Issue with GPT-4 API: Limitation on Output Tokens While Using a Vector Database
I’m currently using the gpt-4-2024-08-06 model in combination with a vector database to access files and perform queries. According to the documentation, the model should handle up to 16,000 tokens per request (input + output combined). However, I’m encountering an issue where the model only generates a maximum of 1000 output tokens, despite trying different prompts and input sizes.
Context:
- Model Version: gpt-4-2024-08-06
- Setup: Using a vector database to handle and retrieve file-based data.
- Issue: Despite providing a large enough input context and expecting a large output (up to 16,000 tokens), the model caps its responses at 1000 output tokens.
What I’ve Tried:
- Experimented with different prompt formulations and lengths.
- Ensured the input size allows for more than 1000 tokens of output.
- Checked if there are any specific parameters limiting output tokens in the request.
Has anyone faced a similar issue, or is there a known workaround to make the model utilize the full token limit?
Any help or insights would be appreciated!
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Hi and welcome to the Forum!
In general, the output length that you can achieve depends on the nature of task / query to the model and your prompt. The fact that you are using a vector database for information retrieval has near to no impact on the output length. Likewise, there is no direct correlation between the amount of input you are providing to the model and the amount of output it returns. It’s really the design of your prompt that has the biggest impact on output length.
To that end, it would be helpful if you could share your prompt here so the Community can take a closer look how it could be optimized to create longer outputs.
For additional explanation, it is worth highlighting is that the model gpt-4o-2024-08-06 provides you with a context window of 128,000 token and a maximum output of 16,384 tokens. The context window refers to the combined limit of input and output tokens that the model can process and generate within a single interaction. Depending on the output you are aiming for, you can provide the model with well over 100,000 input tokens.
In reality, it is pretty difficult for the model to provide output that is close to the maximum token numbers. 1,000 output tokens falls within the common range of output a model produces for prompts that are not optimized for length. So what you are experiencing is fairly normal behaviour.
All this to reiterate that we can best further help you if you can share details of your prompt.
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Thank you for your detailed response! I appreciate the clarification regarding the relationship between prompt design and output length. It’s helpful to know that hitting the higher token output range often requires a very targeted and optimized prompt.
2 Likes