Experiencing APIConnectionError with async OpenAI embeddings while synchronous version works properly

[omarR_85]

I’m facing an issue with the async embedding function from OpenAI. My async code triggers the error openai.error.APIConnectionError: Error communicating with OpenAI, but the synchronous version runs without any problems.

Here’s the async function that’s causing trouble:

async def retrieve_embeddings(input_data: list[str]):
    
    return await openai.Embedding.acreate(
        api_key=await obtain_openai_key(),
        model='text-embedding-ada-002',
        input=input_data,
        timeout=60,
    )

On the other hand, the synchronous version works fine:

import openai

openai.api_key = 'your_secret_key'

input_examples = [
    "What is your name?"
]

response_data = openai.Embedding.create(
    model="text-embedding-ada-002",
    input=input_examples
)

embedding_result = response_data["data"]
print(embedding_result)

I really need the async version to function correctly for my project. Any suggestions on what might be causing this connection problem specifically with the async method?

[Sky24]

Check your SSL context and certificate verification settings. I hit this exact APIConnectionError when my async setup couldn’t validate SSL certificates properly - the sync version just worked fine. Async clients sometimes need explicit SSL context config, especially in corporate networks or certain environments. Try adding ssl=False temporarily to your async call. If that fixes it, you know it’s certificate-related and can configure SSL properly. Also watch out for rate limits - async requests hit OpenAI’s rate limiting differently than sync calls. The timing can trigger connection errors that look like network problems but aren’t.

[Ryan_Innovative]

Classic async context issue. Your async function’s probably getting called from sync code, or there’s something wrong with your event loop setup. I’ve hit this same problem mixing async OpenAI calls with sync code. Wrap your async call with asyncio.run() if you’re calling it from sync context, or make sure you’re using await consistently through your whole call stack. Also check that obtain_openai_key() actually returns a valid key - async key retrieval can fail silently and return None, causing connection errors. Verify your OpenAI library version too. Older versions had async quirks. Use a recent version and consider switching to the newer OpenAI client if you’re still on the legacy one.

[JumpingMountain]

Had this exact problem last month - drove me crazy for hours. It’s usually not your code structure, it’s a networking issue with async operations. Your async function might timeout differently than the sync version because of how the HTTP client handles connection pooling. Try adding aiohttp as your HTTP client by setting up a custom session, or just use the newer openai Python client (v1.x) instead of the legacy one. The legacy async methods have known connection issues. Also check that your obtain_openai_key() function isn’t adding delays or returning bad keys. I switched to the new client and haven’t seen this error since.

[lily_luminesce]

The Problem: You’re encountering the openai.error.APIConnectionError: Error communicating with OpenAI error when using OpenAI’s async embedding function, while the synchronous version works correctly. This suggests a problem with how your asynchronous code handles network requests and potentially timeouts or retries.

Understanding the “Why” (The Root Cause):

The core issue is likely related to how asynchronous operations handle network requests, timeouts, and retries compared to synchronous calls. Asynchronous code, by its nature, involves concurrent operations that may interact differently with network resources, potentially leading to connection issues that don’t appear in synchronous code. The openai.error.APIConnectionError often indicates problems with establishing or maintaining a connection to the OpenAI API, which can manifest differently in async contexts due to factors like asynchronous HTTP client behavior, connection pooling strategies, and handling of network interruptions. Simple solutions like increasing the timeout value might not address the root cause if the problem lies in the asynchronous handling of network requests or retries. Directly managing low-level network details in asynchronous code can become quite complex and error-prone.

Step-by-Step Guide:

1. Automate the Embedding Process: Instead of directly managing the async openai.Embedding.acreate call, automate the entire embedding process using a workflow automation tool. This approach abstracts away the complexities of asynchronous networking, handling retries, and managing potential errors. The workflow would take your input data, make the API call to OpenAI’s embedding endpoint (handling retries and timeouts automatically), and return the embeddings. This eliminates the need to deal with lower-level details of asynchronous HTTP requests, connection pooling, and error handling directly in your code.

2. Choose a Workflow Automation Tool: Several workflow automation tools offer visual interfaces to build and manage such pipelines easily. These tools often provide built-in capabilities to handle retries, exponential backoff, and error logging, significantly simplifying the process. (A specific tool is mentioned in the original answer but not repeated here to avoid potential bias, as this information is not available in your provided context).

3. Integrate the Workflow: Integrate the chosen workflow into your application. Your async function will simply trigger the workflow, passing the input data. The workflow will handle the actual API interaction, and you’ll receive the results when the process completes. This keeps your code clean and focused on higher-level logic while handling the complexities of asynchronous API calls robustly.

Common Pitfalls & What to Check Next:

• Rate Limiting: Ensure you’re not exceeding OpenAI’s API rate limits. Asynchronous requests can sometimes trigger these limits more quickly than synchronous calls due to the concurrency of operations.
• Network Configuration: Check your network configuration (proxies, firewalls) to rule out interference with asynchronous requests. Asynchronous calls may be more susceptible to network issues than their synchronous counterparts.
• API Key Management: Double-check that your obtain_openai_key() function is correctly retrieving and returning a valid API key. Asynchronous contexts can obscure errors in key retrieval that might be more apparent in synchronous code.
• Library Version: Using the latest version of the OpenAI Python client is recommended to benefit from bug fixes and improved asynchronous support.

Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!

[Ethan99]

The legacy acreate method has connection bugs that cause random failures. I switched from openai.Embedding.acreate() to the new OpenAI Python client (v1+) and the connection errors vanished. The async implementation in the old library can’t handle HTTP connections properly under load. With the new client, use async_client = AsyncOpenAI() then await async_client.embeddings.create(). Way more stable connections and better error messages when stuff breaks. Your API key setup looks fine - it’s just that the legacy async methods suck.

[Alex_Thunder]

your network config might be messing with async calls. I’ve seen firewalls or proxies block async requests while letting sync ones pass through just fine. Test it from a different network first to rule that out. Also, timeout=60 could be too aggressive - async calls often need more time than their sync counterparts.