5414: Update version for the next release (v1.14.0) in Cargo.toml r=Kerollmops a=meili-bot
⚠️ This PR is automatically generated. Check the new version is the expected one and Cargo.lock has been updated before merging. Fixes https://github.com/meilisearch/meilisearch/issues/5268.
Co-authored-by: Kerollmops <Kerollmops@users.noreply.github.com>
Co-authored-by: Kerollmops <clement@meilisearch.com>
5420: Add support for the progress API of arroy r=Kerollmops a=irevoire
# Pull Request
## Related issue
Fixes https://github.com/meilisearch/meilisearch/issues/5419
## What does this PR do?
- Convert the arroy progress to the meilisearch progress
- Use the new arroy closure to support the progress of arroy
Co-authored-by: Tamo <tamo@meilisearch.com>
5418: Cache embeddings in search r=Kerollmops a=dureuill
# Pull Request
## Related issue
TBD
## What does this PR do?
- Adds a cache for embeddings produced in search
- The cache is disabled by default, and can be enabled following the instructions [here](https://github.com/orgs/meilisearch/discussions/818).
- Had to accommodate the `timeout` test for openai that uses a mock that simulates a timeout on subsequent responses: since the test was reusing the same query, the cache would kick-in and no request would be made to the mock, meaning no timeout any longer and so a failing test 😅
- `Embedder::embed_search` now accepts a reference instead of an owned `String`.
## Manual testing
- I created 4 indexes on a fresh DB with the same settings (one embedder from openai)
- I sent 1/4 of movies.json to each index
- I sent a federated search request against all 4 indexes, with the same query for each index, using the embedder of each index.
Results:
- The first call took 400ms to 1s. Before this change, it took in the 3s range.
- Any repeated call with the same query took in the range of 25ms.
- Looking at the details at trace log level, I can see that the first index that needs the embedding is taking most of the 400ms in `embed_one`. The other indexes report that the query text is found in the cache and they each take a few µs.
Co-authored-by: Louis Dureuil <louis@meilisearch.com>
5369: exhaustive facet search r=ManyTheFish a=ManyTheFish
Fixes#5403
This PR adds an `exhaustiveFacetCount` field to the `/facet-search` API allowing the end-user to have a better facet count when having a distinct attribute set in the index settings.
# Usage
`POST /index/:index_uid/facet-search`
**Body:**
```json
{
"facetQuery": "blob",
"facetName": "genres",
"q": "",
"exhaustiveFacetCount": true
}
```
# Prototype Docker images
```sh
$ docker pull getmeili/meilisearch:prototype-exhaustive-facet-search-00
```
Co-authored-by: ManyTheFish <many@meilisearch.com>
