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The append() method of the LanguageModel interface adds content to the session's context window without generating a model response. It returns a Promise that resolves when the content has been successfully loaded into context. Use this method to preload a context before asking the model a question.
A context may be a document, conversation, history, or background information. You can call the append() method at any point during the session's lifetime.
append(input)
append(input, options)inputThe content to append to the context window. 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 OptionalAn object representing the options that can be passed. Properties include:
signalAn AbortSignal to cancel the append operation.
A Promise that resolves with undefined when the content has been prefilled into the context window, or rejects with one of the following exception values on failure.
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 prefilling fails for any other reason not listed in the other exception types.
QuotaExceededError DOMExceptionThrown if appending input 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.TypeError DOMExceptionThrown if a message's role is system but it was not the first message passed to the context.
See also Adding context with initial and ongoing prompt inputs > Appending extra messages to the context.
This example shows how to append to a context for the user role before calling prompt(). Note that we can just specify text input (documentText) in this case, because user is the default role.
const documentText = "This is my important essay...";
const session = await LanguageModel.create();
// Preload the document text into context
await session.append(documentText);
// Now ask questions about the document
const summary = await session.prompt(
"Summarize the key points of this document.",
);
console.log(summary);An abort signal lets you cancel an append operation. The example below passes an AbortSignal to the signal member and calls its abort() method after 3 seconds.
const controller = new AbortController();
setTimeout(() => controller.abort(), 3000);
try {
await session.append(
"Here is some background context for future questions.",
{
signal: controller.signal,
},
);
console.log("Context appended successfully.");
} catch (err) {
if (err.name === "AbortError") {
console.log("Append was aborted.");
}
}The code below shows how to log the number of tokens used after appending a large amount of context.
const largeDocument = "This is my large body of text...";
const session = await LanguageModel.create();
await session.append(largeDocument);
console.log(
`Context used: ${session.contextUsage} / ${session.contextWindow} tokens`,
);