prompt-executor-openai-client

val chatParams = OpenAIChatParams(
    temperature = 0.7,
    maxTokens = 1000,
    frequencyPenalty = 0.5,
    presencePenalty = 0.5,
    topP = 0.9,
    stop = listOf("\\n", "END"),
    logprobs = true,
    topLogprobs = 5,
    reasoningEffort = ReasoningEffort.MEDIUM,
    parallelToolCalls = true,
    audio = OpenAIAudioConfig(voice = "alloy", format = "mp3"),
    webSearchOptions = OpenAIWebSearchOptions(enabled = true)
)

val responsesParams = OpenAIResponsesParams(
    temperature = 0.7,
    maxTokens = 1000,
    background = true,
    include = listOf("sources", "citations"),
    maxToolCalls = 10,
    reasoning = ReasoningConfig(effort = ReasoningEffort.HIGH),
    truncation = Truncation(type = "auto")
)

dependencies {
    implementation("ai.koog.prompt:prompt-executor-openai-client:$version")
}

val openaiClient = OpenAILLMClient(
    apiKey = "your-openai-api-key",
)

suspend fun main() {
    val client = OpenAILLMClient(
        apiKey = System.getenv("OPENAI_API_KEY"),
    )

    // Text-only example with Chat API
    val response = client.execute(
        prompt = prompt {
            system("You are helpful assistant")
            user("What time is it now?")
        },
        model = OpenAIModels.Chat.GPT5,
        params = OpenAIChatParams(
            temperature = 0.7,
            reasoningEffort = ReasoningEffort.MEDIUM
        )
    )

    // Using Responses API
    val responsesResponse = client.execute(
        prompt = prompt {
            system("You are helpful assistant")
            user("Research the latest developments in AI")
        },
        model = OpenAIModels.Chat.GPT5,
        params = OpenAIResponsesParams(
            background = true,
            include = listOf("sources", "citations"),
            maxToolCalls = 5
        )
    )

    println(response)
}

// Image analysis with GPT-5
val imageResponse = client.execute(
    prompt = prompt {
        user {
            text("What do you see in this image?")
            image("/path/to/image.jpg")
        }
    },
    model = OpenAIModels.Chat.GPT5,
    params = OpenAIChatParams(
        temperature = 0.3,
        reasoningEffort = ReasoningEffort.HIGH
    )
)

// Audio transcription (requires audio models)
val audioData = File("/path/to/audio.wav").readBytes()
val transcriptionResponse = client.execute(
    prompt = prompt {
        user {
            text("Transcribe this audio")
            audio(audioData, "wav")
        }
    },
    model = OpenAIModels.Audio.GPT4oAudio,
    params = OpenAIChatParams(
        audio = OpenAIAudioConfig(voice = "alloy", format = "mp3")
    )
)

// PDF document processing with Responses API
val pdfResponse = client.execute(
    prompt = prompt {
        user {
            text("Summarize this PDF document with citations")
            document("/path/to/document.pdf")
        }
    },
    model = OpenAIModels.Chat.GPT5,
    params = OpenAIResponsesParams(
        include = listOf("sources", "citations"),
        reasoning = ReasoningConfig(effort = ReasoningEffort.MEDIUM)
    )
)

// Embedding example
val embedding = client.embed(
    text = "This is a sample text for embedding",
    model = OpenAIModels.Embeddings.TextEmbedding3Small
)

// Mixed content with custom parameters
val mixedResponse = client.execute(
    prompt = prompt {
        user {
            text("Compare this image with the PDF:")
            image("/path/to/chart.png")
            document("/path/to/report.pdf")
            text("What insights can you provide?")
        }
    },
    model = OpenAIModels.Chat.GPT5,
    params = OpenAIChatParams(
        temperature = 0.5,
        maxTokens = 4000,
        reasoningEffort = ReasoningEffort.HIGH,
        parallelToolCalls = true
    )
)

// Background processing with Responses API
val backgroundResponse = client.execute(
    prompt = prompt {
        user("Research and analyze market trends for renewable energy")
    },
    model = OpenAIModels.Chat.GPT5,
    params = OpenAIResponsesParams(
        background = true,
        include = listOf("sources", "citations", "steps"),
        maxToolCalls = 20,
        reasoning = ReasoningConfig(effort = ReasoningEffort.HIGH)
    )
)