Скачать или смотреть LangChain Conversations (4.1)

This video introduces LangChain Conversations, a key concept for managing dialogue with Large Language Models (LLMs). You’ll see how LangChain extends an LLM’s limited memory by embedding the entire conversation history into each new prompt. Using simple Python functions, the notebook demonstrates how to initialize a chat session, capture user input, and generate responses that recall earlier context—like remembering a user’s name. By walking through these examples step by step, the lesson highlights how conversation state is maintained and how Markdown formatting can improve the readability of responses
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The tutorial then expands into more advanced use cases, such as constraining the model to specific topics with system prompts and testing memory recall across multiple turns of dialogue. By the end, you’ll understand how to create a dynamic, context-aware chat application that feels more natural and personalized. This foundation sets the stage for exploring richer memory strategies in LangChain, such as buffer windows, summaries, and persistence, which are covered in later modules


Code for This Video:
https://github.com/jeffheaton/app_gen...

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