Meilisearch 1.13
Meilisearch 1.13 stabilizes AI-powered search, introduces remote federated search—laying the groundwork for sharding—and makes version upgrades easier.

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.
Meilisearch 1.13 is available on Meilisearch Cloud, too—upgrade now!
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_embedder
error for both search and similarity requests.
It has been split into:
invalid_search_embedder
: for search requestsinvalid_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 embeddingnumberOfEmbeddings
: total number of embeddings in an indexusedDatabaseSize
: storage space used by the database in bytes, excluding unused space claimed by LMDB. UnlikedatabaseSize
, 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.
For more information, subscribe to our monthly newsletter, check out the roadmap, or join our Product Discussions.
For anything else, join our developers community on Discord.