lunr.js VS MeiliSearch

If your content is mostly static, you might want to consider pre-building an index and shipping it as a whole. You could look into something like

* https://stork-search.net/ (Rust/WASM)

* tinysearch: https://github.com/tinysearch/tinysearch (Rust/WASM)

* https://lunrjs.com/ (JS, simple, stable)

* http://elasticlunr.com/ - based on the former, slightly more sophisticated tuning options

Also search results can be pre-indexed and stored in a Json file. Just as an example. https://lunrjs.com/

Would anyone be willing to help make this more accessible and clean? I have some front-end dev experience, but it would be cool to work together with people to make sure we have something that makes sense and looks nicer than what I could do myself. As for functionality, searching on GitHub directly seems to work pretty well, but it might be better to have a page and a search feature maybe using something like Lunr. I would also like to create some sort of easy "API" in case Matt wants to embed some transcripts on his website. It would be cool if it would be as easy as just adding a blank div with a special id and a data attribute with the episode number on the Squarespace page.

which is open source, appears to be free, and claims that it can run in the browser

Is there a way to run it in WASM, to get something like Lunr[1]? We prefer to do our (small-index, <2MB) search client-side for a bunch of reasons, currently using Lunr.js, but it's a bit annoying and the typeahead search is something I improvised and not really official.

[1] https://lunrjs.com/

To ensure cross-platform compatibility, Bitwarden uses Lunr.js for searching. This search engine is a bit quirky, and difficult to get used to.

Slightly more js work required, but this should a more customisable solution: https://lunrjs.com/

OWDX runs entirely in the browser; as such, it cannot offer cutting-edge search functionality of the sort you'd find in a search engine or an expensive piece of enterprise software. The search library I'm using — lunr.js, for anyone who's interested — does, however, offer a nice set of core functionality and a modest but handy query language.

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