What options do I have to compress or limit the size of inputs and outputs to keep Bedrock interactions efficient (for example, truncating unnecessary context or reducing image resolution)?

To manage input and output sizes efficiently in AWS Bedrock interactions, you can apply strategies like truncation, data compression, and model configuration. These methods help reduce computational costs, improve response times, and avoid hitting token or payload limits. The approach depends on the type of data (text, images, structured data) and whether you’re optimizing inputs, outputs, or both.

For text-based inputs, start by truncating or summarizing unnecessary context. For example, if your task involves processing a large document, extract only the relevant paragraphs or sentences using keyword matching or a smaller model to identify critical sections. Bedrock’s models often have token limits (e.g., 4,000 tokens), so preprocessing steps like removing redundant phrases or using abbreviations can help stay within bounds. For structured data (e.g., JSON), remove non-essential fields or compress keys (e.g., shortening "user_id" to "uid"). If working with images, reduce resolution using tools like Pillow or OpenCV before sending them to Bedrock. Convert images to formats like WebP for smaller file sizes, and consider cropping or resizing to the minimum dimensions required for the task (e.g., 256x256 pixels for thumbnail generation).

When handling outputs, configure Bedrock’s parameters to limit response length. For text generation, set max_tokens to cap the output size. For instance, if you need a one-sentence summary, restrict the model to 50 tokens. For image outputs, specify lower resolutions or use lossy compression in post-processing. If Bedrock returns verbose JSON, strip metadata or flatten nested structures. You can also cache frequently requested outputs (e.g., common API responses) to avoid reprocessing. Additionally, monitor usage metrics to identify patterns—such as repeated queries—where precomputed results or batched processing could reduce redundant calls.

Finally, combine these techniques with Bedrock’s native features. Use streaming for text outputs to process chunks incrementally instead of waiting for the full response. For multi-step workflows, split tasks into smaller operations (e.g., summarize a document section-by-section). Evaluate trade-offs: aggressive truncation might sacrifice accuracy, while excessive compression could degrade image quality. Test different thresholds (e.g., token limits, image sizes) to balance efficiency and output quality. By systematically applying these methods, you can optimize Bedrock interactions without compromising functionality.