appendix A Running the code examples
During your journey through AI-Powered Search, we walk through a lot of code and run software examples demonstrating the techniques within this book. This appendix shows you how to easily set up and run the accompanying source code so that you can experiment with live, running examples as you work through the material. We’ll cover how the book’s source code is packaged, pulling and building the source code, and working with Jupyter notebooks and Docker to run the examples.
Building an AI-powered search system requires integrating many components and libraries. For our default search engine, we will use Apache Solr, which internally relies on Apache ZooKeeper. You can also swap out Solr with many other popular search engines and vector databases—see appendix B for instructions.
For significant data processing and machine learning tasks, we use Apache Spark. We use Python as our programming language for all the code examples and rely on many Python library dependencies, in addition to other system dependencies (like Java), which several of our systems require. Of course, we also need to execute our code examples and see the results in a user-friendly way, which we accomplish with the use of Jupyter notebooks.