OpenAI-Organization header should match organization for API key Error

I am encountering an error while using the OpenAI API. The error message is:

“OpenAI API error: OpenAI-Organization header should match organization for API key”

I have verified that my API key and organization ID are correctly set.

Steps I have taken to resolve the issue:

  1. Verified the API key and organization ID from the OpenAI dashboard.
  2. Hardcoded the values directly in the script to avoid any dotenv loading issues.
  3. Ensured there are no typos or extra spaces in the credentials.

Despite these steps, I am still encountering the same error. Could you please assist me in resolving this issue?

Thank you,

Welcome @c-prashant.dwivedi

Are you added to more than one org?

1 Like

Hi @sps
NO,
API key and org id provided by admin.

If you have been issued an API key, there should be no need to specify an organization when making an API call.

The parameter is only needed when your own OpenAI account is a member of multiple organizations by invitation, and you need to specify the billing for particular API calls instead of setting a default organization for all API keys you generate.

Hi @_j
OpenAI API error: OpenAI-Organization header should match organization for API key

I’m still getting this error when i’m using only API Key :

import openai

# Directly set environment variables for testing
api_key = "■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■xxxx8" 

# Set API key directly
openai.api_key = api_key

# Verify environment variables
print("OpenAI API Key:", openai.api_key)

# Test API call with updated interface
try:
    response = openai.Completion.create(
        model="text-davinci-003",
        prompt="Say this is a test!",
        max_tokens=5
    )
    print("API call successful:", response)
except openai.OpenAIError as e:
    print(f"OpenAI API error: {str(e)}")
except Exception as e:
    print(f"Error: {str(e)}")

Hi @c-prashant.dwivedi

You exhibited code that cannot work. It uses obsolete library methods and attempts to use an AI model that has been shut down. It employs an endpoint that is not for the latest chat models.

Here is example code from the API reference employing the latest openai Python library.

from openai import OpenAI
client = OpenAI()

completion = client.chat.completions.create(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Are you a good AI?"}
  ]
)
print(completion.choices[0].message.content)

Note the need for messages with one of three roles (the other being assistant, for sending back what the AI wrote previously), and the 3.5 series model name alias that points to the currently-recommended AI model.

The output is a pydantic model with the method-based parsing shown when printing to extract part of the response object.


Here is example code of my own doing, demonstrating NOT using the python library, but using Python’s built-in http library for making API calls directly. It also uses the moderations endpoint for checking for undesired inputs against policy, uses asyncio, is a real chatbot that produces an in-memory chat history, and asks for input after the AI introduces itself. For the intermediate coder to use immediately and learn from, assuming only that OpenAI API knowledge is what’s missing from your repertoire.

Both rely on you setting OPENAI_API_KEY as an environment variable. No organization is needed.

import os
import json
import asyncio
import aiohttp

# Global variables for API responses for your inspection
global chat_history, moderation_scores, chat_response


async def fetch_url(session, url, headers, data):
    async with session.post(url, headers=headers, data=data) as response:
        return await response.json()


async def moderate_input(user_input):
    global moderation_scores
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
    }
    to_moderate = {"input": ", ".join(str(d) for d in (chat_history[-2:] + user_input))}
    async with aiohttp.ClientSession() as session:
        moderation_scores = await fetch_url(
            session,
            "https://api.openai.com/v1/moderations",
            headers,
            json.dumps(to_moderate).encode(),
        )
        if moderation_scores["results"][0]["flagged"]:
            print(moderation_scores["results"][0]["categories"])
            return False
        return True


async def get_chat_response(user_input):
    global chat_response
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
    }
    params_template = {"model": "gpt-3.5-turbo", "max_tokens": 666, "top_p": 0.9}
    request = {
        **params_template,
        **{
            "messages": [
                {"role": "system", "content": "You are ChatAPI, an AI assistant."}
            ]
            + chat_history[-10:]
            + user_input
        },
    }
    async with aiohttp.ClientSession() as session:
        chat_response = await fetch_url(
            session,
            "https://api.openai.com/v1/chat/completions",
            headers,
            json.dumps(request).encode(),
        )
        return chat_response["choices"][0]["message"]["content"]


async def main():
    global chat_history
    chat_history = []
    user_input = [{"role": "user", "content": "Introduce yourself."}]
    while user_input[0]["content"] != "exit":
        if await moderate_input(user_input):
            reply = await get_chat_response(user_input)
            print(reply)  # This line is the output point
            chat_history += user_input + [{"role": "assistant", "content": reply}]
        user_input = [
            {"role": "user", "content": input("\nPrompt: ")}
        ]  # This line is the input point


if __name__ == "__main__":
    asyncio.run(main())

Not used is streaming a response as it is generated, a vital part of user experience.

Since this code is kind of obtuse, uses methods simplified by openai’s library, and was made for my own satisfaction, I had the AI write about the API and the code to my specification.

API chat code tutorial

Tutorial: Building an AI Chatbot with OpenAI’s API

Welcome to this intermediate-level tutorial on creating an AI chatbot using Python and OpenAI’s API. This tutorial will help you understand how to make HTTPS RESTful API calls, manage a chat session that simulates memory, and prepare the groundwork for expanding this single-session chatbot into a multiuser application.

Overview of API Methods for HTTPS RESTful Calls

The OpenAI API allows you to interact with powerful AI models via HTTP requests. To use it effectively, you’ll need to understand two key types of API requests:

  1. Moderations API: This endpoint is used to analyze user inputs for potential issues, such as inappropriate content. A POST request is sent to the API with the user input, and a response is received indicating whether the input is flagged.

  2. Chat Completions API: This endpoint generates AI responses based on a series of messages that provide context. Again, a POST request is sent, this time including both the user input and prior messages to maintain context. The response includes the AI-generated text.

Both types of requests use JSON for sending data and receiving responses. Here’s a basic structure of how these requests are made using Python’s aiohttp library, which allows for asynchronous HTTP requests:

async with aiohttp.ClientSession() as session:
    response = await session.post(url, headers=headers, data=json_data)
    data = await response.json()

Chatbot Flow and Chat History Management

The chatbot simulates a conversational memory by keeping a rolling log of the last few exchanges. This history is used to provide context to the AI, which helps in generating coherent and contextually appropriate responses.

Flow:

  • Start: The chatbot initializes with a system message and waits for user input.
  • Input Processing: Each input is first sent to the moderation API to ensure it’s appropriate.
  • Response Generation: If the input is clean, it’s added to the current chat history, and a request is made to the chat completions API, which includes the recent messages as context.
  • Output: The AI’s response is displayed to the user, and the cycle repeats until the user exits.

This approach not only simulates a short-term memory but also structures the interaction as a continuous dialogue, enhancing the user experience.

Overview of the Code

Here’s a breakdown of the main components of the chatbot code:

  • Global Variables: chat_history, moderation_scores, and chat_response are used to store data that might be useful across different parts of the program or for debugging.

  • Asynchronous Functions:

    • fetch_url(): Handles the actual sending of requests to the API. It’s abstracted to be reusable for different types of API requests.
    • moderate_input(): Checks if the user input is appropriate using the Moderations API.
    • get_chat_response(): Sends the user input along with the last few messages to the Chat Completions API and retrieves the AI-generated response.
  • Error Handling: The use of aiohttp for handling HTTP sessions inherently manages some lower-level network errors, but additional handling is integrated to ensure any API-specific errors are caught and managed.

  • Main Loop: main() orchestrates the chat flow, repeatedly collecting user input, processing it through the moderation and chat APIs, and handling responses or errors.

Expanding to Multiuser Sessions

The current implementation manages a single chat session in a console environment. However, this structure serves as a foundation for scaling up:

  • Session Management: By encapsulating each user’s chat in a separate asynchronous task or thread, the application can handle multiple users simultaneously. Each session would maintain its own chat_history.

  • Web Backend: The chatbot logic can be integrated into a web application framework like Flask or Django. Each session could be handled via websockets or HTTP sessions to interact with web clients.

  • Scalability and Reliability: Leveraging cloud services or containers can help manage load, improve reliability, and dynamically scale the number of active sessions according to demand.

By understanding these core concepts and structures, you can not only build a functional AI chatbot but also adapt and expand it into a robust, multiuser application. This tutorial should provide you with both the tools and the knowledge to explore further applications of AI APIs in real-world scenarios.

Thanks @_j now it is connected with open AI.