Optimize MistralAiEmbeddingModel dimensions method

- Calculate and cache values for unknown models only if necessary
- Make known embedding dimensions a mutable map attribute
- Verify the cache mechanism with MistralAiEmbeddingModelTests

Signed-off-by: Nicolas Krier <7557886+nicolaskrier@users.noreply.github.com>

Author: nicolaskrier

models/spring-ai-mistral-ai/src/main/java/org/springframework/ai/mistralai/MistralAiEmbeddingModel.java

@@ -16,6 +16,7 @@
 
 package org.springframework.ai.mistralai;
 
+import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
 
@@ -56,16 +57,14 @@ public class MistralAiEmbeddingModel extends AbstractEmbeddingModel {
 
 	private static final Logger logger = LoggerFactory.getLogger(MistralAiEmbeddingModel.class);
 
+	private static final EmbeddingModelObservationConvention DEFAULT_OBSERVATION_CONVENTION = new DefaultEmbeddingModelObservationConvention();
+
 	/**
 	 * Known embedding dimensions for Mistral AI models. Maps model names to their
 	 * respective embedding vector dimensions. This allows the dimensions() method to
 	 * return the correct value without making an API call.
 	 */
-	private static final Map<String, Integer> KNOWN_EMBEDDING_DIMENSIONS = Map.of(
-			MistralAiApi.EmbeddingModel.EMBED.getValue(), 1024, MistralAiApi.EmbeddingModel.CODESTRAL_EMBED.getValue(),
-			1536);
-
-	private static final EmbeddingModelObservationConvention DEFAULT_OBSERVATION_CONVENTION = new DefaultEmbeddingModelObservationConvention();
+	private final Map<String, Integer> knownEmbeddingDimensions = createKnownEmbeddingDimensions();
 
 	private final MistralAiEmbeddingOptions defaultOptions;
 
@@ -85,6 +84,14 @@ public class MistralAiEmbeddingModel extends AbstractEmbeddingModel {
 	 */
 	private EmbeddingModelObservationConvention observationConvention = DEFAULT_OBSERVATION_CONVENTION;
 
+	private static Map<String, Integer> createKnownEmbeddingDimensions() {
+		Map<String, Integer> knownEmbeddingDimensions = new HashMap<>();
+		knownEmbeddingDimensions.put(MistralAiApi.EmbeddingModel.EMBED.getValue(), 1024);
+		knownEmbeddingDimensions.put(MistralAiApi.EmbeddingModel.CODESTRAL_EMBED.getValue(), 1536);
+
+		return knownEmbeddingDimensions;
+	}
+
 	public MistralAiEmbeddingModel(MistralAiApi mistralAiApi, MetadataMode metadataMode,
 			MistralAiEmbeddingOptions options, RetryTemplate retryTemplate, ObservationRegistry observationRegistry) {
 		Assert.notNull(mistralAiApi, "mistralAiApi must not be null");
@@ -174,7 +181,8 @@ public float[] embed(Document document) {
 
 	@Override
 	public int dimensions() {
-		return KNOWN_EMBEDDING_DIMENSIONS.getOrDefault(this.defaultOptions.getModel(), super.dimensions());
+		return this.knownEmbeddingDimensions.computeIfAbsent(this.defaultOptions.getModel(),
+				model -> super.dimensions());
 	}
 
 	/**

models/spring-ai-mistral-ai/src/test/java/org/springframework/ai/mistralai/MistralAiEmbeddingModelTests.java

@@ -16,6 +16,7 @@
 
 package org.springframework.ai.mistralai;
 
+import java.util.Arrays;
 import java.util.List;
 
 import org.junit.jupiter.api.Test;
@@ -28,6 +29,7 @@
 
 import static org.assertj.core.api.Assertions.assertThat;
 import static org.mockito.ArgumentMatchers.any;
+import static org.mockito.Mockito.verify;
 import static org.mockito.Mockito.when;
 
 /**
@@ -77,7 +79,7 @@ void testDimensionsForCodestralEmbedModel() {
 	void testDimensionsFallbackForUnknownModel() {
 		MistralAiApi mockApi = createMockApiWithEmbeddingResponse(512);
 
-		// Use a model name that doesn't exist in KNOWN_EMBEDDING_DIMENSIONS
+		// Use a model name that doesn't exist in knownEmbeddingDimensions.
 		MistralAiEmbeddingOptions options = MistralAiEmbeddingOptions.builder().withModel("unknown-model").build();
 
 		MistralAiEmbeddingModel model = MistralAiEmbeddingModel.builder()
@@ -87,17 +89,23 @@ void testDimensionsFallbackForUnknownModel() {
 			.retryTemplate(RetryUtils.DEFAULT_RETRY_TEMPLATE)
 			.build();
 
-		// Should fall back to super.dimensions() which detects dimensions from the API
-		// response
+		// For the first call, it should fall back to super.dimensions() which detects
+		// dimensions from the API response.
 		assertThat(model.dimensions()).isEqualTo(512);
+
+		// For the second call, it should use the cache mechanism.
+		assertThat(model.dimensions()).isEqualTo(512);
+
+		// Verify that super.dimensions() has been called once.
+		verify(mockApi).embeddings(any());
 	}
 
 	@Test
 	void testAllEmbeddingModelsHaveDimensionMapping() {
-		// This test ensures that KNOWN_EMBEDDING_DIMENSIONS map stays in sync with the
-		// EmbeddingModel enum
+		// This test ensures that knownEmbeddingDimensions map stays in sync with the
+		// EmbeddingModel enum.
 		// If a new model is added to the enum but not to the dimensions map, this test
-		// will help catch it
+		// will help catch it.
 
 		for (MistralAiApi.EmbeddingModel embeddingModel : MistralAiApi.EmbeddingModel.values()) {
 			MistralAiApi mockApi = createMockApiWithEmbeddingResponse(1024);
@@ -138,16 +146,13 @@ private MistralAiApi createMockApiWithEmbeddingResponse(int dimensions) {
 
 		// Create a mock embedding response with the specified dimensions
 		float[] embedding = new float[dimensions];
-		for (int i = 0; i < dimensions; i++) {
-			embedding[i] = 0.1f;
-		}
+		Arrays.fill(embedding, 0.1f);
 
 		MistralAiApi.Embedding embeddingData = new MistralAiApi.Embedding(0, embedding, "embedding");
 
 		MistralAiApi.Usage usage = new MistralAiApi.Usage(10, 0, 10);
 
-		MistralAiApi.EmbeddingList embeddingList = new MistralAiApi.EmbeddingList("object", List.of(embeddingData),
-				"model", usage);
+		var embeddingList = new MistralAiApi.EmbeddingList<>("object", List.of(embeddingData), "model", usage);
 
 		when(mockApi.embeddings(any())).thenReturn(ResponseEntity.ok(embeddingList));