How Do I Resolve the gpt-3.5-turbo Token Limit Exceeded Error?

Below is an explanation and some recommendations to resolve the token limit issue:

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What's Happening

• Error Meaning:
  The error indicates that the combined tokens from your entire messages (including system, user, and assistant messages) and your max_tokens for the answer exceed the limit for gpt-3.5-turbo (4097 tokens in this case).

• Token Counting:
  Even if you send a short "Hi", the API counts all tokens used in the conversation. This includes any system messages, previous conversation history, and extra tokens for internal formatting. Even small strings can contribute if a large context is being retained.

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Key Points to Consider

• Context History Impact:
  If you set MAX_CONTEXT_QUESTIONS = 10, then the prompt may include tokens from up to 10 previous exchanges. Over time, even if each question or answer is short, the cumulative token count can exceed the available limit.

• The MAX_TOKENS Parameter:
  Setting MAX_TOKENS defines the maximum tokens for the reply, but it doesn’t reduce the tokens used by your prompt or conversation history. The total tokens (prompt + reply) must remain below the model’s context limit.

• Message Formatting Overhead:
  The API adds tokens for the role, delimiters, and additional formatting. This overhead can sometimes contribute significantly when many messages are involved.

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Recommendations to Resolve the Issue

1. Trim Your Conversation Context:

   • Reduce Number of Messages:
     Consider lowering MAX_CONTEXT_QUESTIONS or trimming older conversation history if it is not critical.
   • Summarize Earlier Exchanges:
     Instead of sending full previous messages, summarize the key points to reduce token count.

2. Check Your Token Usage:

   • Use OpenAI’s token counting utilities (like the tiktoken library) to inspect how many tokens your current context uses. Here’s an example code snippet:

   PYTHONCopied
   import tiktoken
   
   # Choose the appropriate encoding for your model
   encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")
   
   # Sample text (this should be replaced by your actual conversation input)
   text = "Your conversation or prompt text here"
   token_count = len(encoding.encode(text))
   print(f"Token count: {token_count}")

3. Adjust max_tokens if Needed:
   Make sure that prompt_tokens + max_tokens is within the limit. You might temporarily lower max_tokens for testing purposes to see which message or conversation length triggers the error.

4. Monitor Hidden Tokens:
   Remember that besides your visible text, some tokens are added for the message structure (e.g., role fields, formatting tokens).

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Next Steps

• Reevaluate the conversation context that is being sent.
• Compute the token count of your current messages, including system and assistant messages.
• Reduce the conversation history by truncating or summarizing previous messages.

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If you need further assistance or encounter additional questions related to managing tokens in API calls, please provide more details about your conversation structure or how you manage storing previous messages.