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Infer DSL and automatic input serialization (#327)
* Infer DSL and automatic input serialization * clean up * get() on Infer class * Spotless apply * Using buildPrompt --------- Co-authored-by: Javi Pacheco <[email protected]>
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‎core/src/commonMain/kotlin/com/xebia/functional/xef/llm/ChatWithFunctions.kt

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@@ -12,6 +12,8 @@ import com.xebia.functional.xef.llm.models.chat.Message
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import com.xebia.functional.xef.llm.models.functions.CFunction
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import com.xebia.functional.xef.llm.models.functions.encodeJsonSchema
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import com.xebia.functional.xef.prompt.Prompt
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import com.xebia.functional.xef.prompt.buildPrompt
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import com.xebia.functional.xef.prompt.templates.user
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import io.github.oshai.kotlinlogging.KotlinLogging
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import kotlinx.serialization.ExperimentalSerializationApi
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import kotlinx.serialization.KSerializer
@@ -62,6 +64,28 @@ interface ChatWithFunctions : Chat {
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promptConfiguration: PromptConfiguration = PromptConfiguration.DEFAULTS,
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): A = prompt(prompt.toMessages(), scope, functions, serializer, promptConfiguration)
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@OptIn(ExperimentalSerializationApi::class)
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@AiDsl
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suspend fun <A, B> prompt(
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input: A,
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scope: Conversation,
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inputSerializer: KSerializer<A>,
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outputSerializer: KSerializer<B>,
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functions: List<CFunction> = generateCFunction(outputSerializer.descriptor),
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promptConfiguration: PromptConfiguration = PromptConfiguration.DEFAULTS,
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): B =
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prompt(
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buildPrompt {
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+user(
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"${inputSerializer.descriptor.serialName}(${Json.encodeToString(inputSerializer, input)})"
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)
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},
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scope,
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functions,
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{ json -> Json.decodeFromString(outputSerializer, json) },
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promptConfiguration
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)
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@AiDsl
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suspend fun <A> prompt(
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prompt: Prompt,

‎core/src/commonMain/kotlin/com/xebia/functional/xef/prompt/lang/Infer.kt

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@@ -0,0 +1,121 @@
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package com.xebia.functional.xef.prompt.lang
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import com.xebia.functional.xef.auto.Conversation
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import com.xebia.functional.xef.auto.PromptConfiguration
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import com.xebia.functional.xef.llm.ChatWithFunctions
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import com.xebia.functional.xef.prompt.Prompt
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import com.xebia.functional.xef.prompt.buildPrompt
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import com.xebia.functional.xef.prompt.templates.system
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import com.xebia.functional.xef.prompt.templates.user
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import kotlinx.serialization.json.Json
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import kotlinx.serialization.serializer
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/**
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* Infer is a DSL for generating structured output from structured input. Infer allows the input
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* values to be inferred with a minimal DSL which is then replaced with the actual values inside the
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* LLM thought process. Assumes a LLM with Sudolang like capabilities or understanding of structured
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* input.
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*/
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class Infer(
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val model: ChatWithFunctions,
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val conversation: Conversation,
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) {
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class Replacement<out A>(val name: String, val value: A, val config: Config)
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class Config(val modifiers: List<Pair<String, String>>) {
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companion object {
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operator fun invoke(vararg modifiers: Pair<String, String>): Config =
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Config(modifiers.toList())
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}
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}
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class Scope {
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val replacements: MutableList<Replacement<*>> = mutableListOf()
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val inferInt: Int
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get() = inferInt()
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val inferFloat: Float
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get() = inferFloat()
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val inferDouble: Double
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get() = inferDouble()
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val inferString: String
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get() = inferString()
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val placeholder
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get() = "${'$'}generate${'$'}"
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fun inferInt(config: Config = Config()): Int = infer(placeholder, Int.MAX_VALUE, config)
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fun inferFloat(config: Config = Config()): Float = infer(placeholder, Float.MAX_VALUE, config)
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fun inferDouble(config: Config = Config()): Double =
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infer(placeholder, Double.MAX_VALUE, config)
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fun inferString(config: Config = Config()): String = infer(placeholder, placeholder, config)
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private fun <A> infer(name: String, value: A, config: Config): A {
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replacements.add(Replacement(name, value, config))
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return value
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}
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}
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suspend inline operator fun <reified A, reified B> invoke(
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prompt: Prompt,
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block: Scope.() -> A
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): B {
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val scope = Scope()
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val request = block(scope)
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var input = Json.encodeToString(serializer<A>(), request)
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scope.replacements.forEach { replacement ->
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input = replaceInferPlaceholder(input, replacement)
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}
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return model.prompt(
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messages =
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buildPrompt {
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+system(prompt.message)
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+system("Stay in role and follow the directives of the function `Process`")
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+system(
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"""
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Process(input) {
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STOP, Carefully consider all instructions in this function
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STOP, first replace all placeholders ${scope.placeholder} in input
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Consider in your output ALL properties in the input that are not placeholders
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Reflect their information in your output
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target
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|> critique
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|> fix(critique)
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|> applyCritique(target)
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produce output in valid json
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}
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"""
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.trimIndent()
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)
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+user("Process($input)")
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},
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scope = conversation,
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serializer = serializer<B>(),
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promptConfiguration = PromptConfiguration { temperature(0.0) }
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)
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}
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fun replaceInferPlaceholder(input: String, replacement: Replacement<*>): String =
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input.replaceFirst(
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oldValue = replacement.value.toString(),
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newValue =
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if (replacement.config.modifiers.isEmpty()) {
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replacement.name
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} else {
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replacement.name +
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":" +
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replacement.config.modifiers.joinToString(",") { (k, v) -> "$k:$v" } +
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""
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}
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)
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companion object {}
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}

‎core/src/commonMain/kotlin/com/xebia/functional/xef/prompt/lang/Program.kt

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@@ -0,0 +1,20 @@
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package com.xebia.functional.xef.prompt.lang
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import com.xebia.functional.xef.auto.Conversation
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import com.xebia.functional.xef.llm.ChatWithFunctions
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import kotlinx.serialization.serializer
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class Program(
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val model: ChatWithFunctions,
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val conversation: Conversation,
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val description: String,
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) {
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suspend inline operator fun <reified A, reified B> invoke(input: A): B {
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return model.prompt(
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input = input,
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scope = conversation,
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inputSerializer = serializer<A>(),
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outputSerializer = serializer<B>(),
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)
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}
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}

‎core/src/commonMain/kotlin/com/xebia/functional/xef/prompt/lang/PromptLanguage.kt

-90
This file was deleted.

‎examples/kotlin/src/main/kotlin/com/xebia/functional/xef/auto/StructuredInput.kt

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package com.xebia.functional.xef.auto
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import com.xebia.functional.xef.auto.llm.openai.OpenAI
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import com.xebia.functional.xef.auto.llm.openai.prompt
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import kotlinx.serialization.Serializable
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@Serializable data class Question(val question: String)
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@Serializable data class Answer(val answer: String)
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/** Demonstrates how to use any structured serializable input as a prompt. */
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suspend fun main() {
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OpenAI.conversation {
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val question = Question("What is your name?")
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println("question: $question")
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val answer: Answer = prompt(question)
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println("answer: $answer")
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}
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}

‎examples/kotlin/src/main/kotlin/com/xebia/functional/xef/auto/expressions/DigitalDetoxPlanner.kt

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@@ -0,0 +1,79 @@
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package com.xebia.functional.xef.auto.expressions
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import com.xebia.functional.xef.auto.Description
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import com.xebia.functional.xef.auto.llm.openai.OpenAI
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import com.xebia.functional.xef.prompt.Prompt
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import com.xebia.functional.xef.prompt.lang.Infer
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import kotlinx.serialization.Serializable
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enum class DigitalActivity {
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SocialMedia,
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Work,
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Entertainment,
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Gaming,
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News,
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Browsing,
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Infer
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}
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@Serializable data class DigitalHabits(val activity: DigitalActivity, val dailyHours: Float)
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@Serializable
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data class DetoxState(
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val currentScreenTime: Float, // in hours
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val primaryActivities: List<DigitalHabits>,
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val detoxGoal: String // e.g., "Reduce Social Media consumption by 50%"
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)
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@Serializable data class GenerateDetoxPlan(val state: DetoxState)
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@Serializable
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data class DetoxRecommendationPrompt(
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@Description(
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[
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"Craft a digital detox plan based on the user's current habits and desired goals.",
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"Recommend actionable steps and alternative non-digital activities to aid the detox process."
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]
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)
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val dayByDayActions:
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List<DetoxPlan>, // e.g., {"Day 1": ["1-hour nature walk", "Read a book for 30 minutes"]}
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val summary: String
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)
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@Serializable data class DetoxPlan(val day: String, val actions: List<String>)
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suspend fun main() {
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OpenAI.conversation {
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val infer = Infer(OpenAI.FromEnvironment.DEFAULT_SERIALIZATION, conversation)
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val detoxPlan: DetoxRecommendationPrompt =
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infer(
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Prompt(
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"""
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Assume the role of a digital wellbeing coach. Based on the user's digital habits and detox goals, suggest a holistic detox plan.
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"""
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.trimIndent()
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)
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) {
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GenerateDetoxPlan(
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state =
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DetoxState(
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currentScreenTime = 6.0f,
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primaryActivities =
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listOf(
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DigitalHabits(DigitalActivity.SocialMedia, 3.0f),
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DigitalHabits(DigitalActivity.Work, 2.0f),
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DigitalHabits(DigitalActivity.Entertainment, 1.0f)
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),
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detoxGoal = "Reduce Social Media consumption by 50%"
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)
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)
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}
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println("Digital Detox Plan:")
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detoxPlan.dayByDayActions.forEach { (day, actions) ->
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println("\n$day:")
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actions.forEach { action -> println("- $action") }
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}
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println("\nSummary: ${detoxPlan.summary}")
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}
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}

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