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Long-Term Memory Implementation in AI Chatbots

Sep 4, 2024

Lecture Notes on Long-Term Memory with Generative AI

Introduction

  • Discussed the importance of note-taking for memory retention.
  • Transitioned into the topic of long-term memory in generative AI, particularly focusing on large language models (LLMs).
  • Key point: LLMs do not inherently have memory or state.

Short-Term Memory vs. Long-Term Memory

  • Short-Term Memory in Chatbots:

    • When using a chatbot, it can remember things during a single session because it saves conversational history.
    • Example: If a user provides information about themselves, the chatbot can recall it within the same session.
  • Long-Term Memory Issues:

    • If the chat is reset (simulating a new day), the chatbot cannot recall previous information without the provided history.
    • Proposed solution: Summarize and store conversational history at the end of the session using another LLM.

Implementing Long-Term Memory with Amazon Bedrock

  • Transition to how to create an agent in Amazon Bedrock with long-term memory capabilities.

  • Steps to Create an Agent:

    1. Go to the agents section in the Amazon Bedrock console.
    2. Click on "Create Agent" and provide a name (e.g., "My Demo Agent").
    3. Choose a model (e.g., Sonet or Hau).
    4. Enable memory with the option to set its duration (default is 30 days).
    5. Provide instructions for chatbot behavior.
  • Testing the Agent:

    • After creating the agent, simulate a conversation to test memory functionality.
    • Example interaction: User states preferences (e.g., enjoys tea and beach walks).
    • End the session, and the agent summarizes the conversation for future reference.

Code Implementation in Jupyter Notebook

  • Overview of a Jupyter notebook provided to demonstrate implementation:
    • Libraries: Uses Boto3 (AWS SDK for Python).
    • Agent Creation Code:
      • Create agent, define roles, and manage permissions through policies.
      • Enable memory configuration in the agent creation code.
    • Agent Interaction:
      • Invoke the agent using the defined session ID and memory ID.
      • Receive and handle responses, including memory traces.

Summary of Features

  • Memory allows the agent to remember user preferences across multiple sessions.
  • The agent can summarize previous conversations and use that information in future interactions.
  • Can handle user requests more intelligently based on prior sessions.

Conclusion

  • Amazon Bedrock's agent feature provides a way to implement long-term memory in chatbots.
  • Importance of feedback on this service as it is in public preview.
  • Encouragement to engage with the content and subscribe for more information on generative AI.