AuthenticationError

There has been some issues with openAI in the newer versions.

Did you try setting up the key like?

yes I did basically I tried every method available on the Open Ai

This is strange, what is the version that you are trying with.
Also can you share your code

Yea the above has always worked for me (in addition to setting it in your env)

You could also print(os.environ["OPENAI_API_KEY"]) to confirm its in your env, and then use that to set the openai module

import gradio as gr
from llama_index import SimpleDirectoryReader, LLMPredictor, PromptHelper, StorageContext, ServiceContext, \
GPTVectorStoreIndex, load_index_from_storage
from langchain.chat_models import ChatOpenAI
import openai
import sys

import os
from dotenv import load_dotenv

load_dotenv()
openai_api_key = os.environ["OPENAI_API_KEY"]


def create_service_context():
max_input_size = 4096
num_outputs = 512
max_chunk_overlap = 0.2
chunk_size_limit = 6000

prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)

# LLMPredictor is a wrapper class around LangChain's LLMChain that allows easy integration into LlamaIndex
llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.5, model_name="gpt-3.5-turbo", max_tokens=num_outputs))

# Constructs service_context
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
return service_context


directory_path = r'C:\Users\tejas\OneDrive\Desktop\docs'


def data_ingestion_indexing(directory_path):
documents = SimpleDirectoryReader(directory_path).load_data()

index = GPTVectorStoreIndex.from_documents(
documents, service_context=create_service_context()
)

index.storage_context.persist()
return index


def data_querying(input_text):
storage_context = StorageContext.from_defaults(persist_dir="./storage")

index = load_index_from_storage(storage_context, service_context=create_service_context())

response = index.as_query_engine().query(input_text)

return response.response


iface = gr.Interface(
fn=data_querying,
inputs=gr.components.Textbox(lines=7, label="Enter your query"),
outputs="text",
title="llamaIndex"
)

index = data_ingestion_indexing(directory_path)
iface.launch(share=False)

okay let me try that