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import { PUBLIC_MAX_INPUT_TOKENS, PUBLIC_SEP_TOKEN } from "$env/static/public";
import { buildPrompt } from "$lib/buildPrompt";
import { collections } from "$lib/server/database.js";
import { modelEndpoint } from "$lib/server/modelEndpoint.js";
import { trimPrefix } from "$lib/utils/trimPrefix.js";
import { trimSuffix } from "$lib/utils/trimSuffix.js";
import { textGeneration } from "@huggingface/inference";
import { error } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
export async function POST({ params, locals, fetch }) {
const convId = new ObjectId(params.id);
const conversation = await collections.conversations.findOne({
_id: convId,
sessionId: locals.sessionId,
});
if (!conversation) {
throw error(404, "Conversation not found");
}
const firstMessage = conversation.messages.find((m) => m.from === "user");
const userPrompt =
`Please summarize the following message as a single sentence of less than 5 words:\n` +
firstMessage?.content;
const prompt = buildPrompt([{ from: "user", content: userPrompt }]);
const parameters = {
temperature: 0.9,
top_p: 0.95,
repetition_penalty: 1.2,
top_k: 50,
watermark: false,
max_new_tokens: 1024,
truncate: parseInt(PUBLIC_MAX_INPUT_TOKENS),
stop: [PUBLIC_SEP_TOKEN],
return_full_text: false,
};
const endpoint = modelEndpoint();
let { generated_text } = await textGeneration(
{
model: endpoint.endpoint,
inputs: prompt,
parameters,
},
{
fetch: (url, options) =>
fetch(url, {
...options,
headers: { ...options?.headers, Authorization: endpoint.authorization },
}),
}
);
generated_text = trimSuffix(trimPrefix(generated_text, "<|startoftext|>"), PUBLIC_SEP_TOKEN);
if (generated_text) {
await collections.conversations.updateOne(
{
_id: convId,
sessionId: locals.sessionId,
},
{
$set: { title: generated_text },
}
);
}
return new Response(
JSON.stringify(
generated_text
? {
title: generated_text,
}
: {}
),
{ headers: { "Content-Type": "application/json" } }
);
}
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