I have the following code where I am asking questions based on my context, and am able to get the respective outputs in streaming format. However,  I am creating an api for the same and not able to replicate similar results
from langchain import OpenAI
from types import FunctionType
from llama_index import ServiceContext, GPTVectorStoreIndex, LLMPredictor, PromptHelper, SimpleDirectoryReader, load_index_from_storage
import sys
import os
import time 
from llama_index.response.schema import StreamingResponse
import uvicorn 
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import uvicorn 



os.environ["OPENAI_API_KEY"] = "your key here" # gpt 3.5 turbo


app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=['*'],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

from llama_index import StorageContext, load_index_from_storage, ServiceContext
from langchain.chat_models import ChatOpenAI

def construct_index(directory_path):
    max_input_size = 4096
    num_outputs = 5000
    max_chunk_overlap = 256
    chunk_size_limit = 3900
    file_metadata = lambda x : {"filename": x}
    reader = SimpleDirectoryReader(directory_path, file_metadata=file_metadata)
    
    documents = reader.load_data()

    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
    
    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)

    index = GPTVectorStoreIndex.from_documents(
        documents=documents, service_context = service_context
    )
    
    index.storage_context.persist("./jsons/contentstack_llm")
    return index
   
def get_index():
    max_input_size = 4000
    num_outputs = 1024
    max_chunk_overlap = 512
    chunk_size_limit = 3900
    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
    llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo", max_tokens=num_outputs, streaming = True))
    
    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
    
    return service_context  

# construct_index("./docs")
storage_context = StorageContext.from_defaults(persist_dir="./jsons/contentstack_llm")
service_context = get_index()
index = load_index_from_storage(storage_context, service_context = service_context)

query_engine = index.as_query_engine(streaming = True)
class Item(BaseModel):
    input_text: str

@app.post("/question_answering")
async def create_item(item: Item):
    input_sentence = item.input_text
    response = query_engine.query(input_sentence)
    links = []
    return StreamingResponse(query_engine.query(input_sentence).response_gen)
        

Upon executing the following code, TypeError: cannot pickle 'generator' object, I received the following error. Is there any workaround to do it in fastapi ? I am able to stream the answers in my console, but I would like to create a stream between my api and the output. Also, if not FastAPI, can we do a similar thing in Flask ?