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    AnalysisGenAI Agents

    Memory-Enhanced Conversational

    This tutorial outlines the process of creating a conversational AI agent with enhanced memory capabilities. The agent incorporates both short-term and long-term memory to maintain context and improve the quality of interactions over time.

    Traditional chatbots often struggle with maintaining context beyond immediate interactions. This limitation can lead to disjointed conversations and a lack of personalization. By implementing both short-term and long-term memory, we aim to create an agent that can: - Maintain context within a single conversation - Remember important information across multiple sessions - Provide more coherent and personalized responses

    What you'll learn

    • 1
      Language Model: The core AI component for understanding and generating responses.
    • 2
      Short-term Memory: Stores the immediate conversation history.
    • 3
      Long-term Memory: Retains important information across multiple conversations.
    • 4
      Prompt Template: Structures the input for the language model, incorporating both types of memory.
    • 5
      Memory Manager: Handles the storage and retrieval of information in both memory types.

    About this tutorial

    This hands-on Jupyter notebook is part of GenAI Agents, a free open-source repository by Nir Diamant covering ai agents techniques with runnable code examples and detailed explanations.

    Free and open-sourceRunnable Jupyter notebookActive community support
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