This hands-on tutorial will teach you how to build a complete search solution using Elasticsearch. In this tutorial you will learn:
- How to perform full-text keyword searches on a dataset, optionally with filters
- How to generate, store and search dense vector embeddings using a Machine Learning model
- How to use the ELSER model to generate and search sparse vectors
- How to combine search results from the methods listed above using Elastic's Reciprocal Rank Fusion (RRF) algorithm
This most important aspect of this tutorial is that it will show you how to implement all these features on a project that you will run on your own computer, all done in small incremental steps.
The examples you will learn are written in Python, but the concepts are universal and can be applied to your favorite language or technology stack.
To get the most out of this tutorial, we recommend that you follow along and run all the examples.