gutenberg VS MeiliSearch

Case study 3: Zola

So after shopping around a bit I found a simple, dependency-less static site generator called Zola. The lack of dependencies sounded very attractive after all the headaches trying to update my Gatsby modules. I wanted to give Zola a try and see what tradeoffs I would need to make coming form a React-based framework to this Rust-based generator.

I think you're thinking about Zola: https://github.com/getzola/zola

But yes, if I were to recommend something, it'd be Zola given that there's just one executable that you need to run and there's absolutely no setup required.

If I were to start again from scratch, I'd likely use Zola as SSG (https://www.getzola.org/)

I've used Zola (https://github.com/getzola/zola) for a static project homepage a few years ago to showcase examples with a simple description and a wasm app embedded in the page, it worked perfectly for me and the docs was clear on how to use it. It was very easy to set up along with a GitHub action to automatically update the wasm binaries when needed. It is definitely a tool I keep in my mental toolbox as a good default.

I'm currently learning https://www.getzola.org/.

It's more manual than idy like but it's gonna be for a small personal and work website so I don't mind much.

It's super fast.

Doesn't seem to fit your use casr but still.

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