Implementing Conversational Memory Using LangChain and Amazon DynamoDB

Utilizing conversational memory with LangChain and DynamoDB for persistent storage enhances user experience by enabling applications to maintain context across interactions, resulting in more coherent conversations. This approach provides scalability and persistence, allowing for long-term retention of conversation history while integrating seamlessly with other AWS services. Additionally, it supports the development of sophisticated AI applications that can adapt based on past interactions, offering significant value in various domains such as customer service and personal assistance.

In this lab, you will learn how to add in-memory and persistent conversation history to an application using LangChain and Amazon DynamoDB.

Learning objectives

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

• Create a LangChain runnable with access to conversation history
• Use in-memory conversation history in an LLM application using LangChain
• Use persistent conversation history in an LLM application using LangChain and Amazon DynamoDB

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:

• LangChain
• Amazon Bedrock
• Python

The following content can be used to fulfill the prerequisites:

• Working With Conversational Memory and the Amazon Bedrock Converse API
• 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