To manage input and output sizes in AWS Bedrock, you can apply compression, truncation, or preprocessing techniques tailored to your data type. For text, prioritize reducing unnecessary content. For images, adjust resolution or format. For outputs, set limits via API parameters or post-process results. Here’s a breakdown of practical methods:
1. Input Compression Techniques
For text inputs, truncate or summarize content to remove redundancy. For example, use libraries like transformers to truncate text to a token limit (e.g., 512 tokens) before sending it to Bedrock. If working with large documents, extract key sections or use a summarization model (like Amazon Titan) to condense text. For images, reduce resolution using tools like Python’s Pillow or AWS Lambda with ImageMagick. Convert formats to JPEG or WebP for smaller file sizes, and crop images to focus on relevant areas. If using Bedrock’s multimodal capabilities, ensure images are optimized to avoid unnecessary bandwidth and processing costs.
2. Output Size Management
Control output length directly via Bedrock’s API parameters. For text models, set max_tokens or response_length to cap response size. For example, limiting a response to 200 tokens forces concise answers. For image-generating models, specify lower resolutions (e.g., 512x512 instead of 1024x1024) or compressed formats in the request. Post-process outputs programmatically—use regex to filter irrelevant text or libraries like OpenCV to downsample images. If streaming responses, process chunks incrementally to avoid holding large outputs in memory.
3. Preprocessing and Model Optimization
Preprocess data before invoking Bedrock. Use services like Amazon Rekognition to extract text or metadata from images, reducing input size. For repetitive queries, cache frequent inputs/outputs to avoid reprocessing. Choose Bedrock models optimized for efficiency—for example, selecting Titan Express for faster, shorter responses over larger models. Adjust model parameters like temperature to reduce randomness (and verbosity) in text generation. Structure prompts to explicitly request concise answers (e.g., “Respond in one sentence”), which can reduce output size without code changes.