Fix the review problems

langchain4j-azure-ai-search-spring-boot-starter/src/main/java/dev/langchain4j/azure/aisearch/spring/AutoConfig.java

@@ -0,0 +1,47 @@
+package dev.langchain4j.azure.aisearch.spring;
+
+import com.azure.search.documents.indexes.models.SearchIndex;
+import dev.langchain4j.model.embedding.EmbeddingModel;
+import dev.langchain4j.rag.content.retriever.azure.search.AzureAiSearchContentRetriever;
+import dev.langchain4j.store.embedding.azure.search.AzureAiSearchEmbeddingStore;
+import org.springframework.boot.autoconfigure.AutoConfiguration;
+import org.springframework.boot.autoconfigure.condition.ConditionalOnProperty;
+import org.springframework.boot.context.properties.EnableConfigurationProperties;
+import org.springframework.context.annotation.Bean;
+import org.springframework.lang.Nullable;
+
+import static dev.langchain4j.azure.aisearch.spring.Properties.PREFIX;
+
+@AutoConfiguration
+@EnableConfigurationProperties(Properties.class)
+public class AutoConfig {
+    @Bean
+    @ConditionalOnProperty(PREFIX + ".content-retriver.api-key")
+    public AzureAiSearchContentRetriever azureAiSearchContentRetriever(Properties properties, @Nullable EmbeddingModel embeddingModel, @Nullable SearchIndex index) {
+        Properties.NestedProperties nestedProperties = properties.getContentRetriver();
+        return AzureAiSearchContentRetriever.builder()
+                .endpoint(nestedProperties.getEndpoint())
+                .apiKey(nestedProperties.getApiKey())
+                .createOrUpdateIndex(nestedProperties.isCreateOrUpdateIndex())
+                .embeddingModel(embeddingModel)
+                .dimensions(nestedProperties.getDimensions())
+                .index(index)
+                .maxResults(nestedProperties.getMaxResults())
+                .minScore(nestedProperties.getMinScore())
+                .queryType(nestedProperties.getQueryType())
+                .build();
+    }
+
+    @Bean
+    @ConditionalOnProperty(PREFIX + ".embedding-store.api-key")
+    public AzureAiSearchEmbeddingStore azureAiSearchEmbeddingStore(Properties properties, @Nullable EmbeddingModel embeddingModel, @Nullable SearchIndex index) {
+        Properties.NestedProperties nestedProperties = properties.getEmbeddingStore();
+        return AzureAiSearchEmbeddingStore.builder()
+                .endpoint(nestedProperties.getEndpoint())
+                .apiKey(nestedProperties.getApiKey())
+                .createOrUpdateIndex(nestedProperties.isCreateOrUpdateIndex())
+                .dimensions(nestedProperties.getDimensions())
+                .index(index)
+                .build();
+    }
+}

langchain4j-azure-ai-search-spring-boot-starter/src/main/java/dev/langchain4j/azure/aisearch/spring/Properties.java

@@ -0,0 +1,33 @@
+package dev.langchain4j.azure.aisearch.spring;
+
+import dev.langchain4j.rag.content.retriever.azure.search.AzureAiSearchQueryType;
+import lombok.Getter;
+import lombok.Setter;
+import org.springframework.boot.context.properties.ConfigurationProperties;
+import org.springframework.boot.context.properties.NestedConfigurationProperty;
+
+@Getter
+@Setter
+@ConfigurationProperties(prefix = Properties.PREFIX)
+public class Properties {
+
+    static final String PREFIX = "langchain4j.azure.ai-search";
+
+    @NestedConfigurationProperty
+    NestedProperties contentRetriver;
+
+    @NestedConfigurationProperty
+    NestedProperties embeddingStore;
+
+    @Getter
+    @Setter
+    public static class NestedProperties {
+        String endpoint;
+        String apiKey;
+        int dimensions;
+        boolean createOrUpdateIndex;
+        int maxResults = 3;
+        double minScore;
+        AzureAiSearchQueryType queryType;
+    }
+}

langchain4j-azure-ai-search-spring-boot-starter/src/test/java/dev/langchain4j/azure/aisearch/spring/AutoConfigIT.java

@@ -0,0 +1,79 @@
+package dev.langchain4j.azure.aisearch.spring;
+
+import dev.langchain4j.model.embedding.AllMiniLmL6V2EmbeddingModel;
+import dev.langchain4j.model.embedding.EmbeddingModel;
+import dev.langchain4j.rag.content.retriever.ContentRetriever;
+import dev.langchain4j.rag.content.retriever.azure.search.AzureAiSearchContentRetriever;
+import dev.langchain4j.store.embedding.EmbeddingStore;
+import dev.langchain4j.store.embedding.azure.search.AzureAiSearchEmbeddingStore;
+import org.junit.jupiter.api.Test;
+import org.springframework.boot.autoconfigure.AutoConfigurations;
+import org.springframework.boot.test.context.runner.ApplicationContextRunner;
+
+import static org.assertj.core.api.Assertions.assertThat;
+
+class AutoConfigIT {
+
+    private static final String AZURE_AISEARCH_API_KEY = System.getenv("AZURE_AISEARCH_API_KEY");
+    private static final String AZURE_AISEARCH_ENDPOINT = System.getenv("AZURE_AISEARCH_ENDPOINT");
+    private static final String AZURE_AISEARCH_DIMENSIONS = System.getenv("AZURE_AISEARCH_DIMENSIONS");
+    private static final String AZURE_AISEARCH_MAX_RESULTS = System.getenv("AZURE_AISEARCH_MAX_RESULTS");
+    private static final String AZURE_AISEARCH_MIN_SCORE = System.getenv("AZURE_AISEARCH_MIN_SCORE");
+
+    ApplicationContextRunner contextRunner = new ApplicationContextRunner()
+            .withConfiguration(AutoConfigurations.of(AutoConfig.class));
+
+    @Test
+    void should_provide_ai_search_retriver_only_search() {
+        contextRunner
+                .withPropertyValues(
+                        Properties.PREFIX + ".content-retriver.api-key=" + AZURE_AISEARCH_API_KEY,
+                        Properties.PREFIX + ".content-retriver.endpoint=" + AZURE_AISEARCH_ENDPOINT,
+                        Properties.PREFIX + ".content-retriver.create-or-update-index=" + "false",
+                        Properties.PREFIX + ".content-retriver.query-type=" + "FULL_TEXT"
+                ).run(context -> {
+                    ContentRetriever contentRetriever = context.getBean(ContentRetriever.class);
+                    assertThat(contentRetriever).isInstanceOf(AzureAiSearchContentRetriever.class);
+
+                    assertThat(context.getBean(AzureAiSearchContentRetriever.class)).isSameAs(contentRetriever);
+                });
+    }
+
+    @Test
+    void should_provide_ai_search_retrive_create_or_update_indexr() {
+        contextRunner
+                .withPropertyValues(
+                        Properties.PREFIX + ".content-retriver.api-key=" + AZURE_AISEARCH_API_KEY,
+                        Properties.PREFIX + ".content-retriver.endpoint=" + AZURE_AISEARCH_ENDPOINT,
+                        Properties.PREFIX + ".content-retriver.dimensions=" + AZURE_AISEARCH_DIMENSIONS,
+                        Properties.PREFIX + ".content-retriver.create-or-update-index=" + "true",
+                        Properties.PREFIX + ".content-retriver.max-results=" + AZURE_AISEARCH_MAX_RESULTS,
+                        Properties.PREFIX + ".content-retriver.min-score=" + AZURE_AISEARCH_MIN_SCORE,
+                        Properties.PREFIX + ".content-retriver.query-type=" + "VECTOR"
+                ).withBean(EmbeddingModel.class, AllMiniLmL6V2EmbeddingModel::new)
+                .run(context -> {
+                    ContentRetriever contentRetriever = context.getBean(ContentRetriever.class);
+                    assertThat(contentRetriever).isInstanceOf(AzureAiSearchContentRetriever.class);
+
+                    assertThat(context.getBean(AzureAiSearchContentRetriever.class)).isSameAs(contentRetriever);
+                });
+    }
+
+    @Test
+    void should_provide_ai_search_embedding_store() {
+        contextRunner
+                .withPropertyValues(
+                        Properties.PREFIX + ".embedding-store.api-key=" + AZURE_AISEARCH_API_KEY,
+                        Properties.PREFIX + ".embedding-store.endpoint=" + AZURE_AISEARCH_ENDPOINT,
+                        Properties.PREFIX + ".embedding-store.dimensions=" + AZURE_AISEARCH_DIMENSIONS,
+                        Properties.PREFIX + ".embedding-store.create-or-update-index=" + "true"
+                ).withBean(EmbeddingModel.class, AllMiniLmL6V2EmbeddingModel::new)
+                .run(context -> {
+                    EmbeddingStore embeddingStore = context.getBean(EmbeddingStore.class);
+                    assertThat(embeddingStore).isInstanceOf(AzureAiSearchEmbeddingStore.class);
+
+                    assertThat(context.getBean(AzureAiSearchEmbeddingStore.class)).isSameAs(embeddingStore);
+                });
+    }
+
+}

langchain4j-azure-openai-spring-boot-starter/pom.xml → langchain4j-azure-open-ai-spring-boot-starter/pom.xml

@@ -56,8 +56,10 @@
 
         <dependency>
             <groupId>dev.langchain4j</groupId>
-            <artifactId>langchain4j-open-ai</artifactId>
-            <version>0.29.0-SNAPSHOT</version>
+            <artifactId>langchain4j-core</artifactId>
+            <version>${project.version}</version>
+            <classifier>tests</classifier>
+            <type>test-jar</type>
             <scope>test</scope>
         </dependency>
 

langchain4j-azure-openai-spring-boot-starter/src/test/java/dev/langchain4j/azure/openai/AutoConfigIT.java → langchain4j-azure-open-ai-spring-boot-starter/src/test/java/dev/langchain4j/azure/openai/AutoConfigIT.java

@@ -2,7 +2,9 @@
 
 import dev.langchain4j.azure.openai.spring.AutoConfig;
 import dev.langchain4j.data.message.AiMessage;
+import dev.langchain4j.model.ExampleTestTokenizer;
 import dev.langchain4j.model.StreamingResponseHandler;
+import dev.langchain4j.model.Tokenizer;
 import dev.langchain4j.model.azure.AzureOpenAiChatModel;
 import dev.langchain4j.model.azure.AzureOpenAiEmbeddingModel;
 import dev.langchain4j.model.azure.AzureOpenAiImageModel;
@@ -11,7 +13,6 @@
 import dev.langchain4j.model.chat.StreamingChatLanguageModel;
 import dev.langchain4j.model.embedding.EmbeddingModel;
 import dev.langchain4j.model.image.ImageModel;
-import dev.langchain4j.model.openai.OpenAiTokenizer;
 import dev.langchain4j.model.output.Response;
 import org.junit.jupiter.api.Test;
 import org.springframework.boot.autoconfigure.AutoConfigurations;
@@ -116,7 +117,7 @@ void should_provide_embedding_model() {
                 .withPropertyValues("langchain4j.azure.open-ai.embedding-model.api-key=" + AZURE_OPENAI_KEY,
                         "langchain4j.azure.open-ai.embedding-model.endpoint=" + AZURE_OPENAI_ENDPOINT,
                         "langchain4j.azure.open-ai.embedding-model.deployment-name=" + AZURE_EMBEDDING_DEPLOYMENT_NAME)
-                .withBean(OpenAiTokenizer.class, () -> new OpenAiTokenizer("gpt-3.5-turbo"))
+                .withBean(Tokenizer.class, ExampleTestTokenizer::new)
                 .run(context -> {
 
                     EmbeddingModel embeddingModel = context.getBean(EmbeddingModel.class);