This is the intro application to Vespa. Learn how to configure the schema for simple recommendation and search use cases. Try the apps on Vespa Cloud or using Docker
This set of sample apps goes deeper. Starting with blog-search, a more complex search app is build. Then transform this into an app for making recommendations in blog-recommendation. Finally, use machine learning to train and use a model for use in recommendations.
Create an end-to-end E-Commerce shopping engine using use-case-shopping and an Amazon product data set.
text-search dives deep into text ranking, using Vespa's nativerank and BM25 implementations. It uses the MS Marco dataset.
semantic-qa-retrieval takes text search to the next level using the Stanford Question Answering Dataset (SQuAD), text embeeddings and tensor ranking. This sample app demonstrates how to return answers to questions.
model-evaluation: A sample Vespa application which demonstrates Stateless ML Model Evaluation.
boolean-search: Learn how to use prediate fields to implement boolean indexing. I.e. how to express in a document a range of values to match, like "this fits readers in age range 20 to 30".
multiple-bundles: Build a Java application using components and dependencies in other bundles (jars).
basic-search-*: Simple application that can be deployed in different environments
Note: Applications with pom.xml must be built before being deployed. Refer to vespa plugins for more information.
Contribute to the Vespa sample applications.
Travis-CI build status:
