solr VS orama

Using the Galaxy UI, knowledge workers can systematically review the best results from all configured services including Apache Solr, ChatGPT, Elastic, OpenSearch, PostgreSQL, Google BigQuery, plus generic HTTP/GET/POST with configurations for premium services like Google's Programmable Search Engine, Miro and Northern Light Research.

Apache Solr can be used to index and search text-based documents. It supports a wide range of file formats including PDFs, Microsoft Office documents, and plain text files. https://solr.apache.org/

Solr https://solr.apache.org/

if so, then https://solr.apache.org/ can be a solution, though there's a bit of setup involved. oh yea, you get to write your own "search interface" too which would end up calling solr's api to find stuff.

Orama, an open-source, edge-first hybrid search engine highlights the industry's shift towards more efficient, accurate, and scalable solutions. Recent trends indicate a shift from traditional search solutions to more modern and efficient answering engines like Orama, evidenced by the search features on both Node.js and SolidJS that were formerly powered by Algolia, but are now powered by Orama.

Let's break into the steps to utilize Orama and analyze how it works. I won't dig into the technical stuff because, hey, it's an open-source project, which means you can easily peek at the source code, no problemo!

If you are interested in it, you can learn more about it in the official documentation. And don't forget to follow Orama on Twitter and Michere Riva its CTO.

I'll use the Lyra repository for this article, so thanks to the Lyra contributors if this article has a great git history and awesome code.

You can checkout lyra, its in-memory full text search engine for javascript