3986: Fix geo bounding box with strings r=ManyTheFish a=irevoire
# Pull Request
When sending a document with one geofield of type string (i.e.: `{ "_geo": { "lat": 12, "lng": "13" }}`), the geobounding box would exclude this document.
This PR fixes this issue by automatically parsing the string value in case we're working on a geofield.
## Related issue
Fixes https://github.com/meilisearch/meilisearch/issues/3973
## What does this PR do?
- Automatically parse the facet value iif we're working on a geofield.
- Make insta works with snapshots in loops or closure executed multiple times. (you may need to update your cli if it panics after this PR: `cargo install cargo-insta`).
- Add one integration test in milli and in meilisearch to ensure it works forever.
- Add three snapshots for the dump that mysteriously disappeared I don't know how
Co-authored-by: Tamo <tamo@meilisearch.com>
3963: Fix the milli crate r=ManyTheFish a=irevoire
Milli was using the serde feature of either without enabling it first; thus, it wasn't working.
It was working in meilisearch, though, because `meilisearch-types` was using the feature which enables it globally for all the other crates.
## Related issue
Fixes https://github.com/meilisearch/meilisearch/issues/3962
Co-authored-by: Tamo <tamo@meilisearch.com>
3952: Use the new safe `read-txn-no-tls` heed feature r=ManyTheFish a=Kerollmops
[We recently found out](https://github.com/meilisearch/heed/issues/191#issuecomment-1650280513) that the `read-sync-txn` heed feature was invalid and must be removed from this crate. We were declaring it in milli/meilisearch but, fortunately, not sharing the `RoTxn`s across threads 😮💨
[I recently introduced the `read-txn-no-tls` heed feature](https://github.com/meilisearch/heed/pull/194), which implements `RoTxn: Send` and allows multiple read transactions on a single thread (which we use).
This PR removes the `sync-read-txn` heed feature from the _Cargo.toml_ file. I will fix this in heed v0.20.0 and will fill a RustSec advisory in the meantime.
Co-authored-by: Clément Renault <clement@meilisearch.com>
3942: Normalize for the search the facets values r=ManyTheFish a=Kerollmops
This PR improves and fixes the search for facet values feature. Searching for _bre_ wasn't returning facet values like _brévent_ or _brô_.
The issue was related to the fact that facets are normalized but not in the same way as the `searchableAttributes` are. We decided to normalize them further and add another intermediate database where the key is the normalized facet value, and the value is a set of the non-normalized facets. We then use these non-normalized ones to get the correct counts by fetching the associated databases.
### What's missing in this PR?
- [x] Apply the change to the whole set of `SearchForFacetValue::execute` conditions.
- [x] Factorize the code that does an intermediate normalized value fetch in a function.
- [x] Add or modify the search for facet value test.
Co-authored-by: Clément Renault <clement@meilisearch.com>
Co-authored-by: Kerollmops <clement@meilisearch.com>
3921: Deactivate camel case segmentation r=dureuill a=ManyTheFish
# Pull Request
This PR deactivates the camel case segmentation to retrieve the possibility to accept typos over camel-cased words
## Related issue
Fixes#3869Fixes#3818
## What does this PR do?
- deactivates camelcase segmentation
related to #3919
Co-authored-by: ManyTheFish <many@meilisearch.com>
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>
3821: Add normalized and detailed scores to documents returned by a query r=dureuill a=dureuill
# Pull Request
## Related issue
Fixes#3771
## What does this PR do?
### User standpoint
<details>
<summary>Request ranking score</summary>
```
echo '{
"q": "Badman dark knight returns",
"showRankingScore": true,
"limit": 10,
"attributesToRetrieve": ["title"]
}' | mieli search -i index-word-count-10-count
```
</details>
<details>
<summary>Response</summary>
```json
{
"hits": [
{
"title": "Batman: The Dark Knight Returns, Part 1",
"_rankingScore": 0.947520325203252
},
{
"title": "Batman: The Dark Knight Returns, Part 2",
"_rankingScore": 0.947520325203252
},
{
"title": "Batman Unmasked: The Psychology of the Dark Knight",
"_rankingScore": 0.6657594086021505
},
{
"title": "Legends of the Dark Knight: The History of Batman",
"_rankingScore": 0.6654905913978495
},
{
"title": "Angel and the Badman",
"_rankingScore": 0.2196969696969697
},
{
"title": "Angel and the Badman",
"_rankingScore": 0.2196969696969697
},
{
"title": "Batman",
"_rankingScore": 0.11553030303030302
},
{
"title": "Batman Begins",
"_rankingScore": 0.11553030303030302
},
{
"title": "Batman Returns",
"_rankingScore": 0.11553030303030302
},
{
"title": "Batman Forever",
"_rankingScore": 0.11553030303030302
}
],
"query": "Badman dark knight returns",
"processingTimeMs": 12,
"limit": 10,
"offset": 0,
"estimatedTotalHits": 46
}
```
</details>
- If adding a `showRankingScore` parameter to the search query, then documents returned by a search now contain an additional field `_rankingScore` that is a float bigger than 0 and lower or equal to 1.0. This field represents the relevancy of the document, relatively to the search query and the settings of the index, with 1.0 meaning "perfect match" and 0 meaning "not matching the query" (Meilisearch should never return documents not matching the query at all).
- The `sort` and `geosort` ranking rules do not influence the `_rankingScore`.
<details>
<summary>Request detailed ranking scores</summary>
```
echo '{
"q": "Badman dark knight returns",
"showRankingScoreDetails": true,
"limit": 5,
"attributesToRetrieve": ["title"]
}' | mieli search -i index-word-count-10-count
```
</details>
<details>
<summary>Response</summary>
```json
{
"hits": [
{
"title": "Batman: The Dark Knight Returns, Part 1",
"_rankingScoreDetails": {
"words": {
"order": 0,
"matchingWords": 4,
"maxMatchingWords": 4,
"score": 1.0
},
"typo": {
"order": 1,
"typoCount": 1,
"maxTypoCount": 4,
"score": 0.8
},
"proximity": {
"order": 2,
"score": 0.9545454545454546
},
"attribute": {
"order": 3,
"attributes_ranking_order": 1.0,
"attributes_query_word_order": 0.926829268292683,
"score": 0.926829268292683
},
"exactness": {
"order": 4,
"matchType": "noExactMatch",
"score": 0.26666666666666666
}
}
},
{
"title": "Batman: The Dark Knight Returns, Part 2",
"_rankingScoreDetails": {
"words": {
"order": 0,
"matchingWords": 4,
"maxMatchingWords": 4,
"score": 1.0
},
"typo": {
"order": 1,
"typoCount": 1,
"maxTypoCount": 4,
"score": 0.8
},
"proximity": {
"order": 2,
"score": 0.9545454545454546
},
"attribute": {
"order": 3,
"attributes_ranking_order": 1.0,
"attributes_query_word_order": 0.926829268292683,
"score": 0.926829268292683
},
"exactness": {
"order": 4,
"matchType": "noExactMatch",
"score": 0.26666666666666666
}
}
},
{
"title": "Batman Unmasked: The Psychology of the Dark Knight",
"_rankingScoreDetails": {
"words": {
"order": 0,
"matchingWords": 3,
"maxMatchingWords": 4,
"score": 0.75
},
"typo": {
"order": 1,
"typoCount": 1,
"maxTypoCount": 3,
"score": 0.75
},
"proximity": {
"order": 2,
"score": 0.6666666666666666
},
"attribute": {
"order": 3,
"attributes_ranking_order": 1.0,
"attributes_query_word_order": 0.8064516129032258,
"score": 0.8064516129032258
},
"exactness": {
"order": 4,
"matchType": "noExactMatch",
"score": 0.25
}
}
},
{
"title": "Legends of the Dark Knight: The History of Batman",
"_rankingScoreDetails": {
"words": {
"order": 0,
"matchingWords": 3,
"maxMatchingWords": 4,
"score": 0.75
},
"typo": {
"order": 1,
"typoCount": 1,
"maxTypoCount": 3,
"score": 0.75
},
"proximity": {
"order": 2,
"score": 0.6666666666666666
},
"attribute": {
"order": 3,
"attributes_ranking_order": 1.0,
"attributes_query_word_order": 0.7419354838709677,
"score": 0.7419354838709677
},
"exactness": {
"order": 4,
"matchType": "noExactMatch",
"score": 0.25
}
}
},
{
"title": "Angel and the Badman",
"_rankingScoreDetails": {
"words": {
"order": 0,
"matchingWords": 1,
"maxMatchingWords": 4,
"score": 0.25
},
"typo": {
"order": 1,
"typoCount": 0,
"maxTypoCount": 1,
"score": 1.0
},
"proximity": {
"order": 2,
"score": 1.0
},
"attribute": {
"order": 3,
"attributes_ranking_order": 1.0,
"attributes_query_word_order": 0.8181818181818182,
"score": 0.8181818181818182
},
"exactness": {
"order": 4,
"matchType": "noExactMatch",
"score": 0.3333333333333333
}
}
}
],
"query": "Badman dark knight returns",
"processingTimeMs": 9,
"limit": 5,
"offset": 0,
"estimatedTotalHits": 46
}
```
</details>
- If adding a `showRankingScoreDetails` parameter to the search query, then the returned documents will now contain an additional `_rankingScoreDetails` field that is a JSON object containing one field per ranking rule that was applied, whose value is a JSON object with the following fields:
- `order`: a number indicating the order this rule was applied (0 is the first applied ranking rule)
- `score` (except for `sort` and `geosort`): a float indicating how the document matched this particular rule.
- other fields that are specific to the rule, indicating for example how many words matched for a document and how many typos were counted in a matching document.
- If the `displayableAttributes` list is defined in the settings of the index, any ranking rule using an attribute **not** part of that list will be marked as `<hidden-rule>` in the `_rankingScoreDetails`.
- Search queries that are part of a `multi-search` requests are modified in the same way and each of the queries can take the `showRankingScore` and `showRankingScoreDetails` parameters independently. The results are still returned in separate lists and providing a unified list of results between multiple queries is not in the scope of this PR (but is unblocked by this PR and can be done manually by using the scores of the various documents).
### Implementation standpoint
- Fix difference in how the position of terms were computed at indexing time and query time: this difference meant that a query containing a hard separator would fail the exactness check.
- Fix the id reported by the sort ranking rule (very minor)
- Change how the cost of removing words is computed. After this change the cost no longer works for any other ranking rule than `words`. Also made `words` have a cost of 0 such that the entire cost of `words` is given by the termRemovalStrategy. The new cost computation makes it so the score is computed in a way consistent with the number of words in the query. Additionally, the words that appear in phrases in the query are also counted as matching words.
- When any score computation is requested through `showRankingScore` or `showRankingScoreDetails`, remove optimization where ranking rules are not executed on buckets of a single document: this is important to allow the computation of an accurate score.
- add virtual conditions to fid and position to always have the max cost: this ensures that the score is independent from the dataset
- the Position ranking rule now takes into account the distance to the position of the word in the query instead of the distance to the position 0.
- modified proximity ranking rule cost calculation so that the cost is 0 for documents that are perfectly matching the query
- Add a new `milli::score_details` module containing all the types that are involved in score computation.
- Make it so a bucket of result now contains a `ScoreDetails` and changed the ranking rules to produce their `ScoreDetails`.
- Expose the scores in the REST API.
- Add very light analytics for scoring.
- Update the search tests to add the expected scores.
Co-authored-by: Louis Dureuil <louis@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>
3835: Add more documentation to graph-based ranking rule algorithms + comment cleanup r=Kerollmops a=loiclec
In addition to documenting the `cheapest_path.rs` file, this PR cleans up a few outdated comments as well as some TODOs. These TODOs have been moved to https://github.com/meilisearch/meilisearch/issues/3776
Co-authored-by: Loïc Lecrenier <loic.lecrenier@icloud.com>
