1 files changed, 42 insertions, 36 deletions

diff --git a/container-search/src/main/java/com/yahoo/search/ranking/HitRescorer.java b/container-search/src/main/java/com/yahoo/search/ranking/HitRescorer.java
index cce6b42d323..fee4f5b4160 100644
--- a/container-search/src/main/java/com/yahoo/search/ranking/HitRescorer.java
+++ b/container-search/src/main/java/com/yahoo/search/ranking/HitRescorer.java
@@ -1,57 +1,63 @@
-// Copyright Yahoo. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+// Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
 package com.yahoo.search.ranking;
 
 import com.yahoo.search.result.FeatureData;
 import com.yahoo.search.result.Hit;
-import static com.yahoo.searchlib.rankingexpression.Reference.RANKING_EXPRESSION_WRAPPER;
+import com.yahoo.tensor.Tensor;
 
+import java.util.List;
 import java.util.function.Supplier;
 import java.util.logging.Logger;
 
 class HitRescorer {
 
     private static final Logger logger = Logger.getLogger(HitRescorer.class.getName());
-    
-    private final Supplier<Evaluator> evaluatorSource;
 
-    public HitRescorer(Supplier<Evaluator> evaluatorSource) {
-        this.evaluatorSource = evaluatorSource;
+    private final Supplier<Evaluator> mainEvalSrc;
+    private final List<String> mainFromMF;
+    private final List<NormalizerContext> normalizers;
+
+    public HitRescorer(Supplier<Evaluator> mainEvalSrc, List<String> mainFromMF, List<NormalizerContext> normalizers) {
+        this.mainEvalSrc = mainEvalSrc;
+        this.mainFromMF = mainFromMF;
+        this.normalizers = normalizers;
     }
 
-    boolean rescoreHit(Hit hit) {
-        var features = hit.getField("matchfeatures");
-        if (features instanceof FeatureData matchFeatures) {
-            var scorer = evaluatorSource.get();
-            for (String argName : scorer.needInputs()) {
-                var asTensor = matchFeatures.getTensor(argName);
-                if (asTensor == null) {
-                    asTensor = matchFeatures.getTensor(alternate(argName));
-                }
-                if (asTensor != null) {
-                    scorer.bind(argName, asTensor);
-                } else {
-                    logger.warning("Missing match-feature for Evaluator argument: " + argName);
-                    return false;
-                }
-            }
-            double newScore = scorer.evaluateScore();
-            hit.setRelevance(newScore);
-            return true;
-        } else {
-            logger.warning("Hit without match-features: " + hit);
-            return false;
+    void preprocess(WrappedHit wrapped) {
+        for (var n : normalizers) {
+            var scorer = n.evalSource().get();
+            double val = evalScorer(wrapped, scorer, n.fromMF());
+            wrapped.setIdx(n.normalizer().addInput(val));
+        }
+    }
+
+    void runNormalizers() {
+        for (var n : normalizers) {
+            n.normalizer().normalize();
         }
     }
 
-    private static final String RE_PREFIX = RANKING_EXPRESSION_WRAPPER + "(";
-    private static final String RE_SUFFIX = ")";
-    private static final int RE_PRE_LEN = RE_PREFIX.length();
-    private static final int RE_SUF_LEN = RE_SUFFIX.length();
+    double rescoreHit(WrappedHit wrapped) {
+        var scorer = mainEvalSrc.get();
+        for (var n : normalizers) {
+            double normalizedValue = n.normalizer().getOutput(wrapped.getIdx());
+            scorer.bind(n.name(), Tensor.from(normalizedValue));
+        }
+        double newScore = evalScorer(wrapped, scorer, mainFromMF);
+        wrapped.setScore(newScore);
+        return newScore;
+    }
 
-    static String alternate(String argName) {
-        if (argName.startsWith(RE_PREFIX) && argName.endsWith(RE_SUFFIX)) {
-            return argName.substring(RE_PRE_LEN, argName.length() - RE_SUF_LEN);
+    private static double evalScorer(WrappedHit wrapped, Evaluator scorer, List<String> fromMF) {
+        for (String argName : fromMF) {
+            var asTensor = wrapped.getTensor(argName);
+            if (asTensor != null) {
+                scorer.bind(argName, asTensor);
+            } else {
+                logger.warning("Missing match-feature for Evaluator argument: " + argName);
+                return 0.0;
+            }
         }
-        return argName;
+        return scorer.evaluateScore();
     }
 }