Vespa is an open-source search and recommendation engine designed for handling large-scale data and real-time querying. It is optimized for search applications that require fast indexing and retrieval of structured and unstructured data, such as text, images, and videos. Vespa supports full-text search, faceting, filtering, and ranking, as well as machine learning models for personalized ranking.
In IR, Vespa can be used for tasks like semantic search, recommendation systems, and document retrieval, enabling developers to combine traditional keyword-based searches with machine learning-based ranking models. Vespa supports rich queries that allow users to filter, rank, and combine data in various ways to improve the relevance of results.
Vespa’s ability to integrate with machine learning models and its support for real-time data processing make it ideal for use cases like e-commerce search engines, news aggregators, and personalized content recommendations. It also scales horizontally to handle large volumes of data and traffic.