> ## Documentation Index
> Fetch the complete documentation index at: https://www.meilisearch.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Meilisearch v1.55 release notes

> Released October 12, 2026

## New Features

### Preview the effect of a dynamic search rule

You can now apply an inline-specified dynamic search rule to a search query without saving it permanently. This lets you test how a rule would affect search results before deploying it.

The `POST /indexes/{:indexUid}/search` and `POST /multi-search` endpoints now accept an optional `inlineRule` parameter in the search query object. When provided, Meilisearch will apply the rule's actions to that specific search as if it had been saved.

When you include the `Meili-Include-Metadata: true` header, the response's `metadata` will show whether the inline rule's conditions were satisfied and why (if not).

**Note:** This feature requires Meilisearch Enterprise Edition and the `dynamicSearchRules` experimental feature.

**Example: Apply a user-specific rule to boost Batman movies**

```json theme={null}
{
  "q": "super hero",
  "inlineRule": {
    "uid": "user-likes-batman",
    "actions": {
      "scale": [
        {
          "filter": "hero = batman AND release_time = fresh",
          "weight": 1.2
        }
      ]
    }
  }
}
```

**Example: Compare search results with and without a rule**

```json theme={null}
{
  "queries": [
    {
      "q": "fashion",
      "filter": "color = blue AND category = shirt"
    },
    {
      "q": "fashion",
      "filter": "color = blue AND category = shirt",
      "inlineRule": {
        "uid": "special-sales",
        "conditions": {
          "query": {
            "words": "pants"
          },
          "filter": {
            "values": {
              "color": "yellow",
              "stock_intent": "destock"
            }
          }
        }
      }
    }
  ]
}
```

## Improvements

### Better performance tracking for facet distribution

The `performanceDetails` in search responses now includes timing information for facet distribution computation. When you set `showPerformanceDetails` to `true`, you'll see separate entries for:

* `compute facet distribution`: Time spent computing facet counts
* `compute facet stats`: Time spent computing facet statistics
* `facet distribution`: Total time for all facet-related operations

This helps you identify whether facet computation is affecting your search performance.

**Example response with facet performance details**

```json theme={null}
{
  "performanceDetails": {
    "process > execute local > facet distribution > compute facet distribution": "315.75µs",
    "process > execute local > facet distribution > compute facet stats": "27.71µs",
    "process > execute local > facet distribution": "432.62µs"
  }
}
```

## Other

### Fixed ranking score threshold performance

Meilisearch now stops searching immediately after finding the first document below the `rankingScoreThreshold`, rather than continuing to evaluate additional documents. This significantly improves search performance when using this parameter.

**Note:** This fix may change result ordering in some cases, as allowed by our compatibility policy.

### Breaking change: Binary quantization cannot be disabled

If your embedder is configured with binary quantization, you can no longer disable it by passing `binaryQuantization: null` in an embedder settings update. Doing so now returns an error. Binary quantization is permanent for an index because the exact embedding values cannot be recovered after quantization.

[Find more information on GitHub](https://github.com/meilisearch/meilisearch/releases/tag/v1.55.0)


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