This hands-on tutorial will teach you how to build a complete search solution using 

How to perform full-text keyword searches on a dataset, optionally with filters

How to generate, <strong>store</strong> and search dense vector embeddings using a Machine Learning model

 model to generate and search sparse vectors

How to combine search results from the methods listed above using Elastic's 

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 

, 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.

Welcome

To follow this tutorial you will need to install the following components:

An installation of Elasticsearch, based on our hosted 

 service (which includes a free trial period), or a self-hosted service that you run on your own computer. See the 

 section above for installation instructions.

 interpreter. Make sure it is a recent version, such as Python 3.8 or newer.

The tutorial assumes that you have no previous knowledge of Elasticsearch or general search topics, but it expects you to have a basic familiarity with the following technologies, at least at a beginner level:

The command prompt or terminal application in your operating system.

Requirements

Download the starter search application by clicking on the link below.

Download the Tutorial Starter Application

Find a suitable parent directory for your project, such as your 

 directory, and extract the contents of the zip file there. This should add a 

 directory with several sub-directories and files inside.

 directory created in the previous section.

Following Python best practices, you are now going to create a 

, a private Python environment dedicated to this project. Do this with the following command:

This command creates a Python virtual environment in a 

 in this command with any name that you like. Note that in some installations of Python, you may need to use 

 the virtual environment, which is a way to make this virtual environment the active Python environment for the terminal session you are in. If you are working on a UNIX-based operating system such as Linux or macOS, activate the virtual environment as follows:

The above activation command would also work if you are working inside a WSL environment on a Microsoft Windows computer. But if you are using the Windows command prompt or PowerShell, the activation command is different:

When the virtual environment is activated, the command-line prompt changes to show the name of the environment:

The last step to configure the Python environment is to install a few packages that are needed by the starter application. Make sure that the virtual environment was activated in the previous step, and then run the following command to install these dependencies:

At this point you should be able to start the application with the following command:

To confirm that the application is running, open your browser and navigate to 

The Flask application is configured to run in development mode. When it detects that a source file has been changed, it will restart itself automatically to incorporate the changes. You can leave this terminal session with the application running while you continue with the tutorial, and as you make changes the application will restart to update.

Project Setup

Python Client Setup

Set Up the Elasticsearch Python Client

Create an Index

Create an Elasticsearch Index

Search Basics

Pagination

Filters

Faceted Search

In this section of the tutorial you are going to learn how to work with the full-text search capabilities of Elasticsearch. This will introduce you to several core topics such as working with the Python client library, creating and updating indices, making queries, and working with search results, all of which also apply to the more advanced search methods you will learn later.

Full-Text Search

Embeddings Intro

Introduction to Embeddings

Generating Embeddings

Generate Embeddings

Storing Embeddings

<strong>store</strong> Embeddings in Elasticsearch

k-Nearest Neighbor Search

k-Nearest Neighbor (kNN) Search

Hybrid Search

Hybrid Search: Combined Full-Text and kNN Results

This section will introduce you to a different way of searching that leverages Machine Learning (ML) techniques to interpret meaning and context.

Vector Search

ELSER Model

The ELSER Model

Semantic Queries

Hybrid Search: Combined Full-Text and ELSER Results

In this section you are going to learn about another Machine Learning approach to search that utilizes the 

Elastic Learned Sparse EncodeR model, or ELSER

, a Natural Language Processing model trained by Elastic to perform semantic search.

Semantic Search

Semantic Search using ELSER

Congratulations, you have finished the Search Tutorial!

We hope that this tutorial has given you a base from which you can start to experiment and create your search solution with Elasticsearch!

Welcome

Ready to build state of the art search experiences?