From 14c9607787f824dd20eb90404a6e7ac64d3744c4 Mon Sep 17 00:00:00 2001 From: ylzhangah <1194926515@qq.com> Date: Wed, 24 Jul 2024 16:13:07 +0800 Subject: [PATCH 01/19] update file --- docs/eulercopilot_deploy.md | 155 ++++++++++++++++++ ...\347\275\262\346\226\271\346\241\2101.png" | Bin 0 -> 312430 bytes ...\347\275\262\346\226\271\346\241\2102.png" | Bin 0 -> 234100 bytes ...\344\274\240\346\214\207\345\215\227.docx" | Bin 0 -> 484967 bytes script/README.md | 4 + script/check_env.sh | 2 + script/download_file.sh | 1 + script/install_tools.sh | 1 + 8 files changed, 163 insertions(+) create mode 100644 docs/eulercopilot_deploy.md create mode 100644 "docs/pictures/EulerCopilot\351\203\250\347\275\262\346\226\271\346\241\2101.png" create mode 100644 "docs/pictures/EulerCopilot\351\203\250\347\275\262\346\226\271\346\241\2102.png" create mode 100644 "docs/\346\234\254\345\234\260\350\257\255\346\226\231\344\270\212\344\274\240\346\214\207\345\215\227.docx" create mode 100644 script/README.md create mode 100644 script/check_env.sh create mode 100644 script/download_file.sh create mode 100644 script/install_tools.sh diff --git a/docs/eulercopilot_deploy.md b/docs/eulercopilot_deploy.md new file mode 100644 index 0000000..c045cb9 --- /dev/null +++ b/docs/eulercopilot_deploy.md @@ -0,0 +1,155 @@ +# EulerCopilot部署指南 +## EulerCopilot介绍 +NeoCopilot是一款智能问答工具,使用NeoCopilot可以解决操作系统知识获取的便捷性,并且为OS领域模型赋能开发者及运维人员。作为获取操作系统知识,使能操作系统生产力工具(如A-ops/Atune/X2openEuler/Eulermake/EulerDevops/stratovirt/iSulad等),颠覆传统命令交付方式,由传统命令交付方式向自然语义进化,并结合智能体任务规划能力,降低开发、使用操作系统特性的门槛。 + +### 功能描述 + - 通过web/shell/公众号方式,覆盖在openEuler社区/社区公众号/shell 命令行界面; + - 面向普通用户支持语言对话交流的方式,获取操作系统基础知识、欧拉基础知识、生成命令; + - 面向运维人员支持自然语义目标意图理解,自动生成工作流,基于openEuler A-OPS/ATUNE生产力工具实现启发式诊断、调优; + - 面向企业用户支持语义级功能特性调用,使能openEuler 主要特性(stratovirt/iSulad等) +### 应用场景 + - openEuler 在社区入口,通过自然语言方式,进行智能问答,包括但不限于:检索欧拉社区相关知识,案例;生成简单命令;对接社区基础设施;获取社区代办事项;构建社区流水线;构建软件包等。 + - shell 终端用户,基于shell 命令行增强自然语言识别能力,构建启发式运维、部署等能力,包括但不限于:智能诊断、智能调优、智能部署。 + - 端侧设备用户,通过公众号,复用社区入口能力 +### 解决痛点 + - 随着操作系统规模剧增,当前操作系统覆盖的软件包超过3000+,整个规模达3.6万+,操作系统可调参数达13000+,依靠积累需要大量的学习成本;调优诊断依靠专家经验,10年以上专家诊断、调优效率是新员工的10倍以上;调优、诊断、部署过程需要掌握大量的命令,使用门槛较高。 +### 关键价值 + - 通过NeoCopilot统一知识问答,知识准确率超过80%,降低重复性知识咨询问题,通过自然语言启发式运维,调优诊断部署效率倍级提升。工具通过自然语言交付,实现意图理解,生成工作流,生成命令,生成报告,生成建议,为初学者构建专家系统; + +### 组件介绍 + +| 组件 | 端口 | 说明 | +| ----------------------------- | --------------- | -------------------- | +| euler-copilot-framework | 8002 (内部端口) | 智能体框架服务 | +| euler-copilot-web | 8080 | 智能体前端界面 | +| euler-copilot-rag | 8005 (内部端口) | 检索增强服务 | +| euler-copilot-vectorize-agent | 8001 (内部端口) | 文本向量化服务 | +| secret_ingect | 无 | 配置文件安全复制工具 | +| mysql | 3306 (内部端口) | MySQL数据库 | +| redis | 6379 (内部端口) | Redis数据库 | +| postgres | 5432 (内部端口) | 向量数据库 | + +## 环境要求 + +### 软件要求 + +| 软件 | 版本要求 | 说明 | +| ---------- | ------------------------------------|--------------------------------------| +| 操作系统 | openEuler 22.03 LTS及以上版本 | 无 +| K3s | >= v1.29.0,带有Traefik Ingress工具 | K3s提供轻量级的 Kubernetes集群,易于部署和管理 | +| Docker | >= v25.4.0 | docker提供一个独立的运行应用程序环境,作用是用来部署模型 | +| Helm | >= v3.14.4 | Helm是一个 Kubernetes的包管理工具,其目的是快速安装、升级、卸载Eulercopilot服务 | + +#### 部署方案1 + + +| 硬件要求 | 说明 | +| ------------- | ------------------------------------------------------------------- | +| 服务器 | 需要2台服务器 | +| CPU | 鲲鹏,x86_64 >= 32 cores | +| RAM | >= 64GB | +| 存储 | >= 500 GB | +| GPU/NPU | [基于所选模型评估硬件资源需求](https://huggingface.co/spaces/hf-accelerate/model-memory-usage)| + +部署视图如下图1所示 + +![user-flow](./pictures/EulerCopilot部署方案1.png) + +#### 部署方案2 + +| 硬件类型 | 说明 | +| -------- | ------------------- | +| 服务器 | 1台 | +| CPU | x86_64, >= 32 cores | +| RAM | >= 32GB | +| 存储 | >= 200 GB | + +部署视图如下图2所示 + +![user-flow](./pictures/EulerCopilot部署方案2.png) + + +## 环境初始化 +如果您的服务器准备完成后,就可以对环境进行初始化操作了,以下是操作步骤的说明和脚本,可以按照顺序去执行 + +| 步骤 | 脚本路径 | 说明 | +| ------------------ | ---------------------------|------------------------------------------ | +| 1)环境检查 | ![user-flow](../script/euler-copilot-helm/check_env.sh) | 主要对服务器的主机名、DNS、防火墙设置 | +| 2)k3s安装与配置 | ![user-flow](../script/install_k3s.sh) | k3s的部署 | +| 3)models文件下载 | ![user-flow](../script/download_file.sh) | 模型bge-reranker-large、bge-mixed-modetext2和分词工具vec-base-chinese-paraphrase的下载 | +| 4)helm安装 | ![user-flow](../script/install_helm.sh) | 安装helm工具 | | +| 5)大模型准备 | NA | 提供openai接口或按照附录部建议方式部署(需要在附录提供大模型推荐参数列表,最小列表) | + + +## EulerCopilot安装 +您的环境已准备好了,就可以开始安装EulerCopilot了 + +### 1. 编辑YAML + - euler-copilot-helm是EulerCopilot安装时需要的配置文件库,需要在安装前根据实际情况修改完成values.yaml: + `cd ../script/euler-copilot-helm/chat/values.yaml` +### 2. 安装EulerCopilot + ```bash + helm install -n euler-copilot 服务名称 . + # 服务名称可任意指定 + ``` +### 3. 查看组件状态 + ```bash + kubectl -n euler-copilot get pods + # 注意:镜像拉取需要等待一分钟左右,若Pod状态均为Running,则部署成功 + ``` + ``` + # 如果部署有如下报错,可查看部署日志和pod日志 + kubectl -n euler-copilot get events + kubectl logs $(pod_id) -n euler-copilot + ``` + +## 测试 + +到目前为止,您已经完成EulerCopilot的部署,现在就可以开启体验智能问答之旅了 + +### 公网环境 + - 在浏览器输入https://<当前机器公网IP地址>:8080/,进行智能问答测试 +### 内网环境 + - 内网下使用eulerCopilot + - 修改values.yaml中的domain的值为内网ip + - 按照如下方式修改相关配置文件: + ``` + vim euler-copilot-helm/chat_ssl/traefik-config.yml + # 内容如下 + websecure: + exposedPort: port + # 将该处的port修改成要转发的端口 + kubectl apply -f traefik-config.yml + ``` + - 网页登录验证:`https://$(内网ip):$(port)` + +## 构建专有领域的问答 +- 构建openEuler专业知识领域的智能问答 + - 1. 修改values.yaml的pg的镜像仓为`pg-data` + - 2. 修改values.yaml的rag部分的字段`knowledgebaseID: openEuler_2bb3029f` + - 3. 将euler-copilot-helm/chat/templates/pgsql里面pgsql-deployment.yaml的volume相关字段注释掉 +- 构建项目专属知识领域的智能问答 + ![user-flow](./本地语料上传指南.docx) +## 附录 +### 大模型部署 +GPU环境可参考以下方式部署 +``` +# 下载权重: +huggingface-cli download --resume-download Qwen/Qwen1.5-14B-Chat --local-dir Qwen1.5-14B-Chat +# 创建终端,screen -S contol +python3 -m fastchat.serve.controller +按ctrl A+D置于后台 +# 创建新终端,screen -S api +python3 -m fastchat.serve.openai_api_server --host 0.0.0.0 --port 30000 --api-keys sk-123456 +# 如果当前环境的python版本是3.12或者3.9可以创建python3.10的conda虚拟环境 +mkdir -p /root/python-py310 +conda create --prefix=/root/python-py310 python==3.10.14 +conda activate /root/python-py310 +screen -S worker +screen -r worker +安装fastchat和vllm +pip install fschat vllm +安装依赖: +pip install fschat[model_worker] +python3 -m fastchat.serve.vllm_worker --model-path /root/models/Qwen1.5-14B-Chat/ --model-name qwen1.5 --num-gpus 8 --gpu-memory-utilization=0.7 --dtype=half +``` diff --git "a/docs/pictures/EulerCopilot\351\203\250\347\275\262\346\226\271\346\241\2101.png" "b/docs/pictures/EulerCopilot\351\203\250\347\275\262\346\226\271\346\241\2101.png" new file mode 100644 index 0000000000000000000000000000000000000000..97ea4f384fa0e7779139e48238ac77cf02005403 GIT binary patch literal 312430 zcmagFcRX9+|2JG!REwZ$HN>i*Rn#64d(^B^Rcf?aMN3h&rAF-$v1e-3s#T+AQ8Uz5 ztEE=#9T5@{Px}4czvp#7_v?P{`>(uSCs$6+^*Pu1T<>+Fj19HvuW??xbms6aeOu&+fxsp|GOpns+4E@XV%`ovlbMn`aQ)wVx>K+jvg-mSA}!8 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