Grouping Results
demonstrates Vespa grouping and faceting for query time result analytics.
Read more in Vespa grouping.
predicate-fields
uses Vespa's predicate field type to implement indexing of document side boolean expressions.
Boolean document side constraints allows the document to specify which type of queries it can be retrieved for.
Predicate fields allow expressing logic like "this document should only be visible in search for readers in age range 20 to 30" or
"this product should only be visible in search during campaign hours".
The vespa-chinese-linguistics app
demonstrates integrating custom linguistic processing,
in this case a Chinese tokenizer Jieba.
http-api-using-request-handlers-and-processors
demonstrates how to build custom HTTP apis, building REST interfaces with custom handlers and renderers.
See also Custom HTTP Api tutorial.
multiple-bundles is a technical sample application
demonstrating how to use multiple OSGI bundles for custom plugins (searchers, handlers, renderers).
Joins shows possibilities for doing distributed query time joins.
This is for use cases where parent-child is not sufficient.
Document-processingbuilds on
album-recommendation to show
some of the possibilities for doing custom document processing in Java.
generic-request-processing
Generic request-response processing sample application.
lucene-linguistics contains two sample application packages:
- A bare minimal app.
- Shows advanced configuration of the Lucene based
Linguisticsimplementation.
aws/lambda and
google-cloud/cloud-functions
have examples of (lambda) functions for accessing data and logs with the cloud providers.
embedder-auto-training-evaluation does
automatic data generation using the ChatGPT API.
This in order to train an embedder to perform better for information retrieval on specific datasets
without labor-intensive and expensive manual training data annotation.
Machine learned embedder models enable efficient similarity computations, but training these models requires large amounts of (often manually) annotated data. The aim of this app is to investigate whether Large Language Models (LLMs), such as GPT-3.5-turbo, can be employed to generate synthetic data for training embedder, without extensive manual intervention.
The repository contains scripts and notebooks to:
- Prepare datasets
- Generate training data for datasets using an LLM
- Train an embedder
- Evaluate performance
Read more in the blog post.
embedding-service
demonstrates how a Java handler component can be used to process HTTP requests.
In this application, a handler is used to implement an embedding service,
which takes a string as an input and returns a vector embedding of that string.
FastHTML Vespa frontend
is a simple frontend for the Vespa search engine.
It is built using FastHTML and written in pure Python. Features:
- Simple search interface, with links to search results.
- Accordion with full JSON-response from Vespa.
- SQLite DB for storing queries.
- Admin authentication for viewing and downloading queries.
- Deployment options - Docker + Huggingface spaces.
Use 
model-deployment to generate a model in ONNX format in the models directory,
by running the ONNXModelExport notebook.
reranker is a stateless application which re-ranks results obtained from another Vespa application.
While this does not result in good performance and is not recommended for production,
it is useful when you want to quickly do ranking experiments without rewriting application data.
In-Context Learning This is a set of scripts/installs to back up the presentation using In-Context Learning at:
For any questions, please register at the Vespa Slack and discuss in the general channel.
See operations for sample applications for multinode clusters, deployed in various infrastructure like Kubernetes. Also find examples for CI/CD, security and monitoring.
Note: Applications with pom.xml are Java/Maven projects and must be built before being deployed. Refer to the Developer Guide for more information.
Contribute to the Vespa sample applications.