4580: Update the search logs r=Kerollmops a=irevoire
# Pull Request
## Related issue
Fixes https://github.com/meilisearch/meilisearch/issues/4579
## What does this PR do?
- Update the debug implementation of the search query and search results so it’s way smaller and doesn’t display useless information
Co-authored-by: Tamo <tamo@meilisearch.com>
4566: Bring back changes from v1.7.6 to main r=irevoire a=dureuill
Co-authored-by: Louis Dureuil <louis@meilisearch.com>
Co-authored-by: dureuill <dureuill@users.noreply.github.com>
4560: Bring back change from v1.7.5 to main r=curquiza a=irevoire
Co-authored-by: Tamo <tamo@meilisearch.com>
Co-authored-by: irevoire <irevoire@users.noreply.github.com>
Co-authored-by: meili-bors[bot] <89034592+meili-bors[bot]@users.noreply.github.com>
4554: Update version for the next release (v1.7.5) in Cargo.toml r=curquiza a=meili-bot
⚠️ This PR is automatically generated. Check the new version is the expected one and Cargo.lock has been updated before merging.
Co-authored-by: irevoire <irevoire@users.noreply.github.com>
4548: v1.8 hybrid search changes r=dureuill a=dureuill
Implements the search changes from the [usage page](https://meilisearch.notion.site/v1-8-AI-search-API-usage-135552d6e85a4a52bc7109be82aeca42#40f24df3da694428a39cc8043c9cfc64)
### ⚠️ Breaking changes in an experimental feature:
- Removed the `_semanticScore`. Use the `_rankingScore` instead.
- Removed `vector` in the response of the search (output was too big).
- Removed all the vectors from the `vectorSort` ranking score details
- target vector appearing in the name of the rule
- matched vector appearing in the details of the rule
### Other user-facing changes
- Added `semanticHitCount`, indicating how many hits were returned from the semantic search. This is especially useful in the hybrid search.
- Embed lazily: Meilisearch no longer generates an embedding when the keyword results are "good enough".
- Graceful embedding failure in hybrid search: when doing hybrid search (`semanticRatio in ]0.0, 1.0[`), an embedding failure no longer causes the search request to fail. Instead, only the keyword search is performed. When doing a full vector search (`semanticRatio==1.0`), a failure to embed will still result in failing that search.
Co-authored-by: Louis Dureuil <louis@meilisearch.com>
4549: Hugging Face embedder improvements r=dureuill a=dureuill
Architectural changes/Internal improvements
### 1. Prefer safetensors weights over pytorch weights when available
safetensors weights are memory mapped, which reduces memory usage of supported models.
### 2. Update candle
Updates candle to `0.4.1`, now targeting crates.io and the tokenizers to `v0.15.2` (still on github).
This might fix https://github.com/meilisearch/meilisearch/issues/4399 thanks to the now included https://github.com/huggingface/candle/issues/1454
Co-authored-by: Louis Dureuil <louis@meilisearch.com>
