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Meilisearch – search engine API bringing AI-powered hybrid search

(github.com)
151 points modinfo | 1 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. jimmydoe ◴[15 Apr 25 16:55 UTC] No.43695500[source]▶
+1 typesense is really fast. the only drawback is starting up is slow when index getting larger. the good thing is full text search (excl vector) is relatively stable feature set, so if your use case is just FTS, you won't need to restart very often for version upgrade.