Chat

Use GPT to chat

The llm.chat() function is a text-based conversational interface that uses GPT (Generative Pre-trained Transformer) to generate responses based on the context of a conversation. This documentation provides details on how to use the llm.chat() function to generate responses.

Function Signature

fun chat(
    context: Context,
    numTurns: Int = 5,
    prompt: String = "The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly.",
    personaName: String = context.currentPersona.name,
    systemPrompt: String = "",
    config: LLMConfig = LLMConfig(),
    taskName: String = ""
): String

Parameters

The chat() function accepts the following parameters:

• context: The current context of the conversation, represented as a Context object.

• numTurns: The number of previous turns to consider when generating the response. Defaults to 5.

• prompt: The prompt text to direct the generated response. Defaults to "The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly.".

• systemPrompt: The system prompt text. It describes the overall properties of conversation or personality of digital persona.

• personaName: The name of the persona or system generating the response. Defaults to name of current persona.

• config: A configuration object of type LLMConfig. This parameter is optional and defaults to the default configuration values.

• taskName: A string intended for analytical purposes.

Return Value

The chat() function returns a string representing the generated response based on the input context.

Example Usage

Here's an example of how to use the llm.chat() function:

val response = llm.chat(
    context, 
    numTurns = 5, 
    prompt = "You are digital persona Kai", 
    systemPrompt = "You answer in riddles",
    personaName = "Kai")
logger.info(response)

In this example, we pass context, number of turns, prompt and persona's name to the llm.chat() function, which generates a response based on the input context. Finally, we print the generated response.