prompt-model/ai.koog.prompt.dsl/Prompt

Prompt

data class Prompt(val messages: List<Message>, val id: String, val params: LLMParams = LLMParams())(source)

Represents a data structure for a prompt, consisting of a list of messages, a unique identifier, and optional parameters for language model settings.

Constructors

Prompt

constructor(messages: List<Message>, id: String, params: LLMParams = LLMParams())

Types

Companion

object Companion

Companion object for the Prompt class, providing utilities and constants for creating instances of Prompt.

LLMParamsUpdateContext

class LLMParamsUpdateContext

Represents a mutable context for updating the parameters of an LLM (Language Learning Model). The class is used internally to facilitate changes to various configurations, such as temperature, speculation, schema, and tool choice, before converting back to an immutable LLMParams instance.

Properties

val id: String

The unique identifier for the prompt.

latestTokenUsage

val latestTokenUsage: Int

Represents the total token usage of the most recent response message in the current prompt.

messages

val messages: List<Message>

The list of Message objects associated with the prompt.

params

val params: LLMParams

The language model pa rameters associated with the prompt. Defaults to LLMParams.

totalTimeSpent

val totalTimeSpent: Duration

Represents the total time spent across all messages within the prompt (measured in milliseconds)

Functions

withMessages

fun withMessages(update: (List<Message>) -> List<Message>): Prompt

Creates a copy of the Prompt with updated messages, allowing to modify the existing list of messages or provide a new one.

withParams

fun withParams(newParams: LLMParams): Prompt

Returns a new instance of the Prompt class with updated language model parameters.

withUpdatedParams

fun withUpdatedParams(update: Prompt.LLMParamsUpdateContext.() -> Unit): Prompt

Creates a new instance of Prompt with updated parameters based on the modifications provided in the given update lambda. The update is applied to a mutable context representing the current LLM parameters, allowing selective modifications, which are then returned as a new set of parameters.