Learning LangChain

Chapter 9. Deployment: Launching Your AI Application Into Production

A Note for Early Release Readers

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This will be the 9th 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 explored the key concepts, ideas, and tools to help you build the core functionality of your AI application. You’ve learned how to utilize LangChain to generate LLM outputs, index and retrieve data, and enable memory and agency.

But your application is limited to your local server environment, so external users can’t access its features yet.

In this chapter, you’ll learn the best practices for deploying your AI application into production. We’ll also explore various tools to debug, collaborate, test, and monitor your LLM applications.

Let’s get started.

Prerequisites

In order to effectively deploy your AI application, you need to utilize various services to host your application, store and retrieve data, and monitor your application.

In the deployment example discussed in this chapter, we will incorporate the following services:

Vector store ...