In PR #2773, I added the `chinese`, `hebrew`, `japanese` and `thai`
feature flags to allow melisearch to be built without huge specialed
tokenizations that took up 90% of the melisearch binary size.
Unfortunately, due to some recent changes, this doesn't work anymore.
The problem lies in excessive use of the `default` feature flag, which
infects the dependency graph.
Instead of adding `default-features = false` here and there, it's easier
and more future-proof to not declare `default` in `milli` and
`meilisearch-types`. I've renamed it to `all-tokenizers`, which also
makes it a bit clearer what it's about.
Conflicts | resolution
----------|-----------
Cargo.lock | added mimalloc
Cargo.toml | took origin/main version
milli/src/search/criteria/exactness.rs | deleted after checking it was only clippy changes
milli/src/search/query_tree.rs | deleted after checking it was only clippy changes
3347: Enhance language detection r=irevoire a=ManyTheFish
## Summary
Some completely unrelated Languages can share the same characters, in Meilisearch we detect the Languages using `whatlang`, which works well on large texts but fails on small search queries leading to a bad segmentation and normalization of the query.
This PR now stores the Languages detected during the indexing in order to reduce the Languages list that can be detected during the search.
## Detail
- Create a 19th database mapping the scripts and the Languages detected with the documents where the Language is detected
- Fill the newly created database during indexing
- Create an allow-list with this database and pass it to Charabia
- Add a test ensuring that a Japanese request containing kanjis only is detected as Japanese and not Chinese
## Related issues
Fixes#2403Fixes#3513
Co-authored-by: f3r10 <frledesma@outlook.com>
Co-authored-by: ManyTheFish <many@meilisearch.com>
Co-authored-by: Many the fish <many@meilisearch.com>
3505: Csv delimiter r=irevoire a=irevoire
Fixes https://github.com/meilisearch/meilisearch/issues/3442
Closes https://github.com/meilisearch/meilisearch/pull/2803
Specified in https://github.com/meilisearch/specifications/pull/221
This PR is a reimplementation of https://github.com/meilisearch/meilisearch/pull/2803, on the new engine. Thanks for your idea and initial PR `@MixusMinimax;` sorry I couldn’t update/merge your PR. Way too many changes happened on the engine in the meantime.
**Attention to reviewer**; I had to update deserr to implement the support of deserializing `char`s
-------
It introduces four new error messages;
- Invalid value in parameter csvDelimiter: expected a string of one character, but found an empty string
- Invalid value in parameter csvDelimiter: expected a string of one character, but found the following string of 5 characters: doggo
- csv delimiter must be an ascii character. Found: 🍰
- The Content-Type application/json does not support the use of a csv delimiter. The csv delimiter can only be used with the Content-Type text/csv.
And one error code;
- `invalid_index_csv_delimiter`
The `invalid_content_type` error code is now also used when we encounter the `csvDelimiter` query parameter with a non-csv content type.
Co-authored-by: Tamo <tamo@meilisearch.com>
3461: Bring v1 changes into main r=curquiza a=Kerollmops
Also bring back changes in milli (the remote repository) into main done during the pre-release
Co-authored-by: Loïc Lecrenier <loic.lecrenier@me.com>
Co-authored-by: bors[bot] <26634292+bors[bot]@users.noreply.github.com>
Co-authored-by: curquiza <curquiza@users.noreply.github.com>
Co-authored-by: Tamo <tamo@meilisearch.com>
Co-authored-by: Philipp Ahlner <philipp@ahlner.com>
Co-authored-by: Kerollmops <clement@meilisearch.com>
765: Update version for the next release (v0.39.1) in Cargo.toml files r=curquiza a=meili-bot
⚠️ This PR is automatically generated. Check the new version is the expected one before merging.
Co-authored-by: curquiza <curquiza@users.noreply.github.com>
764: Update deserr to latest version r=irevoire a=loiclec
Update deserr to 0.1.5, which changes the `DeserializeFromValue` trait, getting rid of the `default()` method.
Co-authored-by: Loïc Lecrenier <loic.lecrenier@me.com>
736: Update charabia r=curquiza a=ManyTheFish
Update Charabia to the last version.
> We are now Romanizing Chinese characters into Pinyin.
> Note that we keep the accent because they are in fact never typed directly by the end-user, moreover, changing an accent leads to a different Chinese character, and I don't have sufficient knowledge to forecast the impact of removing accents in this context.
Co-authored-by: ManyTheFish <many@meilisearch.com>
693: use the lmdb-master.3 branch r=Kerollmops a=irevoire
After investigating https://github.com/meilisearch/meilisearch/issues/3017, we found out that it was due to lmdb and that, without any code change on our side, bumping using the lmdb-master-3 branch fix our issues.
But, we’re not really confident about what changed between the `mdb.master` and `mdb.master3` branches; thus this is a temporary change, and we hope we’ll be able to move to the new version of heed asap (either before the end of the pre-release or for the next release).
--------
The bug is hard to reproduce; I can reproduce it 100% of the time on my archlinux personal computer. But on a scaleway archlinux bare-metal machine, it doesn’t reproduce. It’s flaky on our test suite, but `@loiclec` was able to write a minimal test that reproduces it every time on macOS.
Basically, what happens is when there are multiple threads opening databases in a different directory at the same time.
If there are 10 or more threads running at the same time, lmdb starts throwing the `Invalid argument (os error 22)` error for no reason, we believe.
I would like to submit an issue to lmdb, but I don’t really have the time to write a test in C without heed currently.
`@hyc,` if you want to take a look at it, here is the repo that reproduces the issue on macOS: https://github.com/irevoire/heed-bug
Co-authored-by: Irevoire <tamo@meilisearch.com>
e.g. add one facet value incrementally with a group_size = X and then
add another one with group_size = Y
It is not actually possible to do so with the public API of milli,
but I wanted to make sure the algorithm worked well in those cases
anyway.
The bugs were found by fuzzing the code with fuzzcheck, which I've added
to milli as a conditional dev-dependency. But it can be removed later.
635: Use an unstable algorithm for `grenad::Sorter` when possible r=Kerollmops a=loiclec
# Pull Request
## What does this PR do?
Use an unstable algorithm to sort the internal vector used by `grenad::Sorter` whenever possible to speed up indexing.
In practice, every time the merge function creates a `RoaringBitmap`, we use an unstable sort. For every other merge function, such as `keep_first`, `keep_last`, etc., a stable sort is used.
Co-authored-by: Loïc Lecrenier <loic@meilisearch.com>
We need to store all the external id (primary key) in a hashmap
associated to their internal id during.
The smartstring remove heap allocation / memory usage and should
improve the cache locality.
436: Speed up the word prefix databases computation time r=Kerollmops a=Kerollmops
This PR depends on the fixes done in #431 and must be merged after it.
In this PR we will bring the `WordPrefixPairProximityDocids`, `WordPrefixDocids` and, `WordPrefixPositionDocids` update structures to a new era, a better era, where computing the word prefix pair proximities costs much fewer CPU cycles, an era where this update structure can use the, previously computed, set of new word docids from the newly indexed batch of documents.
---
The `WordPrefixPairProximityDocids` is an update structure, which means that it is an object that we feed with some parameters and which modifies the LMDB database of an index when asked for. This structure specifically computes the list of word prefix pair proximities, which correspond to a list of pairs of words associated with a proximity (the distance between both words) where the second word is not a word but a prefix e.g. `s`, `se`, `a`. This word prefix pair proximity is associated with the list of documents ids which contains the pair of words and prefix at the given proximity.
The origin of the performances issue that this struct brings is related to the fact that it starts its job from the beginning, it clears the LMDB database before rewriting everything from scratch, using the other LMDB databases to achieve that. I hope you understand that this is absolutely not an optimized way of doing things.
Co-authored-by: Clément Renault <clement@meilisearch.com>
Co-authored-by: Kerollmops <clement@meilisearch.com>
