133 lines
3.5 KiB
Markdown
133 lines
3.5 KiB
Markdown
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<div align="center">
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> 简体中文 < | <a href="https://www.google.com/search?q=best+website+to+learn+chinese&newwindow=1&sca_esv=a76695392a9980a7&rlz=1C1CHBD_zh-HKHK1072HK1072&sxsrf=ADLYWIKib09skMzGw8JqpTv2AWB6Xk8uZQ%3A1716986674152&ei=MiNXZsP3COrk2roPt-SrgA0&oq=best+website+to+learn+chin&gs_lp=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-YAwCIBgGQBgq6BgYIARABGBSSBwoxLjE2LjUuMS4xoAf2rwE&sclient=gws-wiz-serp" target="_blank">English</a>
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</div>
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## 环境搭建
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```bash
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git clone xxxxxx
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npm install pm2 -g
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yarn
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pip install -r requirements.txt
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```
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下载最新版本的 Lagrange.core ，并完成初始化。完成后根目录为
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```
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📁bot
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📁rag
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📁app
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├─📁publish
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│ ├─🏷️...
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│ ├─🏷️appsettings.json
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│ └─🪛Lagrange.OneBot
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└─🎗️...
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```
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---
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## 架构
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```mermaid
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graph TB
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core(Lagrage.Core)
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onebot(Lagrange.onebot)
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vecdb(vecdb)
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llm(LLM)
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intent(intent tree)
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core(Lagrange.Core) --> onebot(Lagrange.onebot)
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onebot -->|query| intent
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intent -->|intent| onebot
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subgraph Intent Recognition
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intent -->|query| vecdb
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vecdb -->|ktop| intent
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intent -->|ktop,query| llm
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llm -->|intent| intent
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end
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subgraph execution
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onebot --> command{intent}
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command --> query
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command --> upload
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command --> ...
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end
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subgraph third party
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LLM
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Google
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server
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end
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query --> LLM
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query --> Google
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upload --> server
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```
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- `Lagrange.onebot` --> 📁bot
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- `vecdb` --> 📁rag
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- `intent tree` --> 📁prompt
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---
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## 接口规范
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http 接口满足 `HttpResponse` 所示。
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```typescript
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interface HttpResponse<T> {
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code: number,
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data: CommonResponse<T>
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}
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interface CommonResponse<T> {
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code: number,
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data?: T,
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msg?: string
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}
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```
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---
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## 开发须知
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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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``` |