[DERCBOT-919] Fix TU

Author: assouktim

bot/admin/server/src/main/kotlin/service/RAGValidationService.kt

@@ -37,22 +37,22 @@ object RAGValidationService {
     private val vectorStoreProviderService: VectorStoreProviderService get() = injector.provide()
 
     fun validate(ragConfig: BotRAGConfiguration): Set<ErrorMessage> {
+        val observabilitySetting = ObservabilityService.getObservabilityConfiguration(
+            ragConfig.namespace, ragConfig.botId, enabled = true
+        )?.setting
+
         return mutableSetOf<ErrorMessage>().apply {
             val questionCondensingLlmErrors = llmProviderService.checkSetting(
                 LLMProviderSettingStatusQuery(
                     ragConfig.questionCondensingLlmSetting!!,
-                    ObservabilityService.getObservabilityConfiguration(
-                        ragConfig.namespace, ragConfig.botId, enabled = true
-                    )?.setting
+                    observabilitySetting
                 )
             ).getErrors("LLM setting check failed (for question condensing)")
 
             val questionAnsweringLlmErrors = llmProviderService.checkSetting(
                 LLMProviderSettingStatusQuery(
                     ragConfig.questionAnsweringLlmSetting!!,
-                    ObservabilityService.getObservabilityConfiguration(
-                        ragConfig.namespace, ragConfig.botId, enabled = true
-                    )?.setting
+                    observabilitySetting
                 )
             ).getErrors("LLM setting check failed (for question answering)")
 

bot/admin/server/src/test/kotlin/service/RAGServiceTest.kt

@@ -70,8 +70,13 @@ class RAGServiceTest : AbstractTest() {
             namespace = NAMESPACE,
             botId = BOT_ID,
             enabled = false,
-            questionCondensingLlmSetting = null,
-            questionCondensingPrompt = null,
+            questionCondensingLlmSetting = OpenAILLMSettingDTO(
+                apiKey = "apikey",
+                model = MODEL,
+                temperature = TEMPERATURE,
+                baseUrl = "https://api.openai.com/v1"
+            ),
+            questionCondensingPrompt = PromptTemplate(template = PROMPT),
             questionAnsweringLlmSetting = OpenAILLMSettingDTO(
                 apiKey = "apikey",
                 model = MODEL,
@@ -86,7 +91,11 @@ class RAGServiceTest : AbstractTest() {
                 model = "model",
                 apiBase = "url"
             ),
-            noAnswerSentence = "No answer sentence"
+            noAnswerSentence = "No answer sentence",
+            documentsRequired = true,
+            debugEnabled = false,
+            maxDocumentsRetrieved = 2,
+            maxMessagesFromHistory = 2,
         )
 
         private val DEFAULT_BOT_CONFIG = aApplication.copy(namespace = NAMESPACE, botId = BOT_ID)

bot/admin/server/src/test/kotlin/service/RAGValidationServiceTest.kt

@@ -79,12 +79,16 @@ class RAGValidationServiceTest {
     private val ragConfiguration = BotRAGConfigurationDTO(
         namespace = "namespace",
         botId = "botId",
-        questionCondensingLlmSetting = null,
-        questionCondensingPrompt = null,
+        questionCondensingLlmSetting = openAILLMSetting,
+        questionCondensingPrompt = PromptTemplate(template = "test"),
         questionAnsweringLlmSetting = openAILLMSetting,
         questionAnsweringPrompt = PromptTemplate(template = "How to bike in the rain"),
         emSetting = azureOpenAIEMSetting,
         noAnswerSentence = " No answer sentence",
+        documentsRequired = true,
+        debugEnabled = false,
+        maxDocumentsRetrieved = 2,
+        maxMessagesFromHistory = 2,
     )
 
     @Test
@@ -167,7 +171,7 @@ class RAGValidationServiceTest {
     fun `validation of the RAG configuration when the Orchestrator returns 2 errors for LLM and 1 for Embedding model, the RAG function has not been activated`() {
 
         // GIVEN
-        // - 3 errors returned by Generative AI Orchestrator for LLM (2) and EM (1)
+        // - 3 errors returned by Generative AI Orchestrator for LLM (4 = 2 for condensing + 2 for answering) and EM (1)
         // - RAG is not enabled
         every {
             llmProviderService.checkSetting(any())
@@ -191,11 +195,13 @@ class RAGValidationServiceTest {
         )
 
         // THEN :
-        // Check that 3 errors have been found
-        assertEquals(2, errors.size)
+        // Check that 3 groups of errors have been found
+        assertEquals(3, errors.size)
         assertEquals("10", (((errors.elementAt(0).params) as List<*>)[0] as ErrorResponse).code)
         assertEquals("20", (((errors.elementAt(0).params) as List<*>)[1] as ErrorResponse).code)
-        assertEquals("30", (((errors.elementAt(1).params) as List<*>)[0] as ErrorResponse).code)
+        assertEquals("10", (((errors.elementAt(1).params) as List<*>)[0] as ErrorResponse).code)
+        assertEquals("20", (((errors.elementAt(1).params) as List<*>)[1] as ErrorResponse).code)
+        assertEquals("30", (((errors.elementAt(2).params) as List<*>)[0] as ErrorResponse).code)
     }
 
     private fun createFakeErrorResponse(code: String) = ErrorResponse(