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meilisearch-enterprise-macos-amd64 2026-02-23 136.0 MB
meilisearch-windows-amd64.exe 2026-02-23 136.6 MB
meilisearch-enterprise-windows-amd64.exe 2026-02-23 138.0 MB
meilisearch-macos-amd64 2026-02-23 134.4 MB
meilisearch-macos-apple-silicon 2026-02-23 130.1 MB
meilisearch-enterprise-macos-apple-silicon 2026-02-23 131.5 MB
meilisearch.deb 2026-02-23 88.4 MB
meilisearch-enterprise-linux-amd64 2026-02-23 143.1 MB
meilisearch-linux-amd64 2026-02-23 141.3 MB
meilisearch-enterprise-linux-aarch64 2026-02-23 137.4 MB
meilisearch-linux-aarch64 2026-02-23 135.9 MB
meilisearch-openapi.json 2026-02-23 597.2 kB
README.md 2026-02-19 3.3 kB
v1.36.0 source code.tar.gz 2026-02-19 20.2 MB
v1.36.0 source code.zip 2026-02-19 21.2 MB
Totals: 15 Items   1.5 GB 6

Version v1.36.0 introduces an exciting update to the ranking rules to improve the engine's relevance. It's actually the first time we've made such a change since v1.0, and we're really happy about this improvement!

✨ Enhancement

  • Introduce the attributeRank and wordPosition criteria by @Kerollmops in https://github.com/meilisearch/meilisearch/pull/6154, https://github.com/meilisearch/meilisearch/pull/6155, and https://github.com/meilisearch/meilisearch/pull/6164

    We released two new ranking rules that Meilisearch had already been using internally for the attribute one, which is basically both ranking rules applied one after the other:

    • attributeRank: A document is considered better if the query words match in a higher searchable attribute. It ignores the position of the query words in this attribute.
    • wordPosition: A document is considered better if the query words match closer to the beginning of an attribute. The attribute rank is ignored by this rule.

    We continue our policy of migrating everyone to use a homemade HNSW by introducing a new dumpless upgrade step that migrates index uses the old annoy vector store to the new Hannoy one. Changing the vector store backend affects the ranking score. This step can take a couple of minutes when the number of embeddings is high, and we recommend changing the vector store backend beforehand to gain more control if needed. To do so, you must enable the vectorStoreSetting experimental feature and set the vectorStore root setting to experimental.

🪲 Bug fixes

🔒 Security

🔩 Miscellaneous

[!WARNING] Breaking change: the meilisearch-openapi-mintlify.json file will not be available in the release assets anymore. If you were using it, please refer to the one that is now available in our public documentation repository.

❤️ Thanks to @zen-zap for contributing to this release!

Source: README.md, updated 2026-02-19