Why can't I batch multiple independent prompts in a single ChatCompletion API call?

Below is an explanation that clarifies the behavior and your options:

Why Your Example Doesn’t Produce Separate Completions

The Chat Completions endpoint is designed for conversations. When you supply a list of messages, the API treats them as part of the same conversation history. In your example, the two prompts are combined as sequential messages in one conversation context. This is why you receive a single completion that references the overall conversation rather than two independent responses.

Current Batching Support for Chat Completions

Unlike the standard completions endpoint that supports batching by accepting a list of prompts, the Chat Completions endpoint (e.g., with gpt-3.5-turbo) does not currently support batching multiple independent conversations within a single API call. The approach of sending multiple prompts as separate conversations in one request isn’t available at this time.

What You Can Do

1. Separate API Calls:
   To get independent responses, you can iterate over your prompts and make separate ChatCompletion.create calls for each conversation. For example:

   PYTHONCopied
   for prompt in ["this is prompt 1", "this is prompt 2"]:
       response = openai.ChatCompletion.create(
           model="gpt-3.5-turbo",
           messages=[{'role': 'user', 'content': prompt}],
       )
       print(response['choices'][0]['message']['content'])

2. Batch via Application Logic:
   Depending on your use case and scale, you might implement batching on the client side, sending multiple asynchronous API calls concurrently. This isn’t the same as a single API call with multiple independent conversations, but it can still help you manage throughput.

Final Note

The batching technique described in the OpenAI API documentation applies to endpoints that accept a list of prompts (like the completions.create endpoint). However, for Chat Completions, you currently need to handle multiple conversation initiations separately.

If you have further questions on optimizing your API usage or handling rate limits while processing multiple conversations, please let me know!