PydanticAI
This tutorial outlines the process of creating a conversational agent that maintains context across multiple interactions. We'll use a modern AI framework to build an agent capable of engaging in more natural and coherent conversations.
Many simple chatbots lack the ability to maintain context, leading to disjointed and frustrating user experiences. This tutorial aims to solve that problem by implementing a conversational agent that can remember and refer to previous parts of the conversation, enhancing the overall interaction quality.
What you'll learn
- 1Language Model: The core AI component that generates responses.
- 2Prompt Template: Defines the structure of our conversations.
- 3History Manager: Manages conversation history and context.
- 4Message Store: Stores the messages for each conversation session.
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.
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