Add l2_norm normalization support to linear retriever

docs/changelog/128504.yaml

@@ -0,0 +1,5 @@
+pr: 128504
+summary: Add l2_norm normalization support to linear retriever
+area: Relevance
+type: enhancement
+issues: []

docs/reference/elasticsearch/rest-apis/retrievers.md

@@ -276,7 +276,7 @@ Each entry specifies the following parameters:
 `normalizer`
 :   (Optional, String)
 
-    Specifies how we will normalize the retriever’s scores, before applying the specified `weight`. Available values are: `minmax`, and `none`. Defaults to `none`.
+    Specifies how we will normalize the retriever’s scores, before applying the specified `weight`. Available values are: `minmax`, `l2_norm`, and `none`. Defaults to `none`.
 
     * `none`
     * `minmax` : A `MinMaxScoreNormalizer` that normalizes scores based on the following formula
@@ -285,6 +285,11 @@ Each entry specifies the following parameters:
         score = (score - min) / (max - min)
         ```
 
+    * `l2_norm` : An `L2ScoreNormalizer` that normalizes scores so that the L2 norm (Euclidean norm) of the score vector is 1. Each score is divided by the square root of the sum of squares of all scores:
+
+        ```
+        score = score / sqrt(sum(score_i^2))
+        ```
 
 See also [this hybrid search example](docs-content://solutions/search/retrievers-examples.md#retrievers-examples-linear-retriever) using a linear retriever on how to independently configure and apply normalizers to retrievers.
 

x-pack/plugin/rank-rrf/src/main/java/org/elasticsearch/xpack/rank/linear/L2ScoreNormalizer.java

@@ -0,0 +1,57 @@
+
+/*
+ * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
+ * or more contributor license agreements. Licensed under the Elastic License
+ * 2.0; you may not use this file except in compliance with the Elastic License
+ * 2.0.
+ */
+
+package org.elasticsearch.xpack.rank.linear;
+
+import org.apache.lucene.search.ScoreDoc;
+
+public class L2ScoreNormalizer extends ScoreNormalizer {
+
+    public static final L2ScoreNormalizer INSTANCE = new L2ScoreNormalizer();
+
+    public static final String NAME = "l2_norm";
+
+    private static final float EPSILON = 1e-6f;
+
+    public L2ScoreNormalizer() {}
+
+    @Override
+    public String getName() {
+        return NAME;
+    }
+
+    @Override
+    public ScoreDoc[] normalizeScores(ScoreDoc[] docs) {
+        if (docs.length == 0) {
+            return docs;
+        }
+        double sumOfSquares = 0.0;
+        boolean atLeastOneValidScore = false;
+        for (ScoreDoc rd : docs) {
+            if (Float.isNaN(rd.score) == false) {
+                atLeastOneValidScore = true;
+                sumOfSquares += rd.score * rd.score;
+            }
+        }
+        if (atLeastOneValidScore == false) {
+            // No valid scores to normalize
+            return docs;
+        }
+        double norm = Math.sqrt(sumOfSquares);
+        if (norm < EPSILON) {
+            // Avoid division by zero, return original scores (or set all to zero if you prefer)
+            return docs;
+        }
+        ScoreDoc[] scoreDocs = new ScoreDoc[docs.length];
+        for (int i = 0; i < docs.length; i++) {
+            float score = (float) (docs[i].score / norm);
+            scoreDocs[i] = new ScoreDoc(docs[i].doc, score, docs[i].shardIndex);
+        }
+        return scoreDocs;
+    }
+}

x-pack/plugin/rank-rrf/src/main/java/org/elasticsearch/xpack/rank/linear/ScoreNormalizer.java

@@ -17,6 +17,9 @@ public abstract class ScoreNormalizer {
     public static ScoreNormalizer valueOf(String normalizer) {
         if (MinMaxScoreNormalizer.NAME.equalsIgnoreCase(normalizer)) {
             return MinMaxScoreNormalizer.INSTANCE;
+        } else if (L2ScoreNormalizer.NAME.equalsIgnoreCase(normalizer)) {
+            return L2ScoreNormalizer.INSTANCE;
+
         } else if (IdentityScoreNormalizer.NAME.equalsIgnoreCase(normalizer)) {
             return IdentityScoreNormalizer.INSTANCE;
 

x-pack/plugin/rank-rrf/src/yamlRestTest/resources/rest-api-spec/test/linear/10_linear_retriever.yml

@@ -265,6 +265,59 @@ setup:
   - match: { hits.hits.3._id: "3" }
   - close_to: { hits.hits.3._score: { value: 0.0, error: 0.001 } }
 
+---
+"should normalize initial scores with l2_norm":
+  - do:
+      search:
+        index: test
+        body:
+          retriever:
+            linear:
+              retrievers: [
+                {
+                  retriever: {
+                    standard: {
+                      query: {
+                        bool: {
+                          should: [
+                            { constant_score: { filter: { term: { keyword: { value: "one" } } }, boost: 3.0 } },
+                            { constant_score: { filter: { term: { keyword: { value: "two" } } }, boost: 4.0 } }
+                          ]
+                        }
+                      }
+                    }
+                  },
+                  weight: 10.0,
+                  normalizer: "l2_norm"
+                },
+                {
+                  retriever: {
+                    standard: {
+                      query: {
+                        bool: {
+                          should: [
+                            { constant_score: { filter: { term: { keyword: { value: "three" } } }, boost: 6.0 } },
+                            { constant_score: { filter: { term: { keyword: { value: "four" } } }, boost: 8.0 } }
+                          ]
+                        }
+                      }
+                    }
+                  },
+                  weight: 2.0,
+                  normalizer: "l2_norm"
+                }
+              ]
+
+  - match: { hits.total.value: 4 }
+  - match: { hits.hits.0._id: "2" }
+  - match: { hits.hits.0._score: 8.0 }
+  - match: { hits.hits.1._id: "1" }
+  - match: { hits.hits.1._score: 6.0 }
+  - match: { hits.hits.2._id: "4" }
+  - close_to: { hits.hits.2._score: { value: 1.6, error: 0.001 } }
+  - match: { hits.hits.3._id: "3" }
+  - match: { hits.hits.3._score: 1.2 }
+
 ---
 "should throw on unknown normalizer":
   - do: