When working with large knowledge bases, responses from generative AI models can be lengthy and often take time to fully generate. In traditional setups, users must wait until the complete response is ready before seeing any output, causing delays and poor interactivity. This is where response streaming becomes important. By streaming data as it’s being generated, you can deliver responses chunk by chunk in real time, improving the user experience and making applications feel faster and more conversational.
In this Cloud Lab, you’ll learn how to stream query results from Amazon Bedrock Knowledge Bases using AWS Lambda response streaming. You’ll start by storing source documents in Amazon S3 and creating a knowledge base using an embedding model, which transforms input data into vector representations. You will then store these embeddings in an S3 vector bucket, ensuring structured and efficient storage for easy retrieval.
Next, you’ll build two AWS Lambda functions, one using response streaming and one using buffered responses, to query the knowledge base with a text model. Finally, you’ll integrate both functions into a Flask web app, allowing you to compare real-time streamed output with traditional buffered responses and clearly see how streaming improves interactivity in GenAI applications.
After completing this Cloud Lab, you’ll have a strong understanding of how response streaming works in AWS Lambda, how to use Amazon Bedrock Knowledge Bases for contextual question answering, and how to connect these services with a simple web frontend.
The following is the high-level architecture diagram of the infrastructure you’ll create in this Cloud Lab:
