Learning LangChain

Chapter 5. Cognitive Architectures with LangGraph

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This will be the 5th chapter of the final book. Please note that the GitHub repo will be made active later on.

If you have comments about how we might improve the content and/or examples in this book, or if you notice missing material within this chapter, please reach out to the editor at [e-mail].

So far, we’ve looked at the most common features of LLM applications:

• Prompting techniques in the Preface and Chapter 1

• RAG in Chapters 2 and 3

• Memory in Chapter 4

The next question should be: how do we assemble these pieces into a coherent application that achieves the goal we set out to solve? To draw a parallel with the world of bricks and mortar, a swimming pool and a one-story house are built of the same materials, but obviously serve very different purposes. What makes them uniquely suited to their different purposes is the plan for how those materials are combined—that is, their architecture. The same is true when building LLM applications. The most important decisions you have to make are how to assemble the different components you have at your disposal (such as RAG, prompting techniques, memory) into something that achieves your purpose.

Before we look at specific ...