repositories {
    mavenCentral()
}

dependencies {
    implementation("io.github.bay73:generic-ai:0.6.6")
}

import com.bay.aiclient.AiClient

fun getResponse() {
    // Create a client for specified AI provider.
    val client = AiClient.get(AiClient.Type.OPEN_AI) { // Choose provider
        apiKey = "put your API key here"
        defaultModel = "gpt-4o-mini"    // Choose model
    }
    // Start request to the AI model.
    val job = client.generateText { prompt = "When the first LLM was created?" }
    // Wait for execution and get response.
    println(job.await().getOrThrow().response)
    
    // Get list of available models
    val models = client.models()
    models.models.forEach { println(it.id) }  
}

fun customizeRequest() {
    val response = cleint.generateText {
        model = "model_id" // Model id to use for a specific request.
        prompt = "" // User prompt which initiates generation 
        systemInstructions = "" // Additional system instruction to adjust AI behavior.
        responseFormat = ResponseFormat.JSON_OBJECT // Allows to specify response format. See details below.
        chatHistory = listOf<TextMessage>() // A list of chat messages in chronological order, representing a conversation between the user and the model.
        maxOutputTokens = 100 // The maximum number of tokens that can be generated as part of the response.
        stopSequences = listOf<String>() // A list of strings that the model uses to stop generation.
        temperature = 0.1 // A non-negative float that tunes the degree of randomness in generation. Lower temperatures mean less random generations.
        topP = 0.5 // An alternative way controlling the diversity of the model's responses. It's recommended to use either temperature or topP.
    }
}

fun getResponseWithSpecificParameters() {
    // Create a client for specified AI provider.
    val client = AiClient.get(CohereClient::class) { // Choose provider
        apiKey = "put your API key here"
        defaultModel = "command-r"    // Choose model
    }
    // Start request to the AI model.
    val response = client.generateText { 
        prompt = "When the first LLM was created?"
        seed = 5  // see provider documentation for usage of specific parameters 
        frequencyPenalty = 0.1
    }
}

fun getBedrockClient() {
    // You can use either [DefaultCredentialsProvider](https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/credentials-chain.html) or specify access key and token directly.
    val credentials = BedrockClient.Credentials("region", true)
    // or 
    val credentials = BedrockClient.Credentials("region", false, "accessKeyId", "secretAccessKey", "sessionToken ")

    // Create a client using the credentials provider
    val client = BedrockClient.Builder(creadentals).build()
}

fun getAzureOpenAiClient() {
    // Create a client using specific builder
    val client = AzureOpenAiClient.Builder().apply {
        resourceName = "your-resource-name"
        apiKey = "your-api-key"
    }.build()
}

fun getYandexOpenAiClient() {
    // Create a client using specific builder
    val client = YandexOpenAiClient.Builder().apply {
        resourceFolder = "your-resource-name"
        apiKey = "your-api-key"
    }.build()
}