Searchkick VS MeiliSearch

I run a large scale production application that does something along these lines. If the data needs to be close to real-time, I'd say use `searchkick` + Elasticsearch, and use `searchkick`'s async feature to "stream" the data from your table to the ES index. Your dashboard will then just query from the ES index via searchkick.

You're right, that's actually what we implemented, application-level hooks, but they needed development and maintenance effort that come for free with the adapter we're using for OpenSearch integration, which also comes with welcome features: synonyms, partial matches, and many others.

Spoiler, the adapter is Searchkick: https://github.com/ankane/searchkick

Searchkick

Normally for Rails applications you would use a gem like searchkick since it greatly reduces the initial Elasticsearch complexity.

We lean heavily on Elasticsearch at CompanyCam. One of it's primary use cases is serving our highly filterable project feed. It is incredibly fast, even when you apply multiple filters to your query and are searching a largish data set. Our primary interface for interacting with Elasticsearch is using the Searchkick gem. Searchkick is a powerhouse and provides so many features out of the box. One place where we bump up against the edges is when trying to reindex a large collection.

Convinced? Ok read on and I’ll show you what switching from Elasticsearch to Meilisearch looked like for a real production app — ScribeHub. We also moved from Ankane’s excellent Searchkick gem to the first party meilisearch-rails gem and I’ll show you the changes there as well.

If you want to use a look-aside pattern (like you might have used with Searchkick + Elasticsearch), you should check out ActiveStash: https://github.com/cipherstash/activestash

Searchkick learns what your users are looking for. As more people search, it gets smarter and the results get better. It’s friendly for developers - and magical for your users. BONUS: it's written and supported by "ankane" who has flawless reputation amongst the Ruby community.

We needed to introduce a new service for search. As we settled on using meilisearch, we needed a way to sync updates on our models with the records in meilisearch. We could've continued to use callbacks but we needed something better.

Meilisearch

In this case, a good strategy is to use vector search only when the keyword/prefix search returns none or just a small number of results. A good candidate for this is MeiliSearch. It uses custom ranking rules to provide results as fast as the user can type.

With the VertexAI endpoint set up and tested, our next step is to work with LibreChat. LibreChat is an open-source ChatGPT clone that can integrate with various AI models, including the PaLM 2 models via the VertexAI API. It's built using React, MongoDB, and Meilisearch technologies.

Meilisearch seems like it is the best open source option.

https://www.meilisearch.com/

Starting with v1.3, you can use Meilisearch as a vector store. Meilisearch allows you to store vector embeddings alongside your documents conveniently. You will need to create the vector embeddings using your third-party tool of choice (Hugging Face, OpenAI). As we published the first v1.3 release candidate, you can try out vector search today.

I work at Meilisearch, an open-source search engine built in Rust. 🦀

https://www.meilisearch.com/ https://github.com/meilisearch