Appendix A: Running the code examples
This appendix covers
- How this book’s source code examples are packaged
- Pulling the AI-Powered Search source code
- Building and running the examples
- Working with Jupyter
- Working with Docker
During your journey through AI-Powered Search, we walk through a lot of code and running 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.
A.1 Overall Structure of Code Examples
Building an AI-powered search system requires integrating many components and libraries. For our default search engine, we will leverage Apache Solr, which internally leverages Apache Zookeeper. We also support swapping 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 leverage Apache Spark. We use Python as our programming language for all 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 the ability to actually execute our code examples and see the results in a user-friendly way, which we’ll accomplish through the use of Jupyter notebooks.