AWS Developers | Solving LLM Amnesia: Cross Session Memory @awsdevelopers | Uploaded 2 months ago | Updated 1 hour ago
(Managed long term memory for Amazon Bedrock is available now in public preview.)
This video explores the challenge of maintaining memory across multiple conversations with AI chatbots and demonstrates how to implement long-term memory using Agents for Amazon Bedrock. Key points covered:
💡 Explanation of why traditional large language models lack persistent memory
💡 Introduction to Amazon Bedrock's agent feature with long-term memory capabilities
💡 Step-by-step guide on creating an agent with memory in the Amazon Bedrock console
💡 Detailed walkthrough of implementing long-term memory programmatically using Python and boto3
💡 Demonstration of how the agent retains information across separate chat sessions
💡 Discussion of memory storage, retrieval, and integration into the AI's decision-making process
The video provides both conceptual understanding and practical implementation details, making it valuable for developers looking to enhance AI applications with persistent memory features.
🛠️ Link to code used in this video: github.com/build-on-aws/agents-for-amazon-bedrock-sample-feature-notebooks/blob/main/notebooks/preview-agent-long-memory.ipynb
Follow AWS Developers!
🐦 Twitter: twitter.com/awsdevelopers
💼 LinkedIn: linkedin.com/showcase/aws-developers
👾 Twitch: twitch.tv/aws
📺 Instagram: instagram.com/awsdevelopers/?hl=en
#AmazonBedrock #LongTermMemory #AIAgent #MachineLearning #PythonProgramming
(Managed long term memory for Amazon Bedrock is available now in public preview.)
This video explores the challenge of maintaining memory across multiple conversations with AI chatbots and demonstrates how to implement long-term memory using Agents for Amazon Bedrock. Key points covered:
💡 Explanation of why traditional large language models lack persistent memory
💡 Introduction to Amazon Bedrock's agent feature with long-term memory capabilities
💡 Step-by-step guide on creating an agent with memory in the Amazon Bedrock console
💡 Detailed walkthrough of implementing long-term memory programmatically using Python and boto3
💡 Demonstration of how the agent retains information across separate chat sessions
💡 Discussion of memory storage, retrieval, and integration into the AI's decision-making process
The video provides both conceptual understanding and practical implementation details, making it valuable for developers looking to enhance AI applications with persistent memory features.
🛠️ Link to code used in this video: github.com/build-on-aws/agents-for-amazon-bedrock-sample-feature-notebooks/blob/main/notebooks/preview-agent-long-memory.ipynb
Follow AWS Developers!
🐦 Twitter: twitter.com/awsdevelopers
💼 LinkedIn: linkedin.com/showcase/aws-developers
👾 Twitch: twitch.tv/aws
📺 Instagram: instagram.com/awsdevelopers/?hl=en
#AmazonBedrock #LongTermMemory #AIAgent #MachineLearning #PythonProgramming
