Added Orchestration LLM Config Convenience

docs/guides/ORCHESTRATION_CHAT_COMPLETION.md

@@ -77,7 +77,7 @@ To use the Orchestration service, create a client and a configuration object:
 var client = new OrchestrationClient();
 
 var config = new OrchestrationModuleConfig()
-        .withLlmConfig(LLMModuleConfig.create().modelName("gpt-35-turbo").modelParams(Map.of()));
+        .withLlmConfig(OrchestrationAiModel.GPT_4O);
 ```
 
 Please also refer to [our sample code](../../sample-code/spring-app/src/main/java/com/sap/ai/sdk/app/controllers/OrchestrationController.java) for this and all following code examples.
@@ -218,13 +218,11 @@ In this example, the input will be masked before the call to the LLM. Note that
 Change your LLM module configuration to add model parameters:
 
 ```java
-var llmConfig =
-    LLMModuleConfig.create()
-        .modelName("gpt-35-turbo")
-        .modelParams(
-            Map.of(
-                "max_tokens", 50,
-                "temperature", 0.1,
-                "frequency_penalty", 0,
-                "presence_penalty", 0));
+LLMModuleConfig llmConfig =
+    OrchestrationAiModel.GPT_4O.modelParams(
+        Map.of(
+            "max_tokens", 50,
+            "temperature", 0.1,
+            "frequency_penalty", 0,
+            "presence_penalty", 0));
 ```

orchestration/src/main/java/com/sap/ai/sdk/orchestration/OrchestrationAiModel.java

@@ -0,0 +1,121 @@
+package com.sap.ai.sdk.orchestration;
+
+import com.sap.ai.sdk.orchestration.client.model.LLMModuleConfig;
+import java.util.Map;
+import javax.annotation.Nonnull;
+
+/** Large language models available in Orchestration. */
+// https://help.sap.com/docs/sap-ai-core/sap-ai-core-service-guide/models-and-scenarios-in-generative-ai-hub
+@SuppressWarnings("unused") // tested in OrchestrationTest.orchestrationModelAvailability()
+public class OrchestrationAiModel extends LLMModuleConfig {
+
+  /** IBM Granite 13B chat completions model */
+  public static final OrchestrationAiModel IBM_GRANITE_13B_CHAT =
+      new OrchestrationAiModel("ibm--granite-13b-chat");
+
+  /** MistralAI Mistral Large Instruct model */
+  public static final OrchestrationAiModel MISTRAL_LARGE_INSTRUCT =
+      new OrchestrationAiModel("mistralai--mistral-large-instruct");
+
+  /** MistralAI Mixtral 8x7B Instruct v01 model */
+  public static final OrchestrationAiModel MIXTRAL_8X7B_INSTRUCT_V01 =
+      new OrchestrationAiModel("mistralai--mixtral-8x7b-instruct-v01");
+
+  /** Meta Llama3 70B Instruct model */
+  public static final OrchestrationAiModel LLAMA3_70B_INSTRUCT =
+      new OrchestrationAiModel("meta--llama3-70b-instruct");
+
+  /** Meta Llama3.1 70B Instruct model */
+  public static final OrchestrationAiModel LLAMA3_1_70B_INSTRUCT =
+      new OrchestrationAiModel("meta--llama3.1-70b-instruct");
+
+  /** Anthropic Claude 3 Sonnet model */
+  public static final OrchestrationAiModel CLAUDE_3_SONNET =
+      new OrchestrationAiModel("anthropic--claude-3-sonnet");
+
+  /** Anthropic Claude 3 Haiku model */
+  public static final OrchestrationAiModel CLAUDE_3_HAIKU =
+      new OrchestrationAiModel("anthropic--claude-3-haiku");
+
+  /** Anthropic Claude 3 Opus model */
+  public static final OrchestrationAiModel CLAUDE_3_OPUS =
+      new OrchestrationAiModel("anthropic--claude-3-opus");
+
+  /** Anthropic Claude 3.5 Sonnet model */
+  public static final OrchestrationAiModel CLAUDE_3_5_SONNET =
+      new OrchestrationAiModel("anthropic--claude-3.5-sonnet");
+
+  /** Amazon Titan Embed Text model */
+  public static final OrchestrationAiModel TITAN_EMBED_TEXT =
+      new OrchestrationAiModel("amazon--titan-embed-text");
+
+  /** Amazon Titan Text Lite model */
+  public static final OrchestrationAiModel TITAN_TEXT_LITE =
+      new OrchestrationAiModel("amazon--titan-text-lite");
+
+  /** Amazon Titan Text Express model */
+  public static final OrchestrationAiModel TITAN_TEXT_EXPRESS =
+      new OrchestrationAiModel("amazon--titan-text-express");
+
+  /** Azure OpenAI GPT-3.5 Turbo chat completions model */
+  public static final OrchestrationAiModel GPT_35_TURBO = new OrchestrationAiModel("gpt-35-turbo");
+
+  /** Azure OpenAI GPT-3.5 Turbo chat completions model */
+  public static final OrchestrationAiModel GPT_35_TURBO_16K =
+      new OrchestrationAiModel("gpt-35-turbo-16k");
+
+  /** Azure OpenAI GPT-4 chat completions model */
+  public static final OrchestrationAiModel GPT_4 = new OrchestrationAiModel("gpt-4");
+
+  /** Azure OpenAI GPT-4-32k chat completions model */
+  public static final OrchestrationAiModel GPT_4_32K = new OrchestrationAiModel("gpt-4-32k");
+
+  /** Azure OpenAI Text Embedding Ada 002 model */
+  public static final OrchestrationAiModel TEXT_EMBEDDING_ADA_002 =
+      new OrchestrationAiModel("text-embedding-ada-002");
+
+  /** Azure OpenAI Text Embedding 3 Small model */
+  public static final OrchestrationAiModel TEXT_EMBEDDING_3_SMALL =
+      new OrchestrationAiModel("text-embedding-3-small");
+
+  /** Azure OpenAI Text Embedding 3 Large model */
+  public static final OrchestrationAiModel TEXT_EMBEDDING_3_LARGE =
+      new OrchestrationAiModel("text-embedding-3-large");
+
+  /** Azure OpenAI GPT-4o chat completions model */
+  public static final OrchestrationAiModel GPT_4O = new OrchestrationAiModel("gpt-4o");
+
+  /** Azure OpenAI GPT-4o-mini chat completions model */
+  public static final OrchestrationAiModel GPT_4O_MINI = new OrchestrationAiModel("gpt-4o-mini");
+
+  /** Google Cloud Platform Text Bison model */
+  public static final OrchestrationAiModel TEXT_BISON = new OrchestrationAiModel("text-bison");
+
+  /** Google Cloud Platform Chat Bison model */
+  public static final OrchestrationAiModel CHAT_BISON = new OrchestrationAiModel("chat-bison");
+
+  /** Google Cloud Platform Text Embedding Gecko model */
+  public static final OrchestrationAiModel TEXT_EMBEDDING_GECKO =
+      new OrchestrationAiModel("textembedding-gecko");
+
+  /** Google Cloud Platform Text Embedding Gecko Multilingual model */
+  public static final OrchestrationAiModel TEXT_EMBEDDING_GECKO_MULTILINGUAL =
+      new OrchestrationAiModel("textembedding-gecko-multilingual");
+
+  /** Google Cloud Platform Gemini 1.0 Pro model */
+  public static final OrchestrationAiModel GEMINI_1_0_PRO =
+      new OrchestrationAiModel("gemini-1.0-pro");
+
+  /** Google Cloud Platform Gemini 1.5 Pro model */
+  public static final OrchestrationAiModel GEMINI_1_5_PRO =
+      new OrchestrationAiModel("gemini-1.5-pro");
+
+  /** Google Cloud Platform Gemini 1.5 Flash model */
+  public static final OrchestrationAiModel GEMINI_1_5_FLASH =
+      new OrchestrationAiModel("gemini-1.5-flash");
+
+  OrchestrationAiModel(@Nonnull final String modelName) {
+    setModelName(modelName);
+    setModelParams(Map.of());
+  }
+}

orchestration/src/test/java/com/sap/ai/sdk/orchestration/OrchestrationUnitTest.java

@@ -57,14 +57,12 @@
 @WireMockTest
 class OrchestrationUnitTest {
   static final LLMModuleConfig LLM_CONFIG =
-      LLMModuleConfig.create()
-          .modelName("gpt-35-turbo-16k")
-          .modelParams(
-              Map.of(
-                  "max_tokens", 50,
-                  "temperature", 0.1,
-                  "frequency_penalty", 0,
-                  "presence_penalty", 0));
+      OrchestrationAiModel.GPT_35_TURBO_16K.modelParams(
+          Map.of(
+              "max_tokens", 50,
+              "temperature", 0.1,
+              "frequency_penalty", 0,
+              "presence_penalty", 0));
   private final Function<String, InputStream> fileLoader =
       filename -> Objects.requireNonNull(getClass().getClassLoader().getResourceAsStream(filename));
 

sample-code/spring-app/src/main/java/com/sap/ai/sdk/app/controllers/OrchestrationController.java

@@ -1,5 +1,7 @@
 package com.sap.ai.sdk.app.controllers;
 
+import static com.sap.ai.sdk.orchestration.OrchestrationAiModel.GPT_35_TURBO;
+
 import com.sap.ai.sdk.orchestration.OrchestrationClient;
 import com.sap.ai.sdk.orchestration.OrchestrationModuleConfig;
 import com.sap.ai.sdk.orchestration.OrchestrationPrompt;
@@ -30,12 +32,11 @@
 @RestController
 @RequestMapping("/orchestration")
 class OrchestrationController {
-  static final LLMModuleConfig LLM_CONFIG =
-      LLMModuleConfig.create().modelName("gpt-35-turbo").modelParams(Map.of());
+  LLMModuleConfig llmConfig = GPT_35_TURBO;
 
   private final OrchestrationClient client = new OrchestrationClient();
   private final OrchestrationModuleConfig config =
-      new OrchestrationModuleConfig().withLlmConfig(LLM_CONFIG);
+      new OrchestrationModuleConfig().withLlmConfig(llmConfig);
 
   /**
    * Chat request to OpenAI through the Orchestration service with a simple prompt.
@@ -171,7 +172,7 @@ public CompletionPostResponse maskingAnonymization() {
 
   /**
    * Let the orchestration service a response to a hypothetical user who provided feedback on the AI
-   * SDK. Pseydonymize the user's name and location to protect their privacy.
+   * SDK. Pseudonymize the user's name and location to protect their privacy.
    *
    * @return the result object
    */

sample-code/spring-app/src/test/java/com/sap/ai/sdk/app/controllers/OrchestrationTest.java

@@ -3,15 +3,22 @@
 import static org.assertj.core.api.Assertions.assertThat;
 import static org.assertj.core.api.Assertions.assertThatThrownBy;
 
+import com.sap.ai.sdk.orchestration.OrchestrationAiModel;
 import com.sap.ai.sdk.orchestration.OrchestrationClientException;
 import com.sap.ai.sdk.orchestration.client.model.AzureThreshold;
 import com.sap.ai.sdk.orchestration.client.model.CompletionPostResponse;
+import java.lang.reflect.Field;
+import java.util.ArrayList;
 import java.util.List;
 import java.util.Map;
+import lombok.SneakyThrows;
+import lombok.extern.slf4j.Slf4j;
 import org.assertj.core.api.InstanceOfAssertFactories;
 import org.junit.jupiter.api.BeforeEach;
+import org.junit.jupiter.api.DisplayName;
 import org.junit.jupiter.api.Test;
 
+@Slf4j
 class OrchestrationTest {
   OrchestrationController controller;
 
@@ -41,7 +48,7 @@ void testTemplate() {
     assertThat(llm.getId()).isNotEmpty();
     assertThat(llm.getObject()).isEqualTo("chat.completion");
     assertThat(llm.getCreated()).isGreaterThan(1);
-    assertThat(llm.getModel()).isEqualTo(OrchestrationController.LLM_CONFIG.getModelName());
+    assertThat(llm.getModel()).isEqualTo(controller.llmConfig.getModelName());
     var choices = llm.getChoices();
     assertThat(choices.get(0).getIndex()).isZero();
     assertThat(choices.get(0).getMessage().getContent()).isNotEmpty();
@@ -54,7 +61,7 @@ void testTemplate() {
     assertThat(result.getOrchestrationResult().getObject()).isEqualTo("chat.completion");
     assertThat(result.getOrchestrationResult().getCreated()).isGreaterThan(1);
     assertThat(result.getOrchestrationResult().getModel())
-        .isEqualTo(OrchestrationController.LLM_CONFIG.getModelName());
+        .isEqualTo(controller.llmConfig.getModelName());
     choices = result.getOrchestrationResult().getChoices();
     assertThat(choices.get(0).getIndex()).isZero();
     assertThat(choices.get(0).getMessage().getContent()).isNotEmpty();
@@ -137,4 +144,29 @@ void testMaskingPseudonymization() {
         .doesNotContain("MASKED_PERSON")
         .contains("Mallory");
   }
+
+  @Test
+  @DisplayName("Declared OrchestrationAiModels must match Orchestration's list of available models")
+  @SneakyThrows
+  void orchestrationModelAvailability() {
+    // TODO: Change this test to be like ScenarioTest.openAiModelAvailability() once the
+    //  Orchestration service has made the "available models endpoint".
+    //  Right now this test cannot tell if we are lacking models.
+
+    // Gather our declared Orchestration models
+    List<OrchestrationAiModel> declaredOrchestrationModelList = new ArrayList<>();
+    for (Field field : OrchestrationAiModel.class.getFields()) {
+      if (field.getType().equals(OrchestrationAiModel.class)) {
+        declaredOrchestrationModelList.add(((OrchestrationAiModel) field.get(null)));
+      }
+    }
+
+    declaredOrchestrationModelList.parallelStream()
+        .forEach(
+            model -> {
+              controller.llmConfig = model;
+              log.info("Testing completion for model: {}", model.getModelName());
+              assertThat(controller.completion()).isNotNull();
+            });
+  }
 }

sample-code/spring-app/src/test/java/com/sap/ai/sdk/app/controllers/ScenarioTest.java

@@ -11,12 +11,12 @@
 import org.junit.jupiter.api.DisplayName;
 import org.junit.jupiter.api.Test;
 
-public class ScenarioTest {
+class ScenarioTest {
 
   @Test
   @DisplayName("Declared OpenAI models must match AI Core's available OpenAI models")
   @SneakyThrows
-  public void openAiModelAvailability() {
+  void openAiModelAvailability() {
 
     // Gather AI Core's list of available OpenAI models
     final var aiModelList = new ScenarioController().getModels().getResources();