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+---
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+mapped_pages:
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+ - https://www.elastic.co/guide/en/elasticsearch/reference/current/rescore-search-results.html
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+applies_to:
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+ stack: all
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+ serverless: all
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+---
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+
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+# Rescore search results
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+
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+Rescoring can help to improve precision by reordering just the top
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+(e.g. 100 - 500) documents returned by initial retrieval phase
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+(query, knn search) by using a secondary (usually more costly) algorithm,
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+instead of applying the costly algorithm to all documents in the index.
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+
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+## How `rescore` works [rescore]
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+
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+A `rescore` request is executed on each shard before it returns its results
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+to be sorted by the node handling the overall search request.
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+
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+The rescore API has 3 options:
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+
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+1. `query` rescorer that executes a provided `rescore_query` on the top documents
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+2. `script` rescorer that uses a script to modify the scores of the top documents
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+3. `learning_to_rank` rescorer that uses an LTR model to re-rank the top documents
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+
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+All rescores have the `window_size` parameter that controls how many top
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+documents will be considered for rescoring. The default is 10.
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+
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+::::{note}
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+When implementing pagination, keep the `window_size` consistent across pages.
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+Changing it while advancing through results (by using different `from` values)
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+can cause the top hits to shift, leading to a confusing user experience.
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+::::
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+
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+## Query Rescorer [query-rescorer]
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+
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+The query rescorer executes a second query only on the top documents returned
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+from the previous phase. The number of docs which is examined on each shard
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+can be controlled by the `window_size` parameter.
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+
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+By default, the scores from the original query and the rescore query are combined
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+linearly to produce the final `_score` for each document.
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+The relative importance of the original query and of the rescore query can be
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+controlled with the `query_weight` and `rescore_query_weight` respectively.
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+Both default to `1`.
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+
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+For example:
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+
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+```console
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+POST /_search
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+{
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+ "query" : {
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+ "match" : {
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+ "message" : {
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+ "operator" : "or",
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+ "query" : "the quick brown"
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+ }
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+ }
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+ },
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+ "rescore" : {
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+ "window_size" : 10,
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+ "query" : {
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+ "rescore_query" : {
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+ "match_phrase" : {
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+ "message" : {
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+ "query" : "the quick brown",
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+ "slop" : 2
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+ }
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+ }
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+ },
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+ "query_weight" : 0.7,
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+ "rescore_query_weight" : 1.2
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+ }
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+ }
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+}
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+```
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+
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+::::{note}
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+An error will be thrown if an explicit [`sort`](/reference/elasticsearch/rest-apis/sort-search-results.md)
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+(other than `_score` in descending order) is provided with a `rescore` query.
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+::::
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+
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+
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+The way the scores are combined can be controlled with the `score_mode`:
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+
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+| Score Mode | Description |
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+| --- |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+| `total` | Add the original score and the rescore query score. The default. |
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+| `multiply` | Multiply the original score by the rescore query score. Useful for [`function query`](/reference/query-languages/query-dsl/query-dsl-function-score-query.md) rescores. |
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+| `avg` | Average the original score and the rescore query score. |
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+| `max` | Take the max of original score and the rescore query score. |
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+| `min` | Take the min of the original score and the rescore query score. |
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+
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+## Script rescorer [script-rescorer]
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+```{applies_to}
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+stack: ga 9.2
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+```
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+
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+`script` rescorer uses a script to rescore the top documents returned
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+from the previous phase. The script has access to the original score as well
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+as values of document fields.
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+
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+For example, the following script rescores documents based on the document's
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+original query score and the value of field `num_likes`:
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+
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+```console
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+POST /_search
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+{
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+ "query" : {
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+ "match" : {
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+ "message" : {
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+ "operator" : "or",
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+ "query" : "the quick brown"
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+ }
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+ }
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+ },
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+ "rescore" : {
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+ "window_size" : 10,
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+ "script" : {
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+ "script" : {
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+ "source": "doc['num_likes'].value * params.multiplier + _score",
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+ "parameters": {
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+ "multiplier": 0.1
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+ }
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+ }
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+ }
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+ }
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+}
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+```
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+
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+## Learning to rank rescorer [learning-to-rank-rescorer]
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+
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+`learning_to_rank` uses an LTR model to rescore the top documents. You must
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+provide the `model_id` of a deployed model, as well as any named parameters
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+required by the query templates for features used by the model.
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+
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+```console
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+GET my-index/_search
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+{
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+ "query": {
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+ "multi_match": {
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+ "fields": ["title", "content"],
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+ "query": "the quick brown fox"
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+ }
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+ },
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+ "rescore": {
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+ "learning_to_rank": {
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+ "model_id": "ltr-model",
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+ "params": {
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+ "query_text": "the quick brown fox"
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+ }
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+ },
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+ "window_size": 100
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+ }
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+}
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+```
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+
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+## Multiple rescores [multiple-rescores]
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+
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+You can apply multiple rescoring operations in sequence. The first rescorer
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+works on the top documents from the initial retrieval phase, while the second
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+rescorer works on the output of the first rescorer, and so on. A common practice
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+is to use a larger window for the first rescorer and smaller windows for more
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+expensive subsequent rescorers.
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+
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+```console
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+POST /_search
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+{
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+ "query": {
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+ "match": {
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+ "message": {
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+ "operator": "or",
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+ "query": "the quick brown"
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+ }
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+ }
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+ },
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+ "rescore": [
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+ {
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+ "window_size": 10,
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+ "query": {
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+ "rescore_query": {
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+ "match_phrase": {
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+ "message": {
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+ "query": "the quick brown",
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+ "slop": 2
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+ }
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+ }
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+ },
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+ "query_weight": 0.7,
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+ "rescore_query_weight": 1.2
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+ }
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+ },
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+ {
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+ "window_size": 5,
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+ "query": {
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+ "score_mode": "multiply",
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+ "rescore_query": {
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+ "function_score": {
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+ "script_score": {
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+ "script": {
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+ "source": "Math.log10(doc.count.value + 2)"
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ ]
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+}
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+```
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Mayya Sharipova