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Building
File size: 3,527 Bytes
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import {
getQuery,
getSettings,
getTextGenerationState,
updateModelLoadingProgress,
updateModelSizeInMegabytes,
updateResponse,
updateTextGenerationState,
} from "./pubSub";
import {
ChatGenerationError,
canStartResponding,
getFormattedSearchResults,
} from "./textGenerationUtilities";
export async function generateTextWithWllama() {
if (!getSettings().enableAiResponse) return;
const response = await generateWithWllama(getQuery(), updateResponse, true);
updateResponse(response);
}
export async function generateChatWithWllama(
messages: import("gpt-tokenizer/GptEncoding").ChatMessage[],
onUpdate: (partialResponse: string) => void,
) {
return generateWithWllama(
messages[messages.length - 1].content,
onUpdate,
false,
);
}
async function initializeWllamaInstance(
progressCallback?: ({
loaded,
total,
}: {
loaded: number;
total: number;
}) => void,
) {
const { initializeWllama, wllamaModels } = await import("./wllama");
const model = wllamaModels[getSettings().wllamaModelId];
updateModelSizeInMegabytes(model.fileSizeInMegabytes);
const wllama = await initializeWllama(model.url, {
wllama: {
suppressNativeLog: true,
},
model: {
n_threads: getSettings().cpuThreads,
n_ctx: model.contextSize,
cache_type_k: model.cacheType,
embeddings: false,
allowOffline: true,
progressCallback,
},
});
return { wllama, model };
}
async function generateWithWllama(
input: string,
onUpdate: (partialResponse: string) => void,
shouldCheckCanRespond = false,
) {
let loadingPercentage = 0;
const { wllama, model } = await initializeWllamaInstance(
shouldCheckCanRespond
? ({ loaded, total }) => {
const progressPercentage = Math.round((loaded / total) * 100);
if (loadingPercentage !== progressPercentage) {
loadingPercentage = progressPercentage;
updateModelLoadingProgress(progressPercentage);
}
}
: undefined,
);
if (shouldCheckCanRespond) {
await canStartResponding();
updateTextGenerationState("preparingToGenerate");
}
const prompt = await model.buildPrompt(
wllama,
input,
getFormattedSearchResults(model.shouldIncludeUrlsOnPrompt),
);
let streamedMessage = "";
await wllama.createCompletion(prompt, {
nPredict: 2048,
stopTokens: model.stopTokens,
sampling: model.sampling,
onNewToken: (_token, _piece, currentText, { abortSignal }) => {
if (shouldCheckCanRespond && getTextGenerationState() === "interrupted") {
abortSignal();
throw new ChatGenerationError("Chat generation interrupted");
}
if (shouldCheckCanRespond && getTextGenerationState() !== "generating") {
updateTextGenerationState("generating");
}
streamedMessage = handleWllamaCompletion(
model,
currentText,
abortSignal,
onUpdate,
);
},
});
await wllama.exit();
return streamedMessage;
}
function handleWllamaCompletion(
model: import("./wllama").WllamaModel,
currentText: string,
abortSignal: () => void,
onUpdate: (text: string) => void,
) {
let text = currentText;
if (model.stopStrings) {
for (const stopString of model.stopStrings) {
if (text.slice(-(stopString.length * 2)).includes(stopString)) {
abortSignal();
text = text.slice(0, -stopString.length);
break;
}
}
}
onUpdate(text);
return text;
}
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