roaring-rs VS MeiliSearch

There are issues and pull requests but I advise you to look at the milli folder in the Meilisearch repository, it’s where all the logic is done. We extensively use RoaringBitmaps, heed the LMDB wrapper and grenad when indexing.

On some benchmarks, we are faster but most of the time we aren't. We absolutely need to introduce benchmarks with the croaring-rs library and most of those performances gain could also be achieved with other methods to do multi-ops for example.

We tried to release the new version of roaring-rs, better compressed bitset in Rust, but found out that the core library simd module was blocking us. We now have to work on std::simd to release the blocking features.

Yeah, it can be attributed to using the roaring-rs library, but not just that, we have done so much to improve the search performances by reducing the number of set-operations we do.

Given the shoutouts to Burntsushi and Lemire this is almost certainly a bitmap trigram index based engine similar to https://github.com/google/zoekt

The index is likely based on Roaring bitmaps, presumably https://github.com/RoaringBitmap/roaring-rs in this case.

Nice architecture, exactly how I would have done it also.

I have worked on roaring-rs, a very fast library to do set operations like unions and intersections, and improved the four operations by using the standard ops traits.

I have implemented a better way of specifying ranges to be inserted or removed from a RoaringBitmap by using the RangeBounds trait. The roaring-rs library exposes fast data-structures to do set operations, like intersections and unions.

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