Meilisearch v1.3 release notes

New Features

Vector Search (Experimental)

Meilisearch now supports vector search, allowing you to use it as a vector store. You can add vector embeddings generated by third-party tools (such as Hugging Face, Cohere, or OpenAI) and search using vector similarity. Enable vector search via the experimental features endpoint:

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

Add documents with vector embeddings using the _vectors field:

curl -X POST -H 'content-type: application/json' \
 'localhost:7700/indexes/songs/documents' \
 --data-binary '[
 {"id": 0, "_vectors": [0, 0.8, -0.2], "title": "Across The Universe"},
 {"id": 1, "_vectors": [1, -0.2, 0], "title": "All Things Must Pass"},
 {"id": 2, "_vectors": [[0.5, 3, 1], [-0.2, 4, 6]], "title": "And Your Bird Can Sing"}
 ]'

Query using vectors with the /search or /multi-search endpoints:

curl -X POST -H 'content-type: application/json' \
 'localhost:7700/indexes/songs/search' \
 --data-binary '{"vector": [0, 1, 2]}'

Vector search results include a _semanticScore field (0 to 1) indicating relevance:

{
  "hits": [
    {
      "id": 0,
      "_vectors": [
        0,
        0.8,
        -0.2
      ],
      "title": "Across The Universe",
      "_semanticScore": 0.6754
    }
  ]
}

Note: Vector size must be consistent across all documents in an index.

Ranking Score Visibility

Use the showRankingScore search parameter to see how relevant each document is to your query:

curl \
 -X POST 'http://localhost:7700/indexes/movies/search' \
 -H 'Content-Type: application/json' \
 --data-binary '{"q": "Batman Returns", "showRankingScore": true}'

Each document includes a _rankingScore field (0 to 1, higher is more relevant).

Ranking Score Details (Experimental)

Get detailed scoring breakdowns per ranking rule using the experimental showRankingScoreDetails parameter:

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

Then use it in searches:

curl \
 -X POST 'http://localhost:7700/indexes/movies/search' \
 -H 'Content-Type: application/json' \
 --data-binary '{"q": "Batman Returns", "showRankingScoreDetails": true}'

Results include a _rankingScoreDetails object showing scores for each ranking rule (words, typo, proximity, attribute, exactness).

Define Searchable Fields at Query Time

The new attributesToSearchOn search parameter restricts searches to specific attributes:

{
  "q": "adventure",
  "attributesToSearchOn": [
    "genre"
  ]
}

Attributes must be in the searchable attributes list. Given a dataset with documents containing “adventure” in both name and genre fields, this query returns only documents with “adventure” in the genre field. The new POST /indexes/{index}/facet-search endpoint searches within facet values (fields defined as filterableAttributes). It supports prefix search and typo tolerance:

curl \
 -X POST 'http://localhost:7700/indexes/movies/facet-search' \
 -H 'Content-Type: application/json' \
 --data-binary '{"facetName": "genres", "facetQuery": "a"}'

Use the sortFacetValuesBy setting to order facet values by frequency. Sort all facets by count:

curl \
 -X PATCH 'http://localhost:7700/indexes/movies/settings/faceting' \
 -H 'Content-Type: application/json' \
 --data-binary '{"sortFacetValuesBy": {"*": "count"}}'

Or sort individual facets while keeping others alphabetical:

curl \
 -X PATCH 'http://localhost:7700/indexes/movies/settings/faceting' \
 -H 'Content-Type: application/json' \
 --data-binary '{"sortFacetValuesBy": {"*": "alpha", "genre": "count"}}'

Task Queue Visibility

The /tasks route now includes a total property showing the total number of tasks in the queue. You can filter this count, for example /tasks?statuses=succeeded shows the total number of successfully processed tasks.

Improvements

The attribute ranking rule now calculates relevance based on how close a matching word is to that word’s position in the query, rather than its absolute distance from the beginning of the attribute. This provides more intuitive ranking when search terms appear at different positions in document fields.

Language Support Enhancements

Improved Japanese word segmentation
Enhanced separator-based tokenization: words containing underscores (_) are now properly segmented into separate words, and brackets {()} are no longer treated as context separators for the proximity ranking rule

Performance and Size Improvements

Reduced index size by approximately 15% through internal database optimization
Improved deserialization performance
Re-enabled task autobatching for addition and deletion operations
Fixed performance issue on /stats endpoint

Metrics Improvements

The experimental Prometheus /metrics endpoint now provides:

Task queue metrics including number of queued and processing tasks
Real database size used by Meilisearch
“meilisearch” prefix on all metrics
lastUpdate and isIndexing fields in /stats endpoint

Web Interface Update

Updated the local search preview web interface and mini-dashboard to version v0.2.11.

Other

Case-Sensitive Search Fix

Fixed case-sensitive search issues with camelCase words. Searches for dellonghi now properly match documents containing DeLonghi.

Vector and Geo Fixes

Fixed geo bounding box queries with string coordinates (requires re-indexing documents with lat and lng fields)
Fixed handling of null JSON values in the _vectors field
Fixed panic when using multiple vectors with different dimensions
Fixed panic when sorting geo fields represented as strings

Filter Improvements

Fixed filter escaping to properly handle the backslash character at the end of filter values.

Other Notable Changes

Fixed highlighting document issues by properly remapping char map when lowercasing strings
Fixed panic in ranking rule bucket sort algorithm
Added new /experimental-features endpoint for managing experimental features like scoreDetails and vectorStore
Fixed document deletion statistics when using filters

Find more information on GitHub

New Features

Vector Search (Experimental)

Ranking Score Visibility

Ranking Score Details (Experimental)

Define Searchable Fields at Query Time

Search Facet Values

Sort Facets by Count

Task Queue Visibility

Improvements

Attribute Ranking Rule Refinement

Language Support Enhancements

Performance and Size Improvements

Metrics Improvements

Web Interface Update

Other

Case-Sensitive Search Fix

Vector and Geo Fixes

Filter Improvements

Other Notable Changes

​New Features

​Vector Search (Experimental)

​Ranking Score Visibility

​Ranking Score Details (Experimental)

​Define Searchable Fields at Query Time

​Search Facet Values

​Sort Facets by Count

​Task Queue Visibility

​Improvements

​Attribute Ranking Rule Refinement

​Language Support Enhancements

​Performance and Size Improvements

​Metrics Improvements

​Web Interface Update

​Other

​Case-Sensitive Search Fix

​Vector and Geo Fixes

​Filter Improvements

​Other Notable Changes

New Features

Vector Search (Experimental)

Ranking Score Visibility

Ranking Score Details (Experimental)

Define Searchable Fields at Query Time

Search Facet Values

Sort Facets by Count

Task Queue Visibility

Improvements

Attribute Ranking Rule Refinement

Language Support Enhancements

Performance and Size Improvements

Metrics Improvements

Web Interface Update

Other

Case-Sensitive Search Fix

Vector and Geo Fixes

Filter Improvements

Other Notable Changes