Meilisearch 1.7

We're excited to unveil Meilisearch v1.7. In this article, we’ll review the most impactful changes. For an exhaustive listing, check out the changelog on Github.

Improved hybrid & vector search

New OpenAI embedding models

Starting with Meilisearch 1.7, you can use OpenAI’s third-generation embedding models:

• text-embedding-3-small
• text-embedding-3-large

Compared to the previous text-embedding-ada-002 model, these new models offer improvements allowing to balance search results relevancy and cost efficiency.

The small model is more cost-effective, generating smaller vector embeddings that require less storage. The large focuses on delivering higher precision in search results, making it ideal to optimize relevancy.

Configuring OpenAI model dimensions

When using the new text-embedding-3-small and text-embedding-3-large OpenAI models, you can now configure dimensions for your model. When not provided, the model's defaults dimensions are used.

You can do so when updating your settings:

{
  // other settings...

  "embedders": {
    "small": {
      "source": "openAi",
      "model": "text-embedding-3-large",
      "dimensions": 512 // must be >0, must be <= 3072 for "text-embedding-3-large"
    },
  }
}

Higher dimensions typically offer more complex embeddings which can lead to more accurate search results. However, increased dimensions are more costly to compute and require more storage.

Use AI Search on Meilisearch Cloud

Meilisearch hybrid search and vector search are coming to Meilisearch Cloud. We're rolling out invites every day. Join the beta. 👇

Get early access

GPU support for computing Hugging Face embeddings

Meilisearch 1.7 comes with the ability to enable GPU support for self-hosted Meilisearch for computing Hugging Face embeddings. This can significantly speed up the embedding generation process.

Find detailed instructions in our dedicated guide on Using GPU to compute Hugging Face embeddings.

Display ranking scores details at search

Meilisearch 1.7 stabilizes the showRankingScoreDetails search parameter that was introduced in Meilisearch 1.3. This parameter adds a _rankingScoreDetails field to each document in search results, enabling further inspection of the search results.

You can include the parameter when making searches:

curl \
  -X POST 'https://edge.meilisearch.com/indexes/movies/search' \
  -H 'Content-Type: application/json' \
  --data-binary '{
    "q": "batman",
    "showRankingScoreDetails": true
  }'

Learn more about the _rankingScoreDetails object and how to use it with your preferred SDK in the API reference.

Contributors shout-out

We want to give a massive thank you to all contributors that made this release possible: @capJavert, @codesmith-emmy, @Elliot67 and @Gosti for their PRs in Meilisearch, and @agourlay, @choznerol, @ngdbao, @timvisee, and @xshadowlegendx for their work on charabia.

Special thanks to our SDKs maintainers, thanks to whom Meilisearch is available across so many languages. 🫶

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And that’s a wrap for v1.7! This release post highlights the most significant updates. For an exhaustive listing, read the changelog on Github.

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