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>
3319: Transparently resize indexes on MaxDatabaseSizeReached errors r=Kerollmops a=dureuill
# Pull Request
## Related issue
Related to https://github.com/meilisearch/meilisearch/discussions/3280, depends on https://github.com/meilisearch/milli/pull/760
## What does this PR do?
### User standpoint
- Meilisearch no longer fails tasks that encounter the `milli::UserError(MaxDatabaseSizeReached)` error.
- Instead, these tasks are retried after increasing the maximum size allocated to the index where the failure occurred.
### Implementation standpoint
- Add `Batch::index_uid` to get the `index_uid` of a batch of task if there is one
- `IndexMapper::create_or_open_index` now takes an additional `size` argument that allows to (re)open indexes with a size different from the base `IndexScheduler::index_size` field
- `IndexScheduler::tick` now returns a `Result<TickOutcome>` instead of a `Result<usize>`. This offers more explicit control over what the behavior should be wrt the next tick.
- Add `IndexStatus::BeingResized` that contains a handle that a thread can use to await for the resize operation to complete and the index to be available again.
- Add `IndexMapper::resize_index` to increase the size of an index.
- In `IndexScheduler::tick`, intercept task batches that failed due to `MaxDatabaseSizeReached` and resize the index that caused the error, then request a new tick that will eventually handle the still enqueued task.
## Testing the PR
The following diff can be applied to this branch to make testing the PR easier:
<details>
```diff
diff --git a/index-scheduler/src/index_mapper.rs b/index-scheduler/src/index_mapper.rs
index 553ab45a..022b2f00 100644
--- a/index-scheduler/src/index_mapper.rs
+++ b/index-scheduler/src/index_mapper.rs
`@@` -228,13 +228,15 `@@` impl IndexMapper {
drop(lock);
+ std:🧵:sleep_ms(2000);
+
let current_size = index.map_size()?;
let closing_event = index.prepare_for_closing();
- log::info!("Resizing index {} from {} to {} bytes", name, current_size, current_size * 2);
+ log::error!("Resizing index {} from {} to {} bytes", name, current_size, current_size * 2);
closing_event.wait();
- log::info!("Resized index {} from {} to {} bytes", name, current_size, current_size * 2);
+ log::error!("Resized index {} from {} to {} bytes", name, current_size, current_size * 2);
let index_path = self.base_path.join(uuid.to_string());
let index = self.create_or_open_index(&index_path, None, 2 * current_size)?;
`@@` -268,8 +270,10 `@@` impl IndexMapper {
match index {
Some(Available(index)) => break index,
Some(BeingResized(ref resize_operation)) => {
+ log::error!("waiting for resize end");
// Deadlock: no lock taken while doing this operation.
resize_operation.wait();
+ log::error!("trying our luck again!");
continue;
}
Some(BeingDeleted) => return Err(Error::IndexNotFound(name.to_string())),
diff --git a/index-scheduler/src/lib.rs b/index-scheduler/src/lib.rs
index 11b17d05..242dc095 100644
--- a/index-scheduler/src/lib.rs
+++ b/index-scheduler/src/lib.rs
`@@` -908,6 +908,7 `@@` impl IndexScheduler {
///
/// Returns the number of processed tasks.
fn tick(&self) -> Result<TickOutcome> {
+ log::error!("ticking!");
#[cfg(test)]
{
*self.run_loop_iteration.write().unwrap() += 1;
diff --git a/meilisearch/src/main.rs b/meilisearch/src/main.rs
index 050c825a..63f312f6 100644
--- a/meilisearch/src/main.rs
+++ b/meilisearch/src/main.rs
`@@` -25,7 +25,7 `@@` fn setup(opt: &Opt) -> anyhow::Result<()> {
#[actix_web::main]
async fn main() -> anyhow::Result<()> {
- let (opt, config_read_from) = Opt::try_build()?;
+ let (mut opt, config_read_from) = Opt::try_build()?;
setup(&opt)?;
`@@` -56,6 +56,8 `@@` We generated a secure master key for you (you can safely copy this token):
_ => (),
}
+ opt.max_index_size = byte_unit::Byte::from_str("1MB").unwrap();
+
let (index_scheduler, auth_controller) = setup_meilisearch(&opt)?;
#[cfg(all(not(debug_assertions), feature = "analytics"))]
```
</details>
Mainly, these debug changes do the following:
- Set the default index size to 1MiB so that index resizes are initially frequent
- Turn some logs from info to error so that they can be displayed with `--log-level ERROR` (hiding the other infos)
- Add a long sleep between the beginning and the end of the resize so that we can observe the `BeingResized` index status (otherwise it would never come up in my tests)
## Open questions
- Is the growth factor of x2 the correct solution? For a `Vec` in memory it makes sense, but here we're manipulating quantities that are potentially in the order of 500GiBs. For bigger indexes it may make more sense to add at most e.g. 100GiB on each resize operation, avoiding big steps like 500GiB -> 1TiB.
## PR checklist
Please check if your PR fulfills the following requirements:
- [ ] Does this PR fix an existing issue, or have you listed the changes applied in the PR description (and why they are needed)?
- [ ] Have you read the contributing guidelines?
- [ ] Have you made sure that the title is accurate and descriptive of the changes?
Thank you so much for contributing to Meilisearch!
3470: Autobatch addition and deletion r=irevoire a=irevoire
This PR adds the capability to meilisearch to batch document addition and deletion together.
Fix https://github.com/meilisearch/meilisearch/issues/3440
--------------
Things to check before merging;
- [x] What happens if we delete multiple time the same documents -> add a test
- [x] If a documentDeletion gets batched with a documentAddition but the index doesn't exist yet? It should not work
Co-authored-by: Louis Dureuil <louis@meilisearch.com>
Co-authored-by: Tamo <tamo@meilisearch.com>
3499: Use the workspace inheritance r=Kerollmops a=irevoire
Use the workspace inheritance [introduced in rust 1.64](https://blog.rust-lang.org/2022/09/22/Rust-1.64.0.html#cargo-improvements-workspace-inheritance-and-multi-target-builds).
It allows us to define the version of meilisearch once in the main `Cargo.toml` and let all the other `Cargo.toml` uses this version.
`@curquiza` I added you as a reviewer because I had to patch some CI scripts
And `@Kerollmops,` I had to bump the `cargo_toml` crates because our version was getting old and didn't support the feature yet.
Also, in another PR, I would like to unify some of our dependencies to ensure we always stay in sync between all our crates.
Co-authored-by: Tamo <tamo@meilisearch.com>
3490: Fix attributes set candidates r=curquiza a=ManyTheFish
# Pull Request
Fix attributes set candidates for v1.1.0
## details
The attribute criterion was not returning the remaining candidates when its internal algorithm was been exhausted.
We had a loss of candidates by the attribute criterion leading to the bug reported in the issue linked below.
After some investigation, it seems that it was the only criterion that had this behavior.
We are now returning the remaining candidates instead of an empty bitmap.
## Related issue
Fixes#3483
PR on milli for v1.0.1: https://github.com/meilisearch/milli/pull/777
Co-authored-by: ManyTheFish <many@meilisearch.com>
3492: Bump deserr r=Kerollmops a=irevoire
Bump deserr to the latest version;
- We now use the default actix-web extractors that deserr provides (which were copy/pasted from meilisearch)
- We also use the default `JsonError` message provided by deserr instead of defining our own in meilisearch
- Finally, we get the new `did you mean?` error message. Fix#3493
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>
776: Reduce incremental indexing time of `words_prefix_position_docids` DB r=curquiza a=loiclec
Fixes partially https://github.com/meilisearch/milli/issues/605
The `words_prefix_position_docids` can easily contain millions of entries. Thus, iterating
over it can be very expensive. But we do so needlessly for every document addition tasks.
It can sometimes cause indexing performance issues when :
- a user sends many `documentAdditionOrUpdate` tasks that cannot be all batched together (for example if they are interspersed with `documentDeletion` tasks)
- the documents contain long, diverse text fields, thus increasing the number of entries in `words_prefix_position_docids`
- the index has accumulated many soft-deleted documents, further increasing the size of `words_prefix_position_docids`
- the machine running Meilisearch does not have great IO performance (e.g. slow SSD, or quota-limited by the cloud provider)
Note, before approving the PR: the only changed file should be `milli/src/update/words_prefix_position_docids.rs`.
Co-authored-by: Loïc Lecrenier <loic.lecrenier@me.com>
774: Update version for the next release (v0.41.1) 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>
This database can easily contain millions of entries. Thus, iterating
over it can be very expensive.
For regular `documentAdditionOrUpdate` tasks, `del_prefix_fst_words`
will always be empty. Thus, we can save a significant amount of time
by adding this `if !del_prefix_fst_words.is_empty()` condition.
The code's behaviour remains completely unchanged.
763: Fixes error message when lat and lng are unparseable r=loiclec a=ahlner
# Pull Request
## Related issue
Fixes partially [#3007](https://github.com/meilisearch/meilisearch/issues/3007)
## What does this PR do?
- Changes function validate_geo_from_json to return a BadLatitudeAndLongitude if lat or lng is a string and not parseable to f64
- implemented some unittests
- Derived PartialEq for GeoError to use assert_eq! in tests
## 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: Philipp Ahlner <philipp@ahlner.com>
767: Update version for the next release (v0.39.2) 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>
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>
761: Integrate deserr r=irevoire a=loiclec
1. `Setting<T>` now implements `DeserializeFromValue`
2. The settings now store ranking rules as strongly typed `Criterion` instead of `String`, since the validation of the ranking rules will be done on meilisearch's side from now on
Co-authored-by: Loïc Lecrenier <loic.lecrenier@me.com>
759: Change primary key inference error messages r=Kerollmops a=dureuill
# Pull Request
## Related issue
Milli part of https://github.com/meilisearch/meilisearch/issues/3301
## What does this PR do?
- Change error message strings
## 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: Louis Dureuil <louis@meilisearch.com>
733: Avoid a prefix-related worst-case scenario in the proximity criterion r=loiclec a=loiclec
# Pull Request
## Related issue
Somewhat fixes (until merged into meilisearch) https://github.com/meilisearch/meilisearch/issues/3118
## What does this PR do?
When a query ends with a word and a prefix, such as:
```
word pr
```
Then we first determine whether `pre` *could possibly* be in the proximity prefix database before querying it. There are then three possibilities:
1. `pr` is not in any prefix cache because it is not the prefix of many words. We don't query the proximity prefix database. Instead, we list all the word derivations of `pre` through the FST and query the regular proximity databases.
2. `pr` is in the prefix cache but cannot be found in the proximity prefix databases. **In this case, we partially disable the proximity ranking rule for the pair `word pre`.** This is done as follows:
1. Only find the documents where `word` is in proximity to `pre` **exactly** (no derivations)
2. Otherwise, assume that their proximity in all the documents in which they coexist is >= 8
3. `pr` is in the prefix cache and can be found in the proximity prefix databases. In this case we simply query the proximity prefix databases.
Note that if a prefix is longer than 2 bytes, then it cannot be in the proximity prefix databases. Also, proximities larger than 4 are not present in these databases either. Therefore, the impact on relevancy is:
1. For common prefixes of one or two letters: we no longer distinguish between proximities from 4 to 8
2. For common prefixes of more than two letters: we no longer distinguish between any proximities
3. For uncommon prefixes: nothing changes
Regarding (1), it means that these two documents would be considered equally relevant according to the proximity rule for the query `heard pr` (IF `pr` is the prefix of more than 200 words in the dataset):
```json
[
{ "text": "I heard there is a faster proximity criterion" },
{ "text": "I heard there is a faster but less relevant proximity criterion" }
]
```
Regarding (2), it means that two documents would be considered equally relevant according to the proximity rule for the query "faster pro":
```json
[
{ "text": "I heard there is a faster but less relevant proximity criterion" }
{ "text": "I heard there is a faster proximity criterion" },
]
```
But the following document would be considered more relevant than the two documents above:
```json
{ "text": "I heard there is a faster swimmer who is competing in the pro section of the competition " }
```
Note, however, that this change of behaviour only occurs when using the set-based version of the proximity criterion. In cases where there are fewer than 1000 candidate documents when the proximity criterion is called, this PR does not change anything.
---
## Performance
I couldn't use the existing search benchmarks to measure the impact of the PR, but I did some manual tests with the `songs` benchmark dataset.
```
1. 10x 'a':
- 640ms ⟹ 630ms = no significant difference
2. 10x 'b':
- set-based: 4.47s ⟹ 7.42 = bad, ~2x regression
- dynamic: 1s ⟹ 870 ms = no significant difference
3. 'Someone I l':
- set-based: 250ms ⟹ 12 ms = very good, x20 speedup
- dynamic: 21ms ⟹ 11 ms = good, x2 speedup
4. 'billie e':
- set-based: 623ms ⟹ 2ms = very good, x300 speedup
- dynamic: ~4ms ⟹ 4ms = no difference
5. 'billie ei':
- set-based: 57ms ⟹ 20ms = good, ~2x speedup
- dynamic: ~4ms ⟹ ~2ms. = no significant difference
6. 'i am getting o'
- set-based: 300ms ⟹ 60ms = very good, 5x speedup
- dynamic: 30ms ⟹ 6ms = very good, 5x speedup
7. 'prologue 1 a 1:
- set-based: 3.36s ⟹ 120ms = very good, 30x speedup
- dynamic: 200ms ⟹ 30ms = very good, 6x speedup
8. 'prologue 1 a 10':
- set-based: 590ms ⟹ 18ms = very good, 30x speedup
- dynamic: 82ms ⟹ 35ms = good, ~2x speedup
```
Performance is often significantly better, but there is also one regression in the set-based implementation with the query `b b b b b b b b b b`.
Co-authored-by: Loïc Lecrenier <loic.lecrenier@me.com>
732: Interpret synonyms as phrases r=loiclec a=loiclec
# Pull Request
## Related issue
Fixes (when merged into meilisearch) https://github.com/meilisearch/meilisearch/issues/3125
## What does this PR do?
We now map multi-word synonyms to phrases instead of loose words. Such that the request:
```
btw I am going to nyc soon
```
is interpreted as (when the synonym interpretation is chosen for both `btw` and `nyc`):
```
"by the way" I am going to "New York City" soon
```
instead of:
```
by the way I am going to New York City soon
```
This prevents queries containing multi-word synonyms to exceed to word length limit and degrade the search performance.
In terms of relevancy, there is a debate to have. I personally think this could be considered an improvement, since it would be strange for a user to search for:
```
good DIY project
```
and have a result such as:
```
{
"text": "whether it is a good project to do, you'll have to decide for yourself"
}
```
However, for synonyms such as `NYC -> New York City`, then we will stop matching documents where `New York` is separated from `City`. This is however solvable by adding an additional mapping: `NYC -> New York`.
## Performance
With the old behaviour, some long search requests making heavy uses of synonyms could take minutes to be executed. This is no longer the case, these search requests now take an average amount of time to be resolved.
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>
709: Optimise the `ExactWords` sub-criterion within `Exactness` r=loiclec a=loiclec
# Pull Request
## Related issue
Fixes (partially) https://github.com/meilisearch/meilisearch/issues/3116
## What does this PR do?
1. Reduces the algorithmic complexity of finding the documents containing N exact words from something that is exponential to something that is polynomial.
2. Cache intermediary results between different calls to the `exactness` criterion.
## Performance Results
On the `smol_songs.csv` dataset, a request containing 10 common words now takes about 60ms instead of 5 seconds to execute. For example, this is the case with this (admittedly nonsensical) request: `Rock You Hip Hop Folk World Country Electronic Love The`.
Co-authored-by: Loïc Lecrenier <loic.lecrenier@me.com>
