Add support for HSQLDB store

CHANGELOG.md

@@ -4,6 +4,7 @@
 - Upgraded to openai sdk 1.0.0-beta.11
 - Added convenience method `FunctionInvocation.withResultTypeAutoConversion` which sets the return type and registers a
   type converter based on Jackson for the return type.
+- Added localization support for error/debug messages
 
 ### Bug Fixes
 

README.md

@@ -50,7 +50,7 @@ in a different direction, but also to consider the impact on the larger ecosyste
 To learn more and get started:
 
 - Read the [documentation](https://learn.microsoft.com/en-us/semantic-kernel/overview/?tabs=Java&pivots=programming-language-java)
-- Learn how to [contribute](https://learn.microsoft.com/en-us/semantic-kernel/get-started/contributing?tabs=Java&pivots=programming-language-java) to the project
+- Learn how to [contribute](https://learn.microsoft.com/en-us/semantic-kernel/support/contributing?tabs=Java&pivots=programming-language-java) to the project
 - Join the [Discord community](https://aka.ms/SKDiscord)
 - Attend [regular office hours and SK community events](COMMUNITY.md)
 - Follow the team on our [blog](https://aka.ms/sk/blog)

aiservices/openai/src/main/java/com/microsoft/semantickernel/aiservices/openai/textembedding/OpenAITextEmbeddingGenerationService.java

@@ -8,35 +8,37 @@
 import com.microsoft.semantickernel.aiservices.openai.OpenAiService;
 import com.microsoft.semantickernel.exceptions.AIException;
 import com.microsoft.semantickernel.services.openai.OpenAiServiceBuilder;
-import com.microsoft.semantickernel.services.textcompletion.TextGenerationService;
 import com.microsoft.semantickernel.services.textembedding.Embedding;
 import com.microsoft.semantickernel.services.textembedding.TextEmbeddingGenerationService;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import javax.annotation.Nullable;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 import reactor.core.publisher.Mono;
 
-import javax.annotation.Nullable;
-import java.util.ArrayList;
-import java.util.List;
-
 /**
  * An OpenAI implementation of a {@link TextEmbeddingGenerationService}.
- *
  */
 public class OpenAITextEmbeddingGenerationService extends OpenAiService<OpenAIAsyncClient>
     implements TextEmbeddingGenerationService {
+
     private static final Logger LOGGER = LoggerFactory
         .getLogger(OpenAITextEmbeddingGenerationService.class);
     private static final int DEFAULT_DIMENSIONS = 1536;
     private final int dimensions;
 
+    public static final int EMBEDDING_DIMENSIONS_SMALL = 1536;
+    public static final int EMBEDDING_DIMENSIONS_LARGE = 3072;
+
     /**
      * Creates a new {@link OpenAITextEmbeddingGenerationService}.
      *
-     * @param client OpenAI client
+     * @param client         OpenAI client
      * @param deploymentName deployment name
-     * @param modelId OpenAI model id
-     * @param serviceId Service id
+     * @param modelId        OpenAI model id
+     * @param serviceId      Service id
      */
     public OpenAITextEmbeddingGenerationService(
         OpenAIAsyncClient client,
@@ -57,6 +59,24 @@ public static Builder builder() {
         return new Builder();
     }
 
+    /**
+     * Generates embeddings for the given data.
+     *
+     * @param data The data to generate embeddings for.
+     * @return A Mono that completes with the embeddings.
+     */
+    @Override
+    public Mono<Embedding> generateEmbeddingAsync(String data) {
+        return this.internalGenerateTextEmbeddingsAsync(Arrays.asList(data))
+            .flatMap(embeddings -> {
+                if (embeddings.isEmpty()) {
+                    return Mono.empty();
+                }
+
+                return Mono.just(embeddings.get(0));
+            });
+    }
+
     /**
      * Generates embeddings for the given data.
      *
@@ -88,6 +108,7 @@ protected Mono<List<Embedding>> internalGenerateTextEmbeddingsAsync(List<String>
      */
     public static class Builder extends
         OpenAiServiceBuilder<OpenAIAsyncClient, OpenAITextEmbeddingGenerationService, OpenAITextEmbeddingGenerationService.Builder> {
+
         private int dimensions = DEFAULT_DIMENSIONS;
 
         /**

api-test/integration-tests/pom.xml

@@ -122,6 +122,14 @@
             <artifactId>jsonschema-module-jackson</artifactId>
             <scope>test</scope>
         </dependency>
+
+
+        <dependency>
+            <groupId>org.hsqldb</groupId>
+            <artifactId>hsqldb</artifactId>
+            <version>2.7.3</version>
+            <scope>test</scope>
+        </dependency>
     </dependencies>
 
     <dependencyManagement>

api-test/integration-tests/src/test/java/com/microsoft/semantickernel/tests/connectors/memory/jdbc/Hotel.java

@@ -5,6 +5,7 @@
 import com.microsoft.semantickernel.data.vectorstorage.attributes.VectorStoreRecordDataAttribute;
 import com.microsoft.semantickernel.data.vectorstorage.attributes.VectorStoreRecordKeyAttribute;
 import com.microsoft.semantickernel.data.vectorstorage.attributes.VectorStoreRecordVectorAttribute;
+import com.microsoft.semantickernel.data.vectorstorage.definition.DistanceFunction;
 
 import java.util.List;
 
@@ -24,19 +25,19 @@ public class Hotel {
     private final String description;
 
     @JsonProperty("summaryEmbedding1")
-    @VectorStoreRecordVectorAttribute(dimensions = 8, distanceFunction = "euclidean")
+    @VectorStoreRecordVectorAttribute(dimensions = 8, distanceFunction = DistanceFunction.EUCLIDEAN_DISTANCE)
     private final List<Float> euclidean;
 
     @JsonProperty("summaryEmbedding2")
-    @VectorStoreRecordVectorAttribute(dimensions = 8, distanceFunction = "cosineDistance")
+    @VectorStoreRecordVectorAttribute(dimensions = 8, distanceFunction = DistanceFunction.COSINE_DISTANCE)
     private final List<Float> cosineDistance;
 
     @JsonProperty("summaryEmbedding3")
-    @VectorStoreRecordVectorAttribute(dimensions = 8, distanceFunction = "dotProduct")
+    @VectorStoreRecordVectorAttribute(dimensions = 8, distanceFunction = DistanceFunction.DOT_PRODUCT)
     private final List<Float> dotProduct;
 
     @JsonProperty("indexedSummaryEmbedding")
-    @VectorStoreRecordVectorAttribute(dimensions = 8, indexKind = "hnsw", distanceFunction = "euclidean")
+    @VectorStoreRecordVectorAttribute(dimensions = 8, indexKind = "hnsw", distanceFunction = DistanceFunction.EUCLIDEAN_DISTANCE)
     private final List<Float> indexedEuclidean;
     @VectorStoreRecordDataAttribute
     private double rating;