Meilisearch v1.14 release notes - Meilisearch Documentation

New Features

Granular filterable attribute settings

Control which types of filters you want to enable for each attribute in your documents. Use PATCH /indexes/INDEX_NAME/settings to specify filter features like equality, comparison, and facet search on a per-attribute basis:

{
  "filterableAttributes": [
    {
      "attributePatterns": [
        "genre",
        "artist"
      ],
      "features": {
        "facetSearch": true,
        "filter": {
          "equality": true,
          "comparison": false
        }
      }
    },
    {
      "attributePatterns": [
        "rank"
      ],
      "features": {
        "facetSearch": false,
        "filter": {
          "equality": true,
          "comparison": true
        }
      }
    }
  ]
}

This allows you to further optimize indexing speeds by enabling only the filter features you need for each attribute.

Composite embedders

Use different embedders at search and indexing time to optimize AI-powered search performance. For example, use a remote embedder during indexing (higher bandwidth) and a local embedder during search queries (lower latency). To use composite embedders:

Enable the feature with the /experimental-features route:

curl MEILISEARCH_URL/experimental-features \
 -H 'Content-Type: application/json' \
 -d '{"compositeEmbedders": true}'

Create an embedder with source set to "composite", defining both searchEmbedder and indexingEmbedder:

{
  "embedders": {
    "text": {
      "source": "composite",
      "searchEmbedder": {
        "source": "huggingFace",
        "model": "baai/bge-base-en-v1.5",
        "revision": "a5beb1e3e68b9ab74eb54cfd186867f64f240e1a"
      },
      "indexingEmbedder": {
        "source": "rest",
        "url": "https://URL.endpoints.huggingface.cloud",
        "apiKey": "hf_XXXXXXX",
        "documentTemplate": "Your {{doc.template}}",
        "request": {
          "inputs": [
            "{{text}}",
            "{{..}}"
          ]
        },
        "response": [
          "{{embedding}}",
          "{{..}}"
        ]
      }
    }
  }
}

Meilisearch will use the indexingEmbedder during indexing and the searchEmbedder when responding to search queries.

Retrieve multiple documents by ID

Fetch multiple documents at once by providing their IDs:

curl -H 'Content-Type: application/json' MEILISEARCH_URL/indexes/INDEX_UID/documents -d '{ "ids": ["cody", "finn", "brandy", "gambit"] }'

{
  "results": [
    {
      "id": "brandy",
      "info": 13765493
    },
    {
      "id": "finn",
      "info": 35863
    },
    {
      "id": "cody",
      "info": 122263
    },
    {
      "id": "gambit",
      "info": 22222
    }
  ],
  "offset": 0,
  "limit": 20,
  "total": 4
}

Note: Documents are not returned in the queried order, and non-existent documents are ignored.

Improvements

Batch document requests

You can now batch together /documents requests using either PUT or POST methods, improving efficiency when working with multiple documents.

Enhanced batch progress tracking

The /batches route now displays timestamped internal indexing steps, giving you better visibility into the indexing process. Batch progress view has also been extended to include indexing of vectors. The /facet-search route now supports an exhaustiveFacetCount parameter to retrieve an exact facet count instead of estimates.

Reduced memory consumption

Arroy (the vector storage component) now uses less RAM, improving overall memory efficiency for vector operations.

Experimental embedding cache

An experimental feature to cache embeddings during search is now available, potentially improving search performance for repeated queries.

Armenian character handling

Armenian characters are no longer case-sensitive in searches, improving search accuracy for Armenian language content.

Optimized reindexing

Searchable attributes no longer trigger reindexing when only their order changes, reducing unnecessary processing.

Improved task handling

Cancellation tasks can now be accepted even when the disk is full, ensuring better reliability of task management.

Other

Breaking behavior change

Enabling rankingScoreThreshold no longer causes _rankingScore to be miscalculated, fixing a significant issue with ranking score accuracy when using threshold filters. Find more information on GitHub

​New Features

​Granular filterable attribute settings

​Composite embedders

​Retrieve multiple documents by ID

​Improvements

​Batch document requests

​Enhanced batch progress tracking

​Exhaustive facet count parameter

​Reduced memory consumption

​Experimental embedding cache

​Armenian character handling

​Optimized reindexing

​Improved task handling

​Other

​Breaking behavior change