Enum/Classification support for models that do not support logitBias

core/build.gradle.kts

@@ -106,7 +106,10 @@ kotlin {
       }
       val jvmTest by getting {
           dependencies {
-              implementation(libs.kotest.junit5)
+              implementation(libs.ollama.testcontainers)
+              implementation(libs.junit.jupiter.api)
+              implementation(libs.junit.jupiter.engine)
+              implementation(libs.logback)
           }
       }
       val linuxX64Main by getting {

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

@@ -6,6 +6,7 @@ import com.xebia.functional.openai.generated.model.CreateChatCompletionRequestMo
 import com.xebia.functional.xef.conversation.AiDsl
 import com.xebia.functional.xef.conversation.Conversation
 import com.xebia.functional.xef.prompt.Prompt
+import com.xebia.functional.xef.prompt.configuration.PromptConfiguration
 import kotlin.coroutines.cancellation.CancellationException
 import kotlin.reflect.KClass
 import kotlin.reflect.KType
@@ -111,7 +112,19 @@ sealed interface AI {
       config: Config = Config(),
       api: Chat = OpenAI(config).chat,
       conversation: Conversation = Conversation()
-    ): A = chat(Prompt(model, prompt), target, config, api, conversation)
+    ): A =
+      chat(
+        prompt =
+          Prompt(
+            model = model,
+            value = prompt,
+            configuration = PromptConfiguration { supportsLogitBias = config.supportsLogitBias }
+          ),
+        target = target,
+        config = config,
+        api = api,
+        conversation = conversation
+      )
 
     @AiDsl
     suspend inline operator fun <reified A : Any> invoke(

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

@@ -1,6 +1,5 @@
 package com.xebia.functional.xef
 
-import arrow.core.nonEmptyListOf
 import com.xebia.functional.openai.Config as OpenAIConfig
 import com.xebia.functional.openai.generated.api.OpenAI
 import com.xebia.functional.xef.env.getenv
@@ -26,7 +25,8 @@ data class Config(
     classDiscriminator = "_type_"
   },
   val streamingPrefix: String = "data:",
-  val streamingDelimiter: String = "data: [DONE]"
+  val streamingDelimiter: String = "data: [DONE]",
+  val supportsLogitBias: Boolean = true,
 ) {
   companion object {
     val DEFAULT = Config()
@@ -50,7 +50,8 @@ fun OpenAI(
   val token =
     config.token
       ?: getenv(KEY_ENV_VAR)
-      ?: throw AIError.Env.OpenAI(nonEmptyListOf("missing $KEY_ENV_VAR env var"))
+      ?: "<not-provided>" // throw AIError.Env.OpenAI(nonEmptyListOf("missing $KEY_ENV_VAR env
+  // var"))
   val clientConfig: HttpClientConfig<*>.() -> Unit = {
     install(ContentNegotiation) { json(config.json) }
     install(HttpTimeout) {

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

@@ -9,6 +9,8 @@ import com.xebia.functional.xef.llm.models.modelType
 import com.xebia.functional.xef.llm.prompt
 import com.xebia.functional.xef.llm.promptStreaming
 import com.xebia.functional.xef.prompt.Prompt
+import com.xebia.functional.xef.prompt.PromptBuilder.Companion.user
+import com.xebia.functional.xef.prompt.contentAsString
 import kotlin.reflect.KClass
 import kotlin.reflect.KType
 import kotlin.reflect.typeOf
@@ -50,7 +52,7 @@ data class DefaultAI<A : Any>(
     val serializer = serializer()
     return when (serializer.descriptor.kind) {
       SerialKind.ENUM -> {
-        runWithEnumSingleTokenSerializer(serializer, prompt)
+        runWithEnumSerializer(serializer, prompt)
       }
       // else -> runWithSerializer(prompt, serializer)
       PolymorphicKind.OPEN ->
@@ -82,11 +84,61 @@ data class DefaultAI<A : Any>(
     }
   }
 
-  @OptIn(ExperimentalSerializationApi::class)
-  suspend fun runWithEnumSingleTokenSerializer(serializer: KSerializer<A>, prompt: Prompt): A {
+  private suspend fun runWithEnumSerializer(serializer: KSerializer<A>, prompt: Prompt): A =
+    if (prompt.configuration.supportsLogitBias) {
+      runWithEnumSingleTokenSerializer(serializer, prompt)
+    } else {
+      runWithEnumRegexResponseSerializer(serializer, prompt)
+    }
+
+  private suspend fun runWithEnumRegexResponseSerializer(
+    serializer: KSerializer<A>,
+    prompt: Prompt
+  ): A {
+    val cases = casesFromEnumSerializer(serializer)
+    val classificationMessage =
+      user(
+        """
+      <instructions>
+        You are an AI, expected to classify the `context` into one of the `cases`:
+        <context>
+          ${prompt.messages.joinToString("\n") { it.contentAsString() }}
+        <cases>  
+          ${cases.map { s -> "<case>$s</case>" }.joinToString("\n")}
+        </cases>
+        Select the `case` corresponding to the `context`.
+        IMPORTANT. Reply exclusively with the selected `case`.
+      </instructions>
+    """
+          .trimIndent()
+      )
+    val result =
+      api.createChatCompletion(
+        CreateChatCompletionRequest(
+          messages = prompt.messages + classificationMessage,
+          model = model,
+          maxTokens = prompt.configuration.maxTokens,
+          temperature = 0.0
+        )
+      )
+    val casesRegexes = cases.map { ".*$it.*" }
+    val responseContent = result.choices[0].message.content ?: ""
+    val choice =
+      casesRegexes
+        .zip(cases)
+        .firstOrNull {
+          Regex(it.first, RegexOption.IGNORE_CASE).containsMatchIn(responseContent.trim())
+        }
+        ?.second
+    return serializeWithEnumSerializer(choice, enumSerializer)
+  }
+
+  private suspend fun runWithEnumSingleTokenSerializer(
+    serializer: KSerializer<A>,
+    prompt: Prompt
+  ): A {
     val encoding = model.modelType(forFunctions = false).encoding
-    val cases =
-      serializer.descriptor.elementDescriptors.map { it.serialName.substringAfterLast(".") }
+    val cases = casesFromEnumSerializer(serializer)
     val logitBias =
       cases
         .flatMap {
@@ -108,7 +160,17 @@ data class DefaultAI<A : Any>(
         )
       )
     val choice = result.choices[0].message.content
-    val enumSerializer = enumSerializer
+    return serializeWithEnumSerializer(choice, enumSerializer)
+  }
+
+  @OptIn(ExperimentalSerializationApi::class)
+  private fun casesFromEnumSerializer(serializer: KSerializer<A>): List<String> =
+    serializer.descriptor.elementDescriptors.map { it.serialName.substringAfterLast(".") }
+
+  private fun serializeWithEnumSerializer(
+    choice: String?,
+    enumSerializer: ((case: String) -> A)?
+  ): A {
     return if (choice != null && enumSerializer != null) {
       enumSerializer(choice)
     } else {

core/src/commonMain/kotlin/com/xebia/functional/xef/prompt/configuration/PromptConfiguration.kt

@@ -18,6 +18,7 @@ constructor(
   var maxTokens: Int = 500,
   var messagePolicy: MessagePolicy = MessagePolicy(),
   var seed: Int? = null,
+  var supportsLogitBias: Boolean = true,
 ) {
 
   fun messagePolicy(block: MessagePolicy.() -> Unit) = messagePolicy.apply { block() }

core/src/jvmTest/kotlin/com/xebia/functional/xef/ollama/tests/EnumClassificationTest.kt

@@ -0,0 +1,33 @@
+package com.xebia.functional.xef.ollama.tests
+
+import com.xebia.functional.xef.ollama.tests.models.OllamaModels
+import com.xebia.functional.xef.ollama.tests.models.Sentiment
+import kotlinx.coroutines.runBlocking
+import org.junit.jupiter.api.Test
+
+class EnumClassificationTest : OllamaTests() {
+
+  @Test
+  fun `positive sentiment`() {
+    runBlocking {
+      val sentiment =
+        ollama<Sentiment>(
+          model = OllamaModels.Gemma2B,
+          prompt = "The context of the situation is very positive.",
+        )
+      assert(sentiment == Sentiment.POSITIVE) { "Expected POSITIVE but got $sentiment" }
+    }
+  }
+
+  @Test
+  fun `negative sentiment`() {
+    runBlocking {
+      val sentiment =
+        ollama<Sentiment>(
+          model = OllamaModels.LLama3_8B,
+          prompt = "The context of the situation is very negative.",
+        )
+      assert(sentiment == Sentiment.NEGATIVE) { "Expected NEGATIVE but got $sentiment" }
+    }
+  }
+}

core/src/jvmTest/kotlin/com/xebia/functional/xef/ollama/tests/OllamaTests.kt

@@ -0,0 +1,100 @@
+package com.xebia.functional.xef.ollama.tests
+
+import com.github.dockerjava.api.model.Image
+import com.xebia.functional.openai.generated.model.CreateChatCompletionRequestModel
+import com.xebia.functional.xef.AI
+import com.xebia.functional.xef.Config
+import com.xebia.functional.xef.OpenAI
+import io.github.oshai.kotlinlogging.KotlinLogging
+import java.util.concurrent.ConcurrentHashMap
+import org.junit.jupiter.api.AfterAll
+import org.testcontainers.DockerClientFactory
+import org.testcontainers.ollama.OllamaContainer
+import org.testcontainers.utility.DockerImageName
+
+abstract class OllamaTests {
+
+  val logger = KotlinLogging.logger {}
+
+  companion object {
+    private const val OLLAMA_IMAGE = "ollama/ollama:0.1.26"
+
+    private val registeredContainers: MutableMap<String, OllamaContainer> = ConcurrentHashMap()
+
+    @PublishedApi
+    internal fun useModel(model: String): OllamaContainer =
+      if (registeredContainers.containsKey(model)) {
+        registeredContainers[model]!!
+      } else {
+        ollamaContainer(model)
+      }
+
+    private fun ollamaContainer(model: String, imageName: String = model): OllamaContainer {
+      if (registeredContainers.containsKey(model)) {
+        return registeredContainers[model]!!
+      }
+      // create the new image if it is not already a docker image
+      val listImagesCmd: List<Image> =
+        DockerClientFactory.lazyClient().listImagesCmd().withImageNameFilter(imageName).exec()
+
+      val ollama =
+        if (listImagesCmd.isEmpty()) {
+          // ship container emoji: 🚢
+          println("🐳 Creating a new Ollama container with $model image...")
+          val ollama = OllamaContainer(OLLAMA_IMAGE)
+          ollama.start()
+          println("🐳 Pulling $model image...")
+          ollama.execInContainer("ollama", "pull", model)
+          println("🐳 Committing $model image...")
+          ollama.commitToImage(imageName)
+          ollama.withReuse(true)
+        } else {
+          println("🐳 Using existing Ollama container with $model image...")
+          // Substitute the default Ollama image with our model variant
+          val ollama =
+            OllamaContainer(
+                DockerImageName.parse(imageName).asCompatibleSubstituteFor("ollama/ollama")
+              )
+              .withReuse(true)
+          ollama.start()
+          ollama
+        }
+      println("🐳 Starting Ollama container with $model image...")
+      registeredContainers[model] = ollama
+      ollama.execInContainer("ollama", "run", model)
+      return ollama
+    }
+
+    @AfterAll
+    @JvmStatic
+    fun teardown() {
+      registeredContainers.forEach { (model, container) ->
+        println("🐳 Stopping Ollama container for model $model")
+        container.stop()
+      }
+    }
+  }
+
+  protected suspend inline fun <reified A> ollama(
+    model: String,
+    prompt: String,
+  ): A {
+    useModel(model)
+    val config = Config(supportsLogitBias = false, baseUrl = ollamaBaseUrl(model))
+    val api = OpenAI(config = config, logRequests = true).chat
+    val result: A =
+      AI(
+        prompt = prompt,
+        config = config.copy(),
+        api = api,
+        model = CreateChatCompletionRequestModel.Custom(model),
+      )
+    logger.info { "🚀 Inference on model $model: $result" }
+    return result
+  }
+
+  fun ollamaBaseUrl(model: String): String {
+    val ollama = registeredContainers[model]!!
+    return "http://${ollama.host}:${ollama.getMappedPort(ollama.exposedPorts.first())}/v1/"
+  }
+}

core/src/jvmTest/kotlin/com/xebia/functional/xef/ollama/tests/models/OllamaModels.kt

@@ -0,0 +1,8 @@
+package com.xebia.functional.xef.ollama.tests.models
+
+object OllamaModels {
+  const val Gemma2B = "gemma:2b"
+  const val Phi3Latest = "phi3:latest"
+  const val LLama3_8B = "llama3:8b"
+  const val Qwen0_5B = "qwen:0.5b"
+}

core/src/jvmTest/kotlin/com/xebia/functional/xef/ollama/tests/models/Sentiment.kt

@@ -0,0 +1,9 @@
+package com.xebia.functional.xef.ollama.tests.models
+
+import kotlinx.serialization.Serializable
+
+@Serializable
+enum class Sentiment {
+  POSITIVE,
+  NEGATIVE,
+}

core/src/jvmTest/resources/logback.xml

@@ -0,0 +1,26 @@
+<?xml version="1.0" encoding="UTF-8" ?>
+<!DOCTYPE configuration>
+
+<configuration>
+    <statusListener class="ch.qos.logback.core.status.NopStatusListener" />
+
+    <appender name="NOOP" class="ch.qos.logback.core.helpers.NOPAppender" />
+
+    <appender name="STDOUT" class="ch.qos.logback.core.ConsoleAppender">
+        <encoder class="ch.qos.logback.classic.encoder.PatternLayoutEncoder">
+            <pattern>%d{HH:mm:ss.SSS} [%thread] %-5level %logger{36} -%kvp- %msg%n</pattern>
+        </encoder>
+    </appender>
+
+    <root level="info">
+        <appender-ref ref="STDOUT"/>
+    </root>
+
+    <logger name="com.xebia.functional.xef" level="debug">
+        <appender-ref ref="STDOUT" />
+    </logger>
+
+    <logger name="com.gargoylesoftware.htmlunit" level="off">
+        <appender-ref ref="STDOUT" />
+    </logger>
+</configuration>

examples/src/main/kotlin/com/xebia/functional/xef/dsl/chat/EnumOllama.kt

@@ -0,0 +1,18 @@
+package com.xebia.functional.xef.dsl.chat
+
+import com.xebia.functional.openai.generated.model.CreateChatCompletionRequestModel
+import com.xebia.functional.xef.AI
+import com.xebia.functional.xef.Config
+import com.xebia.functional.xef.OpenAI
+
+suspend fun main() {
+  val config = Config(baseUrl = "http://localhost:11434/v1/", supportsLogitBias = false)
+  val sentiment =
+    AI<Sentiment>(
+      prompt = "I love Xef!",
+      model = CreateChatCompletionRequestModel.Custom("orca-mini:3b"),
+      config = config,
+      api = OpenAI(config, logRequests = true).chat,
+    )
+  println(sentiment) // positive
+}

examples/src/main/resources/logback.xml

@@ -12,7 +12,7 @@
         </encoder>
     </appender>
 
-    <root level="info">
+    <root level="trace">
         <appender-ref ref="STDOUT"/>
     </root>