Meilisearch v1.16 introduces two core features, multimodal embeddings allowing to index images, text and other formats; and the Export/Transfer of documents between instances easing the migration of a local Meilisearch into the cloud!

New features and updates 🔥

Multimodal embeddings

Meilisearch now allows conveniently indexing images, text and other formats in documents, and to retrieve documents by searching with an image or a text query.

This new feature leverages multimodal embedders to provide a common semantic representation for images, texts, and any other piece of data in a format supported by the chosen model.

As an example, the sample movies dataset contains movie descriptions and links to movie posters. Using this new feature, it is possible to use text to search both in the descriptions and the posters, or to use an image to search for similar looking posters, or movies with a description matching the query image.

Usage

To use multimodal embeddings, start by enabling the multimodal experimental feature:

curl $MEILISEARCH_URL/experimental-features -X PATCH -H 'Content-type: application/json' -d '{"multimodal": true}'

Then pick an embedder service that supports multimodal such as cohere or VoyageAI to start building the embedding configuration.

An example configuration for VoyageAI to index the description and poster from the movies database, and to search by text or image, might look like the following:

// PATCH /indexes/$INDEX_NAME/settings/embedders
{"voyage": {
  "source": "rest",
  "url": "https://api.voyageai.com/v1/multimodalembeddings",
  "apiKey": "$VOYAGE_API_KEY",
  // describes how we index documents
  "indexingFragments": {
    "text": {
      "value": {
        // this part sticks to the voyage API <https://docs.voyageai.com/reference/multimodal-embeddings-api>
        "content": [
          {
            "type": "text",
            "text": "A movie titled {{doc.title}} whose description starts with {{doc.overview|truncateWords:20}}."
          }
        ]
      }
    },
    // we also send the poster
    "poster": {
      "value": {
        "content": [
          {
            "type": "image_url",
            "image_url": "{{doc.poster}}"
          }
        ]
      }
    }
  },
  // describes how to search in the index
  "searchFragments": {
    // we allow sending a poster at search time
    "poster": {
      "value": {
        "content": [
          {
            "type": "image_url",
            "image_url": "{{media.poster}}"
          }
        ]
      }
    },
    // we also allow inline image data at search time only
    "image": {
      "value": {
        "content": [
          {
            "type": "image_base64",
            "image_base64": "data:{{media.image.mime}};base64,{{media.image.data}}"
          }
        ]
      }
    },
    "text": {
      "value": {
        "content": [
          {
            "type": "text",
            // uses the `q` field from search queries
            "text": "{{q}}"
          }
        ]
      }
    }
  },
  "request": {
    "inputs": [
      "{{fragment}}",
      "{{..}}"
    ],
    "model": "voyage-multimodal-3"
  },
  "response": {
    "data": [
      {
        "embedding": "{{embedding}}"
      },
      "{{..}}"
    ]
  }
}}

Then, to search by poster (image URL):

// POST /indexes/$INDEX_NAME/search
{
  "media": {
    "poster": "https://image.tmdb.org/t/p/w500/6FfCtAuVAW8XJjZ7eWeLibRLWTw.jpg"
  },
  "hybrid": {
    "embedder": "voyage",
    // semanticRatio is specified here for explicitness,
    // but it would default to 1.0 if omitted, as we don't have a text query
    // (no "q") here.
    "semanticRatio": 1.0
  }
}

To search by image data (encoded as base64):

// POST /indexes/$INDEX_NAME/search
{
  "media": {
    "image": {
      "mime": "image/jpeg",
      // image bytes encoded as base64
      "data": "/9j/4AAQSkZJRgABAQAAAQABAAD/2w..."
    }
  "hybrid": {
    "embedder": "voyage",
  }
}

To search by text (performing a hybrid search):

// POST /indexes/$INDEX_NAME/search
{
  // note that since the model is multimodal, this will also compare the produced embedding
  // with the poster images from movie, so describing a poster image here could find the
  // associated movie
  "q": "A movie with lightsabers in space",
  "hybrid": {
    "embedder": "voyage",
    "semanticRatio": 0.5
  }
}

For more information about this feature, please refer to its public usage page

Done by @dureuill in #5596

Export/Transfer documents between instances

Meilisearch now allows to transfer documents from an instance to another without having to create a dump or a snapshot. This feature will ease the migration to the Cloud by streaming all the documents from a local instance to a cloud one.

Usage

We expose one single route on the instance which accepts a bunch of parameters.

• url: Where do we want to send our settings and documents.
• apiKey: (optional) The API key to have the rights to send those requests. Usually the master key of the remote machine.
• payloadSize: (optional) A human readable size defining the size of the payloads to send. Defaults to 50 MiB.
• indexes: (optional) A set of patterns of matching the indexes you want to export. If not defined, defaults to all indexes without filter.
• filter: (optional) A filter defining the subset of documents to actually export.
• overrideSettings: (optional, default false) When false, will not set settings nor update the primary key on indexes that already exist on the remote instance. Will still send the documents

POST: /export

{
  "url": "https://localhost:7711",
  "apiKey": null,
  "payloadSize": "123 MiB",
  "indexes": {
    "*": {
      "filter": null,
      "overrideSettings": true
    }
  }
}

Response:

{
  "taskUid": 2,
  "indexUid": null,
  "status": "enqueued",
  "type": "export",
  "enqueuedAt": "2025-06-26T12:54:10.785864Z"
}

One task can retry requests in case of failure (not indefinitely, though) and can also be canceled. However, keep in mind that canceling this export task will not cancel tasks received by the targeted Meilisearch instance.

Done by @Kerollmops with the help of @Mubelotix in #5670

Other improvements

• Added support for nested wildcards in attributes_to_search_on by @lblack00 in #5548
• Improve the support of geo field extraction from the documents by @nnethercott in #5592
• Use all CPUs when importing dumps by @nnethercott in #5527
• Display the last embedder error live in batches by @Mubelotix in #5707
• Settings indexer edition 2024 by @ManyTheFish in #5687
• Request fragments by @dureuill in #5596

Fixes 🐞

• Improve the performance when managing single-typo words by @dureuill in #5551
• Fix distinct for hybrid search by @dureuill in #5614
• Fix a bug related to Cyrillic having different typo tolerance due to byte counting bug by @arthurgousset in #5617
• Fix Gemini base_url when used with OpenAI clients by @diksipav in #5692
• Remove Gemini from the LLM-providers list due to incompatibility with OpenAI by @Kerollmops in #5708
• Fix the environment variable name of the experimental limit batched tasks total size feature by @Kerollmops in #5705
• Fix disableOnNumbers reset by @Nymuxyzo in #5702
• Make sure to recover from missing update files by @Kerollmops in #5683
• Add analytics to the chat completions by @Kerollmops in #5709

Misc

• Dependencies updates
  • Remove old dependencies by @Mubelotix in #5689
  • Upgrade dependencies by @Kerollmops in #5686
  • Bump the mini-dashboard to v0.2.20 by @Kerollmops in #5728
• CIs and tests (34 PRs, one folk, yes!)
  • perf: Faster IT tests - stats.rs by @martin-g in #5572
  • perf: Faster index::get_index IT tests by @martin-g in #5578
  • tests: Assert succeeded/failed for the index::delete_index IT tests by @martin-g in #5580
  • tests: Faster index::search::mod IT tests by @martin-g in #5584
  • perf: Faster index::update_index IT tests by @martin-g in #5579
  • perf: Faster integration tests for add_documents.rs by @martin-g in #5574
  • tests: Faster search::errors IT tests by @martin-g in #5599
  • tests: Faster search::locales IT tests by @martin-g in #5601
  • tests: Faster search::matching_strategy IT tests by @martin-g in #5602
  • tests: search::pagination IT tests by @martin-g in #5604
  • tests: Faster search::restricted_searchable IT tests by @martin-g in #5605
  • tests: Faster settings::distinct IT tests by @martin-g in #5606
  • tests: Faster settings::proximity_settings IT tests by @martin-g in #5609
  • tests: Faster settings::tokenizer_customization IT tests by @martin-g in #5610
  • tests: Faster search::facet_search IT tests by @martin-g in #5600
  • tests: Faster settings::get_settings IT tests by @martin-g in #5607
  • tests: Faster vector::binary_quantized IT tests by @martin-g in #5618
  • tests: Faster search::distinct IT tests by @martin-g in #5620
  • tests: Faster similar::errors IT tests by @martin-g in #5621
  • tests: Faster documents::get_documents IT tests by @martin-g in #5624
  • tests: Faster documents::delete_documents IT tests by @martin-g in #5619
  • tests: Faster search::geo IT tests by @martin-g in #5623
  • tests: Faster search::hybrid IT tests by @martin-g in #5625
  • tests: Faster tasks::mod IT tests by @martin-g in #5615
  • tests: Faster stats::mod IT tests by @martin-g in #5611
  • tests: Faster search::filters IT tests by @martin-g in #5622
  • tests: Use Server::wait_task() instead of Index::wait_task() in search:: by @martin-g in #5700
  • tests: Use Server::wait_task() instead of Index::wait_task() in index:: by @martin-g in #5698
  • tests: Use Server::wait_task() instead of Index::wait_task() in settings:: by @martin-g in #5699
  • tests: Use Server::wait_task() instead of Index::wait_task() in documents:: by @martin-g in #5697
  • tests: Faster documents::update_documents IT tests by @martin-g in #5682
  • tests: Faster similar::mod IT tests by @martin-g in #5680
  • tests: Faster document::errors IT tests by @martin-g in #5677
  • tests: Faster settings::prefix_search_settings IT tests by @martin-g in #5681
  • tests: Faster search::multi IT tests by @martin-g in #5603
• Misc
  • Ignore flaky test by @dureuill in #5627
  • ci: Use GITHUB_TOKEN secret for the db change check workflow by @martin-g in #5632
  • chore: Fix English grammar in SearchQueue's comments by @martin-g in #5642
  • Typo fix by @mcmah309 in #5589
  • docs: Recommend using a custom path for the benches' data by @martin-g in #5672

❤️ Thanks again to our external contributors:

• Meilisearch: @martin-g, @lblack00, @mcmah309, @nnethercott, @arthurgousset, @Mubelotix, @diksipav, @Nymuxyzo
• Arroy: @nnethercott