builder VS MeiliSearch

I created my first custom GPT based on the Builder.io docs site, forum, and example projects on github and it can now answer detailed questions with code snippets about integrating Builder.io into your site or app. You can try it here (currently requires a paid ChatGPT plan).

Check out Builder.io’s GitHub repository for more details about its features and community.

I’ve maintained a handful of JavaScript SDKs over the years. And for each one, the question of how exactly to provide support for fetch in a Node environment inevitably comes up. In fact, we just recently had to resolve some issues concerning this in our Builder.io SDKs.

16) https://github.com/builderio/builder

Personally, I would reach for builderIO's React SDK. It's pretty simple to set up and allows you to specify a selection of your own components.

Yes, It is. Builder Github. I think you might have mistaken them for their premium services.

Hmm, I guess it really depends on where you want to go. If you want a basic editor, sure, write your own, but I see you're integrating rows and columns. If you want to match current UX expectations, you gonna spend a shit ton of time to add things like drag and drop ordering, color and spacing controls, etc. If you want to have a full blown editing experience, I'd check out sth. like https://builder.io/ or https://craft.js.org/

We’re excited to announce that the builder.io homepage now achieves a 100/100 score on PageSpeed Insights, even on mobile, since we adopted Qwik.

The founders of builder.io are actively involved and have the right mindset. Swell.is also checks all the boxes and I am happy to bet on these two horses.

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