To handle large inputs when calling the OpenAI API, you need to manage token limits, optimize input structure, and use strategies like chunking or summarization. Each OpenAI model has a maximum token limit (e.g., 16k tokens for gpt-3.5-turbo, 128k for gpt-4). If your input exceeds this limit, the API will return an error. Start by calculating the token count of your input using tools like OpenAI’s tiktoken library. For example, tiktoken.get_encoding("cl100k_base").encode(text) returns a token list, letting you verify if your input fits within the model’s limits. If it doesn’t, split the text into smaller sections that stay under the token cap, ensuring logical breaks (e.g., paragraphs or code blocks) to preserve context.
When splitting large inputs, prioritize maintaining coherence. For instance, if processing a long document, divide it into chapters or sections and process each separately. For code analysis, split files into functions or logical modules. Use system prompts to guide the model’s behavior across chunks. For example, if summarizing a 20k-token article with gpt-3.5-turbo (16k limit), split it into two 10k-token sections. Summarize the first chunk, then include the summary as context when processing the second chunk to retain overarching themes. Alternatively, use embeddings to index large datasets and retrieve only relevant snippets for each query, reducing input size while preserving accuracy.
Adjust API parameters to optimize for large inputs. Set max_tokens to reserve enough tokens for the response, ensuring the combined input and output don’t exceed the model’s limit. For iterative tasks, chain multiple API calls: process the first chunk, extract key points, and feed them into subsequent requests. For example, when analyzing a large codebase, break it into files, analyze each for vulnerabilities, then combine results. Always test edge cases—like inputs near the token limit—and implement error handling to retry with smaller chunks if the API rejects a request. By combining token management, logical chunking, and iterative processing, you can efficiently handle large inputs without sacrificing output quality.