> ## 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.
# Ranking score
> Learn how Meilisearch computes the _rankingScore for each document and which index settings influence it.
The `_rankingScore` is a normalized value between `0.0` and `1.0` that represents how relevant a document is to a given search query. A score of `1.0` means the document is a perfect match, while a score closer to `0.0` means it is a weak match. Meilisearch does not return the ranking score by default; you must explicitly request it.
## Requesting the ranking score
To include `_rankingScore` in search results, set `showRankingScore` to `true` in your search request:
```sh theme={null}
curl \
-X POST 'MEILISEARCH_URL/indexes/movies/search' \
-H 'Content-Type: application/json' \
--data-binary '{
"q": "batman dark knight",
"showRankingScore": true
}'
```
Each document in the response will include a `_rankingScore` field:
```json theme={null}
{
"hits": [
{
"id": 155,
"title": "The Dark Knight",
"_rankingScore": 0.9546
},
{
"id": 36657,
"title": "Batman Begins",
"_rankingScore": 0.8103
}
],
"query": "batman dark knight"
}
```
## Requesting a detailed breakdown
For a deeper understanding of why a document received a particular score, set `showRankingScoreDetails` to `true`. This adds a detailed global ranking score field, `_rankingScoreDetails`, to each document in the response. `_rankingScoreDetails` is an object containing one nested object per active ranking rule, showing how each rule contributed to the document's overall score:
```sh theme={null}
curl \
-X POST 'MEILISEARCH_URL/indexes/movies/search' \
-H 'Content-Type: application/json' \
--data-binary '{
"q": "batman dark knight",
"showRankingScore": true,
"showRankingScoreDetails": true
}'
```
The response includes a `_rankingScoreDetails` object for each document:
```json theme={null}
{
"hits": [
{
"id": 155,
"title": "The Dark Knight",
"_rankingScore": 0.9546,
"_rankingScoreDetails": {
"words": { "order": 0, "matchingWords": 3, "maxMatchingWords": 3, "score": 1.0 },
"typo": { "order": 1, "typoCount": 0, "maxTypoCount": 3, "score": 1.0 },
"proximity": { "order": 2, "score": 0.9286 },
"attributeRank": { "order": 3, "attributeRankingOrderScore": 1.0, "score": 1.0 },
"exactness": { "order": 5, "matchType": "noExactMatch", "score": 0.3333 }
}
}
]
}
```
Each key in `_rankingScoreDetails` corresponds to a [ranking rule](/capabilities/full_text_search/relevancy/ranking_rules), and its `score` property shows how well the document performed on that rule.
## How the score is computed
Ranking rules sort documents either by relevancy (`words`, `typo`, `proximity`, `exactness`, `attributeRank`, `wordPosition`) or by the value of a field (`sort`). Since `sort` does not rank documents by relevancy, it does not influence the `_rankingScore`.
Meilisearch computes the overall score by combining the subscores from each ranking rule, weighted by their position in the ranking rules list. Rules listed earlier carry more weight.
A document's ranking score does not change based on the scores of other documents in the same index.
For example, if a document A has a score of `0.5` for a query term, this value remains constant no matter the score of documents B, C, or D.
## Settings that influence the ranking score
The table below details all the index settings that can influence the `_rankingScore`. **Unlisted settings do not influence the ranking score.**
| Index setting | Influences if | Rationale |
| :--------------------- | :--------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `searchableAttributes` | The `attributeRank` ranking rule is used | The `attributeRank` ranking rule rates the document depending on the attribute in which the query terms show up. The order is determined by `searchableAttributes` |
| `searchableAttributes` | The `wordPosition` ranking rule is used | The `wordPosition` ranking rule rates the document based on the position of query terms within attributes |
| `rankingRules` | Always | The score is computed by computing the subscore of each ranking rule with a weight that depends on their order |
| `stopWords` | Always | Stop words influence the `words` ranking rule, which is almost always used |
| `synonyms` | Always | Synonyms influence the `words` ranking rule, which is almost always used |
| `typoTolerance` | The `typo` ranking rule is used | Used to compute the maximum number of typos for a query |
## Example: reading ranking score details
Consider a recipe search with two documents matching "chicken curry", sorted by `prep_time_minutes:asc`:
```json theme={null}
[
{ "id": 1, "title": "Easy Chicken Curry", "prep_time_minutes": 20 },
{ "id": 2, "title": "Chicken Stew with Curry Spices and Vegetables", "prep_time_minutes": 15 }
]
```
With Sort placed **after** Proximity in ranking rules (`["words", "typo", "proximity", "sort", ...]`), walk through the `_rankingScoreDetails` in order:
| Step | Rule | Doc 1 | Doc 2 | Outcome |
| ---- | --------- | -------------------- | -------------------- | ---------- |
| 0 | Words | 2/2, score `1.0` | 2/2, score `1.0` | Tie |
| 1 | Typo | 0 typos, score `1.0` | 0 typos, score `1.0` | Tie |
| 2 | Proximity | score `1.0` | score `0.5` | Doc 1 wins |
Proximity broke the tie: "chicken" and "curry" sit next to each other in Doc 1's title (score `1.0`), but are separated by three words in Doc 2 (score `0.5`). Sort never got a chance to act, so even though Doc 2 has a faster prep time, it ranks second.
Notice that Sort shows a `value` (not a `score`) because it does not measure relevance. This is why a document with a higher `_rankingScore` can still rank lower when Sort takes priority. See [ordering ranking rules](/capabilities/full_text_search/relevancy/ranking_rules#where-to-place-sort) for how Sort placement changes outcomes.
## Use cases
* **Debugging relevancy**: Use `showRankingScoreDetails` to understand exactly why a document ranks higher or lower than expected. This helps you fine-tune ranking rules, searchable attributes, and other settings.
* **Building confidence indicators**: Display the ranking score in your UI as a relevancy badge or progress bar so users can gauge how closely a result matches their query.
* **Setting score thresholds**: Filter out low-quality results in your frontend by only displaying documents above a certain `_rankingScore` threshold (for example, `0.5`).
* **A/B testing ranking configurations**: Compare ranking scores across different index configurations to measure which setup produces better relevancy for your use case.
## Next steps
Understand the ranking rules that determine document relevancy
Add your own ranking rules based on document attributes
Full reference for search parameters including showRankingScore