598: Matching query terms policy r=Kerollmops a=ManyTheFish
## Summary
Implement several optional words strategy.
## Content
Replace `optional_words` boolean with an enum containing several term matching strategies:
```rust
pub enum TermsMatchingStrategy {
// remove last word first
Last,
// remove first word first
First,
// remove more frequent word first
Frequency,
// remove smallest word first
Size,
// only one of the word is mandatory
Any,
// all words are mandatory
All,
}
```
All strategies implemented during the prototype are kept, but only `Last` and `All` will be published by Meilisearch in the `v0.29.0` release.
## Related
spec: https://github.com/meilisearch/specifications/pull/173
prototype discussion: https://github.com/meilisearch/meilisearch/discussions/2639#discussioncomment-3447699
Co-authored-by: ManyTheFish <many@meilisearch.com>
609: Retry downloading the benchmarks datasets r=Kerollmops a=irevoire
Downloading the benchmarks datasets is failing [more and more](https://github.com/meilisearch/milli/pull/607#pullrequestreview-1076023074) often; thus, instead of fixing the issue, I thought we could retry multiple times.
Co-authored-by: Irevoire <tamo@meilisearch.com>
561: Enriched documents batch reader r=curquiza a=Kerollmops
~This PR is based on #555 and must be rebased on main after it has been merged to ease the review.~
This PR contains the work in #555 and can be merged on main as soon as reviewed and approved.
- [x] Create an `EnrichedDocumentsBatchReader` that contains the external documents id.
- [x] Extract the primary key name and make it accessible in the `EnrichedDocumentsBatchReader`.
- [x] Use the external id from the `EnrichedDocumentsBatchReader` in the `Transform::read_documents`.
- [x] Remove the `update_primary_key` from the _transform.rs_ file.
- [x] Really generate the auto-generated documents ids.
- [x] Insert the (auto-generated) document ids in the document while processing it in `Transform::read_documents`.
Co-authored-by: Kerollmops <clement@meilisearch.com>
572: Add reindexing benchmarks r=Kerollmops a=irevoire
With #557 coming, we should add benchmarks that measure our impact on the reindexing process.
Co-authored-by: Tamo <tamo@meilisearch.com>
541: Update version for next release (v0.29.0) r=ManyTheFish a=curquiza
Need to update the version since #540 was merged and breaking
Co-authored-by: Clémentine Urquizar <clementine@meilisearch.com>
483: Enhance matching words r=Kerollmops a=ManyTheFish
# Summary
Enhance milli word-matcher making it handle match computing and cropping.
# Implementation
## Computing best matches for cropping
Before we were considering that the first match of the attribute was the best one, this was accurate when only one word was searched but was missing the target when more than one word was searched.
Now we are searching for the best matches interval to crop around, the chosen interval is the one:
1) that have the highest count of unique matches
> for example, if we have a query `split the world`, then the interval `the split the split the` has 5 matches but only 2 unique matches (1 for `split` and 1 for `the`) where the interval `split of the world` has 3 matches and 3 unique matches. So the interval `split of the world` is considered better.
2) that have the minimum distance between matches
> for example, if we have a query `split the world`, then the interval `split of the world` has a distance of 3 (2 between `split` and `the`, and 1 between `the` and `world`) where the interval `split the world` has a distance of 2. So the interval `split the world` is considered better.
3) that have the highest count of ordered matches
> for example, if we have a query `split the world`, then the interval `the world split` has 2 ordered words where the interval `split the world` has 3. So the interval `split the world` is considered better.
## Cropping around the best matches interval
Before we were cropping around the interval without checking the context.
Now we are cropping around words in the same context as matching words.
This means that we will keep words that are farther from the matching words but are in the same phrase, than words that are nearer but separated by a dot.
> For instance, for the matching word `Split` the text:
`Natalie risk her future. Split The World is a book written by Emily Henry. I never read it.`
will be cropped like:
`…. Split The World is a book written by Emily Henry. …`
and not like:
`Natalie risk her future. Split The World is a book …`
Co-authored-by: ManyTheFish <many@meilisearch.com>
