73 lines
2.3 KiB
TypeScript
73 lines
2.3 KiB
TypeScript
import { mapper, plugins, LagrangeContext, PrivateMessage, GroupMessage, Send, logger } from 'lagrange.onebot'
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import { llm } from '../api/llm';
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import { apiQueryVecdb, apiQueryVecdbDataItem } from '../api/vecdb';
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interface IntentResult {
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id: number,
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name: string,
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uncertainty: number
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}
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function makePrompt(messages: apiQueryVecdbDataItem[]): string {
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const texts = ['你是一个很聪明的AI，下面是你的知识库和参考内容，请根据参考内容回答上述的问题：'];
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for (const msg of messages) {
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texts.push(msg.content);
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}
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return texts.join('\n');
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}
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async function useRagLLM(c: LagrangeContext<GroupMessage>, intentResult: IntentResult) {
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const texts = [];
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for (const msg of c.message.message) {
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if (msg.type === 'text') {
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texts.push(msg.data.text);
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}
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}
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const query = texts.join(' ').trim();
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if (query.length === 0) {
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return undefined;
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}
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const { data } = await apiQueryVecdb({ query, k: 3 });
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if (data.code === 200) {
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const messages: apiQueryVecdbDataItem[] = data.data.filter(m => m.score <= 0.7);
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if (messages.length === 0) {
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return '未在数据库中检索到相关内容。';
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} else {
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const query = makePrompt(messages);
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const res = await llm.answer([
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{
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role: 'user',
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content: query
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}
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]);
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if (typeof res === 'string') {
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const links = messages.map(m => m.source);
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const linkSet = new Set<string>(links);
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const reference = ['参考链接：', ...linkSet].join('\n');
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const anwser = res + '\n\n' + reference;
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return anwser;
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}
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}
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} else {
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logger.error('apiQueryVecdb 接口访问失败: ' + JSON.stringify(data));
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return undefined;
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}
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}
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export async function handleGroupIntent(c: LagrangeContext<GroupMessage>, intentResult: IntentResult) {
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switch (intentResult.name) {
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case 'usage':
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return await useRagLLM(c, intentResult);
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break;
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case 'bug':
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return await useRagLLM(c, intentResult);
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break;
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case 'others':
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break;
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default:
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break;
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}
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} |