pg_search VS MeiliSearch

On the backend, we'll need a few tools. Apart from the classics (ActiveRecord scopes and the pg_search gem), you’ll see how the (yet officially unreleased but production-tested) all_futures gem, built by SR authors, will act as an ideal ephemeral object to temporarily store our filter params and host our search logic. Finally, we’ll use pagy for pagination duties.

You can take a look at pg_search if you’re using Postgres

This was a really good read, thanks. I've got into the habit of jumping straight to PgSearch but could definitely apply this approach to some existing projects.

Cleaner, more performant database queries: Definitely don't just leave your query sitting in the controller! For production use cases, you'd want to consider an option like pg_search

If you are using Rails with Postgres you can use pg_search gem to build the named scopes to take advantage of full text search.

https://github.com/Casecommons/pg_search

Hey guys. Looking to release an app for mobile that will be using a rails API. The app will heavily rely on search. I know the go-to is to use elasticsearch but wanted to see if there was enough user demand for the MVP before shelling out $50/mo for the heroku add on. In the mean time I've been using pg_search. From the eye test it's performing okay but will be adding a table that houses over 350K records. With this in mind I was wondering if you all had any tips for increasing the overall speed for search from the model and controller level. Also should note that I'm open to any other free search gems if they deem bette fit.

There are two basic search configurations with pg_search, a Single Model search scope or a multi Model configuration. In my case I am only using the Single Model configuration, but you can read more about multi-search in the documentation.

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