Vespa sample applications

First-time users should go through the getting-started guides first.

The Vespa sample applications are created to run both self-hosted and on Vespa Cloud. You can easily deploy the sample applications to Vespa Cloud without changing the files - just follow the same steps as for vector-search, adding security credentials.

For operational sample applications, see examples/operations. See also PyVespa examples.

Getting started - Basic Sample Applications

Basic album-recommendation

The album-recommendation is the intro application to Vespa. Learn how to configure the schema for simple recommendation and search use cases.

Vector Search

There is a growing interest in AI-powered vector representations of unstructured multimodal data and searching efficiently over these representations. vector-search describes how your organization can unlock the full potential of multimodal AI-powered vector representations using Vespa Cloud - the industry-leading managed Vector Search Service.

Simple hybrid semantic search

The simple semantic search application demonstrates indexed vector search using HNSW, creating embedding vectors from a transformer language model inside Vespa, and hybrid text and semantic ranking. This app also demonstrates using native Vespa embedders.

Retrieval Augmented Generation (RAG)

The retrieval-augmented-generation sample application demonstrates how to build an end-to-end RAG pipeline with API-based and local LLMs.

Indexing multiple vectors per field

The Vespa Multi-Vector Indexing with HNSW app demonstrates how to index multiple vectors per document field for semantic search for longer documents.

Vespa streaming mode for naturally partitioned data

The vector-streaming-search app demonstrates how to use vector streaming search. See also blog post.

ColBERT token-level embeddings

The colbert application demonstrates how to use the Vespa colbert-embedder for explainable semantic search with better accuracy than regular text embedding models.

ColBERT token-level embeddings for long documents

The colbert-long application demonstrates how to use the Vespa colbert-embedder for explainable semantic search for longer documents.

SPLADE sparse learned weights for ranking

The splade application demonstrates how to use the Vespa splade-embedder for semantic search using sparse vector representations.

Multilingual semantic search

The multilingual sample application demonstrates multilingual semantic search with multilingual text embedding models.

Customizing embeddings

The custom-embeddings application demonstrates customizing frozen document embeddings for downstream tasks.

More advanced sample applications

News search and recommendation tutorial

The news sample application used in the Vespa tutorial. This application demonstrates basic search functionality.

It also demonstrates how to build a recommendation system where the approximate nearest neighbor search in a shared user/item embedding space is used to retrieve recommended content for a user. This app also demonstrates using parent-child relationships in Vespa.

Billion-scale Image Search

This billion-scale-image-search app demonstrates billion-scale image search using CLIP retrieval. It features separation of compute from storage and query time vector similarity de-duping. PCA dimension reduction and more.

State-of-the-art Text Ranking

This msmarco-ranking application demonstrates how to represent state-of-the-art text ranking using Transformer (BERT) models. It uses the MS Marco passage ranking datasets and features bi-encoders, cross-encoders, and late-interaction models (ColBERT).

See also the more simplistic text-search app that demonstrates traditional text search using BM25/Vespa nativeRank.

Next generation E-Commerce Search

The use-case-shopping app creates an end-to-end E-Commerce shopping engine. This use case also bundles a frontend application. It uses the Amazon product data set. It demonstrates building next generation E-commerce Search using Vespa. See also the commerce-product-ranking sample application for using learning-to-rank techniques (Including XGBoost and LightGBM) for improving product search ranking.

Search as you type and query suggestions

The incremental-search application demonstrates search-as-you-type functionality, where for each keystroke of the user, it retrieves matching documents. It also demonstrates search suggestions (query auto-completion).

Vespa as ML inference server (model-inference)

The model-inference application demonstrates using Vespa as a stateless ML model inference server where Vespa takes care of distributing ML models to multiple serving containers, offering horizontal scaling and safe deployment. Model versioning and feature processing pipeline.

Vespa Documentation Search

vespa-documentation-search is the search application that powers search.vespa.ai - refer to this for GitHub Actions automation. This sample app is a good start for automated deployments, as it has system, staging and production test examples. It uses the Document API both for regular PUT operations but also for UPDATE with create-if-nonexistent.

CORD-19 Search

cord19.vespa.ai is a full-featured application, based on the Covid-19 Open Research Dataset:

• cord-19: frontend
• cord-19-search: search backend

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Note: Applications with pom.xml are Java/Maven projects and must be built before deployment. Refer to the Developer Guide for more information.

Contribute to the Vespa sample applications.

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