How I learned Vespa by thinking in Solr

Vespa is a modern search platform that offers structured search, inverted index-based text search, and approximate nearest neighbors (ANN) based vector search, which has intrigued Sujit Pal, Technology Research Director at Elsevier Labs, due to its vector search capabilities. Despite its steep learning curve compared to Solr and Elasticsearch, Pal decided to learn Vespa by drawing analogies to Solr, which helped him quickly set up a minimal viable product application. Vespa is packaged as a Docker image and requires a specific hardware setup, with configuration involving a directory structure reminiscent of Maven projects. The platform supports both text and vector searches using a SQL-like query language (YQL), with extensive configuration possibilities for document types and query profiles. Pal's experiment utilized the CORD-19 dataset to populate the Vespa index, and he found the learning curve less daunting after initial setup, with plans to explore Vespa’s machine learning model integration and two-phase query capabilities in the future.