New Features
Hybrid search enhancements
Meilisearch now supports two new embedder sources for hybrid search: Ollama model - Run language models locally using the Ollama framework:Negative keywords
Exclude specific terms from search results using the- operator:
-escapereturns documents that do not contain “escape”-escape roomreturns documents containing “room” but not “escape”-"on demand"returns documents that do not contain the phrase “on demand”
Search cutoff timeout
Configure a timeout for search requests to prevent crashes and performance issues. Set a custom timeout value using the/settings endpoint:
null to disable the cutoff.
Improvements
Indexing performance
Increased indexing speed when updating settings.Search stability
Added a limit for concurrent search requests to prevent unbounded RAM consumption. Launch your instance with a custom limit:Facet sorting
ThesortFacetValuesBy setting now impacts the /facet-search route for consistent facet value ordering.
Hybrid search improvements
- Return keyword search results even if embedding generation fails during hybrid searches
- Added
semanticHitCountfield to search responses indicating the number of hits from semantic search - Improved search logs to exclude
hitsfrom DEBUG log level output
Tokenizer improvements
Enhanced tokenization with support for:- Markdown formatted code blocks
- Improved Korean segmentation
- Tab character (
\t) recognition as a separator - Optional pinyin normalization for Chinese text
Vector embeddings in dumps
Vectors are now included in database dumps, providing an upgrade path to future versions without requiring regeneration of embeddings for auto-generating embedders.Other
Breaking changes: Semantic search scoring
To improve search response times and reduce bandwidth:_semanticScoreis no longer returned in search responses; use_rankingScoreinstead- The
vectorfield is no longer included in search responses - Query vectors are no longer displayed when
"showRankingScoreDetails": trueis set