3866: Update charabia v0.8.0 r=dureuill a=ManyTheFish
# Pull Request
Update Charabia:
- enhance Japanese segmentation
- enhance Latin Tokenization
- words containing `_` are now properly segmented into several words
- brackets `{([])}` are no more considered as context separators so word separated by brackets are now considered near together for the proximity ranking rule
- fixes#3815
- fixes#3778
- fixes [product#151](https://github.com/meilisearch/product/discussions/151)
> Important note: now the float numbers are segmented around the `.` so `3.22` is segmented as [`3`, `.`, `22`] but the middle dot isn't considered as a hard separator, which means that if we search `3.22` we find documents containing `3.22`
Co-authored-by: ManyTheFish <many@meilisearch.com>
3834: Define searchable fields at runtime r=Kerollmops a=ManyTheFish
## Summary
This feature allows the end-user to search in one or multiple attributes using the search parameter `attributesToSearchOn`:
```json
{
"q": "Captain Marvel",
"attributesToSearchOn": ["title"]
}
```
This feature act like a filter, forcing Meilisearch to only return the documents containing the requested words in the attributes-to-search-on. Note that, with the matching strategy `last`, Meilisearch will only ensure that the first word is in the attributes-to-search-on, but, the retrieved documents will be ordered taking into account the word contained in the attributes-to-search-on.
## Trying the prototype
A dedicated docker image has been released for this feature:
#### last prototype version:
```bash
docker pull getmeili/meilisearch:prototype-define-searchable-fields-at-search-time-1
```
#### others prototype versions:
```bash
docker pull getmeili/meilisearch:prototype-define-searchable-fields-at-search-time-0
```
## Technical Detail
The attributes-to-search-on list is given to the search context, then, the search context uses the `fid_word_docids`database using only the allowed field ids instead of the global `word_docids` database. This is the same for the prefix databases.
The database cache is updated with the merged values, meaning that the union of the field-id-database values is only made if the requested key is missing from the cache.
### Relevancy limits
Almost all ranking rules behave as expected when ordering the documents.
Only `proximity` could miss-order documents if all the searched words are in the restricted attribute but a better proximity is found in an ignored attribute in a document that should be ranked lower. I put below a failing test showing it:
```rust
#[actix_rt::test]
async fn proximity_ranking_rule_order() {
let server = Server::new().await;
let index = index_with_documents(
&server,
&json!([
{
"title": "Captain super mega cool. A Marvel story",
// Perfect distance between words in an ignored attribute
"desc": "Captain Marvel",
"id": "1",
},
{
"title": "Captain America from Marvel",
"desc": "a Shazam ersatz",
"id": "2",
}]),
)
.await;
// Document 2 should appear before document 1.
index
.search(json!({"q": "Captain Marvel", "attributesToSearchOn": ["title"], "attributesToRetrieve": ["id"]}), |response, code| {
assert_eq!(code, 200, "{}", response);
assert_eq!(
response["hits"],
json!([
{"id": "2"},
{"id": "1"},
])
);
})
.await;
}
```
Fixing this would force us to create a `fid_word_pair_proximity_docids` and a `fid_word_prefix_pair_proximity_docids` databases which may multiply the keys of `word_pair_proximity_docids` and `word_prefix_pair_proximity_docids` by the number of attributes in the searchable_attributes list. If we think we should fix this test, I'll suggest doing it in another PR.
## Related
Fixes#3772
Co-authored-by: Tamo <tamo@meilisearch.com>
Co-authored-by: ManyTheFish <many@meilisearch.com>
3842: fix some typos r=dureuill a=cuishuang
# Pull Request
## Related issue
Fixes #<issue_number>
## What does this PR do?
- fix some typos
## PR checklist
Please check if your PR fulfills the following requirements:
- [x] Does this PR fix an existing issue, or have you listed the changes applied in the PR description (and why they are needed)?
- [x] Have you read the contributing guidelines?
- [x] Have you made sure that the title is accurate and descriptive of the changes?
Thank you so much for contributing to Meilisearch!
Co-authored-by: cui fliter <imcusg@gmail.com>
3670: Fix addition deletion bug r=irevoire a=irevoire
The first commit of this PR is a revert of https://github.com/meilisearch/meilisearch/pull/3667. It re-enable the auto-batching of addition and deletion of tasks. No new changes have been introduced outside of `milli`. So all the changes you see on the autobatcher have actually already been reviewed.
It fixes https://github.com/meilisearch/meilisearch/issues/3440.
### What was happening?
The issue was that the `external_documents_ids` generated in the `transform` were used in a very strange way that wasn’t compatible with the deletion of documents.
Instead of doing a clear merge between the external document IDs of the DB and the one returned by the transform + writing it on disk, we were doing some weird tricks with the soft-deleted to avoid writing the fst on disk as much as possible.
The new algorithm may be a bit slower but is way more straightforward and doesn’t change depending on if the soft deletion was used or not. Here is a list of the changes introduced:
1. We now do a clear distinction between the `new_external_documents_ids` coming from the transform and only held on RAM and the `external_documents_ids` coming from the DB.
2. The `new_external_documents_ids` (coming out of the transform) are now represented as an `fst`. We don't need to struggle with the hard, soft distinction + the soft_deleted => That's easier to understand
3. When indexing documents, we merge the `external_documents_ids` coming from the DB and the `new_external_documents_ids` coming from the transform.
### Other things introduced in this PR
Since we constantly have to write small, very specialized fuzzers for this kind of bug, we decided to push the one used to reproduce this bug.
It's not perfect, but it's easy to improve in the future.
It'll also run for as long as possible on every merge on the main branch.
Co-authored-by: Tamo <tamo@meilisearch.com>
Co-authored-by: Loïc Lecrenier <loic.lecrenier@icloud.com>
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
3571: Introduce two filters to select documents with `null` and empty fields r=irevoire a=Kerollmops
# Pull Request
## Related issue
This PR implements the `X IS NULL`, `X IS NOT NULL`, `X IS EMPTY`, `X IS NOT EMPTY` filters that [this comment](https://github.com/meilisearch/product/discussions/539#discussioncomment-5115884) is describing in a very detailed manner.
## What does this PR do?
### `IS NULL` and `IS NOT NULL`
This PR will be exposed as a prototype for now. Below is the copy/pasted version of a spec that defines this filter.
- `IS NULL` matches fields that `EXISTS` AND `= IS NULL`
- `IS NOT NULL` matches fields that `NOT EXISTS` OR `!= IS NULL`
1. `{"name": "A", "price": null}`
2. `{"name": "A", "price": 10}`
3. `{"name": "A"}`
`price IS NULL` would match 1
`price IS NOT NULL` or `NOT price IS NULL` would match 2,3
`price EXISTS` would match 1, 2
`price NOT EXISTS` or `NOT price EXISTS` would match 3
common query : `(price EXISTS) AND (price IS NOT NULL)` would match 2
### `IS EMPTY` and `IS NOT EMPTY`
- `IS EMPTY` matches Array `[]`, Object `{}`, or String `""` fields that `EXISTS` and are empty
- `IS NOT EMPTY` matches fields that `NOT EXISTS` OR are not empty.
1. `{"name": "A", "tags": null}`
2. `{"name": "A", "tags": [null]}`
3. `{"name": "A", "tags": []}`
4. `{"name": "A", "tags": ["hello","world"]}`
5. `{"name": "A", "tags": [""]}`
6. `{"name": "A"}`
7. `{"name": "A", "tags": {}}`
8. `{"name": "A", "tags": {"t1":"v1"}}`
9. `{"name": "A", "tags": {"t1":""}}`
10. `{"name": "A", "tags": ""}`
`tags IS EMPTY` would match 3,7,10
`tags IS NOT EMPTY` or `NOT tags IS EMPTY` would match 1,2,4,5,6,8,9
`tags IS NULL` would match 1
`tags IS NOT NULL` or `NOT tags IS NULL` would match 2,3,4,5,6,7,8,9,10
`tags EXISTS` would match 1,2,3,4,5,7,8,9,10
`tags NOT EXISTS` or `NOT tags EXISTS` would match 6
common query : `(tags EXISTS) AND (tags IS NOT NULL) AND (tags IS NOT EMPTY)` would match 2,4,5,8,9
## What should the reviewer do?
- Check that I tested the filters
- Check that I deleted the ids of the documents when deleting documents
Co-authored-by: Clément Renault <clement@meilisearch.com>
Co-authored-by: Kerollmops <clement@meilisearch.com>
