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Meilisearch – search engine API bringing AI-powered hybrid search

(github.com)
151 points modinfo | 2 comments | 14 Apr 25 12:46 UTC | HN request time: 0s | source
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adeptima ◴[14 Apr 25 14:56 UTC] No.43682013[source]▶
Meilisearch is great, used it for a quick demo

However if you need a full-text search similar to Apache Lucene, my go-to options are based on Tantivy

Tantivy https://github.com/quickwit-oss/tantivy

Asian language, BM25 scoring, Natural query language, JSON fields indexing support are all must-have features for me

Quickwit - https://github.com/quickwit-oss/quickwit - https://quickwit.io/docs/get-started/quickstart

ParadeDB - https://github.com/paradedb/paradedb

I'm still looking for a systematic approach to make a hybrid search (combined full-text with embedding vectors).

Any thoughts on up-to-date hybrid search experience are greatly appreciated

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kk3 ◴[14 Apr 25 16:43 UTC] No.43683227[source]▶
As far as combining full-text search with embedding vectors goes, Typesense has been building features around that - https://typesense.org/docs/28.0/api/vector-search.html

I haven't tried those features but I did try Meilisearch awhile back and I found Typesense to index much faster (which was a bottleneck for my particular use case) and also have many more features to control search/ranking. Although just to say, my use case was not typical for search and I'm sure Meilisearch has come a long way since then, so this is not to speak poorly of Meilisearch, just that Typesense is another great option.

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1. Kerollmops ◴[14 Apr 25 17:57 UTC] No.43684199[source]▶
Meilisearch just improved the indexing speed and simplified the update path. We released v1.12 and highly improved indexing speed [1]. We improved the upgrade path with the dumpless upgrade feature [2].

The main advantage of Meilisearch is that the content is written to disk. Rebooting an instance is instant, and that's quite useful when booting from a snapshot or upgrading to a smaller or larger machine. We think disk-first is a great approach as the user doesn't fear reindexing when restarting the program.

That's where Meilisearch's dumpless upgrade is excellent: all the content you've previously indexed is still written to disk and slightly modified to be compatible with the latest engine version. This differs from Typesense, where upgrades necessitate reindexing the documents in memory. I don't know about embeddings. Do you have to query OpenAI again when upgrading? Meilisearch keeps the embeddings on disk to avoid costs and remove the indexing time.

[1]: https://github.com/meilisearch/meilisearch/releases/tag/v1.1... [2]: https://github.com/meilisearch/meilisearch/releases/tag/v1.1...

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2. kk3 ◴[15 Apr 25 04:15 UTC] No.43688924[source]▶
Thank you for the response here. Not being able to upgrade the machine without completely re-indexing has actually become a huge issue for me. My use case is that I need to upgrade the machine to perform a big indexing operation that happens all at once and then after that reduce the machine resources. Typesense has future plans to persist the index to disk but it's not on the road map yet. And with the indexing improvements, Meilisearch may be a viable option for my use case now. I'll be checking this out!