Add RerankRequestChunker

x-pack/plugin/inference/src/main/java/org/elasticsearch/xpack/inference/action/TransportInferenceAction.java

@@ -74,6 +74,8 @@ protected void doInference(
         InferenceService service,
         ActionListener<InferenceServiceResults> listener
     ) {
+        // var rerankChunker = new RerankRequestChunker(request.getInput());
+
         service.infer(
             model,
             request.getQuery(),

x-pack/plugin/inference/src/main/java/org/elasticsearch/xpack/inference/chunking/RerankRequestChunker.java

@@ -0,0 +1,99 @@
+/*
+ * 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.inference.chunking;
+
+import org.elasticsearch.action.ActionListener;
+import org.elasticsearch.inference.ChunkingSettings;
+import org.elasticsearch.xpack.core.inference.action.InferenceAction;
+import org.elasticsearch.xpack.core.inference.results.RankedDocsResults;
+
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+public class RerankRequestChunker {
+
+    private final ChunkingSettings chunkingSettings;
+    private final List<String> inputs;
+    private final Map<Integer, RerankChunks> rerankChunks;
+
+    public RerankRequestChunker(List<String> inputs) {
+        // TODO: Make chunking settings dependent on the model being used.
+        // There may be a way to do this dynamically knowing the max token size for the model/service and query size
+        // instead of hardcoding it ona model/service basis.
+        this.chunkingSettings = new WordBoundaryChunkingSettings(100, 10);
+        this.inputs = inputs;
+        this.rerankChunks = chunk(inputs, chunkingSettings);
+    }
+
+    private Map<Integer, RerankChunks> chunk(List<String> inputs, ChunkingSettings chunkingSettings) {
+        var chunker = ChunkerBuilder.fromChunkingStrategy(chunkingSettings.getChunkingStrategy());
+        var chunks = new HashMap<Integer, RerankChunks>();
+        var chunkIndex = 0;
+        for (int i = 0; i < inputs.size(); i++) {
+            var chunksForInput = chunker.chunk(inputs.get(i), chunkingSettings);
+            for (var chunk : chunksForInput) {
+                chunks.put(chunkIndex, new RerankChunks(i, inputs.get(i).substring(chunk.start(), chunk.end())));
+                chunkIndex++;
+            }
+        }
+        return chunks;
+    }
+
+    public List<String> getChunkedInputs() {
+        List<String> chunkedInputs = new ArrayList<>();
+        for (RerankChunks chunk : rerankChunks.values()) {
+            chunkedInputs.add(chunk.chunkString());
+        }
+        // TODO: Score the inputs here and only return the top N chunks for each document
+        return chunkedInputs;
+    }
+
+    public ActionListener<InferenceAction.Response> parseChunkedRerankResultsListener(ActionListener<InferenceAction.Response> listener) {
+        return ActionListener.wrap(results -> {
+            if (results.getResults() instanceof RankedDocsResults rankedDocsResults) {
+                listener.onResponse(new InferenceAction.Response(parseRankedDocResultsForChunks(rankedDocsResults)));
+                // TODO: Figure out if the above correctly creates the response or if it loses any info
+
+            } else {
+                listener.onFailure(new IllegalArgumentException("Expected RankedDocsResults but got: " + results.getClass()));
+            }
+
+        }, listener::onFailure);
+    }
+
+    private RankedDocsResults parseRankedDocResultsForChunks(RankedDocsResults rankedDocsResults) {
+        Map<Integer, RankedDocsResults.RankedDoc> bestRankedDocResultPerDoc = new HashMap<>();
+        for (var rankedDoc : rankedDocsResults.getRankedDocs()) {
+            int chunkIndex = rankedDoc.index();
+            int docIndex = rerankChunks.get(chunkIndex).docIndex();
+            if (bestRankedDocResultPerDoc.containsKey(docIndex)) {
+                RankedDocsResults.RankedDoc existingDoc = bestRankedDocResultPerDoc.get(docIndex);
+                if (rankedDoc.relevanceScore() > existingDoc.relevanceScore()) {
+                    bestRankedDocResultPerDoc.put(
+                        docIndex,
+                        new RankedDocsResults.RankedDoc(docIndex, rankedDoc.relevanceScore(), inputs.get(docIndex))
+                    );
+                }
+            } else {
+                bestRankedDocResultPerDoc.put(
+                    docIndex,
+                    new RankedDocsResults.RankedDoc(docIndex, rankedDoc.relevanceScore(), inputs.get(docIndex))
+                );
+            }
+        }
+        var bestRankedDocResultPerDocList = new ArrayList<>(bestRankedDocResultPerDoc.values());
+        bestRankedDocResultPerDocList.sort(
+            (RankedDocsResults.RankedDoc d1, RankedDocsResults.RankedDoc d2) -> Float.compare(d2.relevanceScore(), d1.relevanceScore())
+        );
+        return new RankedDocsResults(bestRankedDocResultPerDocList);
+    }
+
+    public record RerankChunks(int docIndex, String chunkString) {};
+}

x-pack/plugin/inference/src/main/java/org/elasticsearch/xpack/inference/rank/textsimilarity/TextSimilarityRankFeaturePhaseRankCoordinatorContext.java

@@ -17,6 +17,7 @@
 import org.elasticsearch.xpack.core.inference.action.GetInferenceModelAction;
 import org.elasticsearch.xpack.core.inference.action.InferenceAction;
 import org.elasticsearch.xpack.core.inference.results.RankedDocsResults;
+import org.elasticsearch.xpack.inference.chunking.RerankRequestChunker;
 import org.elasticsearch.xpack.inference.services.cohere.rerank.CohereRerankTaskSettings;
 import org.elasticsearch.xpack.inference.services.googlevertexai.rerank.GoogleVertexAiRerankTaskSettings;
 import org.elasticsearch.xpack.inference.services.huggingface.rerank.HuggingFaceRerankTaskSettings;
@@ -119,9 +120,16 @@ protected void computeScores(RankFeatureDoc[] featureDocs, ActionListener<float[
                 inferenceListener.onResponse(new InferenceAction.Response(new RankedDocsResults(List.of())));
             } else {
                 List<String> featureData = Arrays.stream(featureDocs).map(x -> x.featureData).toList();
-                InferenceAction.Request inferenceRequest = generateRequest(featureData);
+                RerankRequestChunker chunker = new RerankRequestChunker(featureData);
+                InferenceAction.Request inferenceRequest = generateRequest(chunker.getChunkedInputs());
                 try {
-                    executeAsyncWithOrigin(client, INFERENCE_ORIGIN, InferenceAction.INSTANCE, inferenceRequest, inferenceListener);
+                    executeAsyncWithOrigin(
+                        client,
+                        INFERENCE_ORIGIN,
+                        InferenceAction.INSTANCE,
+                        inferenceRequest,
+                        chunker.parseChunkedRerankResultsListener(inferenceListener)
+                    );
                 } finally {
                     inferenceRequest.decRef();
                 }
@@ -156,6 +164,7 @@ protected RankFeatureDoc[] preprocess(RankFeatureDoc[] originalDocs, boolean rer
     }
 
     protected InferenceAction.Request generateRequest(List<String> docFeatures) {
+        // TODO: Try running the RerankRequestChunker here.
         return new InferenceAction.Request(
             TaskType.RERANK,
             inferenceId,