When calling the OpenAI API with large inputs, it’s essential to manage the size of your requests effectively. The API has a limit on the total number of tokens, which includes both your input and the model's response. As of now, you generally should keep your input under 4,000 tokens for models like GPT-3. If your input exceeds this limit, you will need to truncate or split your request. For instance, if your text includes lengthy documents or conversations, consider summarizing the content or focusing only on the most relevant sections to ensure you stay within the token limit.
If your input is too large and trimming it down isn't feasible, another approach is to break it into smaller chunks or batches. This technique allows you to send multiple requests to the API and process responses individually. When implementing this, ensure that each chunk contains sufficient context so that the model can generate meaningful responses. For example, if processing a multi-paragraph document, you might split it into several sections and send these sequentially while maintaining coherence by reintroducing necessary context with each call.
Finally, another consideration is how you handle and store the responses. If using multiple calls, you might want to aggregate results from each request into a single output format after processing all the chunks. This way, the end-user receives a coherent response that includes insights from all parts of the large input. When implemented thoughtfully, these strategies will allow you to efficiently manage large inputs when working with the OpenAI API.