Merge branch 'main' into enum-without-logitBias

core/src/commonMain/kotlin/com/xebia/functional/xef/AI.kt

@@ -5,6 +5,7 @@ import com.xebia.functional.openai.generated.api.Images
 import com.xebia.functional.openai.generated.model.CreateChatCompletionRequestModel
 import com.xebia.functional.xef.conversation.AiDsl
 import com.xebia.functional.xef.conversation.Conversation
+import com.xebia.functional.xef.conversation.Description
 import com.xebia.functional.xef.prompt.Prompt
 import com.xebia.functional.xef.prompt.configuration.PromptConfiguration
 import kotlin.coroutines.cancellation.CancellationException
@@ -14,15 +15,74 @@ import kotlin.reflect.typeOf
 import kotlinx.serialization.ExperimentalSerializationApi
 import kotlinx.serialization.InternalSerializationApi
 import kotlinx.serialization.KSerializer
+import kotlinx.serialization.Serializable
 import kotlinx.serialization.descriptors.SerialKind
 import kotlinx.serialization.serializer
 
 sealed interface AI {
 
+  @Serializable
+  @Description("The selected items indexes")
+  data class SelectedItems(
+    @Description("The selected items indexes") val selectedItems: List<Int>,
+  )
+
+  data class Classification(
+    val name: String,
+    val description: String,
+  )
+
   interface PromptClassifier {
     fun template(input: String, output: String, context: String): String
   }
 
+  interface PromptMultipleClassifier {
+    fun getItems(): List<Classification>
+
+    fun template(input: String): String {
+      val items = getItems()
+
+      return """
+       |Based on the <input>, identify whether the user is asking about one or more of the following items
+       |
+       |${
+        items.joinToString("\n") { item -> "<${item.name}>${item.description}</${item.name}>" }
+      }
+       | 
+       |<items>
+       |${
+        items.mapIndexed { index, item -> "\t<item index=\"$index\">${item.name}</item>" }
+          .joinToString("\n")
+      }
+       |</items>
+       |<input>
+       |$input
+       |</input>
+    """
+    }
+
+    @OptIn(ExperimentalSerializationApi::class)
+    fun KType.enumValuesName(
+      serializer: KSerializer<Any?> = serializer(this)
+    ): List<Classification> {
+      return if (serializer.descriptor.kind != SerialKind.ENUM) {
+        emptyList()
+      } else {
+        (0 until serializer.descriptor.elementsCount).map { index ->
+          val name =
+            serializer.descriptor
+              .getElementName(index)
+              .removePrefix(serializer.descriptor.serialName)
+          val description =
+            (serializer.descriptor.getElementAnnotations(index).first { it is Description }
+                as Description)
+              .value
+          Classification(name, description)
+        }
+      }
+    }
+  }
+
   companion object {
 
     fun <A : Any> chat(
@@ -87,7 +147,7 @@ sealed interface AI {
       input: String,
       output: String,
       context: String,
-      model: CreateChatCompletionRequestModel = CreateChatCompletionRequestModel.gpt_4_1106_preview,
+      model: CreateChatCompletionRequestModel = CreateChatCompletionRequestModel.gpt_4o,
       target: KType = typeOf<E>(),
       config: Config = Config(),
       api: Chat = OpenAI(config).chat,
@@ -104,6 +164,28 @@ sealed interface AI {
       )
     }
 
+    @AiDsl
+    @Throws(IllegalArgumentException::class, CancellationException::class)
+    suspend inline fun <reified E> multipleClassify(
+      input: String,
+      model: CreateChatCompletionRequestModel = CreateChatCompletionRequestModel.gpt_4o,
+      config: Config = Config(),
+      api: Chat = OpenAI(config).chat,
+      conversation: Conversation = Conversation()
+    ): List<E> where E : PromptMultipleClassifier, E : Enum<E> {
+      val values = enumValues<E>()
+      val value = values.firstOrNull() ?: error("No enum values found")
+      val selected: SelectedItems =
+        invoke(
+          prompt = value.template(input),
+          model = model,
+          config = config,
+          api = api,
+          conversation = conversation
+        )
+      return selected.selectedItems.mapNotNull { values.elementAtOrNull(it) }
+    }
+
     @AiDsl
     suspend inline operator fun <reified A : Any> invoke(
       prompt: String,

examples/src/main/kotlin/com/xebia/functional/xef/dsl/classify/MultipleClassifier.kt

@@ -0,0 +1,47 @@
+package com.xebia.functional.xef.dsl.classify
+
+import com.xebia.functional.openai.generated.model.CreateChatCompletionRequestModel
+import com.xebia.functional.xef.AI
+import com.xebia.functional.xef.conversation.Description
+import kotlin.reflect.typeOf
+import kotlinx.serialization.Serializable
+
+@Serializable
+enum class Sports : AI.PromptMultipleClassifier {
+  @Description(
+    "Football is a team sport that is played on a rectangular field with goalposts at each end. The objective of the game is to score points by moving the ball into the opposing team's goal. The team with the most points at the end of the game wins."
+  )
+  FOOTBALL,
+  @Description(
+    "The game of basketball is played with a ball and a hoop. The objective is to score points by shooting the ball through the hoop. The game is played on a rectangular court with a hoop at each end. The team with the most points at the end of the game wins."
+  )
+  BASKETBALL,
+  @Description(
+    "The game of tennis is played with a racket and a ball. The objective is to hit the ball over the net and into the opponent's court. The game is played on a rectangular court with a net at the center. The player with the most points at the end of the game wins."
+  )
+  VOLLEYBALL,
+  @Description(
+    "The game of cricket is played with a bat and a ball. The objective is to score runs by hitting the ball and running between the wickets. The game is played on a circular field with a wicket at each end. The team with the most runs at the end of the game wins."
+  )
+  CRICKET,
+  @Description(
+    "The game of chess is played on a square board with 64 squares arranged in an 8x8 grid. The objective is to checkmate the opponent's king by placing it under threat of capture. The player who checkmates the opponent's king wins the game."
+  )
+  CHESS;
+
+  override fun getItems(): List<AI.Classification> = typeOf<Sports>().enumValuesName()
+}
+
+/**
+ * This is a simple example of how to use the `AI.multipleClassify` function to classify a prompt
+ */
+suspend fun main() {
+
+  println(AI.multipleClassify<Sports>("Sport played with a racket"))
+  println(
+    AI.multipleClassify<Sports>(
+      input = "The game is played with a ball",
+      model = CreateChatCompletionRequestModel.gpt_4o
+    )
+  )
+}