add prepend support

2 files changed, 64 insertions, 1 deletions

diff --git a/model-integration/src/main/java/ai/vespa/embedding/huggingface/HuggingFaceEmbedder.java b/model-integration/src/main/java/ai/vespa/embedding/huggingface/HuggingFaceEmbedder.java
index 20d8b6362d3..3e5dcfda3e9 100644
--- a/model-integration/src/main/java/ai/vespa/embedding/huggingface/HuggingFaceEmbedder.java
+++ b/model-integration/src/main/java/ai/vespa/embedding/huggingface/HuggingFaceEmbedder.java
@@ -39,6 +39,10 @@ public class HuggingFaceEmbedder extends AbstractComponent implements Embedder {
     private final OnnxEvaluator evaluator;
     private final PoolingStrategy poolingStrategy;
 
+    private final String prependQuery;
+
+    private final String prependDocument;
+
     @Inject
     public HuggingFaceEmbedder(OnnxRuntime onnx, Embedder.Runtime runtime, HuggingFaceEmbedderConfig config) {
         this.runtime = runtime;
@@ -47,6 +51,8 @@ public class HuggingFaceEmbedder extends AbstractComponent implements Embedder {
         tokenTypeIdsName = config.transformerTokenTypeIds();
         outputName = config.transformerOutput();
         normalize = config.normalize();
+        prependQuery = config.prependQuery();
+        prependDocument = config.prependDocument();
         var tokenizerPath = Paths.get(config.tokenizerPath().toString());
         var builder = new HuggingFaceTokenizer.Builder()
                 .addSpecialTokens(true)
@@ -113,7 +119,7 @@ public class HuggingFaceEmbedder extends AbstractComponent implements Embedder {
         if (!tensorType.dimensions().get(0).isIndexed()) {
             throw new IllegalArgumentException("Error in embedding to type '" + tensorType + "': dimension should be indexed.");
         }
-        var embeddingResult = lookupOrEvaluate(context, text);
+        var embeddingResult = lookupOrEvaluate(context, prependInstruction(text, context));
         IndexedTensor tokenEmbeddings = embeddingResult.output;
         if (tensorType.valueType() == TensorType.Value.INT8) {
             return binaryQuantization(embeddingResult, tensorType);
@@ -123,6 +129,16 @@ public class HuggingFaceEmbedder extends AbstractComponent implements Embedder {
         }
     }
 
+    String prependInstruction(String text, Context context) {
+        if (prependQuery != null && !prependQuery.isEmpty() && context.getDestination().startsWith("query")) {
+            return prependQuery + " " + text;
+        }
+        if (prependDocument != null && !prependDocument.isEmpty()){
+            return prependDocument + " " + text;
+        }
+        return text;
+    }
+
     Tensor normalize(Tensor embedding, TensorType tensorType) {
         double sumOfSquares = 0.0;
 
diff --git a/model-integration/src/test/java/ai/vespa/embedding/huggingface/HuggingFaceEmbedderTest.java b/model-integration/src/test/java/ai/vespa/embedding/huggingface/HuggingFaceEmbedderTest.java
index d504d77cc9b..c2c37db31f6 100644
--- a/model-integration/src/test/java/ai/vespa/embedding/huggingface/HuggingFaceEmbedderTest.java
+++ b/model-integration/src/test/java/ai/vespa/embedding/huggingface/HuggingFaceEmbedderTest.java
@@ -143,6 +143,39 @@ public class HuggingFaceEmbedderTest {
         });
     }
 
+    @Test
+    public void testEmbedderWithNormalizationAndPrefix() {
+        String input = "This is a test";
+        var context = new Embedder.Context("schema.indexing");
+        Tensor result = getNormalizePrefixdEmbedder().embed(input, context, TensorType.fromSpec(("tensor<float>(x[8])")));
+        assertEquals(1.0, result.multiply(result).sum().asDouble(), 1e-3);
+        result = getNormalizePrefixdEmbedder().embed(input, context, TensorType.fromSpec(("tensor<float>(x[16])")));
+        assertEquals(1.0,  result.multiply(result).sum().asDouble(), 1e-3);
+        Tensor binarizedResult = getNormalizePrefixdEmbedder().embed(input, context, TensorType.fromSpec(("tensor<int8>(x[2])")));
+        assertEquals("tensor<int8>(x[2]):[125, 44]", binarizedResult.toAbbreviatedString());
+
+        var queryContext = new Embedder.Context("query.qt");
+        Tensor queryResult = getNormalizePrefixdEmbedder().embed(input, queryContext, TensorType.fromSpec(("tensor<float>(x[8])")));
+        assertEquals(1.0, queryResult.multiply(queryResult).sum().asDouble(), 1e-3);
+        queryResult = getNormalizePrefixdEmbedder().embed(input, queryContext, TensorType.fromSpec(("tensor<float>(x[16])")));
+        assertEquals(1.0,  queryResult.multiply(queryResult).sum().asDouble(), 1e-3);
+        Tensor binarizedResultQuery = getNormalizePrefixdEmbedder().embed(input, queryContext, TensorType.fromSpec(("tensor<int8>(x[2])")));
+        assertNotEquals(binarizedResult.toAbbreviatedString(), binarizedResultQuery.toAbbreviatedString());
+        assertEquals("tensor<int8>(x[2]):[119, -116]", binarizedResultQuery.toAbbreviatedString());
+    }
+
+    @Test
+    public void testPrepend() {
+        var context = new Embedder.Context("schema.indexing");
+        String input = "This is a test";
+        var embedder = getNormalizePrefixdEmbedder();
+        var result = embedder.prependInstruction(input, context);
+        assertEquals("This is a document: This is a test",  result);
+        var queryContext = new Embedder.Context("query.qt");
+        var queryResult = embedder.prependInstruction(input, queryContext);
+        assertEquals("Represent this text: This is a test",  queryResult);
+    }
+
     private static HuggingFaceEmbedder getEmbedder() {
         String vocabPath = "src/test/models/onnx/transformer/real_tokenizer.json";
         String modelPath = "src/test/models/onnx/transformer/embedding_model.onnx";
@@ -165,6 +198,20 @@ public class HuggingFaceEmbedderTest {
         return new HuggingFaceEmbedder(new OnnxRuntime(), Embedder.Runtime.testInstance(), builder.build());
     }
 
+    private static HuggingFaceEmbedder getNormalizePrefixdEmbedder() {
+        String vocabPath = "src/test/models/onnx/transformer/real_tokenizer.json";
+        String modelPath = "src/test/models/onnx/transformer/embedding_model.onnx";
+        assumeTrue(OnnxRuntime.isRuntimeAvailable(modelPath));
+        HuggingFaceEmbedderConfig.Builder builder = new HuggingFaceEmbedderConfig.Builder();
+        builder.tokenizerPath(ModelReference.valueOf(vocabPath));
+        builder.transformerModel(ModelReference.valueOf(modelPath));
+        builder.transformerGpuDevice(-1);
+        builder.normalize(true);
+        builder.prependQuery("Represent this text:");
+        builder.prependDocument("This is a document:");
+        return new HuggingFaceEmbedder(new OnnxRuntime(), Embedder.Runtime.testInstance(), builder.build());
+    }
+
     public static Tensor expandBitTensor(Tensor packed) {
         var unpacker = new UnpackBitsNode(new ReferenceNode("input"), TensorType.Value.DOUBLE, "big");
         var context = new MapContext();