Add scalar parameters

src/main/java/com/penguineering/hareairis/ai/AIChatService.java

@@ -6,9 +6,12 @@
 import com.penguineering.hareairis.model.ChatResponse;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
+import org.springframework.ai.azure.openai.AzureOpenAiChatOptions;
 import org.springframework.ai.chat.client.ChatClient;
 import org.springframework.stereotype.Service;
 
+import java.util.Objects;
+
 /**
  * Service to handle chat requests.
  *
@@ -31,7 +34,10 @@ public AIChatService(ChatClient.Builder chatClientBuilder) {
      */
     public ChatResponse handleChatRequest(ChatRequest chatRequest) {
         try {
+            AzureOpenAiChatOptions options = renderAzureOpenAiChatOptions(chatRequest);
+
             ChatClient chatClient = chatClientBuilder
+                    .defaultOptions(options)
                     .defaultSystem(chatRequest.getSystemMessage())
                     .build();
             var chatResponse = chatClient
@@ -59,4 +65,21 @@ public ChatResponse handleChatRequest(ChatRequest chatRequest) {
             throw new ChatException(response.getStatusCode(), e.getMessage());
         }
     }
+
+    private static AzureOpenAiChatOptions renderAzureOpenAiChatOptions(ChatRequest chatRequest) {
+        AzureOpenAiChatOptions options = new AzureOpenAiChatOptions();
+
+        if (Objects.nonNull(chatRequest.getMaxTokens()))
+            options.setMaxTokens(chatRequest.getMaxTokens());
+        if (Objects.nonNull(chatRequest.getTemperature()))
+            options.setTemperature(chatRequest.getTemperature());
+        if (Objects.nonNull(chatRequest.getTopP()))
+            options.setTopP(chatRequest.getTopP());
+        if (Objects.nonNull(chatRequest.getPresencePenalty()))
+            options.setPresencePenalty(chatRequest.getPresencePenalty());
+        if (Objects.nonNull(chatRequest.getFrequencyPenalty()))
+            options.setFrequencyPenalty(chatRequest.getFrequencyPenalty());
+
+        return options;
+    }
 }

src/main/java/com/penguineering/hareairis/model/ChatRequest.java

@@ -6,6 +6,8 @@
 import lombok.Getter;
 import lombok.NoArgsConstructor;
 
+import java.util.Optional;
+
 @NoArgsConstructor
 @AllArgsConstructor
 @Getter
@@ -16,4 +18,49 @@ public class ChatRequest {
 
     @JsonProperty("prompt")
     private String prompt = "";
+
+    /**
+     * The maximum number of tokens to generate.
+     */
+    @JsonProperty(value = "max-tokens")
+    private Integer maxTokens = null;
+
+    /**
+     * The sampling temperature to use that controls the apparent creativity of generated
+     * completions. Higher values will make output more random while lower values will
+     * make results more focused and deterministic. It is not recommended to modify
+     * temperature and top_p for the same completions request as the interaction of these
+     * two settings is difficult to predict.
+     */
+    @JsonProperty(value = "temperature")
+    private Double temperature = null;
+
+    /**
+     * An alternative to sampling with temperature called nucleus sampling. This value
+     * causes the model to consider the results of tokens with the provided probability
+     * mass. As an example, a value of 0.15 will cause only the tokens comprising the top
+     * 15% of probability mass to be considered. It is not recommended to modify
+     * temperature and top_p for the same completions request as the interaction of these
+     * two settings is difficult to predict.
+     */
+    @JsonProperty(value = "top-p")
+    private Double topP = null;
+
+    /**
+     * A value that influences the probability of generated tokens appearing based on
+     * their existing presence in generated text. Positive values will make tokens less
+     * likely to appear when they already exist and increase the model's likelihood to
+     * output new topics.
+     */
+    @JsonProperty(value = "presence-penalty")
+    private Double presencePenalty = null;
+
+    /**
+     * A value that influences the probability of generated tokens appearing based on
+     * their cumulative frequency in generated text. Positive values will make tokens less
+     * likely to appear as their frequency increases and decrease the likelihood of the
+     * model repeating the same statements verbatim.
+     */
+    @JsonProperty(value = "frequency-penalty")
+    private Double frequencyPenalty = null;
 }