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The prompt() method of the LanguageModel interface sends input to the language model and returns a Promise that resolves with the model's complete response as a string.
prompt(input)
prompt(input, options)inputThe content to prompt the model with. This is either:
roleA string indicating the point of view the message is phrased from. Must be one of:
systemA system-level instruction that guides the model's overall behavior. This must be the first instruction passed to the model.
userA message from the user, which the API should respond to.
assistantAn input that provides context for the AI assistant, such as its persona or the format of its responses. Such messages mainly serve to provide context/history, and further shape how the model responds.
contentA string representing a textual prompt, or an array of objects. Each object includes the following properties:
typeAn enumerated value representing the type of content. This can be one of:
audioAudio content.
imageImage content.
textTextual content.
tool-callA tool invocation issued by the model.
tool-responseThe result of a tool invocation.
valueThe content of the message. If the type is text, this is always a string. If the type is audio or image, the value can be one of several different object types; see What data types are accepted?.
prefix OptionalA boolean, defaulting to false. When true, the message is treated as a prefix for the model's next generated response rather than a complete turn.
options OptionalOptions for creating a prompt. Properties include:
responseConstraintAn object following the structure defined by JSON Schema defining the precise format the model's output should be delivered in. When provided and omitResponseConstraintInput is false, any implementation-defined constraint-description message is included in the measurement.
omitResponseConstraintInputA boolean; when true, the automatic constraint-description message is excluded from the measurement.
signalAn AbortSignal to cancel the operation.
A Promise that resolves with a String containing the model's complete response.
AbortError DOMExceptionThrown if the operation was cancelled via the signal option.
NotAllowedError DOMExceptionThrown if usage of the method is blocked by a language-model Permissions-Policy.
NotSupportedError DOMExceptionThrown if:
role is assistant and its type is anything other than text.type is text and its value is not a string.type is image or audio but the type was not listed in expectedInputs, or the value is not an accepted data type.OperationError DOMExceptionThrown if the prompt fails for any other reason not listed in the other exception types.
QuotaExceededError DOMExceptionThrown if the prompt would cause the session's context usage to exceed the model's LanguageModel.contextWindow.
SyntaxError DOMExceptionThrown if:
prefix property is set to true and:role is not assistant.TypeErrorThrown if:
omitResponseConstraintInput is true but responseConstraint is not provided.role is system but it was not the first message passed to the context.The prompt() method is the primary mechanism for interacting with a language model session. It adds the provided input to the context window and generates a response. The entire response is buffered and returned as a single string when generation completes.
For long responses or streaming use cases, use LanguageModel.promptStreaming() instead to receive the response incrementally. To add content to the context window without generating a response, use LanguageModel.append().
Each call to prompt() adds to the session's context. To branch from a given state without affecting the original session, call LanguageModel.clone().
This example shows basic prompt() usage with a single user text input.
const session = await LanguageModel.create();
const response = await session.prompt(
"Summarize the water cycle in one paragraph.",
);
console.log(response);See also Using the Prompt API > Prompting the model.
const session = await LanguageModel.create();
const reply1 = await session.prompt("My name is Alex.");
console.log(reply1); // "Nice to meet you, Alex!"
const reply2 = await session.prompt("What's my name?");
console.log(reply2); // "Your name is Alex."The following example shows how do pass JSON to the responseConstraint option to specify that you want an array returned by the call to prompt().
const session = await LanguageModel.create();
const raw = await session.prompt("Name three planets in our solar system.", {
responseConstraint: {
type: "object",
properties: {
planets: {
type: "array",
items: { type: "string" },
},
},
required: ["planets"],
},
});
const { planets } = JSON.parse(raw);
console.log(planets); // ["Mercury", "Venus", "Earth"]See also Adding context with initial and ongoing prompt inputs > Adding response constraints.
The following example shows how to enable a user to cancel a prompt with a button. It does this by creating an AbortController. Its abort() is callable from a button's click handler. For this to work, a reference to the controller's signal property must be passed to prompt().
const controller = new AbortController();
// Select your cancel button from the DOM
const cancelButton = document.querySelector("#btn-cancel");
// Trigger the abort when the user clicks the button
cancelButton.addEventListener("click", () => {
controller.abort();
});
try {
const response = await session.prompt("write a very long story.", {
signal: controller.signal,
});
console.log(response);
} catch (err) {
if (err.name === "AbortError") {
console.log("prompt was cancelled.");
} else {
console.error("An unexpected error occurred:", err);
}
}See also Using the Prompt API > Cancelling operations and destroying instances.