Meilisearch 1.13

We're excited to introduce Meilisearch v1.13. In this article, we'll cover the most significant changes. For a full list of updates, visit the changelog on GitHub.

AI-powered search

We are thrilled to announce that AI-powered search is now fully stable!

First released in v1.3, our vector store has evolved to auto-embedding from common providers, semantic and hybrid search, always based on our community's invaluable feedback. This feature is now ready to leave the experimental phase.

Therefore, you no longer need to activate it using the /experimental-features route.

v1.13 introduces the last breaking changes to the AI-powered search feature.

Breaking: accepted Ollama URLs

Ollama URLs will only be accepted if they end with /api/embed and /api/embeddings, passing an unsupported endpoint will now trigger an error on Meilisearch

Breaking: error code modifications

Prior to 1.13, if the embedder configuration was missing or wasn't a string, Meilisearch would return an invalid_embeddererror for both search and similarity requests.

It has been split into:

• invalid_search_embedder: for search requests
• invalid_similar_embedder: for similar requests

When the hybrid search parameter is invalid, the error invalid_search_hybrid_query is now returned instead of invalid_hybrid_search.

Experimental: remote federated search

The new network route enables distributed search across multiple Meilisearch instances. Combined with federated search, this simplifies implementing horizontal database partitioning strategies like sharding.

Activate this feature via the /experimental-features route. If you are using Meilisearch Cloud, contact support to enable this feature in your projects.

Use the remotes object to define the Meilisearch instances that should participate in the search.

PATCH /network
{
  "remotes": {
    "ms-01": {
      "url": "http://ms-1.example.meilisearch.io",
      "searchApiKey": "SEARCH_KEY_1"
    },
    "ms-02": {
      "url": "http://ms-2.example.meilisearch.io",
      "searchApiKey": "SEARCH_KEY_2"
    }
  }
}

Set the same remotes on all set of Meilisearch instances, then configure each instance with its own identifier using self. The value of self must match the corresponding key in remotes:

PATCH /network
{
  "self": "ms-01"
}

You can now perform a remote federated search:

POST /multi-search
{
  "federation": { // enable federated mode
    "limit": 5
  },
  "queries": [
    { // #0
      "q": "Batman",
      "indexUid": "movies",
      "federationOptions": {
        "remote": "ms-01"
      }
    },
    { // #1
      "q": "Batman",
	    "indexUid": "movies",
	    "federationOptions": {
        "remote": "ms-02"
      }
    }
  ]
}

Experimental: new dumpless upgrade

You can now upgrade from Meilisearch >=v1.12 to Meilisearch >=v1.13 without using a dump.

Simply install the latest version of Meilisearch and relaunch it with either instance option:

• the --experimental-dumpless-upgrade flag
• or the MEILI_EXPERIMENTAL_DUMPLESS_UPGRADE environment variable

⚠️ As this is an experimental feature, please use snapshots to back up all important data first.

New stats

Meilisearch 1.3 introduces 3 new fields to the stats object:

• numberOfEmbeddedDocuments: total number of documents with at least one embedding
• numberOfEmbeddings: total number of embeddings in an index
• usedDatabaseSize: storage space used by the database in bytes, excluding unused space claimed by LMDB. Unlike databaseSize, this value decreases when documents are removed.

Here is an example stats object returned (as of Meilisearch 1.13):

GET /stats

{
  "databaseSize": 447819776,
  "usedDatabaseSize": 196608,
  "lastUpdate": "2019-11-15T11:15:22.092896Z",
  "indexes": {
    "movies": {
      "numberOfDocuments": 19654,
      "numberOfEmbeddedDocuments": 1,
      "numberOfEmbeddings": 1,
      "isIndexing": false,
      "fieldDistribution": {
        "poster": 19654,
        "overview": 19654,
        "title": 19654,
        "id": 19654,
        "release_date": 19654
      }
    },
    "books": {
      "numberOfDocuments": 5,
      "numberOfEmbeddedDocuments": 5,
      "numberOfEmbeddings": 10,
      "isIndexing": false,
      "fieldDistribution": {
        "id": 5,
        "title": 5,
        "author": 5,
        "price": 5, 
        "genres": 5
      }
    }
  }
}

Contributors shout-out

We're incredibly grateful to the external contributors who helped make this release possible. Thanks to @takaebato, @Sherlouk, @jameshiew, @K-Kumar-01, @mhmoudr, @eltociear, @Gnosnay, @DerTimonius, @manojks1999, @ellnix, @Guikingone, @migueltarga for their contributions to Meilisearch.

And that’s a wrap for v1.13! These release notes only highlight the most significant updates. For an exhaustive listing, read the changelog on GitHub.

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