Previously, if the primary key was set and a Settings update contained
a primary key, an error would be returned.
However, this error is not needed if the new PK == the current PK.
This commit just checks to see if the PK actually changes
before raising an error.
3568: CI: Fix `publish-aarch64` job that still uses ubuntu-18.04 r=Kerollmops a=curquiza
Fixes#3563
Main change
- add the usage of the `ubuntu-18.04` container instead of the native `ubuntu-18.04` of GitHub actions: I had to install docker in the container.
Small additional changes
- remove useless `fail-fast` and unused/irrelevant matrix inputs (`build`, `linker`, `os`, `use-cross`...)
- Remove useless step in job
Proof of work with this CI triggered on this current branch: https://github.com/meilisearch/meilisearch/actions/runs/4366233882
3569: Enhance Japanese language detection r=dureuill a=ManyTheFish
# Pull Request
This PR is a prototype and can be tested by downloading [the dedicated docker image](https://hub.docker.com/layers/getmeili/meilisearch/prototype-better-language-detection-0/images/sha256-a12847de00e21a71ab797879fd09777dadcb0881f65b5f810e7d1ed434d116ef?context=explore):
```bash
$ docker pull getmeili/meilisearch:prototype-better-language-detection-0
```
## Context
Some Languages are harder to detect than others, this miss-detection leads to bad tokenization making some words or even documents completely unsearchable. Japanese is the main Language affected and can be detected as Chinese which has a completely different way of tokenization.
A [first iteration has been implemented for v1.1.0](https://github.com/meilisearch/meilisearch/pull/3347) but is an insufficient enhancement to make Japanese work. This first implementation was detecting the Language during the indexing to avoid bad detections during the search.
Unfortunately, some documents (shorter ones) can be wrongly detected as Chinese running bad tokenization for these documents and making possible the detection of Chinese during the search because it has been detected during the indexing.
For instance, a Japanese document `{"id": 1, "name": "東京スカパラダイスオーケストラ"}` is detected as Japanese during indexing, during the search the query `東京` will be detected as Japanese because only Japanese documents have been detected during indexing despite the fact that v1.0.2 would detect it as Chinese.
However if in the dataset there is at least one document containing a field with only Kanjis like:
_A document with only 1 field containing only Kanjis:_
```json
{
"id":4,
"name": "東京特許許可局"
}
```
_A document with 1 field containing only Kanjis and 1 field containing several Japanese characters:_
```json
{
"id":105,
"name": "東京特許許可局",
"desc": "日経平均株価は26日 に約8カ月ぶりに2万4000円の心理的な節目を上回った。株高を支える材料のひとつは、自民党総裁選で3選を決めた安倍晋三首相の経済政策への期待だ。恩恵が見込まれるとされる人材サービスや建設株の一角が買われている。ただ思惑が先行して資金が集まっている面 は否めない。実際に政策効果を取り込む企業はどこか、なお未知数だ。"
}
```
Then, in both cases, the field `name` will be detected as Chinese during indexing allowing the search to detect Chinese in queries. Therefore, the query `東京` will be detected as Chinese and only the two last documents will be retrieved by Meilisearch.
## Technical Approach
The current PR partially fixes these issues by:
1) Adding a check over potential miss-detections and rerunning the extraction of the document forcing the tokenization over the main Languages detected in it.
> 1) run a first extraction allowing the tokenizer to detect any Language in any Script
> 2) generate a distribution of tokens by Script and Languages (`script_language`)
> 3) if for a Script we have a token distribution of one of the Language that is under the threshold, then we rerun the extraction forbidding the tokenizer to detect the marginal Languages
> 4) the tokenizer will fall back on the other available Languages to tokenize the text. For example, if the Chinese were marginally detected compared to the Japanese on the CJ script, then the second extraction will force Japanese tokenization for CJ text in the document. however, the text on another script like Latin will not be impacted by this restriction.
2) Adding a filtering threshold during the search over Languages that have been marginally detected in documents
## Limits
This PR introduces 2 arbitrary thresholds:
1) during the indexing, a Language is considered miss-detected if the number of detected tokens of this Language is under 10% of the tokens detected in the same Script (Japanese and Chinese are 2 different Languages sharing the "same" script "CJK").
2) during the search, a Language is considered marginal if less than 5% of documents are detected as this Language.
This PR only partially fixes these issues:
- ✅ the query `東京` now find Japanese documents if less than 5% of documents are detected as Chinese.
- ✅ the document with the id `105` containing the Japanese field `desc` but the miss-detected field `name` is now completely detected and tokenized as Japanese and is found with the query `東京`.
- ❌ the document with the id `4` no longer breaks the search Language detection but continues to be detected as a Chinese document and can't be found during the search.
## Related issue
Fixes#3565
## Possible future enhancements
- Change or contribute to the Library used to detect the Language
- the related issue on Whatlang: https://github.com/greyblake/whatlang-rs/issues/122
Co-authored-by: curquiza <clementine@meilisearch.com>
Co-authored-by: ManyTheFish <many@meilisearch.com>
Co-authored-by: Many the fish <many@meilisearch.com>
3525: Fix phrase search containing stop words r=ManyTheFish a=ManyTheFish
# Summary
A search with a phrase containing only stop words was returning an HTTP error 500,
this PR filters the phrase containing only stop words dropping them before the search starts, a query with a phrase containing only stop words now behaves like a placeholder search.
fixes https://github.com/meilisearch/meilisearch/issues/3521
related v1.0.2 PR on milli: https://github.com/meilisearch/milli/pull/779
Co-authored-by: ManyTheFish <many@meilisearch.com>
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>
