Meilisearch v1.11 release notes

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New Features

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AI-powered search improvements

• Binary quantization for embeddings: Enable the new binaryQuantized option to convert floating-point embeddings into boolean values. This significantly improves performance and reduces database size (up to 10x reduction and 6x faster indexing) but impacts relevancy. This option cannot be reverted once enabled.

curl \
 -X PATCH 'http://localhost:7700/indexes/movies/settings' \
 -H 'Content-Type: application/json' \
 --data-binary '{
 "embedders": {
 "image2text": {
 "binaryQuantized": true
 }
 }
 }'

• Document template improvements: The documentTemplate field now includes a new field.is_searchable property. The default template now filters out empty fields and non-searchable attributes for better embedding quality.
• New embedder option: documentTemplateMaxBytes allows you to truncate document template text when it exceeds a specified byte limit.
• Updated default OpenAI model: The default embedding model is now text-embedding-3-small instead of text-embedding-ada-002.

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Federated search enhancements

• facetsByIndex: Request facet distribution and stats for each index separately in federated searches

POST /multi-search
{
 "federation": {
 "limit": 20,
 "offset": 0,
 "facetsByIndex": {
 "movies": ["title", "id"],
 "comics": ["title"]
 }
 },
 "queries": [
 {
 "q": "Batman",
 "indexUid": "movies"
 },
 {
 "q": "Batman",
 "indexUid": "comics"
 }
 ]
}

• mergeFacets: Merge facet data from multiple indexes into a single result set

POST /multi-search
{
 "federation": {
 "limit": 20,
 "offset": 0,
 "facetsByIndex": {
 "movies": ["title", "id"],
 "comics": ["title"]
 },
 "mergeFacets": {
 "maxValuesPerFacet": 10
 }
 },
 "queries": [
 {
 "q": "Batman",
 "indexUid": "movies"
 },
 {
 "q": "Batman",
 "indexUid": "comics"
 }
 ]
}

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Experimental STARTS WITH filter operator

curl \
 -X PATCH 'http://localhost:7700/experimental-features/' \
 -H 'Content-Type: application/json' \
 --data-binary '{
 "containsFilter": true
 }'

{
  "filter": "hero STARTS WITH spider"
}

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Language support improvements

• Added ISO-639-1 language variants and automatic conversion to ISO-639-3
• New German language tokenizer
• Improved Turkish language support
• Fixed Swedish character normalization so å, ä, and ö are no longer normalized

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Improvements

• Improved error handling when using query.facets with federated search (now returns appropriate error instead of silently ignoring the parameter)
• Fixed facet value truncation to correctly apply maxValuesPerFacet limits
• Improved task cancellation when vectors are used
• Better timeout handling for embedding requests during search (3s timeout added)
• Added timeouts to read and write operations
• Retry logic added for deserialization failures in remote embedding providers (REST/OpenAI/ollama)
• Improved vector display when no custom vectors were provided
• Updated Rhai to fix errors when updating documents with functions
• Batch failed logs now appear at error level
• Removed forced capitalization in search UI fields

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Other

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Breaking changes

• When performing AI-powered searches, hybrid.embedder is now mandatory in GET and POST /indexes/{:indexUid}/search
• hybrid must now be passed even for pure semantic searches
• embedder is now mandatory in GET and POST /indexes/{:indexUid}/similar
• semanticRatio is ignored for queries that include vector but not q (performs pure semantic search instead)
• When using federated search, query.facets at the query level now returns an error instead of being silently ignored. Use federation.facetsByIndex instead.