(
streaming=True,
response_mode="tree_summarize",
)
llm = OpenAI(model="gpt-3.5-turbo")

# assemble query engine
query_engine = RAGStringQueryEngine(
retriever=retriever,
response_synthesizer=response_synthesizer,
llm=llm,
qa_prompt=qa_prompt,
)

answer = str(query_engine.query(question))



here I want to load the index separetly and keep it loaded , I will call this api to get the answer everytime