Working With Conversational Memory and the Amazon Bedrock Converse API

Conversational memory in large language model (LLM) applications like chatbots enables models to retain context across multiple interactions, allowing for more coherent, personalized, and efficient conversations. Developers implementing conversational memory management techniques can create more natural model interactions that enhance the user experience.

The Amazon Bedrock Converse API provides a structured framework for implementing conversational memory in LLM applications, offering features like multi-turn conversation support, context retention, and a consistent interface across different models. Developers can manage conversation history, incorporate system messages, and handle various content types, facilitating the creation of conversational AI applications with effective context awareness and coherent interactions.

In this lab, you will learn how to utilize conversational memory in LLM applications, interact with LLMs using the Amazon Bedrock Converse API, and implement techniques to manage conversation history in Python.

Learning objectives

Upon completion of this beginner-level lab, you will be able to:

• Utilize conversational memory in LLM applications
• Interact with LLMs using the Amazon Bedrock Converse API
• Implement trimming and summarizing techniques to manage conversation history in LLM applications

Intended audience

• Candidates for the AWS Certified Machine Learning Specialty certification
• Cloud Architects
• Software Engineers

Prerequisites

Familiarity with the following will be beneficial but is not required:

• Amazon Bedrock
• Python

The following content can be used to fulfill the prerequisites:

• Invoking Amazon Bedrock Models Using the Bedrock Runtime and AWS Lambda

Updates

July 18th, 2025 - Upgraded the lab to use the Amazon Nova Pro model

December 16th, 2024 - Resolved an issue preventing the lab from deploying