完成 EDL 的构建，完成 QA 的拒答流
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parent
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3
.gitignore
vendored
3
.gitignore
vendored
@ -13,4 +13,5 @@ images
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logs
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*.sklearn
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config/*.story.yml
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config/story.yml
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config/story.yml
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model/*.pth
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32
README.md
32
README.md
@ -99,3 +99,35 @@ interface CommonResponse<T> {
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- 非必要，请不要随意宣传本项目。
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- 虽然曾经无数个 QQ 相关的项目都死了，但是基本的 API 端口算是传承了下来。拉格朗日的返回类型，请参考 [go-cqhttp 帮助中心 - API 篇](https://docs.go-cqhttp.org/api/) 中的内容。
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---
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## 启动
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```bash
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# 1. 启动 拉格朗日
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tsc
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pm2 start dist/main.js --name Lagrange.onebot
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pm2 start rag/main.py --name rag
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```
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---
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## 测试命令
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### 重训练 embedding -> intent 分类层
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```bash
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curl -X POST http://127.0.0.1:8081/intent/retrain-embedding-mapping
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```
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### 获取意图
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```bash
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curl -X POST -H "Content-Type: application/json" -d '{"query": "真的开线程是要tcl指令去改的"}' http://127.0.0.1:8081/intent/get-intent-recogition
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```
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### 获取向量数据库中的 topk
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```bash
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curl -X POST -H "Content-Type: application/json" -d '{"query": "这个插件有什么比较好的文档吗？"}' http://127.0.0.1:8081/vecdb/similarity_search_with_score
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```
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70
bot/api/llm.ts
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70
bot/api/llm.ts
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@ -0,0 +1,70 @@
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// 配置大模型 请求参数
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import axios from "axios";
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import { logger } from "lagrange.onebot";
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interface LlmMessageItem {
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role: 'user' | 'assistant',
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content: string
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}
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class ErineLLM {
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apiKey: string = process.env.BAIDU_API_KEY;
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secretKey: string = process.env.BAIDU_SECRET_KEY;
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accessToken: string = '';
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constructor() {
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if (this.apiKey === '') {
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throw Error('百度 api_key 为空');
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}
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if (this.secretKey === '') {
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throw Error('百度 secret_key 为空');
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}
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this.getAccessToken();
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}
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public async getAccessToken() {
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const url = `https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id=${this.apiKey}&client_secret=${this.secretKey}`;
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const payload = "";
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const headers = {
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'Content-Type': 'application/json',
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'Accept': 'application/json'
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};
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const { data } = await axios.post(url, payload, { headers });
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const accessToken = data.access_token;
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if (accessToken) {
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this.accessToken = accessToken;
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logger.info('成功获取大模型访问令牌');
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} else {
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logger.error('大模型令牌获取失败：' + JSON.stringify(data))
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}
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}
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public async answer(message: LlmMessageItem[]): Promise<string | undefined> {
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if (message.length % 2 === 0) {
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logger.error('大模型的 message 长度必须是奇数，目前为 ' + message.length);
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return undefined;
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}
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const url = 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-lite-8k?access_token=' + this.accessToken;
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const payload = {
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messages: message,
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system: '你是锦恢开发的用于进行问答的 QA 机器人。主要负责解决 Digital IDE 用户群的答疑问题'
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}
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const headers = {
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'Content-Type': 'application/json'
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}
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const { data } = await axios.post(url, payload, { headers });
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const result = data.result;
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if (result) {
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return result;
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} else {
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logger.error('大模型返回结果有误： ' + JSON.stringify(data));
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return undefined;
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}
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}
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}
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export const llm = new ErineLLM();
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@ -4,6 +4,8 @@ import * as fs from 'fs';
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import * as yaml from 'yaml';
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import axios from 'axios';
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// 配置 向量数据库 的请求参数
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// 得到配置文件，组装基础路由
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const vecdbBuffer = fs.readFileSync('./config/vecdb.yml', 'utf-8');
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const vecdbConfig = yaml.parse(vecdbBuffer);
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@ -37,6 +39,7 @@ vecdbRequests.interceptors.response.use(
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}
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);
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export {
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vecdbRequests,
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@ -47,7 +47,8 @@ export interface apiGetIntentRecogitionRequest {
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export interface apiGetIntentRecogitionData {
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id: number,
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name: string
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name: string,
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uncertainty: number
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}
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export interface apiIntentRetrainRequest {
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@ -1,8 +1,8 @@
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import * as fs from 'fs';
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import { server } from 'lagrange.onebot';
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import './test';
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import './digital-ide';
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import './services/test';
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import './services/digital-ide';
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const buffer = fs.readFileSync('./app/publish/appsettings.json', 'utf-8');
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const config = JSON.parse(buffer);
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71
bot/services/intent.ts
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71
bot/services/intent.ts
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@ -0,0 +1,71 @@
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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 { data } = await apiQueryVecdb({ query: texts.join(' '), k: 3 });
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if (data.code === 200) {
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const messages: apiQueryVecdbDataItem[] = data.data.filter(m => m.score <= 0.8);
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if (messages.length === 0) {
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c.sendMessage('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 reference = ['参考链接：', ...links].join('\n');
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const anwser = res + '\n\n' + reference;
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c.sendMessage(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 false;
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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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}
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51
bot/services/test.ts
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51
bot/services/test.ts
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@ -0,0 +1,51 @@
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import '../plugins/image';
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import { mapper, plugins, LagrangeContext, PrivateMessage, GroupMessage, Send } from 'lagrange.onebot'
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import { apiGetIntentRecogition, apiQueryVecdb } from '../api/vecdb';
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import { handleGroupIntent } from './intent';
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export class Impl {
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@mapper.onPrivateUser(1193466151)
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@plugins.use('echo')
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@plugins.use('pm')
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@plugins.use('wget-image')
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async handleJinhui(c: LagrangeContext<PrivateMessage>) {
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c.sendMessage([{
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type: 'image',
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data: {
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file: 'file:///data/zhelonghuang/project/rag-llm/images/bird.png',
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timeout: 10000
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}
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}])
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c.finishSession();
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}
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@mapper.onGroup(956419963, { at: false })
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async handleTestGroup(c: LagrangeContext<GroupMessage>) {
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const texts = [];
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const message = c.message;
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for (const msg of 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 reply: Send.Default[] = [];
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const axiosRes = await apiGetIntentRecogition({ query: texts.join('\n') });
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const res = axiosRes.data;
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if (res.code == 200) {
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const intentResult = res.data;
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// 如果 不确定性 太高，就将意图修改为
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if (intentResult.uncertainty >= 0.33) {
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intentResult.name = 'others';
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}
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const uncertainty = Math.round(intentResult.uncertainty * 1000) / 1000;
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c.sendMessage(`【意图: ${intentResult.name} 不确定度: ${uncertainty}】`);
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handleGroupIntent(c, intentResult);
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} else {
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c.sendMessage('RAG 系统目前离线');
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}
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}
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}
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28
bot/test.ts
28
bot/test.ts
@ -1,28 +0,0 @@
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import './plugins';
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import { mapper, plugins, LagrangeContext, PrivateMessage, GroupMessage, Send } from 'lagrange.onebot'
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import { apiQueryVecdb } from './api/vecdb';
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export class Impl {
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@mapper.onPrivateUser(1193466151)
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@plugins.use('echo')
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@plugins.use('pm')
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@plugins.use('wget-image')
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async handleJinhui(c: LagrangeContext<PrivateMessage>) {
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c.sendMessage([{
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type: 'image',
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data: {
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file: 'file:///data/zhelonghuang/project/rag-llm/images/bird.png',
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timeout: 10000
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}
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}])
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c.finishSession();
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}
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@mapper.onGroup(956419963, { at: false })
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async handleTestGroup(c: LagrangeContext<GroupMessage>) {
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console.log(c.message.message);
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console.log(c.message.raw_message);
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}
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}
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File diff suppressed because one or more lines are too long
@ -19,23 +19,33 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/data/zhelonghuang/miniconda3/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n",
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"/data/zhelonghuang/miniconda3/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
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" warnings.warn(\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"整理得到 304 个文档\n",
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"分块得到 1273 个文档\n",
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"数据库已存储到 blog-vecdb 中\n"
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"分块得到 1276 个文档\n",
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"数据库已存储到 ../blog-vecdb 中\n"
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]
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}
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],
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"source": [
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"embedding = HuggingFaceEmbeddings(model_name='maidalun1020/bce-embedding-base_v1')\n",
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"\n",
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"db_persistent_dir = 'blog-vecdb'\n",
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"db_persistent_dir = '../blog-vecdb'\n",
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"\n",
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"if os.path.exists(db_persistent_dir):\n",
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" db = FAISS.load_local(db_persistent_dir, embedding, allow_dangerous_deserialization=True)\n",
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@ -60,7 +70,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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@ -74,7 +84,7 @@
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" 0.71963197)]"
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]
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},
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"execution_count": 6,
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -88,21 +98,21 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
||||
"[(Document(page_content='除了b站的两个教程外，建议多出文档以及示例工程，一个刚接触FPGA但有不熟悉vivado的萌新就指望这个加快学习进度了\\n请问B站教程标题是啥？我搜digital-ide啥也搜不到\\n请问B站教程标题是啥？我搜digital-ide啥也搜不到\\n《Digital-IDE使用教程(一个DDS的实现)》\\n《Digital-IDE应用之FM调制解调》\\n请问B站教程标题是啥？我搜digital-ide啥也搜不到\\n《Digital-IDE使用教程(一个DDS的实现)》 《Digital-IDE应用之FM调制解调》\\n好的谢谢\\n那个教程是好早之前我出的了，新的版本一直不稳定，今年稳定之后会出后续教程。\\nDigital IDE 的使用教程可以看\\n官方文档为 https://sterben.nitcloud.cn/zh/ 但是目前文档不是很完善。', metadata={'source': '../docs/digital-issue/issue-47/issue.md', 'start_index': 0}),\n",
|
||||
" 0.64211607),\n",
|
||||
"[(Document(page_content='问题标题 基础教程太少\\n除了b站的两个教程外，建议多出文档以及示例工程，一个刚接触FPGA但有不熟悉vivado的萌新就指望这个加快学习进度了\\n请问B站教程标题是啥？我搜digital-ide啥也搜不到\\n请问B站教程标题是啥？我搜digital-ide啥也搜不到\\n《Digital-IDE使用教程(一个DDS的实现)》\\n《Digital-IDE应用之FM调制解调》\\n请问B站教程标题是啥？我搜digital-ide啥也搜不到\\n《Digital-IDE使用教程(一个DDS的实现)》 《Digital-IDE应用之FM调制解调》\\n好的谢谢\\n那个教程是好早之前我出的了，新的版本一直不稳定，今年稳定之后会出后续教程。\\nDigital IDE 的使用教程可以看\\n官方文档为 https://sterben.nitcloud.cn/zh/ 但是目前文档不是很完善。', metadata={'source': '../docs/digital-issue/issue-47/issue.md', 'start_index': 0}),\n",
|
||||
" 0.63593847),\n",
|
||||
" (Document(page_content='home: true\\nheroImage: /icon.png\\ndescription: Vscode 平台上的 ASIC & FPGA 开发扩展\\nactionText: 快速开始 🐳\\nactionLink: /zh/guide/introduction\\nfeatures:\\n- title: ✨ HDL 语言支持\\n details: 支持 verilog, vhdl, systemverilog, tcl 脚本等\\n- title: 🎯 项目管理\\n details: 在你的项目中查看结构化的 HDL 文件\\n- title: 🛠️ 额外的工具\\n details: FSM, Netlist, 一键仿真, 文档化，让你的编程体验更加舒坦。\\n\\n::: slot footer\\nMIT Licensed | Copyright © 2018-present Digital-EDA\\n:::', metadata={'source': '../docs/digital-document/index.md', 'start_index': 0}),\n",
|
||||
" 0.7582667),\n",
|
||||
" (Document(page_content='TODO\\n\\n找到所有 vcd item 的 type 类型，可参考：https://pyvcd.readthedocs.io/en/latest/vcd.common.html\\n\\nbug\\n\\n详见飞书文档：https://nc-ai-lab.feishu.cn/wiki/Z4AxwU1SdilATAk7GuvcYkIDnwh\\n\\n流程\\n\\n目前需要为Digital-IDE设计一个render用于显示VCD文件。\\n\\nIEEE 1364定义VCD(value change dump)文件是含已选变量（信号）的值变化信息存储文件。\\n\\nwavedrom在GitHub上开发过vcd和hdl的wavedrom脚本格式，可以用于进行DIDE的vcd可视化的二次开发，目前发现，为了实现vcd文件，基本的仓库主要是下面这两个：\\n\\nJS层与一个简单的前端：https://github.com/wavedrom/vcdrom\\n\\nwasm 解析：https://github.com/wavedrom/vcd\\n\\n打算基于这两个仓库二次开发一个好用的vcd渲染模块，再加入DIDE中。\\n\\n开发思路\\n\\nvcd 的渲染器分为如下几步进行开发。\\n\\nmermaid\\ngraph LR\\na(读取)-->b(渲染)-->c(解析)\\n\\nVCD 读取和解析并不难，难在如何快速安全地读取，对于一些长时间的模拟和仿真而言， vcd 文件可能会非常大。因此，vcd 需要分块读取，分块解析，为了避免这些不必要的麻烦，我使用了 https://github.com/wavedrom/vcdrom 这个项目的后端进行修改。\\n\\nwasm 解析器\\n\\n原项目写得过于紧凑，并不适合进行修改，因此需要修改一部分代码。\\n\\n整合项目在 ： https://github.com/Digital-EDA/digital-vcd-parser', metadata={'source': '../docs/kirigaya.cn/72.md', 'start_index': 0}),\n",
|
||||
" 0.83628875)]"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
||||
12
package.json
12
package.json
@ -18,22 +18,22 @@
|
||||
"@types/node": "^20.12.12",
|
||||
"axios": "^1.7.2",
|
||||
"fs": "^0.0.1-security",
|
||||
"lagrange.onebot": "^1.0.0",
|
||||
"lagrange.onebot": "^1.0.2",
|
||||
"ws": "^8.17.0",
|
||||
"yaml": "^2.4.2"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@sinonjs/referee-sinon": "^12.0.0",
|
||||
"@types/ws": "^8.5.10",
|
||||
"chai": "4.3.4",
|
||||
"serve": "^14.2.3",
|
||||
"typescript": "^5.4.5",
|
||||
"jsverify": "^0.8.4",
|
||||
"knuth-shuffle": "^1.0.8",
|
||||
"mocha": "^10.4.0",
|
||||
"require-uncached": "^2.0.0",
|
||||
"serve": "^14.2.3",
|
||||
"shelljs": "^0.8.5",
|
||||
"should": "^13.2.3",
|
||||
"sinon": "^18.0.0",
|
||||
"jsverify": "^0.8.4",
|
||||
"knuth-shuffle": "^1.0.8",
|
||||
"@sinonjs/referee-sinon": "^12.0.0"
|
||||
"typescript": "^5.4.5"
|
||||
}
|
||||
}
|
||||
|
||||
@ -73,7 +73,7 @@ class PromptEngine:
|
||||
if children is None:
|
||||
children = []
|
||||
|
||||
if name not in self.intent2id:
|
||||
if name != 'root' and name not in self.intent2id:
|
||||
assign_id = len(self.intent2id)
|
||||
self.intent2id[name] = assign_id
|
||||
self.id2intent[assign_id] = name
|
||||
@ -267,4 +267,4 @@ class TreeIntent(ABC):
|
||||
if __name__ == '__main__':
|
||||
prompt_engine = PromptEngine('./config/story.yml')
|
||||
prompt_engine.merge_stories_from_yml('./config/github-issue.story.yml')
|
||||
print(len(prompt_engine.stories))
|
||||
print(prompt_engine.id2intent)
|
||||
0
rag/__init__.py
Normal file
0
rag/__init__.py
Normal file
11
rag/api/__init__.py
Normal file
11
rag/api/__init__.py
Normal file
@ -0,0 +1,11 @@
|
||||
from .admin import logger, app
|
||||
from .config import necessary_files
|
||||
from .constant import StatusCode, MsgCode
|
||||
|
||||
__all__ = [
|
||||
logger,
|
||||
app,
|
||||
necessary_files,
|
||||
StatusCode,
|
||||
MsgCode
|
||||
]
|
||||
@ -12,4 +12,4 @@ logger.add(
|
||||
format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {message}"
|
||||
)
|
||||
|
||||
app = Flask(__file__)
|
||||
app = Flask(__file__)
|
||||
@ -1,9 +1,8 @@
|
||||
|
||||
necessary_files = {
|
||||
'vecdb-config': './config/vecdb.yml',
|
||||
'intent-story': './config/story.yml',
|
||||
'issue-story': './config/github-issue.story.yml',
|
||||
'blog-vecdb-data': './blog-vecdb/index.faiss',
|
||||
'blog-vecdb-model': './blog-vecdb/index.pkl',
|
||||
'intent-classifier': './model/embedding_mapping.sklearn'
|
||||
'intent-classifier': './model/intent.enn.pth'
|
||||
}
|
||||
6
rag/db/__init__.py
Normal file
6
rag/db/__init__.py
Normal file
@ -0,0 +1,6 @@
|
||||
from .embedding import db, embedding
|
||||
|
||||
__all__ = [
|
||||
db,
|
||||
embedding
|
||||
]
|
||||
12
rag/main.py
12
rag/main.py
@ -1,9 +1,11 @@
|
||||
import os
|
||||
import sys
|
||||
sys.path.append(os.path.abspath('.'))
|
||||
|
||||
from admin import app, logger
|
||||
from intent import *
|
||||
from vecdb import *
|
||||
from configs import necessary_files
|
||||
from rag.api.admin import app, logger
|
||||
from rag.api.config import necessary_files
|
||||
from rag.services.intent import *
|
||||
from rag.services.vecdb import *
|
||||
|
||||
def assert_resource(path: str):
|
||||
assert os.path.exists(path), '{} 不存在'.format(file)
|
||||
@ -14,9 +16,11 @@ for file in necessary_files.values():
|
||||
if __name__ == '__main__':
|
||||
from gevent import pywsgi
|
||||
import yaml
|
||||
|
||||
config: dict = yaml.load(open('./config/vecdb.yml'), Loader=yaml.Loader)
|
||||
addr = config.get('addr', '127.0.0.1')
|
||||
port = int(config.get('port', 8081))
|
||||
|
||||
server = pywsgi.WSGIServer((addr, port), app)
|
||||
logger.info('RAG 系统运行在 http://{}:{}'.format(addr, port))
|
||||
server.serve_forever()
|
||||
5
rag/model/__init__.py
Normal file
5
rag/model/__init__.py
Normal file
@ -0,0 +1,5 @@
|
||||
from .enn import LinearEnn
|
||||
|
||||
__all__ = [
|
||||
LinearEnn
|
||||
]
|
||||
88
rag/model/enn.py
Normal file
88
rag/model/enn.py
Normal file
@ -0,0 +1,88 @@
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import numpy as np
|
||||
|
||||
class LinearEnn(nn.Module):
|
||||
in_dim: int
|
||||
out_dim: int
|
||||
alpha_kl: float
|
||||
def __init__(self, in_dim: int, out_dim: int, focal: int, alpha_kl: float):
|
||||
super().__init__()
|
||||
self.in_dim = in_dim
|
||||
self.out_dim = out_dim
|
||||
self.alpha_kl = alpha_kl
|
||||
self.focal = focal
|
||||
self.classifier = nn.Sequential(
|
||||
nn.Linear(in_dim, out_dim),
|
||||
nn.ELU(),
|
||||
)
|
||||
|
||||
def forward(self, inputs: torch.FloatTensor) -> tuple[torch.FloatTensor, torch.FloatTensor]:
|
||||
logits = self.classifier(inputs)
|
||||
evidence = torch.exp(logits)
|
||||
prob = F.normalize(evidence + 1, p=1, dim=1)
|
||||
return evidence, prob
|
||||
|
||||
def criterion(self, evidence: torch.FloatTensor, label: torch.LongTensor) -> torch.FloatTensor:
|
||||
if len(label.shape) == 1:
|
||||
label = F.one_hot(label, self.out_dim)
|
||||
alpha = evidence + 1
|
||||
alpha_0 = alpha.sum(1).unsqueeze(-1).repeat(1, self.out_dim)
|
||||
loss_ece = torch.sum(label * (torch.digamma(alpha_0) - torch.digamma(alpha)), dim=1)
|
||||
loss_ece = torch.mean(loss_ece)
|
||||
if self.alpha_kl > 0:
|
||||
tilde_alpha = label + (1 - label) * alpha
|
||||
uncertainty_alpha = torch.ones_like(tilde_alpha).cuda()
|
||||
estimate_dirichlet = torch.distributions.Dirichlet(tilde_alpha)
|
||||
uncertainty_dirichlet = torch.distributions.Dirichlet(uncertainty_alpha)
|
||||
kl = torch.distributions.kl_divergence(estimate_dirichlet, uncertainty_dirichlet)
|
||||
loss_kl = torch.mean(kl)
|
||||
else:
|
||||
loss_kl = 0
|
||||
return loss_ece + self.alpha_kl * loss_kl
|
||||
|
||||
def predict(self, inputs: torch.FloatTensor | list[list[float]] | np.ndarray) -> tuple[torch.FloatTensor, torch.FloatTensor]:
|
||||
"""
|
||||
返回每个类别的预测概率 和 当前的预测的不确定度 uncertainty
|
||||
"""
|
||||
if not isinstance(inputs, torch.FloatTensor):
|
||||
inputs = torch.FloatTensor(inputs)
|
||||
with torch.no_grad():
|
||||
evidence, prob = self.forward(inputs)
|
||||
alpha = evidence + 1
|
||||
S = alpha.sum(dim=1)
|
||||
u = self.out_dim / S
|
||||
|
||||
return prob, u
|
||||
|
||||
|
||||
def train_enn(enn_model: LinearEnn, embedding: np.ndarray | torch.FloatTensor, labels: np.ndarray | torch.LongTensor, bs: int = 64, lr: float = 1e-3, epoch: int = 100):
|
||||
optimizer = torch.optim.AdamW(enn_model.parameters(), lr=lr)
|
||||
|
||||
sample_num = len(embedding)
|
||||
sample_indice = np.arange(sample_num)
|
||||
bs_num = int(np.ceil(sample_num / bs))
|
||||
|
||||
training_losses = []
|
||||
|
||||
for i in range(epoch):
|
||||
alpha_kl = min(0.9, i / 10)
|
||||
np.random.shuffle(sample_indice)
|
||||
train_loss = 0
|
||||
for bs_i in range(bs_num):
|
||||
start = bs_i * bs
|
||||
end = min(sample_num, start + bs)
|
||||
data_indice = sample_indice[start: end]
|
||||
data = torch.FloatTensor(embedding[data_indice])
|
||||
label = torch.LongTensor(labels[data_indice])
|
||||
evidence, prob = enn_model(data)
|
||||
loss = enn_model.criterion(evidence, label)
|
||||
train_loss += loss.item()
|
||||
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
training_losses.append(train_loss / bs_num)
|
||||
0
rag/services/__init__.py
Normal file
0
rag/services/__init__.py
Normal file
@ -2,12 +2,14 @@ from flask import Flask, request, jsonify
|
||||
import numpy as np
|
||||
import joblib
|
||||
import json
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
import torch
|
||||
|
||||
from embedding import embedding
|
||||
from constant import StatusCode, MsgCode
|
||||
from admin import app
|
||||
from configs import necessary_files
|
||||
|
||||
from rag.db.embedding import embedding
|
||||
from rag.api.constant import StatusCode, MsgCode
|
||||
from rag.api.admin import app
|
||||
from rag.api.config import necessary_files
|
||||
from rag.model.enn import LinearEnn, train_enn
|
||||
|
||||
import sys
|
||||
import os
|
||||
@ -17,16 +19,22 @@ from prompt import PromptEngine
|
||||
|
||||
class IntentRecogition:
|
||||
def __init__(self) -> None:
|
||||
self.embed_intent_classificator = joblib.load(necessary_files['intent-classifier'])
|
||||
self.engine = PromptEngine(necessary_files['intent-story'])
|
||||
self.classifier = LinearEnn(in_dim=768, out_dim=7, focal=0, alpha_kl=0)
|
||||
self.classifier.load_state_dict(torch.load(necessary_files['intent-classifier']))
|
||||
|
||||
def get_intent_recogition(self, query: str) -> dict:
|
||||
query_embed = embedding.embed_documents([query])
|
||||
result_id = self.embed_intent_classificator.predict(query_embed)[0]
|
||||
result_id = int(result_id)
|
||||
prob, u = self.classifier.predict(query_embed)
|
||||
|
||||
result_id = prob.argmax(dim=1)
|
||||
u = u.item()
|
||||
|
||||
result_id = int(result_id.item())
|
||||
return {
|
||||
'id': result_id,
|
||||
'name': self.engine.id2intent[result_id]
|
||||
'id': int(result_id),
|
||||
'name': self.engine.id2intent[result_id],
|
||||
'uncertainty': float(u)
|
||||
}
|
||||
|
||||
intent_recogition = IntentRecogition()
|
||||
@ -34,7 +42,7 @@ intent_recogition = IntentRecogition()
|
||||
@app.route('/intent/reload-embedding-mapping', methods=['post'])
|
||||
def reload_embedding_mapping():
|
||||
try:
|
||||
intent_recogition.embed_intent_classificator = joblib.load(necessary_files['intent-classifier'])
|
||||
intent_recogition.classifier.load_state_dict(torch.load(necessary_files['intent-classifier']))
|
||||
except Exception as e:
|
||||
response = jsonify({
|
||||
'code': StatusCode.process_error.value,
|
||||
@ -56,21 +64,18 @@ def reload_embedding_mapping():
|
||||
def retrain_embedding_mapping():
|
||||
engine = PromptEngine(necessary_files['intent-story'])
|
||||
engine.merge_stories_from_yml(necessary_files['issue-story'])
|
||||
model = LogisticRegression()
|
||||
sentences = []
|
||||
labels = []
|
||||
for story in engine.stories:
|
||||
sentences.append(story.message)
|
||||
labels.append(engine.intent2id[story.intent])
|
||||
|
||||
try:
|
||||
labels = np.array(labels)
|
||||
embed = embedding.embed_documents(sentences)
|
||||
model.fit(embed, labels)
|
||||
enn_model = intent_recogition.classifier
|
||||
train_enn(enn_model, embed, labels, bs=64, lr=1e-3, epoch=100)
|
||||
torch.save(enn_model.state_dict(), necessary_files['intent-classifier'])
|
||||
|
||||
intent_recogition.engine = engine
|
||||
intent_recogition.embed_intent_classificator = model
|
||||
joblib.dump(model, necessary_files['intent-classifier'])
|
||||
except Exception as e:
|
||||
response = jsonify({
|
||||
'code': StatusCode.process_error.value,
|
||||
@ -80,7 +85,6 @@ def retrain_embedding_mapping():
|
||||
response.status_code = StatusCode.success.value
|
||||
return response
|
||||
|
||||
|
||||
response = jsonify({
|
||||
'code': StatusCode.success.value,
|
||||
'data': 'save data to ' + necessary_files['intent-classifier'],
|
||||
@ -1,10 +1,10 @@
|
||||
from flask import Flask, request, jsonify
|
||||
import json
|
||||
|
||||
from embedding import db
|
||||
from constant import StatusCode, MsgCode
|
||||
from url_mapping import urlmapping
|
||||
from admin import app
|
||||
from rag.api.admin import app
|
||||
from rag.db.embedding import db
|
||||
from rag.api.constant import StatusCode, MsgCode
|
||||
from rag.utils.url_mapping import urlmapping
|
||||
|
||||
@app.route('/vecdb/similarity_search_with_score', methods=['post'])
|
||||
def post_similarity_search_with_score():
|
||||
@ -31,13 +31,14 @@ def post_similarity_search_with_score():
|
||||
source = meta.get('source', '')
|
||||
if len(source) > 0:
|
||||
source = urlmapping.url_from_mapping(source)
|
||||
|
||||
result_data.append({
|
||||
'content': page_content.strip(),
|
||||
'meta': meta,
|
||||
'source': source,
|
||||
'score': float(score)
|
||||
})
|
||||
|
||||
|
||||
response = jsonify({
|
||||
'code': StatusCode.success.value,
|
||||
'data': result_data,
|
||||
3
rag/utils/__init__.py
Normal file
3
rag/utils/__init__.py
Normal file
@ -0,0 +1,3 @@
|
||||
from .url_mapping import urlmapping
|
||||
|
||||
__all__ = [ urlmapping ]
|
||||
@ -13,6 +13,7 @@ class UrlMappingRegister:
|
||||
return lambda func: register(start, func)
|
||||
|
||||
def url_from_mapping(self, source: str) -> str:
|
||||
source = source.lstrip('.').lstrip('/')
|
||||
for pattern in self.startsWith_patterns:
|
||||
func = self.startsWith_patterns[pattern]
|
||||
if source.startswith(pattern):
|
||||
@ -32,6 +33,7 @@ def kirigaya_cn(source: str) -> str:
|
||||
template = f'https://kirigaya.cn/blog/article?seq={article_id}'
|
||||
return template
|
||||
|
||||
|
||||
# 样例： docs/digital-document/guide/quick-start.md
|
||||
# 目标： https://sterben.nitcloud.cn/zh/guide/quick-start.html
|
||||
@urlmapping.startsWith('docs/digital-document')
|
||||
@ -42,6 +44,16 @@ def digital_document(source: str) -> str:
|
||||
return router
|
||||
|
||||
|
||||
# 样例： docs/digital-issue/issue-2/issue.md
|
||||
# 目标： https://github.com/Digital-EDA/Digital-IDE/issues/2
|
||||
@urlmapping.startsWith('docs/digital-issue')
|
||||
def digital_document(source: str) -> str:
|
||||
parts = source.split('/')
|
||||
issue_id = parts[2].split('-')[-1]
|
||||
router = f'https://github.com/Digital-EDA/Digital-IDE/issues/{issue_id}'
|
||||
return router
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
print(kirigaya_cn('docs/kirigaya.cn/129.md'))
|
||||
print(kirigaya_cn('docs/kirigaya.cn/21.md'))
|
||||
@ -41,7 +41,7 @@ suite('test intent recogition', () => {
|
||||
{ input: '我的反撤回还能用', expect: 'others' },
|
||||
{ input: '因为这是养蛊的虚拟机，放了些国产垃圾软件，得用国产流氓之王才能镇得住他们', expect: 'others' },
|
||||
{ input: '你咋装了个360', expect: 'others' },
|
||||
{ input: '？？？', expect: 'others' },
|
||||
{ input: '？？？', expect: 'expression,others' },
|
||||
];
|
||||
|
||||
for (const s of intent_suites) {
|
||||
|
||||
206
yarn.lock
206
yarn.lock
@ -4,7 +4,7 @@
|
||||
|
||||
"@pm2/agent@~2.0.0":
|
||||
version "2.0.3"
|
||||
resolved "https://registry.npmjs.org/@pm2/agent/-/agent-2.0.3.tgz"
|
||||
resolved "https://registry.yarnpkg.com/@pm2/agent/-/agent-2.0.3.tgz#6b47fda837f185864767fe1e048f61d1de31fc45"
|
||||
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|
||||
to-regex-range@^5.0.1:
|
||||
@ -2236,22 +2236,22 @@ trampa@^1.0.0:
|
||||
|
||||
tslib@1.9.3:
|
||||
version "1.9.3"
|
||||
resolved "https://registry.npmjs.org/tslib/-/tslib-1.9.3.tgz"
|
||||
resolved "https://registry.yarnpkg.com/tslib/-/tslib-1.9.3.tgz#d7e4dd79245d85428c4d7e4822a79917954ca286"
|
||||
integrity sha512-4krF8scpejhaOgqzBEcGM7yDIEfi0/8+8zDRZhNZZ2kjmHJ4hv3zCbQWxoJGz1iw5U0Jl0nma13xzHXcncMavQ==
|
||||
|
||||
tslib@^2.0.1:
|
||||
version "2.6.2"
|
||||
resolved "https://registry.npmjs.org/tslib/-/tslib-2.6.2.tgz"
|
||||
integrity sha512-AEYxH93jGFPn/a2iVAwW87VuUIkR1FVUKB77NwMF7nBTDkDrrT/Hpt/IrCJ0QXhW27jTBDcf5ZY7w6RiqTMw2Q==
|
||||
version "2.6.3"
|
||||
resolved "https://registry.yarnpkg.com/tslib/-/tslib-2.6.3.tgz#0438f810ad7a9edcde7a241c3d80db693c8cbfe0"
|
||||
integrity sha512-xNvxJEOUiWPGhUuUdQgAJPKOOJfGnIyKySOc09XkKsgdUV/3E2zvwZYdejjmRgPCgcym1juLH3226yA7sEFJKQ==
|
||||
|
||||
tv4@^1.3.0:
|
||||
version "1.3.0"
|
||||
resolved "https://registry.npmjs.org/tv4/-/tv4-1.3.0.tgz"
|
||||
resolved "https://registry.yarnpkg.com/tv4/-/tv4-1.3.0.tgz#d020c846fadd50c855abb25ebaecc68fc10f7963"
|
||||
integrity sha512-afizzfpJgvPr+eDkREK4MxJ/+r8nEEHcmitwgnPUqpaP+FpwQyadnxNoSACbgc/b1LsZYtODGoPiFxQrgJgjvw==
|
||||
|
||||
tx2@~1.0.4:
|
||||
version "1.0.5"
|
||||
resolved "https://registry.npmjs.org/tx2/-/tx2-1.0.5.tgz"
|
||||
resolved "https://registry.yarnpkg.com/tx2/-/tx2-1.0.5.tgz#ee0b0e5e2c351f8d23e54bdf46dd60afa3bbc73d"
|
||||
integrity sha512-sJ24w0y03Md/bxzK4FU8J8JveYYUbSs2FViLJ2D/8bytSiyPRbuE3DyL/9UKYXTZlV3yXq0L8GLlhobTnekCVg==
|
||||
dependencies:
|
||||
json-stringify-safe "^5.0.1"
|
||||
@ -2324,7 +2324,7 @@ vary@~1.1.2:
|
||||
|
||||
vizion@~2.2.1:
|
||||
version "2.2.1"
|
||||
resolved "https://registry.npmjs.org/vizion/-/vizion-2.2.1.tgz"
|
||||
resolved "https://registry.yarnpkg.com/vizion/-/vizion-2.2.1.tgz#04201ea45ffd145d5b5210e385a8f35170387fb2"
|
||||
integrity sha512-sfAcO2yeSU0CSPFI/DmZp3FsFE9T+8913nv1xWBOyzODv13fwkn6Vl7HqxGpkr9F608M+8SuFId3s+BlZqfXww==
|
||||
dependencies:
|
||||
async "^2.6.3"
|
||||
@ -2387,7 +2387,7 @@ wrappy@1:
|
||||
|
||||
ws@^7.0.0:
|
||||
version "7.5.9"
|
||||
resolved "https://registry.npmjs.org/ws/-/ws-7.5.9.tgz"
|
||||
resolved "https://registry.yarnpkg.com/ws/-/ws-7.5.9.tgz#54fa7db29f4c7cec68b1ddd3a89de099942bb591"
|
||||
integrity sha512-F+P9Jil7UiSKSkppIiD94dN07AwvFixvLIj1Og1Rl9GGMuNipJnV9JzjD6XuqmAeiswGvUmNLjr5cFuXwNS77Q==
|
||||
|
||||
ws@^8.17.0:
|
||||
@ -2397,7 +2397,7 @@ ws@^8.17.0:
|
||||
|
||||
ws@~7.4.0:
|
||||
version "7.4.6"
|
||||
resolved "https://registry.npmjs.org/ws/-/ws-7.4.6.tgz"
|
||||
resolved "https://registry.yarnpkg.com/ws/-/ws-7.4.6.tgz#5654ca8ecdeee47c33a9a4bf6d28e2be2980377c"
|
||||
integrity sha512-YmhHDO4MzaDLB+M9ym/mDA5z0naX8j7SIlT8f8z+I0VtzsRbekxEutHSme7NPS2qE8StCYQNUnfWdXta/Yu85A==
|
||||
|
||||
y18n@^5.0.5:
|
||||
@ -2407,7 +2407,7 @@ y18n@^5.0.5:
|
||||
|
||||
yallist@^4.0.0:
|
||||
version "4.0.0"
|
||||
resolved "https://registry.npmjs.org/yallist/-/yallist-4.0.0.tgz"
|
||||
resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"
|
||||
integrity sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==
|
||||
|
||||
yaml@^2.4.2:
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user