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v1.11.0

meilisearch/meilisearch

版本发布时间: 2024-10-28 16:36:44

meilisearch/meilisearch最新发布版本:v1.12.0-rc.6(2024-12-16 23:19:51)

Meilisearch v1.11 introduces AI-powered search performance improvements thanks to binary quantization and various usage changes, all of which are steps towards a future stabilization of the feature. We have also improved federated search usage following user feedback.

🧰 All official Meilisearch integrations (including SDKs, clients, and other tools) are compatible with this Meilisearch release. Integration deployment happens between 4 to 48 hours after a new version becomes available.

Some SDKs might not include all new features. Consult the project repository for detailed information. Is a feature you need missing from your chosen SDK? Create an issue letting us know you need it, or, for open-source karma points, open a PR implementing it (we'll love you for that ❤️).

New features and updates 🔥

Experimental - AI-powered search improvements

This release is Meilisearch's first step towards stabilizing AI-powered search and introduces a few breaking changes to its API. Consult the PRD for full usage details.

Done by @dureuill in #4906, #4920, #4892, and #4938.

⚠️ Breaking changes

Addition & improvements

v1.11:

{% for field in fields %}
  {% if field.is_searchable and not field.value == nil %}
    {{ field.name }}: {{ field.value }}\n
  {% endif %}
{% endfor %}

v1.10:

{% for field in fields %}
  {{ field.name }}: {{ field.value }}\n
{% endfor %}

Embedders using the v1.10 document template will continue working as before. The new default document template will only work with newly created embedders.

Vector database indexing performance improvements

v1.11 introduces a new embedder option, binaryQuantized:

curl \
  -X PATCH 'http://localhost:7700/indexes/movies/settings' \
  -H 'Content-Type: application/json' \
  --data-binary '{
    "embedders": {
      "image2text": {
        "binaryQuantized": true
      }
    }
  }'

Enable binary quantization to convert embeddings of floating point numbers into embeddings of boolean values. This will negatively impact the relevancy of AI-powered searches but significantly improve performance in large collections with more than 100 dimensions.

In our benchmarks, this reduced the size of the database by a factor of 10 and divided the indexing time by a factor of 6 with little impact on search times.

[!WARNING] Enabling this feature will update all of your vectors to contain only 1s or -1s, significantly impacting relevancy.

You cannot revert this option once you enable it. Before setting binaryQuantized to true, Meilisearch recommends testing it in a smaller or duplicate index in a development environment.

Done by @irevoire in #4941.

Federated search improvements

Facet distribution and stats for federated searches

This release adds two new federated search options, facetsByIndex and mergeFacets. These allow you to request a federated search for facet distributions and stats data.

Facet information by index

To obtain facet distribution and stats for each separate index, use facetsByIndex when querying the POST /multi-search endpoint:

POST /multi-search
{
  "federation": {
    "limit": 20,
    "offset": 0,
	"facetsByIndex": {
	  "movies": ["title", "id"],
	  "comics": ["title"],
	}
  },
  "queries": [
    {
      "q": "Batman",
      "indexUid": "movies"
    },
    {
      "q": "Batman",
      "indexUid": "comics"
    }
  ]
}

The multi-search response will include a new field, facetsByIndex with facet data separated per index:

{
  "hits": […],
  …
  "facetsByIndex": {
      "movies": {
        "distribution": {
          "title": {
            "Batman returns": 1
          },
          "id": {
            "42": 1
          }
        },
        "stats": {
          "id": {
            "min": 42,
            "max": 42
          }
        }
      },
     …
  }
}

Merged facet information

To obtain facet distribution and stats for all indexes merged into a single, use both facetsByIndex and mergeFacets when querying the POST /multi-search endpoint:

POST /multi-search
{

  "federation": {
    "limit": 20,
    "offset": 0,
	  "facetsByIndex": {
	    "movies": ["title", "id"],
	    "comics": ["title"],
	  },
	  "mergeFacets": {
	    "maxValuesPerFacet": 10,
	  }
  }
  "queries": [
    {
      "q": "Batman",
      "indexUid": "movies"
    },
    {
      "q": "Batman",
      "indexUid": "comics"
    }
  ]
}

The response includes two new fields, facetDistribution and facetStarts:

{
  "hits": […],
  …
  "facetDistribution": {
    "title": {
      "Batman returns": 1
      "Batman: the killing joke":
    },
    "id": {
      "42": 1
    }
  },
  "facetStats": {
    "id": {
      "min": 42,
      "max": 42
    }
  }
}

Done by @dureuill in #4929.

Experimental — New STARTS WITH filter operator

Enable the experimental feature to use the STARTS WITH filter operator:

curl \
  -X PATCH 'http://localhost:7700/experimental-features/' \
  -H 'Content-Type: application/json' \
  --data-binary '{
    "containsFilter": true
  }'

Use the STARTS WITH operator when filtering:

curl \
  -X POST http://localhost:7700/indexes/movies/search \
  -H 'Content-Type: application/json' \
  --data-binary '{
    "filter": "hero STARTS WITH spider"
  }'

🗣️ This is an experimental feature, and we need your help to improve it! Share your thoughts and feedback on this GitHub discussion.

Done by @Kerollmops in #4939.

Other improvements

Fixes 🐞

Misc

❤️ Thanks again to our external contributors:

相关地址：原始地址 下载(tar) 下载(zip)

1、 meilisearch-linux-aarch64 114.21MB

2、 meilisearch-linux-amd64 115.75MB

3、 meilisearch-macos-amd64 111.6MB

4、 meilisearch-macos-apple-silicon 109.6MB

5、 meilisearch-windows-amd64.exe 111.42MB

6、 meilisearch.deb 74.98MB

查看：2024-10-28发行的版本

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