fix: adding cache for embedding models in EmbeddingModelFactory

redis-om-spring-ai/src/main/java/com/redis/om/spring/vectorize/EmbeddingModelFactory.java

@@ -3,6 +3,7 @@
 import java.time.Duration;
 import java.util.Arrays;
 import java.util.Map;
+import java.util.concurrent.ConcurrentHashMap;
 import java.util.stream.Collectors;
 
 import org.springframework.ai.azure.openai.AzureOpenAiEmbeddingModel;
@@ -46,6 +47,8 @@
 public class EmbeddingModelFactory {
   private final AIRedisOMProperties properties;
   private final SpringAiProperties springAiProperties;
+  private final Map<String, Object> modelCache = new ConcurrentHashMap<>();
+
   private final RestClient.Builder restClientBuilder;
   private final WebClient.Builder webClientBuilder;
   private final ResponseErrorHandler responseErrorHandler;
@@ -62,7 +65,60 @@ public EmbeddingModelFactory(AIRedisOMProperties properties, SpringAiProperties
     this.observationRegistry = observationRegistry;
   }
 
+  /**
+   * Generates a cache key for a model based on its type and parameters
+   * 
+   * @param modelType The type of the model
+   * @param params    Parameters that uniquely identify the model configuration
+   * @return A string key for caching
+   */
+  private String generateCacheKey(String modelType, String... params) {
+    StringBuilder keyBuilder = new StringBuilder(modelType);
+    for (String param : params) {
+      keyBuilder.append(":").append(param);
+    }
+    return keyBuilder.toString();
+  }
+
+  /**
+   * Clears the model cache, forcing new models to be created on next request.
+   * This can be useful when configuration changes or to free up resources.
+   */
+  public void clearCache() {
+    modelCache.clear();
+  }
+
+  /**
+   * Removes a specific model from the cache.
+   *
+   * @param modelType The type of the model (e.g., "openai", "transformers")
+   * @param params    Parameters that were used to create the model
+   * @return true if a model was removed, false otherwise
+   */
+  public boolean removeFromCache(String modelType, String... params) {
+    String cacheKey = generateCacheKey(modelType, params);
+    return modelCache.remove(cacheKey) != null;
+  }
+
+  /**
+   * Returns the current number of models in the cache.
+   *
+   * @return The number of cached models
+   */
+  public int getCacheSize() {
+    return modelCache.size();
+  }
+
   public TransformersEmbeddingModel createTransformersEmbeddingModel(Vectorize vectorize) {
+    String cacheKey = generateCacheKey("transformers", vectorize.transformersModel(), vectorize.transformersTokenizer(),
+        vectorize.transformersResourceCacheConfiguration(), String.join(",", vectorize.transformersTokenizerOptions()));
+
+    TransformersEmbeddingModel cachedModel = (TransformersEmbeddingModel) modelCache.get(cacheKey);
+
+    if (cachedModel != null) {
+      return cachedModel;
+    }
+
     TransformersEmbeddingModel embeddingModel = new TransformersEmbeddingModel();
 
     if (!vectorize.transformersModel().isEmpty()) {
@@ -89,6 +145,8 @@ public TransformersEmbeddingModel createTransformersEmbeddingModel(Vectorize vec
       throw new RuntimeException("Error initializing TransformersEmbeddingModel", e);
     }
 
+    modelCache.put(cacheKey, embeddingModel);
+
     return embeddingModel;
   }
 
@@ -97,6 +155,13 @@ public OpenAiEmbeddingModel createOpenAiEmbeddingModel(EmbeddingModel model) {
   }
 
   public OpenAiEmbeddingModel createOpenAiEmbeddingModel(String model) {
+    String cacheKey = generateCacheKey("openai", model, properties.getOpenAi().getApiKey());
+    OpenAiEmbeddingModel cachedModel = (OpenAiEmbeddingModel) modelCache.get(cacheKey);
+
+    if (cachedModel != null) {
+      return cachedModel;
+    }
+
     String apiKey = properties.getOpenAi().getApiKey();
     if (!StringUtils.hasText(apiKey)) {
       apiKey = springAiProperties.getOpenai().getApiKey();
@@ -109,8 +174,11 @@ public OpenAiEmbeddingModel createOpenAiEmbeddingModel(String model) {
     OpenAiApi openAiApi = OpenAiApi.builder().apiKey(properties.getOpenAi().getApiKey()).restClientBuilder(RestClient
         .builder().requestFactory(factory)).build();
 
-    return new OpenAiEmbeddingModel(openAiApi, MetadataMode.EMBED, OpenAiEmbeddingOptions.builder().model(model)
-        .build(), RetryUtils.DEFAULT_RETRY_TEMPLATE);
+    OpenAiEmbeddingModel embeddingModel = new OpenAiEmbeddingModel(openAiApi, MetadataMode.EMBED, OpenAiEmbeddingOptions
+        .builder().model(model).build(), RetryUtils.DEFAULT_RETRY_TEMPLATE);
+
+    modelCache.put(cacheKey, embeddingModel);
+    return embeddingModel;
   }
 
   private OpenAIClient getOpenAIClient() {
@@ -126,6 +194,16 @@ private OpenAIClient getOpenAIClient() {
   }
 
   public AzureOpenAiEmbeddingModel createAzureOpenAiEmbeddingModel(String deploymentName) {
+    String cacheKey = generateCacheKey("azure-openai", deploymentName, properties.getAzure().getOpenAi().getApiKey(),
+        properties.getAzure().getOpenAi().getEndpoint(), String.valueOf(properties.getAzure().getEntraId()
+            .isEnabled()));
+
+    AzureOpenAiEmbeddingModel cachedModel = (AzureOpenAiEmbeddingModel) modelCache.get(cacheKey);
+
+    if (cachedModel != null) {
+      return cachedModel;
+    }
+
     String apiKey = properties.getAzure().getOpenAi().getApiKey();
     if (!StringUtils.hasText(apiKey)) {
       apiKey = springAiProperties.getAzure().getApiKey(); // Fallback to Spring AI property
@@ -142,10 +220,23 @@ public AzureOpenAiEmbeddingModel createAzureOpenAiEmbeddingModel(String deployme
 
     AzureOpenAiEmbeddingOptions options = AzureOpenAiEmbeddingOptions.builder().deploymentName(deploymentName).build();
 
-    return new AzureOpenAiEmbeddingModel(openAIClient, MetadataMode.EMBED, options);
+    AzureOpenAiEmbeddingModel embeddingModel = new AzureOpenAiEmbeddingModel(openAIClient, MetadataMode.EMBED, options);
+
+    modelCache.put(cacheKey, embeddingModel);
+
+    return embeddingModel;
   }
 
   public VertexAiTextEmbeddingModel createVertexAiTextEmbeddingModel(String model) {
+    String cacheKey = generateCacheKey("vertex-ai", model, properties.getVertexAi().getApiKey(), properties
+        .getVertexAi().getEndpoint(), properties.getVertexAi().getProjectId(), properties.getVertexAi().getLocation());
+
+    VertexAiTextEmbeddingModel cachedModel = (VertexAiTextEmbeddingModel) modelCache.get(cacheKey);
+
+    if (cachedModel != null) {
+      return cachedModel;
+    }
+
     String apiKey = properties.getVertexAi().getApiKey();
     if (!StringUtils.hasText(apiKey)) {
       apiKey = springAiProperties.getVertexAi().getApiKey(); // Fallback to Spring AI property
@@ -183,16 +274,32 @@ public VertexAiTextEmbeddingModel createVertexAiTextEmbeddingModel(String model)
 
     VertexAiTextEmbeddingOptions options = VertexAiTextEmbeddingOptions.builder().model(model).build();
 
-    return new VertexAiTextEmbeddingModel(connectionDetails, options);
+    VertexAiTextEmbeddingModel embeddingModel = new VertexAiTextEmbeddingModel(connectionDetails, options);
+
+    modelCache.put(cacheKey, embeddingModel);
+
+    return embeddingModel;
   }
 
   public OllamaEmbeddingModel createOllamaEmbeddingModel(String model) {
+    String cacheKey = generateCacheKey("ollama", model, properties.getOllama().getBaseUrl());
+
+    OllamaEmbeddingModel cachedModel = (OllamaEmbeddingModel) modelCache.get(cacheKey);
+
+    if (cachedModel != null) {
+      return cachedModel;
+    }
+
     OllamaApi api = OllamaApi.builder().baseUrl(properties.getOllama().getBaseUrl()).restClientBuilder(
         restClientBuilder).webClientBuilder(webClientBuilder).responseErrorHandler(responseErrorHandler).build();
 
     OllamaOptions options = OllamaOptions.builder().model(model).truncate(false).build();
 
-    return OllamaEmbeddingModel.builder().ollamaApi(api).defaultOptions(options).build();
+    OllamaEmbeddingModel embeddingModel = OllamaEmbeddingModel.builder().ollamaApi(api).defaultOptions(options).build();
+
+    modelCache.put(cacheKey, embeddingModel);
+
+    return embeddingModel;
   }
 
   private AwsCredentials getAwsCredentials() {
@@ -218,6 +325,16 @@ private AwsCredentials getAwsCredentials() {
   }
 
   public BedrockCohereEmbeddingModel createCohereEmbeddingModel(String model) {
+    String cacheKey = generateCacheKey("bedrock-cohere", model, properties.getAws().getAccessKey(), properties.getAws()
+        .getSecretKey(), properties.getAws().getRegion(), String.valueOf(properties.getAws().getBedrockCohere()
+            .getResponseTimeOut()));
+
+    BedrockCohereEmbeddingModel cachedModel = (BedrockCohereEmbeddingModel) modelCache.get(cacheKey);
+
+    if (cachedModel != null) {
+      return cachedModel;
+    }
+
     String region = properties.getAws().getRegion();
     if (!StringUtils.hasText(region)) {
       region = springAiProperties.getBedrock().getAws().getRegion(); // Fallback to Spring AI property
@@ -228,10 +345,24 @@ public BedrockCohereEmbeddingModel createCohereEmbeddingModel(String model) {
         properties.getAws().getRegion(), ModelOptionsUtils.OBJECT_MAPPER, Duration.ofMinutes(properties.getAws()
             .getBedrockCohere().getResponseTimeOut()));
 
-    return new BedrockCohereEmbeddingModel(cohereEmbeddingApi);
+    BedrockCohereEmbeddingModel embeddingModel = new BedrockCohereEmbeddingModel(cohereEmbeddingApi);
+
+    modelCache.put(cacheKey, embeddingModel);
+
+    return embeddingModel;
   }
 
   public BedrockTitanEmbeddingModel createTitanEmbeddingModel(String model) {
+    String cacheKey = generateCacheKey("bedrock-titan", model, properties.getAws().getAccessKey(), properties.getAws()
+        .getSecretKey(), properties.getAws().getRegion(), String.valueOf(properties.getAws().getBedrockTitan()
+            .getResponseTimeOut()));
+
+    BedrockTitanEmbeddingModel cachedModel = (BedrockTitanEmbeddingModel) modelCache.get(cacheKey);
+
+    if (cachedModel != null) {
+      return cachedModel;
+    }
+
     String region = properties.getAws().getRegion();
     if (!StringUtils.hasText(region)) {
       region = springAiProperties.getBedrock().getAws().getRegion(); // Fallback to Spring AI property
@@ -242,6 +373,10 @@ public BedrockTitanEmbeddingModel createTitanEmbeddingModel(String model) {
         properties.getAws().getRegion(), ModelOptionsUtils.OBJECT_MAPPER, Duration.ofMinutes(properties.getAws()
             .getBedrockTitan().getResponseTimeOut()));
 
-    return new BedrockTitanEmbeddingModel(titanEmbeddingApi, observationRegistry);
+    BedrockTitanEmbeddingModel embeddingModel = new BedrockTitanEmbeddingModel(titanEmbeddingApi, observationRegistry);
+
+    modelCache.put(cacheKey, embeddingModel);
+
+    return embeddingModel;
   }
 }

tests/src/test/resources/vss_on.yaml

@@ -2,4 +2,4 @@ redis:
   om:
     spring:
       ai:
-        \enabled: true
+        enabled: true