product VS beir

We’re excited to walk our first steps toward semantic search. We can’t wait to hear your thoughts on integrating Meilisearch as a vector store. You can give your feedback in this Github discussion.

Looks good in fact! We will eventually let users use third-party API like OpenAi and Hugging Face to compute the embedding of the documents and queries. You can try our first prototype if you want.

Hey @jiripospisil,

Indeed Meilisearch does not offer an aggregation feature yet although it will be possible to get stats for the `min` and `max` values of a faceted field in the next version (v1.1)

Please tell us more about what you mean by aggregation and why it is critical for your use-case by creating a discussion on Github here (https://github.com/meilisearch/product/discussions) or by proposing a new idea on our public portal here (https://roadmap.meilisearch.com) if you don't have a Github account.

Thank you!

If you remove the URLs from indexation, it'll generally save a ton of place and will be much, much faster to index. We are thinking about not indexing URLs by default; you can help us by explaining your use case here -> https://github.com/meilisearch/product/discussions/553

Just a detail, if you're making a `du -sh` on your computer, the size on the disk will stay unchanged because we are doing soft deletion ;). Don't worry. It will be physically deleted after a while if you need it in the future.

If you kept the default configuration of Meilisearch, the maximum size of the HTTP payload is 100Mb (for security). You change it here -> https://docs.meilisearch.com/learn/configuration/instance_op...

addDocumentsInBatches() is just an helper to send your big json array into multiple parts, not absolutely sure you'll need it. (Code -> https://github.com/meilisearch/meilisearch-js/blob/807a6d827...)

I advise you to fill out a discussion on our product repository for us to evaluate your needs, and use case and then see what we plan about that.

The BEIR project might be what you're looking for: https://github.com/beir-cellar/beir/wiki/Leaderboard

Custom datasets can also be evaluated using this method as specified in this link. This article and the associated benchmarks script can be reused to evaluate what method works best on your data.

> Meilisearch focuses on simplicity, relevancy, and performance.

> excellent relevance out of the box

> if ease of use, performance, and relevancy are important to you, Meilisearch was made for you

Is there a benchmark that shows Meilisearch outperforming Elasticsearch in terms of relevance score? I couldn't find Meilisearch listed on https://github.com/beir-cellar/beir.

There are actually benchmarks that allow measuring search relevancy objectively, e.g. BEIR[1]. Manticore Search team did an effort to make a PR to include it to the list. The results are here [2]. Unfortunately the BEIR team seems to be too busy to review a whole pile of PRs including about Vespa. Nevertheless it would be nice to have both Meilisearch and Typesense there too since it's interesting what performance those non-tf-idf based search engines would show compared to BM25-based and vector search engines.

[1] https://github.com/beir-cellar/beir

True! Here's a pull request to BEIR to compare Manticore with Elasticsearch in terms of relevance https://github.com/beir-cellar/beir/pull/92. Spoiler: in this test Manticore provides better relevance than Elasticsearch in average. Of course you can tune both further and Elasticsearch now has KNN which when combined with BM25 can give even better relevance. In general I would say for most users the results quality in terms of full-text relevance is about the same in Elasticseach and Manticore.

But there's for example BEIR that compared BM25 vs state of the art ML language models and it turned out BM25 is in average better than all of them unless you rerank top 100 results from Elasticsearch using the language models. With Manticore you can get even better relevance than with Elasticsearch. We made a pull-request to BEIR to demonstrate that https://docs.google.com/spreadsheets/d/1_ZyYkPJ_K0st9FJBrjbZqX14nmCCPVlE_y3a_y5KkYI/edit#gid=0