diff --git a/docs/en/ai_full_stack/ai_container_image_userguide/_toc.yaml b/docs/en/ai_full_stack/ai_container_image_userguide/_toc.yaml index 8782a2915974178dadfa7b97efcb19df99069a72..77d171c0a48790d75b2e7e709c27fb91f8ef8e47 100644 --- a/docs/en/ai_full_stack/ai_container_image_userguide/_toc.yaml +++ b/docs/en/ai_full_stack/ai_container_image_userguide/_toc.yaml @@ -1,6 +1,6 @@ -label: AI容器镜像用户指南 +label: AI Container Image User Guide isManual: true -description: openEuler AI 容器镜像封装了 AI 框架等软件，提高 AI 应用开发或使用效率 +description: The openEuler AI container image packages AI frameworks and other software to improve the efficiency of AI application development and usage. sections: - - label: AI容器镜像用户指南 - href: ./ai-container-image-user-guide.md + - label: AI Container Image User Guide + href: ./ai_container_image_user_guide.md diff --git a/docs/zh/ai_full_stack/ai_container_image_userguide/ai-container-image-user-guide.md b/docs/en/ai_full_stack/ai_container_image_userguide/ai_container_image_user_guide.md similarity index 33% rename from docs/zh/ai_full_stack/ai_container_image_userguide/ai-container-image-user-guide.md rename to docs/en/ai_full_stack/ai_container_image_userguide/ai_container_image_user_guide.md index b433f2f4dcb8d609d014e8ecf3f77d37d5fe5773..b6b21e1878e509df93f20ac70a9ef0f3f1cd4baa 100644 --- a/docs/zh/ai_full_stack/ai_container_image_userguide/ai-container-image-user-guide.md +++ b/docs/en/ai_full_stack/ai_container_image_userguide/ai_container_image_user_guide.md @@ -1,54 +1,54 @@ -# openEuler AI 容器镜像用户指南 +# openEuler AI Container Image User Guide -## 简介 +## Introduction -openEuler AI 容器镜像封装了不同硬件算力的 SDK 以及 AI 框架、大模型应用等软件，用户只需要在目标环境中加载镜像并启动容器，即可进行 AI 应用开发或使用，大大减少了应用部署和环境配置的时间，提升效率。 +The openEuler AI container images package SDKs for different hardware accelerators, along with AI frameworks and large-model applications. You only need to load the image and start a container in the target environment to develop or use AI applications, significantly reducing deployment and environment configuration time and improving efficiency. -## 获取镜像 +## Obtain Images -目前，openEuler 已发布支持 Ascend 和 NVIDIA 平台的容器镜像，获取路径如下： +Currently, openEuler provides container images for both Ascend and NVIDIA platforms. You can find them here: -- [openeuler/cann](https://hub.docker.com/r/openeuler/cann) 存放 SDK 类镜像，在 openEuler 基础镜像之上安装 CANN 系列软件，适用于 Ascend 环境。 -- [openeuler/cuda](https://hub.docker.com/r/openeuler/cuda) 存放 SDK 类镜像，在 openEuler 基础镜像之上安装 CUDA 系列软件，适用于 NVIDIA 环境。 -- [openeuler/pytorch](https://hub.docker.com/r/openeuler/pytorch) 存放 AI 框架类镜像，在 SDK 镜像基础之上安装 PyTorch，根据安装的 SDK 软件内容区分适用平台。 -- [openeuler/tensorflow](https://hub.docker.com/r/openeuler/tensorflow) 存放 AI 框架类镜像，在 SDK 镜像基础之上安装 TensorFlow，根据安装的 SDK 软件内容区分适用平台。 -- [openeuler/llm](https://hub.docker.com/r/openeuler/tensorrt) 存放模型应用类镜像，在 AI 框架镜像之上包含特定大模型及工具链，根据安装的 SDK 软件内容区分适用平台。 +- `docker.io/openeuler/cann`: SDK-type images that install the CANN software stack on top of the openEuler base image, for Ascend environments. +- `docker.io/openeuler/cuda`: SDK-type images that install the CUDA software stack on top of the openEuler base image, for NVIDIA environments. +- `docker.io/openeuler/pytorch`: AI framework images that install PyTorch on top of an SDK image; the applicable platform depends on the installed SDK. +- `docker.io/openeuler/tensorflow`: AI framework images that install TensorFlow on top of an SDK image; the applicable platform depends on the installed SDK. +- `docker.io/openeuler/llm`: Model application images that include specific large models and toolchains on top of an AI framework image; the applicable platform depends on the installed SDK. -详细的 AI 容器镜像分类和镜像 tag 的规范说明见[oEEP-0014](https://gitee.com/openeuler/TC/blob/master/oEEP/oEEP-0014%20openEuler%20AI容器镜像软件栈规范.md)。 +For detailed classifications and tag conventions of AI container images, see [oEEP-0014](https://gitee.com/openeuler/TC/blob/master/oEEP/oEEP-0014%20openEuler%20AI容器镜像软件栈规范.md). -由于 AI 容器镜像的体积一般较大，推荐用户在启动容器前先通过如下命令将镜像拉取到开发环境中。 +Because AI container images are typically large, it is recommended to pull the image to your development environment before starting a container: ```sh docker pull image:tag ``` -其中，`image`为仓库名，如`openeuler/cann`，`tag`为目标镜像的 TAG，待镜像拉取完成后即可启动容器。注意，使用`docker pull`命令需按照下文方法安装`docker`软件。 +Here, `image` is the repository name, such as `openeuler/cann`, and `tag` is the target image tag. After the image is pulled, you can start the container. Note that to use the `docker pull` command, Docker must be installed as described below. -## 启动容器 +## Start a Container -1. 在环境中安装`docker`，官方安装方法见[Install Docker Engine](https://docs.docker.com/engine/install/)，也可直接通过如下命令进行安装。 +1. Install `docker` in your environment. Refer to the official guide `https://docs.docker.com/engine/install/`, or install directly with the following commands: ```sh yum install -y docker ``` - 或 + or ```sh apt-get install -y docker ``` -2. NVIDIA环境安装`nvidia-container` +2. For NVIDIA environments, install `nvidia-container` components. - 1）配置yum或apt repo - - 使用yum安装时，执行： + (1) Configure yum or apt repositories + - For yum-based systems, run: ```sh curl -s -L https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo | \ sudo tee /etc/yum.repos.d/nvidia-container-toolkit.repo ``` - - 使用apt安装时，执行： + - For apt-based systems, run: ```sh curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg @@ -60,33 +60,33 @@ docker pull image:tag sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list ``` - 2）安装`nvidia-container-toolkit`,`nvidia-container-runtime`，执行： + (2) Install `nvidia-container-toolkit` and `nvidia-container-runtime`: ```sh - # yum安装 + # yum installation yum install -y nvidia-container-toolkit nvidia-container-runtime ``` ```sh - # apt安装 + # apt installation apt-get install -y nvidia-container-toolkit nvidia-container-runtime ``` - 3）配置docker + (3) Configure Docker ```sh nvidia-ctk runtime configure --runtime=docker systemctl restart docker ``` - 非NVIDIA环境不执行此步骤。 + Skip this step if you are not on an NVIDIA platform. -3. 确保环境中安装`driver`及`firmware`，用户可从[NVIDIA](https://www.nvidia.com/)或[Ascend](https://www.hiascend.com/)官网获取正确版本进行安装。安装完成后 Ascend 平台使用`npu-smi`命令、NVIDIA 平台使用`nvidia-smi`进行测试，正确显示硬件信息则说明安装正常。 +3. Ensure the appropriate `driver` and `firmware` are installed. You can obtain the correct versions from the official websites of [NVIDIA](https://www.nvidia.com/) or [Ascend](https://www.hiascend.com/). After installation, test with `npu-smi` for Ascend platforms or `nvidia-smi` for NVIDIA platforms. If hardware information is displayed correctly, the installation is successful. -4. 完成上述操作后，即可使用`docker run`命令启动容器。 +4. After completing the above steps, use the `docker run` command to start a container. ```sh -# Ascend环境启动容器 +# Start a container on an Ascend environment docker run --rm --network host \ --device /dev/davinci0:/dev/davinci0 \ --device /dev/davinci_manager --device /dev/devmm_svm --device /dev/hisi_hdc \ @@ -96,6 +96,6 @@ docker run --rm --network host \ ``` ```sh -# NVIDIA环境启动容器 +# Start a container on an NVIDIA environment docker run --gpus all -d -ti image:tag ``` diff --git a/docs/en/ai_full_stack/ai_large_model_service_images_userguide/_toc.yaml b/docs/en/ai_full_stack/ai_large_model_service_images_userguide/_toc.yaml index bed3b23bcec452195f1b6a830c6c7e3a017413b0..660ee8766f50c8295e09f1fdd32947a3894f9598 100644 --- a/docs/en/ai_full_stack/ai_large_model_service_images_userguide/_toc.yaml +++ b/docs/en/ai_full_stack/ai_large_model_service_images_userguide/_toc.yaml @@ -1,6 +1,6 @@ -label: AI大模型服务镜像使用指南 +label: AI Large Model Service Image User Guide isManual: true -description: 支持百川、chatglm、星火等AI大模型的容器化封装 +description: Containerized packaging for AI large models such as Baichuan, ChatGLM, and Spark. sections: - - label: AI大模型服务镜像使用指南 - href: ./llm-service-image-user-guide.md + - label: AI Large Model Service Image User Guide + href: ./llm_service_image_user_guide.md diff --git a/docs/en/ai_full_stack/ai_large_model_service_images_userguide/llm-service-image-user-guide.md b/docs/en/ai_full_stack/ai_large_model_service_images_userguide/llm_service_image_user_guide.md similarity index 38% rename from docs/en/ai_full_stack/ai_large_model_service_images_userguide/llm-service-image-user-guide.md rename to docs/en/ai_full_stack/ai_large_model_service_images_userguide/llm_service_image_user_guide.md index c7c492b104b74e25ac87980c2d8580885a43df0e..b27604c65cc581cf8143300d1c19a7cd2849ba03 100644 --- a/docs/en/ai_full_stack/ai_large_model_service_images_userguide/llm-service-image-user-guide.md +++ b/docs/en/ai_full_stack/ai_large_model_service_images_userguide/llm_service_image_user_guide.md @@ -1,41 +1,41 @@ -# 支持百川、chatglm、星火等AI大模型的容器化封装 +# Containerized Packaging for AI Large Models Supporting Baichuan, ChatGLM, Spark -已配好相关依赖，分为CPU和GPU版本，降低使用门槛，开箱即用。 +Pre-configured with relevant dependencies, available in both CPU and GPU versions to lower the barrier to use and enable out-of-the-box deployment. -## 拉取镜像(CPU版本) +## Pull Image (CPU Version) ```bash docker pull openeuler/llm-server:1.0.0-oe2203sp3 ``` -## 拉取镜像(GPU版本) +## Pull Image (GPU Version) ```bash docker pull icewangds/llm-server:1.0.0 ``` -## 下载模型, 并转换为gguf格式 +## Download Model and Convert to GGUF Format ```bash -# 安装huggingface +# Install huggingface pip install huggingface-hub -# 下载你想要部署的模型 +# Download the model you want to deploy export HF_ENDPOINT=https://hf-mirror.com huggingface-cli download --resume-download baichuan-inc/Baichuan2-13B-Chat --local-dir /root/models/Baichuan2-13B-Chat --local-dir-use-symlinks False -# gguf格式转换 +# GGUF format conversion cd /root/models/ git clone https://github.com/ggerganov/llama.cpp.git python llama.cpp/convert-hf-to-gguf.py ./Baichuan2-13B-Chat -# 生成的gguf格式的模型路径 /root/models/Baichuan2-13B-Chat/ggml-model-f16.gguf +# Generated GGUF format model path: /root/models/Baichuan2-13B-Chat/ggml-model-f16.gguf ``` -## 启动方式 +## Startup Methods -需要Docker v25.0.0及以上版本。 +Requires Docker v25.0.0 or higher. -若使用GPU镜像，需要OS上安装nvidia-container-toolkit，安装方式见。 +If using GPU images, nvidia-container-toolkit must be installed on the OS. Installation instructions can be found at . docker-compose.yaml: @@ -43,19 +43,19 @@ docker-compose.yaml: version: '3' services: model: - image: : #镜像名称与tag + image: : # Image name and tag restart: on-failure:5 ports: - - 8001:8000 #监听端口号，修改“8001”以更换端口 + - 8001:8000 # Listening port, modify "8001" to change the port volumes: - - /root/models:/models # 大模型挂载目录 + - /root/models:/models # Large model mount directory environment: - - MODEL=/models/Baichuan2-13B-Chat/ggml-model-f16.gguf # 容器内的模型文件路径 - - MODEL_NAME=baichuan13b # 自定义模型名称 - - KEY=sk-12345678 # 自定义API Key - - CONTEXT=8192 # 上下文大小 - - THREADS=8 # CPU线程数，仅CPU部署时需要 - deploy: # 指定GPU资源, 仅GPU部署时需要 + - MODEL=/models/Baichuan2-13B-Chat/ggml-model-f16.gguf # Model file path inside container + - MODEL_NAME=baichuan13b # Custom model name + - KEY=sk-12345678 # Custom API Key + - CONTEXT=8192 # Context size + - THREADS=8 # CPU thread count, only needed for CPU deployment + deploy: # Specify GPU resources, only needed for GPU deployment resources: reservations: devices: @@ -71,12 +71,12 @@ docker-compose -f docker-compose.yaml up docker run: ```text -cpu部署: docker run -d --restart on-failure:5 -p 8001:8000 -v /root/models:/models -e MODEL=/models/Baichuan2-13B-Chat/ggml-model-f16.gguf -e MODEL_NAME=baichuan13b -e KEY=sk-12345678 openeuler/llm-server:1.0.0-oe2203sp3 +CPU deployment: docker run -d --restart on-failure:5 -p 8001:8000 -v /root/models:/models -e MODEL=/models/Baichuan2-13B-Chat/ggml-model-f16.gguf -e MODEL_NAME=baichuan13b -e KEY=sk-12345678 openeuler/llm-server:1.0.0-oe2203sp3 -gpu部署: docker run -d --gpus all --restart on-failure:5 -p 8001:8000 -v /root/models:/models -e MODEL=/models/Baichuan2-13B-Chat/ggml-model-f16.gguf -e MODEL_NAME=baichuan13b -e KEY=sk-12345678 icewangds/llm-server:1.0.0 +GPU deployment: docker run -d --gpus all --restart on-failure:5 -p 8001:8000 -v /root/models:/models -e MODEL=/models/Baichuan2-13B-Chat/ggml-model-f16.gguf -e MODEL_NAME=baichuan13b -e KEY=sk-12345678 icewangds/llm-server:1.0.0 ``` -## 调用大模型接口测试，成功返回则表示大模型服务已部署成功 +## Test Large Model API Call - Successful response indicates successful deployment ```bash curl -X POST http://127.0.0.1:8001/v1/chat/completions \ @@ -85,8 +85,8 @@ curl -X POST http://127.0.0.1:8001/v1/chat/completions \ -d '{ "model": "baichuan13b", "messages": [ - {"role": "system", "content": "你是一个社区助手，请回答以下问题。"}, - {"role": "user", "content": "你是谁?"} + {"role": "system", "content": "You are a community assistant, please answer the following question."}, + {"role": "user", "content": "Who are you?"} ], "stream": false, "max_tokens": 1024 diff --git a/docs/en/intelligent_foundation/sysHAX/_toc.yaml b/docs/en/intelligent_foundation/sysHAX/_toc.yaml deleted file mode 100644 index 4336146e16b5e642686555ba704c654cac564b5a..0000000000000000000000000000000000000000 --- a/docs/en/intelligent_foundation/sysHAX/_toc.yaml +++ /dev/null @@ -1,6 +0,0 @@ -label: sysHAX用户指南 -isManual: true -description: 异构协同加速运行 -sections: - - label: sysHAX部署指南 - href: ./deploy_guide/sysHax-deployment-guide.md diff --git a/docs/en/intelligent_foundation/sysHAX/deploy_guide/sysHax-deployment-guide.md b/docs/en/intelligent_foundation/sysHAX/deploy_guide/sysHax-deployment-guide.md deleted file mode 100644 index 06025bedca3484c6e8176c13f985ce9743f6bcb6..0000000000000000000000000000000000000000 --- a/docs/en/intelligent_foundation/sysHAX/deploy_guide/sysHax-deployment-guide.md +++ /dev/null @@ -1,151 +0,0 @@ -# sysHAX部署指南 - -sysHAX当前处于快速迭代阶段，基于vllm v0.6.6+npu进行验证。vllm上游发布的正式支持npu的版本为v0.7.1rc1，而当前用的vllm版本处于验证阶段，未合入主线。因此，在当前创新版本中暂不以源码形式发布，而是以容器化的形式为大家提供技术尝鲜。也欢迎开发者在使用过程中有任何问题和建议，可以在sig-Intelligence组中进行充分交流。 - -vllm是一款**高吞吐、低内存占用**的**大语言模型（LLM）推理与服务引擎**，支持**CPU 计算加速**，提供高效的算子下发机制，包括： - -- **Schedule（调度）**：优化任务分发，提高并行计算效率 -- **Prepare Input（准备数据）**：高效的数据预处理，加速输入构建 -- **Ray 框架**：利用分布式计算提升推理吞吐 -- **Sample（模型后处理）**：优化采样策略，提升生成质量 -- **框架后处理**：融合多种优化策略，提升整体推理性能 - -该引擎结合**高效计算调度与优化策略**，为 LLM 推理提供**更快、更稳定、更可扩展**的解决方案。 - -## 环境准备 - -| 服务器型号 | Atlas 800T/I A2 训练/推理服务器 | -| --------------- | --------------------------------------------------------- | -| 操作系统 | openEuler 22.03 LTS及以上 | -| NPU驱动版本 | Ascend-hdk-910b-npu-driver_24.1.rc3_linux-aarch64.run | -| 固件版本 | Ascend-hdk-910b-firmware_7.5.0.1.129.run | - -### **安装驱动固件** - -- 创建驱动运行用户HwHiAiUser（运行驱动进程的用户），安装驱动时无需指定运行用户，默认即为HwHiAiUser。 - -```shell -groupadd -g 1000 HwHiAiUser -useradd -g HwHiAiUser -u 1000 -d /home/HwHiAiUser -m HwHiAiUser -s /bin/bash -``` - -- 将驱动包和固件包上传到服务器任意目录如“/home”。 -- 执行如下命令，增加驱动和固件包的可执行权限。 - -```shell -chmod +x Ascend-hdk-910b-npu-driver_24.1.rc3_linux-aarch64.run -chmod +x Ascend-hdk-910b-firmware_7.5.0.1.129.run -``` - -- 安装驱动 - -```shell -./Ascend-hdk-910b-npu-driver_24.1.rc3_linux-aarch64.run --full --install-for-all - -# 若执行上述安装命令出现类似如下回显信息 -# [ERROR]The list of missing tools: lspci,ifconfig, -# 请执行yum install -y net-tools pciutils - -# 若系统出现如下关键回显信息，则表示驱动安装成功。 -# Driver package installed successfully! -``` - -- 安装固件 - -```shell -./Ascend-hdk-910b-firmware_7.5.0.1.129.run --full - -# 若系统出现如下关键回显信息，表示固件安装成功。 -# Firmware package installed successfully! Reboot now or after driver installation for the installation/upgrade to take effect -``` - -- 执行reboot命令重启系统。 -- 执行npu-smi info查看驱动加载是否成功。 - -## 容器部署场景 - -### 部署Ascend-Docker（容器引擎插件） - -- 参考版本："Ascend-docker-runtime_6.0.RC3.1_linux-aarch64.run" - -```shell -# 将软件包”Ascend-docker-runtime_6.0.RC3.1_linux-aarch64.run”上传到服务器任意目录（如“/home”）。 -chmod +x Ascend-docker-runtime_6.0.RC3.1_linux-aarch64.run -``` - -```shell -./Ascend-docker-runtime_6.0.RC3.1_linux-aarch64.run --install -# 安装完成后，若显示类似如下信息，则说明软件安装成功: -xxx install success -``` - -- 执行systemctl restart docker命令重启docker，使容器引擎插件在docker配置文件中添加的内容生效。 - -### 容器场景vllm搭建 - -```shell -docker pull hub.oepkgs.net/neocopilot/vllm@sha256:c72a0533b8f34ebd4d352ddac3a969d57638c3d0c9c4af9b78c88400c6edff7a - -# /home路径不要全部映射，防止覆盖/home/HwHiAiUser -docker run -itd \ - -p 1111:22 \ - --name vllm_oe \ - --shm-size 16G \ - --device /dev/davinci0 \ - --device /dev/davinci1 \ - --device /dev/davinci2 \ - --device /dev/davinci3 \ - --device /dev/davinci4 \ - --device /dev/davinci5 \ - --device /dev/davinci6 \ - --device /dev/davinci7 \ - --device /dev/davinci_manager \ - --device /dev/devmm_svm \ - --device /dev/hisi_hdc \ - -v /usr/local/dcmi:/usr/local/dcmi \ - -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \ - -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \ - -w /home \ - hub.oepkgs.net/neocopilot/vllm:0.6.6-aarch64-910B-oe2203-sp3 bash - -# 启动vllm，模型自行下载 -vllm serve /home/models/DeepSeek-R1-Distill-Llama-70B --distributed-executor-backend ray --tensor-parallel-size 8 --block-size 32 --preemption_mode swap -``` - -## 纯CPU推理环境部署 - -- 模型文件准备 - -1. 准备模型文件，放在`/home/model/`路径下 - - - **注意**：当前镜像版本支持DeepSeek 7B、32B以及Qwen系列模型 - -2. 拉取镜像，镜像地址：docker pull hub.oepkgs.net/neocopilot/syshax/vllm-cpu@sha256:3983071e1928b9fddc037a51f2fc6b044d41a35d5c1e75ff62c8c5e6b1c157a3 - -3. 启动容器： - -```bash -docker run --name vllm_server_sysHAX \ - -p 7001:7001 \ - -v /home/model:/home/model/ \ - -itd hub.oepkgs.net/neocopilot/syshax/vllm-cpu:0.1.2.4 bash -``` - -- 在容器中启动服务 - -```bash -cd /home/vllm_syshax -python3 vllm/entrypoints/openai/api_server.py \ - --model /home/model/DeepSeek-R1-Distill-Qwen-7B \ - --served-model-name=ds7b \ - --host 0.0.0.0 \ - --port 7001 \ - --dtype=half \ - --swap_space=16 \ - --block_size=16 \ - --preemption_mode=swap \ - --max_model_len=8192 & -``` - -**注意**：`--model`使用实际模型路径，`--served-model-name`可自己指定模型名字，端口两个需要对应，可不用7001 -部署完成，然后向7001端口发送请求即可，请求需满足OpenAPI格式。 diff --git a/docs/en/intelligent_foundation/syshax/deploy_guide/_toc.yaml b/docs/en/intelligent_foundation/syshax/deploy_guide/_toc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..be322a13a3e1446113febb826c3d8b305f50c724 --- /dev/null +++ b/docs/en/intelligent_foundation/syshax/deploy_guide/_toc.yaml @@ -0,0 +1,6 @@ +label: sysHAX User Guide +isManual: true +description: Heterogeneous Collaborative Acceleration +sections: + - label: sysHAX Deployment Guide + href: ./syshax_deployment_guide.md diff --git a/docs/zh/intelligent_foundation/sysHAX/deploy_guide/pictures/syshax-deploy.png b/docs/en/intelligent_foundation/syshax/deploy_guide/pictures/syshax-deploy.png similarity index 100% rename from docs/zh/intelligent_foundation/sysHAX/deploy_guide/pictures/syshax-deploy.png rename to docs/en/intelligent_foundation/syshax/deploy_guide/pictures/syshax-deploy.png diff --git a/docs/en/intelligent_foundation/syshax/deploy_guide/syshax_deployment_guide.md b/docs/en/intelligent_foundation/syshax/deploy_guide/syshax_deployment_guide.md new file mode 100644 index 0000000000000000000000000000000000000000..e6f6e3f4c44cb1ac82bc04d17de0bf92326030a5 --- /dev/null +++ b/docs/en/intelligent_foundation/syshax/deploy_guide/syshax_deployment_guide.md @@ -0,0 +1,212 @@ +# sysHAX Deployment Guide + +## Overview + +sysHAX is positioned as K+X heterogeneous fusion inference acceleration, mainly containing two functional components: + +- Dynamic inference scheduling +- CPU inference acceleration + +**Dynamic inference scheduling**: For inference tasks, the prefill phase belongs to compute-intensive tasks, while the decode phase belongs to memory-intensive tasks. Therefore, from the perspective of computational resources, the prefill phase is suitable for execution on GPU/NPU and other hardware, while the decode phase can be executed on CPU and other hardware. + +**CPU inference acceleration**: Accelerates CPU inference performance through NUMA affinity, parallel optimization, operator optimization, and other methods on the CPU. + +sysHAX consists of two delivery components: + +![syshax-deploy](pictures/syshax-deploy.png) + +The delivery components include: + +- sysHAX: Responsible for request processing and scheduling of prefill and decode requests +- vllm: vllm is a large model inference service that includes both GPU/NPU and CPU during deployment, used for processing prefill and decode requests respectively. From the perspective of developer usability, vllm will be released using containerization. + +vllm is a **high-throughput, low-memory-usage large language model (LLM) inference and service engine** that supports **CPU computation acceleration** and provides efficient operator dispatch mechanisms, including: + +- Schedule: Optimizes task distribution to improve parallel computation efficiency +- Prepare Input: Efficient data preprocessing to accelerate input construction +- Ray framework: Utilizes distributed computing to improve inference throughput +- Sample (model post-processing): Optimizes sampling strategies to improve generation quality +- Framework post-processing: Integrates multiple optimization strategies to improve overall inference performance + +This engine combines **efficient computation scheduling and optimization strategies** to provide **faster, more stable, and more scalable** solutions for LLM inference. + +## Environment Preparation + +| Server Model | Kunpeng 920 Series CPU | +| ------------ | ---------------------- | +| GPU | Nvidia A100 | +| OS | openEuler 22.03 LTS and above | +| Python | 3.9 and above | +| Docker | 25.0.3 and above | + +- Docker 25.0.3 can be installed via `dnf install moby`. + +## Deployment Process + +First, check whether NVIDIA drivers and CUDA drivers are already installed using `nvidia-smi` and `nvcc -V`. If not, you need to install NVIDIA drivers and CUDA drivers first. + +### Install NVIDIA Container Toolkit (Container Engine Plugin) + +If NVIDIA Container Toolkit is already installed, you can skip this step. Otherwise, follow the installation process below: + + + +- Execute the `systemctl restart docker` command to restart docker, making the container engine plugin configuration in the docker config file effective. + +### Container-based vllm Setup + +The following process deploys vllm in a GPU container. + +```shell +docker pull hub.oepkgs.net/neocopilot/syshax/syshax-vllm-gpu:0.2.0 + +docker run --name vllm_gpu \ + --ipc="shareable" \ + --shm-size=64g \ + --gpus=all \ + -p 8001:8001 \ + -v /home/models:/home/models \ + -w /home/ \ + -itd hub.oepkgs.net/neocopilot/syshax/syshax-vllm-gpu:0.2.0 bash +``` + +In the above script: + +- `--ipc="shareable"`: Allows the container to share IPC namespace for inter-process communication. +- `--shm-size=64g`: Sets the container shared memory to 64G. +- `--gpus=all`: Allows the container to use all GPU devices on the host +- `-p 8001:8001`: Port mapping, mapping the host's port 8001 to the container's port 8001. Developers can modify this as needed. +- `-v /home/models:/home/models`: Directory mounting, mapping the host's `/home/models` to `/home/models` inside the container for model sharing. Developers can modify the mapping directory as needed. + +```shell +vllm serve /home/models/DeepSeek-R1-Distill-Qwen-32B \ + --served-model-name=ds-32b \ + --host 0.0.0.0 \ + --port 8001 \ + --dtype=half \ + --swap_space=16 \ + --block_size=16 \ + --preemption_mode=swap \ + --max_model_len=8192 \ + --tensor-parallel-size 2 \ + --gpu_memory_utilization=0.8 +``` + +In the above script: + +- `--tensor-parallel-size 2`: Enables tensor parallelism, splitting the model across 2 GPUs. Requires at least 2 GPUs. Developers can modify this as needed. +- `--gpu_memory_utilization=0.8`: Limits GPU memory usage to 80% to avoid service crashes due to memory exhaustion. Developers can modify this as needed. + +The following process deploys vllm in a CPU container. + +```shell +docker pull hub.oepkgs.net/neocopilot/syshax/syshax-vllm-cpu:0.2.0 + +docker run --name vllm_cpu \ + --ipc container:vllm_gpu \ + --shm-size=64g \ + --privileged \ + -p 8002:8002 \ + -v /home/models:/home/models \ + -w /home/ \ + -itd hub.oepkgs.net/neocopilot/syshax/syshax-vllm-cpu:0.2.0 bash +``` + +In the above script: + +- `--ipc container:vllm_gpu`: Shares the IPC (inter-process communication) namespace with the container named vllm_gpu. Allows this container to exchange data directly through shared memory, avoiding cross-container copying. + +```shell +INFERENCE_OP_MODE=fused OMP_NUM_THREADS=160 CUSTOM_CPU_AFFINITY=0-159 SYSHAX_QUANTIZE=q8_0 \ +vllm serve /home/models/DeepSeek-R1-Distill-Qwen-32B \ + --served-model-name=ds-32b \ + --host 0.0.0.0 \ + --port 8002 \ + --dtype=half \ + --block_size=16 \ + --preemption_mode=swap \ + --max_model_len=8192 +``` + +In the above script: + +- `INFERENCE_OP_MODE=fused`: Enables CPU inference acceleration +- `OMP_NUM_THREADS=160`: Specifies the number of CPU inference threads to start as 160. This environment variable only takes effect after specifying INFERENCE_OP_MODE=fused. +- `CUSTOM_CPU_AFFINITY=0-159`: Specifies the CPU binding scheme, which will be explained in detail later. +- `SYSHAX_QUANTIZE=q8_0`: Specifies the quantization scheme as q8_0. The current version supports 2 quantization schemes: `q8_0`, `q4_0`. + +Note: The GPU container must be started first before starting the CPU container. + +Use lscpu to check the current machine's hardware configuration, focusing on: + +```shell +Architecture: aarch64 + CPU op-mode(s): 64-bit + Byte Order: Little Endian +CPU(s): 160 + On-line CPU(s) list: 0-159 +Vendor ID: HiSilicon + BIOS Vendor ID: HiSilicon + Model name: - + Model: 0 + Thread(s) per core: 1 + Core(s) per socket: 80 + Socket(s): 2 +NUMA: + NUMA node(s): 4 + NUMA node0 CPU(s): 0-39 + NUMA node1 CPU(s): 40-79 + NUMA node2 CPU(s): 80-119 + NUMA node3 CPU(s): 120-159 +``` + +This machine has 160 physical cores, no SMT enabled, 4 NUMA nodes, with 40 cores on each NUMA. + +Use these two environment variables to set the CPU binding scheme: `OMP_NUM_THREADS=160 CUSTOM_CPU_AFFINITY=0-159`. In these two environment variables, the first one is the number of CPU inference threads to start, and the second one is the IDs of the CPUs to bind. In CPU inference acceleration, to achieve NUMA affinity, CPU binding operations are required, following these rules: + +- The number of started threads must match the number of bound CPUs; +- The number of CPUs used on each NUMA must be the same to maintain load balancing. + +For example, in the above script, CPUs 0-159 are bound. Among them, 0-39 belong to NUMA node 0, 40-79 belong to NUMA node 1, 80-119 belong to NUMA node 2, and 120-159 belong to NUMA node 3. Each NUMA uses 40 CPUs, ensuring load balancing across all NUMAs. + +### sysHAX Installation + +sysHAX installation: + +```shell +dnf install sysHAX +``` + +Before starting sysHAX, some basic configuration is needed: + +```shell +syshax init +syshax config services.gpu.port 8001 +syshax config services.cpu.port 8002 +syshax config services.conductor.port 8010 +syshax config models.default ds-32b +``` + +Additionally, you can use `syshax config --help` to view all configuration commands. + +After configuration is complete, start the sysHAX service with the following command: + +```shell +syshax run +``` + +When starting the sysHAX service, service connectivity testing will be performed. sysHAX complies with openAPI standards. Once the service is started, you can use APIs to call the large model service. You can test it with the following script: + +```shell +curl http://0.0.0.0:8010/v1/chat/completions -H "Content-Type: application/json" -d '{ + "model": "ds-32b", + "messages": [ + { + "role": "user", + "content": "Introduce openEuler." + } + ], + "stream": true, + "max_tokens": 1024 +}' +``` diff --git "a/docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/pictures/CPU\346\216\250\347\220\206\351\203\250\347\275\262/CPU\351\200\273\350\276\221\346\240\270\345\277\203.png" "b/docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/pictures/CPU\346\216\250\347\220\206\351\203\250\347\275\262/CPU\351\200\273\350\276\221\346\240\270\345\277\203.png" deleted file mode 100644 index 74ae942b5a5217b8a5e34a2b2cd8d32a49be7a00..0000000000000000000000000000000000000000 Binary files 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"b/docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/pictures/\346\234\254\345\234\260\350\265\204\344\272\247\345\272\223\346\236\204\345\273\272/\351\207\215\345\244\215\345\210\233\345\273\272\350\265\204\344\272\247\345\244\261\350\264\245.png" deleted file mode 100644 index a5ecd6b65abc97320e7467f00d82ff1fd9bf0e44..0000000000000000000000000000000000000000 Binary files "a/docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/pictures/\346\234\254\345\234\260\350\265\204\344\272\247\345\272\223\346\236\204\345\273\272/\351\207\215\345\244\215\345\210\233\345\273\272\350\265\204\344\272\247\345\244\261\350\264\245.png" and /dev/null differ diff --git a/docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/witChainD_deployment.md b/docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/witChainD_deployment.md deleted file mode 100644 index 7aa0d750ae4421365b42ed38e45d3e06d88767ff..0000000000000000000000000000000000000000 --- a/docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/witChainD_deployment.md +++ /dev/null @@ -1,122 +0,0 @@ -# 本地资产库构建指南 - -本平台设计的目的为了为企业（用户）提供一个可视化入口实现对本地文档资产的管理，功能方面分为以下几个部分： - -- 用户管理： - - 您可以通过账号登入witchainD平台并在平台配置大模型，为后续某些文档解析模式提供能力。 -- 资产管理 - - 通过指定资产名称、资产简介、资产默认解析方法、资产默认分块大小和embedding模型等条目创建、删除、导入资产、编辑资产或资产信息 -- 文档管理 - - 支持用户上传限定大小和限定数量的文件，也允许用户下载自己上传的文件，用户可以通过点击资产卡片的形式进入资产，此时文档以条目的形式展示。 - - 支持txt、md、xlsx、docx和doc以及pdf文档的文档解析 - - 文档解析方式有三种general、ocr和enhanced模式，general模式下只提取文字和表格，ocr模式下不仅提取文字和表格嗨提取部分文档的图片内容，enhanced在ocr的基础上对图片中提取的内容进行总结。 - -- 文档解析结果管理： - - 通过指定chunk的类别和关键字过滤目标chunk或者可以指定chunk是否启用，来评判或消除chunk对检索造成的影响。 -- 任务管理 - - 查看当前导入导出资产和文档解析任务的状态、取消或移除导入导出资产库和文档解析任务 -- 检索增强： - - 本平台最终解析的结果通过向量化、关键字的形式对外呈现检索结果，也提供了token压缩和问题补全等技术增强结果命中的概率，也使用了上下文随机关联的形式补全检索结果。 - -## 登录管理平台 - -请在浏览器中输入 `https://$(wichaind域名)`访问 EulerCopilot 的 WitChainD 网页, -登入界面如下，输入账号(admin)、密码(123456)点击登录按钮登录系统。 - -![witchaind登录界面](./pictures/witChainD/witchaind登录界面.png) - -## 新建资产 - -### 1. 查看资产库 - -进入资产卡片显示页面，卡片展示了资产的名称、简介、文档篇数、创建时间和资产ID。 - -![查看资产库界面](./pictures/witChainD/查看资产库界面.png) - -可通过红框中的按钮将卡片展示的资产转换为条目显示。 - -### 2. 新建资产库 - -点击新建资产，会跳出资产信息配置的框图 - -![新建资产库界面](./pictures/witChainD/新建资产库界面.png) - -填写资产库名称、资产库描述（可选）、语言、嵌入模型、默认解析方法和默认文件分块大小后，点击确定。 -![新建资产库填写展示界面](./pictures/witChainD/新建资产库填写展示界面.png) - -资产库建立完毕之后会显示是否添加文档，点击确定 - -![资产库创建完成界面](./pictures/witChainD/资产库创建完成界面.png) - -点击确定完成后进入资产库 - -![资产库创建完成界面](./pictures/witChainD/进入资产库界面.png) - -## 导入文档 - -### 单次导入 - -点击导入文档按钮跳出文档上传框，选择本地文件并勾选进行上传 - -![导入文档界面](./pictures/witChainD/导入文档界面.png) - -![选择文件](./pictures/witChainD/选择文件.png) - -### 批量导入 - -点击1批量导入资产，2选择本地资产，3勾选本地资产，最后点击确定进行资产导入。 - -![批量导入界面](./pictures/witChainD/批量导入界面.png) - -资产导入中 - -![批量正在导入界面](./pictures/witChainD/批量正在导入界面.png) - -资产导入成功 - -![批量导入成功界面](./pictures/witChainD/批量导入成功界面.png) - -## 解析文档 - -等待解析中，点击取消可以停止文档解析。 - -![解析中界面](./pictures/witChainD/解析中界面.png) - -解析完成后，解析状态会显示解析成功。 - -![解析成功界面](./pictures/witChainD/解析成功界面.png) - -文档解析方式有三种general、ocr和enhanced模式，请根据需要选择合适的文档解析方法 - -![解析方法选择界面](./pictures/witChainD/解析方法选择界面.png) - -解析完毕可以通过点击文件名进入文档解析结果展示详情，可以查看文档解析结果，如下图所示： - -![解析详情界面](./pictures/witChainD/解析详情界面.png) - -可以通过1过滤文档解析的片段、表格和图片等内容，通过2可以通过关键字检索模糊检索对应的片段，通过3可以设定是否在检索中是否启用片段，如下图所示： - -![解析结果过滤界面](./pictures/witChainD/解析结果过滤界面.png) - -## 导出资产 - -点击回到首页 - -![回到首页](./pictures/witChainD/回到首页.png) - -再点击导出资产 - -![导出资产界面](./pictures/witChainD/导出资产界面.png) - -显示资产正在导出中，如下图所示： - -![正在导出界面](./pictures/witChainD/正在导出界面.png) - -导出成功点击下载，显示下载成功 - -![导出成功](./pictures/witChainD/导出成功.png) diff --git a/docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/witChainD_guidance.md b/docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/witChainD_guidance.md deleted file mode 100644 index 4759a57baa4e35ee529e9f4da70e1d1405612e6e..0000000000000000000000000000000000000000 --- a/docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/witChainD_guidance.md +++ /dev/null @@ -1,87 +0,0 @@ -# witChainD 使用指南——知识库管理 - -完成 witChainD 部署之后，即可使用 witChainD 进行知识库管理管理。 - -下文会从页面的维度进行 witChainD 的功能介绍。 - -## 1. 资产库管理界面 - -该页面为资产库管理界面，用户登录后将会进入该界面。 - -![资产库管理界面](./pictures/资产库管理界面.png) - -**支持操作：** - -- 配置模型：点击右上角的设置按键，可以修改模型相关的配置。 - - ![模型配置](./pictures/模型配置.png) - -- 新增资产库：点击新建资产库按钮新建，支持自定义名称、描述、语言、嵌入模型、解析方法、文件分块大小、文档类别。注意：重复名称会自动将名称修改成资产库id。 - - ![新增资产库](./pictures/新增资产库.png) - -- 编辑资产库：点击资产库的编辑按钮编辑，支持修改名称、描述、语言、解析方法、文件分块大小、文档类别。注意：不能修改成重复名称。 - - ![编辑资产库](./pictures/编辑资产库0.png) - - ![编辑资产库](./pictures/编辑资产库.png) - -- 导出资产库：点击资产库的导出按钮导出，导出完成后需要按任务列表中的下载任务下载对应资产库到本地。 - - ![导出资产库](./pictures/导出资产库.png) - -- 批量导入资产库：点击批量导入，上传本地文件后选中即可导入。 - - ![批量导入资产库](./pictures/批量导入资产库.png) - -- 搜索资产库：在搜索栏中键入文本，可以搜索得到名称包含对应文本的资产库。 - -## 2. 文档管理界面 - -在资产管理界面点击对应资产库，可以进入文档管理界面。 - -![文档管理界面](./pictures/文档管理界面.png) - -**支持操作：** - -- 导入文档：点击导入文档，从本地上传文件导入，导入后会自动以该资产库默认配置开始解析。 - - ![导入文档](./pictures/导入文档.png) - -- 解析文档：点击操作中的解析，对文档进行解析。也可以选中多个文档批量解析。 - - ![文档解析](./pictures/文档解析.png) - - ![文档解析2](./pictures/文档解析2.png) - - ![解析完成](./pictures/解析完成.png) - -- 编辑文档配置：点击编辑对文档配置进行编辑，支持编辑文档名称、解析方法、类别、文件分块大小。 - - ![编辑文档配置](./pictures/编辑文档配置.png) - -- 下载文档：点击下载即可将文档下载至本地，也可以选中多个文档批量下载。 - -- 删除文档：点击删除即可将文档从资产库中删除，也可以选中多个文档批量删除。 - -- 搜索文档：点击文档名称旁的搜索键，在弹出的搜索框中键入搜索的文本，可以搜索得到名称包含这些文本的文档。 - - ![搜索文档](./pictures/搜索文档.png) - -- 编辑资产库配置：支持编辑资产库名称、描述、语言、默认解析方法、文件分块大小、文档信息类别。 - - ![编辑资产库配置](./pictures/编辑资产库配置.png) - -## 3. 解析结果管理界面 - -点击解析完成的文档，可以进入文档的解析结果管理界面。界面中会按照顺序显示文档解析后的文本块内容预览，每个文本块会附带一个标签，表示该文本块中的信息来源于文档中的段落、列表或者是图片。右侧的开关表示该文本块是否被启用。 - -![文本块结果预览](./pictures/文本块结果预览.png) - -**支持操作**： - -- 关闭/启用文本块：点击文本块右侧的开关即可关闭/启用对应文本块，也可以选中多个文本块批量关闭/启用。 - - ![批量启用](./pictures/批量启用.png) - -- 搜索文本块：在搜索框中键入内容，可以查找包含对应内容的文本块。 diff --git a/docs/en/openEuler_intelligence/intelligent_assistant/advance/work_flow/deploy_guide/workflow_deployment.md b/docs/en/openEuler_intelligence/intelligent_assistant/advance/work_flow/deploy_guide/workflow_deployment.md deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/docs/en/openEuler_intelligence/intelligent_assistant/advance/work_flow/user_guide/workflow_guidance.md b/docs/en/openEuler_intelligence/intelligent_assistant/advance/work_flow/user_guide/workflow_guidance.md deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/shell.md b/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/shell.md deleted file mode 100644 index bb88c52f55a984d8115227454e67ed1970423583..0000000000000000000000000000000000000000 --- a/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/shell.md +++ /dev/null @@ -1,157 +0,0 @@ -# 命令行助手使用指南 - -## 简介 - -EulerCopilot 命令行助手是一个命令行（Shell）AI 助手，您可以通过它来快速生成 Shell 命令并执行，从而提高您的工作效率。除此之外，基于 Gitee AI 在线服务的标准版本还内置了 openEuler 的相关知识，可以助力您学习与使用 openEuler 操作系统。 - -## 环境要求 - -- 操作系统：openEuler 22.03 LTS SP3，或者 openEuler 24.03 LTS 及以上版本 -- 命令行软件： - - Linux 桌面环境：支持 GNOME、KDE、DDE 等桌面环境的内置终端 - - 远程 SSH 链接：支持兼容 xterm-256 与 UTF-8 字符集的终端 - -## 安装 - -EulerCopilot 命令行助手支持通过 OEPKGS 仓库进行安装。 - -### 配置 OEPKGS 仓库 - -```bash -sudo dnf config-manager --add-repo https://repo.oepkgs.net/openeuler/rpm/`sed 's/release //;s/[()]//g;s/ /-/g' /etc/openEuler-release`/extras/`uname -m` -``` - -```bash -sudo dnf clean all -``` - -```bash -sudo dnf makecache -``` - -### 安装命令行助手 - -```bash -sudo dnf install eulercopilot-cli -``` - -若遇到 `Error: GPG check FAILED` 错误，使用 `--nogpgcheck` 跳过检查。 - -```bash -sudo dnf install --nogpgcheck eulercopilot-cli -``` - -## 初始化 - -```bash -copilot --init -``` - -然后根据提示输入 API Key 完成配置。 - -![shell-init](./pictures/shell-init.png) - -初次使用前请先退出终端或重新连接 SSH 会话使配置生效。 - -- **查看助手帮助页面** - - ```bash - copilot --help - ``` - - ![shell-help](./pictures/shell-help.png) - -## 使用 - -在终端中输入问题，按下 `Ctrl + O` 提问。 - -### 快捷键 - -- 输入自然语言问题后，按下 `Ctrl + O` 可以直接向 AI 提问。 -- 直接按下 `Ctrl + O` 可以自动填充命令前缀 `copilot`，输入参数后按下 `Enter` 即可执行。 - -### 智能问答 - -命令行助手初始化完成后，默认处于智能问答模式。 -命令提示符**左上角**会显示当前模式。 -若当前模式不是“智能问答”，执行 `copilot -c` (`copilot --chat`) 切换到智能问答模式。 - -![chat-ask](./pictures/shell-chat-ask.png) - -AI 回答完毕后，会根据历史问答生成推荐问题，您可以复制、粘贴到命令行中进行追问。输入追问的问题后，按下 `Enter` 提问。 - -![chat-next](./pictures/shell-chat-continue.png) - -![chat-next-result](./pictures/shell-chat-continue-result.png) - -智能问答模式下支持连续追问，每次追问最多可以关联3条历史问答的上下文。 - -输入 `exit` 可以退出智能问答模式，回到 Linux 命令行。 - -![chat-exit](./pictures/shell-chat-exit.png) - -- 若问答过程中遇到程序错误，可以按下 `Ctrl + C` 立即退出当前问答，再尝试重新提问。 - -### Shell 命令 - -AI 会根据您的问题返回 Shell 命令，EulerCopilot 命令行助手可以解释、编辑或执行这些命令，并显示命令执行结果。 - -![shell-cmd](./pictures/shell-cmd.png) - -命令行助手会自动提取 AI 回答中的命令，并显示相关操作。您可以通过键盘上下键选择操作，按下 `Enter` 确认。 - -![shell-cmd-interact](./pictures/shell-cmd-interact.png) - -#### 解释 - -如果 AI 仅返回了一条命令，选择解释后会直接请求 AI 解释命令，并显示回答。 -若 AI 回答了多条命令，选择后会显示命令列表，您每次可以选择**一条**请求 AI 解释。 - -![shell-cmd-explain-select](./pictures/shell-cmd-explain-select.png) - -完成解释后，您可以继续选择其他操作。 - -![shell-cmd-explain-result](./pictures/shell-cmd-explain-result.png) - -#### 编辑 - -![shell-cmd-edit](./pictures/shell-cmd-edit.png) - -选择一条命令进行编辑，编辑完成后按下 `Enter` 确认。 - -![shell-cmd-edit-result](./pictures/shell-cmd-edit-result.png) - -完成编辑后，您可以继续编辑其他命令或选择其他操作。 - -#### 执行 - -如果 AI 仅返回了一条命令，选择执行后会直接执行命令，并显示执行结果。 -若 AI 回答了多条命令，选择后会显示命令列表，您每次可以选择**多条**命令来执行。 - -您可以通过键盘上下键移动光标，按下 `空格键` 选择命令，按下 `Enter` 执行所选命令。 -被选中的命令会显示**蓝色高亮**，如图所示。 - -![shell-cmd-exec-multi-select](./pictures/shell-cmd-exec-multi-select.png) - -若不选择任何命令，直接按下 `Enter`，则会跳过执行命令，直接进入下一轮问答。 - -按下 `Enter` 后，被选中的命令会从上到下依次执行。 - -![shell-cmd-exec-result](./pictures/shell-cmd-exec-result.png) - -若执行过程中遇到错误，命令行助手会显示错误信息，并**终止执行命令**，进入下一轮问答。 -您可以在下一轮问答中提示 AI 更正命令，或要求 AI 重新生成命令。 - -## 卸载 - -```bash -sudo dnf remove eulercopilot-cli -``` - -然后使用以下命令删除配置文件。 - -```bash -rm ~/.config/eulercopilot/config.json -``` - -卸载完成后请重启终端或重新连接 SSH 会话使配置还原。 diff --git a/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/offline.md b/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/offline.md deleted file mode 100644 index 8167907479569db279a838dc9dcf3e17597abbfd..0000000000000000000000000000000000000000 --- a/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/offline.md +++ /dev/null @@ -1,620 +0,0 @@ -# **EulerCopilot 智能助手部署指南** - -版本信息 -当前版本：v0.9.5 -更新日期：2025年4月1日 - -## 产品概述 - -EulerCopilot 是一款智能问答工具，使用 EulerCopilot 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 / A-Tune / x2openEuler / EulerMaker / 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。 - -本指南提供基于自动化脚本的EulerCopilot智能助手系统部署说明，支持一键自动部署和手动分步部署两种方式。 - -### 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 | 9988 (内部端口) | 检索增强服务 | -| authhub-backend-service | 11120 (内部端口) | 鉴权服务后端 | -| authhub-web-service | 8000 | 鉴权服务前端 | -| mysql | 3306 (内部端口) | MySQL数据库 | -| redis | 6379 (内部端口) | Redis数据库 | -| minio | 9000 (内部端口) 9001(外部部端口) | minio数据库 | -| mongo | 27017 (内部端口) | mongo数据库 | -| postgres | 5432 (内部端口) | 向量数据库 | -| secret_inject | 无 | 配置文件安全复制工具 | - -### 软件要求 - -| 类型 | 版本要求 | 说明 | -|----------------| -------------------------------------|--------------------------------------| -| 操作系统 | openEuler 22.03 LTS 及以上版本 | 无 | -| K3s | >= v1.30.2，带有 Traefik Ingress 工具 | K3s 提供轻量级的 Kubernetes 集群，易于部署和管理 | -| Helm | >= v3.15.3 | 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 | -| python | >=3.9.9 | python3.9.9 以上版本为模型的下载和安装提供运行环境 | - ---- - -### 硬件规格 - -| 硬件资源 | 最小配置 | 推荐配置 | -|--------------|----------------------------|------------------------------| -| CPU | 4 核心 | 16 核心及以上 | -| RAM | 4 GB | 64 GB | -| 存储 | 32 GB | 64G | -| 大模型名称 | deepseek-llm-7b-chat | DeepSeek-R1-Llama-8B | -| 显存 (GPU) | NVIDIA RTX A4000 8GB | NVIDIA A100 80GB * 2 | - -**关键说明**： - -- 纯CPU环境，建议通过调用 OpenAI 接口或使用自带的模型部署方式来实现功能。 -- 如果k8s集群环境，则不需要单独安装k3s，要求version >= 1.28 - ---- - -### 部署视图 - -![部署图](./pictures/部署视图.png) - ---- - -### 域名配置 - -需准备以下两个服务域名： - -- authhub认证服务：authhub.eulercopilot.local -- EulerCopilot web服务：www.eulercopilot.local - -```bash -# 本地Windows主机中进行配置 -# 打开 C:\Windows\System32\drivers\etc\hosts 添加记录 -# 替换127.0.0.1为目标服务器的IP -127.0.0.1 authhub.eulercopilot.local -127.0.0.1 www.eulercopilot.local -``` - -## 快速开始 - -### 1. 资源获取 - -- **获取部署脚本** - [EulerCopilot 的官方Git仓库](https://gitee.com/openeuler/euler-copilot-framework) - 切换至dev分支点击下载ZIP, 并上传至目标服务器 - - ```bash - unzip euler-copilot-framework.tar -d /home - ``` - -- **资源清单** - - - **下载地址** - [EulerCopilot 资源仓库](https://repo.oepkgs.net/openEuler/rpm/openEuler-22.03-LTS/contrib/eulercopilot) - - **镜像文件** - - ```bash - # 使用脚本自动下载所有镜像（需在联网环境执行） - cd /home/euler-copilot-framework/deploy/scripts/9-other-script/ - # 执行镜像下载（版本号可替换） - ./save_images.sh 0.9.5 # 自动保存到目录/home/eulercopilot/images - # 上传至目标服务器 - scp /home/eulercopilot/images/* root@target-server:/home/eulercopilot/images/ - ``` - - - **模型部署文件**：`bge-m3-Q4_K_M.gguf`, `deepseek-llm-7b-chat-Q4_K_M.gguf`, `ollama-linux-arm64.tgz/ollama-linux-amd64.tgz` - - **工具包**：`helm-v3.15.0-linux-arm64.tar.gz/helm-v3.15.0-linux-amd64.tar.gz`, `k3s-airgap-images-arm64.tar.zst/k3s-airgap-images-amd64.tar.zst`,`k3s-arm64/k3s-amd64`, `k3s-install.sh` - -- **关键说明** - - - **网络要求** - - 手动下载需确保存在可访问外网文件的Windows环境，全部下载完成后传输至离线环境 - - 脚本下载需在联网服务器执行，仅完成镜像下载，完成传输至离线环境 - - **确保目标服务器存在以下目录** - - ```bash - /home/eulercopilot/ - ├── images/ # 存放镜像文件 - ├── models/ # 存放模型文件 - └── tools/ # 存放工具包 - ``` - -### 2. 部署EulerCopilot - -#### 一键部署 - -```bash -cd /home/euler-copilot-framework/deploy/scripts -``` - -```bash -bash deploy.sh -``` - -```bash -# 输入0进行一键自动部署 -============================== - 主部署菜单 -============================== -0) 一键自动部署 -1) 手动分步部署 -2) 重启服务 -3) 卸载所有组件并清除数据 -4) 退出程序 -============================== -请输入选项编号（0-3）: 0 -``` - ---- - -#### 手动分步部署 - -```bash -# 选择1 -> 1 进入手动分步部署 -============================== - 主部署菜单 -============================== -0) 一键自动部署 -1) 手动分步部署 -2) 重启服务 -3) 卸载所有组件并清除数据 -4) 退出程序 -============================== -请输入选项编号（0-3）: 1 -``` - -```bash -# 输入选项编号（0-9），逐步部署 -============================== - 手动分步部署菜单 -============================== -1) 执行环境检查脚本 -2) 安装k3s和helm -3) 安装Ollama -4) 部署Deepseek模型 -5) 部署Embedding模型 -6) 安装数据库 -7) 安装AuthHub -8) 安装EulerCopilot -9) 返回主菜单 -============================== -请输入选项编号（0-9）: -``` - ---- - -**关键说明**： - -1. 安装部署前需要预先准备好部署所需的资源 -2. 在部署过程中，您需要输入 Authhub 域名和 EulerCopilot 域名, 不输入则使用默认域名`authhub.eulercopilot.local`, `www.eulercopilot.local`。 - -#### 重启服务 - -```bash -# 输入选项重启服务 -============================== - 服务重启菜单 -============================== -可重启的服务列表： -1) authhub-backend -2) authhub -3) framework -4) minio -5) mongo -6) mysql -7) pgsql -8) rag -9) rag-web -10) redis -11) web -12) 返回主菜单 -============================== -请输入要重启的服务编号（1-12）: -``` - -#### 卸载所有组件 - -```bash -sudo ./deploy.sh -# 选择2进行完全卸载 -============================== - 主部署菜单 -============================== -0) 一键自动部署 -1) 手动分步部署 -2) 卸载所有组件并清除数据 -3) 退出程序 -============================== -请输入选项编号（0-3）: 2 -``` - ---- - -**关键说明**： - -- 在部署过程中，您需要输入 Authhub 域名和 EulerCopilot 域名, 不输入则使用默认域名`authhub.eulercopilot.local`, `www.eulercopilot.local`。 -- 资源不足时可参考 FAQ 中的评估资源可用性解决 -- 查看组件日志 - -```bash -kubectl logs -n euler-copilot -``` - -- 查看服务状态 - -```bash -kubectl get pod -n euler-copilot -``` - -- 大模型配置修改并更新EulerCopilot - -```bash -cd /home/euler-copilot-framework/deploy/chart/euler-copilot -``` - -```bash -vim values.yaml -``` - -```bash -helm upgrade euler-copilot -n euler-copilot . -``` - -## 验证安装 - -恭喜您，**EulerCopilot** 已成功部署！为了开始您的体验，请在浏览器中输入 `https://您的EulerCopilot域名` 链接访问 EulerCopilot 的网页界面： - -首次访问时，您需要点击页面上的 **立即注册** 按钮来创建一个新的账号，并完成登录过程。 - -![Web登录界面](./pictures/WEB登录界面.png) -![Web 界面](./pictures/WEB界面.png) - -## 构建专有领域智能问答 - -点击知识库，可登录本地知识库管理页面，详细信息请参考文档 [本地资产库构建指南](../../../advance/knowledge_base/deploy_guide/witChainD_deployment.md) -**知识库登录默认账号 `admin`, 密码 `123456`** - ---- - -## 附录 - -### 大模型准备 - -#### GPU 环境 - -可直接使用部署的deepseek大模型参考以下方式进行部署 - -1. 下载模型文件 - - ```bash - huggingface-cli download --resume-download Qwen/Qwen1.5-14B-Chat --local-dir Qwen1.5-14B-Chat - ``` - -2. 创建终端 control - - ```bash - screen -S control - ``` - - ```bash - python3 -m fastchat.serve.controller - ``` - - 按 Ctrl A+D 置于后台 - -3. 创建新终端 api - - ```bash - screen -S api - ``` - - ```bash - python3 -m fastchat.serve.openai_api_server --host 0.0.0.0 --port 30000 --api-keys sk-123456 - ``` - - 按 Ctrl A+D 置于后台 - 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 - - ```bash - mkdir -p /root/py310 - ``` - - ```bash - conda create --prefix=/root/py310 python==3.10.14 - ``` - - ```bash - conda activate /root/py310 - ``` - -4. 创建新终端 worker - - ```bash - screen -S worker - ``` - - ```bash - screen -r worker - ``` - - 安装 fastchat 和 vllm - - ```bash - pip install fschat vllm - ``` - - 安装依赖： - - ```bash - pip install fschat[model_worker] - ``` - - ```bash - 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 - ``` - - 按 Ctrl A+D 置于后台 - -5. 按照如下方式修改配置的大模型参数，并更新服务。 - - ```bash - vim /home/euler-copilot-framework/deploy/chart/euler_copilot/values.yaml - ``` - - 修改如下部分 - - ```yaml - # 模型设置 - models: - # 用于问答的大模型；需要为OpenAI兼容接口 - answer: - # [必填] 接口URL（无需带上“v1”后缀） - url: http://172.168.178.107:11434 - # [必填] 接口API Key；默认置空 - key: sk-123456 - # [必填] 模型名称 - name: deepseek-llm-7b-chat:latest - # [必填] 模型最大上下文数；建议>=8192 - ctx_length: 8192 - # 模型最大输出长度，建议>=2048 - max_tokens: 2048 - # 用于Function Call的模型；建议使用特定推理框架 - functioncall: - # 推理框架类型，默认为ollama - # 可用的框架类型：["vllm", "sglang", "ollama", "openai"] - backend: - # 模型地址；不填则与问答模型一致 - url: ollama - # API Key；不填则与问答模型一致 - key: - # 模型名称；不填则与问答模型一致 - name: - # 模型最大上下文数；不填则与问答模型一致 - ctx_length: - # 模型最大输出长度；不填则与问答模型一致 - max_tokens: - # 用于数据向量化（Embedding）的模型 - embedding: - # 推理框架类型，默认为openai - # [必填] Embedding接口类型：["openai", "mindie"] - type: openai - # [必填] Embedding URL（需要带上“v1”后缀） - url: http://172.168.178.107:11434 - # [必填] Embedding 模型API Key - key: sk-123456 - # [必填] Embedding 模型名称 - name: bge-m3:latest - ``` - - ```bash - # 更新服务 - helm upgrade -n euler-copilot euler-copilot . - # 重启framework服务 - kubectl get pod -n euler-copilot - kubectl delete pod framework-deploy-65b669fc58-q9bw7 -n euler-copilot - ``` - -#### NPU 环境 - -NPU 环境部署可参考链接 [MindIE安装指南](https://www.hiascend.com/document/detail/zh/mindie/10RC2/whatismindie/mindie_what_0001.html) - -### FAQ - -#### 1. 解决 Hugging Face 连接错误 - -如果遇到如下连接错误： - -```text -urllib3.exceptions.NewConnectionError: : Failed to establish a new connection: [Errno 101] Network is unreachable -``` - -尝试以下解决方案： - -- 更新 `huggingface_hub` 包到最新版本。 - - ```bash - pip3 install -U huggingface_hub - ``` - -- 如果网络问题依旧存在，可以尝试使用镜像站点作为端点。 - - ```bash - export HF_ENDPOINT=https://hf-mirror.com - ``` - -#### 2. 在 RAG 容器中调用问答接口 - -进入对应的 RAG Pod 后，可以通过 `curl` 命令发送 POST 请求来获取问答结果。请确保在请求体中提供具体的问题文本。 - -```bash -curl -k -X POST "http://localhost:9988/kb/get_answer" \ - -H "Content-Type: application/json" \ - -d '{ - "question": "您的问题", - "kb_sn": "default_test", - "fetch_source": true - }' -``` - -#### 3. 解决 `helm upgrade` 错误 - -当 Kubernetes 集群不可达时，您可能会遇到类似下面的错误信息： - -```text -Error: UPGRADE FAILED: Kubernetes cluster unreachable -``` - -确保设置了正确的 KUBECONFIG 环境变量指向有效的配置文件。 - -```bash -echo "export KUBECONFIG=/etc/rancher/k3s/k3s.yaml" >> /root/.bashrc -source /root/.bashrc -``` - -#### 4. 查看 Pod 日志失败 - -如果您遇到查看 Pod 日志时权限被拒绝的问题，检查是否正确配置了代理设置，并将本机 IP 地址添加到 `no_proxy` 环境变量中。 - -```bash -cat /etc/systemd/system/k3s.service.env -``` - -编辑文件并确保包含： - -```bash -no_proxy=XXX.XXX.XXX.XXX -``` - -#### 5. GPU环境中大模型流式回复问题 - -对于某些服务执行 curl 大模型时无法进行流式回复的情况，尝试修改请求中的 `"stream"` 参数为 `false`。此外，确认已安装兼容版本的 Pydantic 库。 - -```bash -pip install pydantic==1.10.13 -``` - -#### 6. sglang 模型部署指南 - -按照以下步骤部署基于 sglang 的模型： - -```bash -# 1. 激活名为 `myenv` 的 Conda 环境，该环境基于 Python 3.10 创建： -conda activate myenv - -# 2. 安装 sglang 及其所有依赖项，指定版本为 0.3.0 -pip install "sglang[all]==0.3.0" - -# 3. 从特定索引安装 flashinfer，确保与您的 CUDA 和 PyTorch 版本兼容 -pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/ - -# 4. 使用 sglang 启动服务器，配置如下： -python -m sglang.launch_server \ - --served-model-name Qwen2.5-32B \ - --model-path Qwen2.5-32B-Instruct-AWQ \ - --host 0.0.0.0 \ - --port 8001 \ - --api-key "sk-12345" \ - --mem-fraction-static 0.5 \ - --tp 8 -``` - -- 验证安装 - - ```bash - pip show sglang - pip show flashinfer - ``` - -**注意事项：** - -- API Key：请确保 `--api-key` 参数中的 API 密钥是正确的 -- 模型路径： 确保 `--model-path` 参数中的路径是正确的，并且模型文件存在于该路径下。 -- CUDA 版本：确保你的系统上安装了 CUDA 12.1 和 PyTorch 2.4，因为 `flashinfer` 包依赖于这些特定版本。 -- 线程池大小：根据你的GPU资源和预期负载调整线程池大小。如果你有 8 个 GPU，那么可以选择 --tp 8 来充分利用这些资源。 - -#### 7. 获取 Embedding - -使用 curl 发送 POST 请求以获取 embedding 结果： - -```bash -curl -k -X POST http://localhost:11434/v1/embeddings \ - -H "Content-Type: application/json" \ - -d {"input": "The food was delicious and the waiter...", "model": "bge-m3", "encoding_format": "float"} -``` - -#### 8. 生成证书 - -为了生成自签名证书，首先下载 [mkcert](https://github.com/FiloSottile/mkcert/releases)工具，然后运行以下命令： - -```bash -mkcert -install -mkcert example.com -``` - -最后，将生成的证书和私钥拷贝到 values.yaml 中, 并应用至 Kubernetes Secret。 - -```bash -vim /home/euler-copilot-framework_openeuler/deploy/chart_ssl/traefik-secret.yaml -``` - -```bash -kubectl apply -f traefik-secret.yaml -``` - -#### 9. 问题排查方法 - -1. **获取集群事件信息** - - 为了更好地定位 Pod 失败的原因，请首先检查 Kubernetes 集群中的事件 (Events)。这可以提供有关 Pod 状态变化的上下文信息。 - - ```bash - kubectl get events -n euler-copilot - ``` - -2. **验证镜像拉取状态** - - 确认容器镜像是否成功拉取。如果镜像未能正确加载，可能是由于网络问题或镜像仓库配置错误。 - - ```bash - k3s crictl images - ``` - -3. **审查 Pod 日志** - - 检查相关 Pod 的日志，以寻找可能的错误信息或异常行为。这对于诊断应用程序级别的问题特别有用。 - - ```bash - kubectl logs rag-deploy-service-5b7887644c-sm58z -n euler-copilot - ``` - -4. **评估资源可用性** - - 确保 Kubernetes 集群有足够的资源（如 CPU、内存和存储）来支持 Pod 的运行。资源不足可能导致镜像拉取失败或其他性能问题，或使得 Pod 状态从 Running 变为 Pending 或 Completed。可查看磁盘空间并保证至少有 30% 的可用空间。这有助于维持 Pod 的稳定运行状态。参考该链接挂载空间较大的磁盘[How to move k3s data to another location](https://mrkandreev.name/snippets/how_to_move_k3s_data_to_another_location/) - - ```bash - kubectl top nodes - ``` - -5. **确认 k3s 版本兼容性** - - 如果遇到镜像拉取失败且镜像大小为 0 的问题，请检查您的 k3s 版本是否符合最低要求（v1.30.2 或更高）。较低版本可能存在不兼容的问题。 - - ```bash - k3s -v - ``` - -6. **检查配置** - - 检查 `values.yaml` 文件中关于 OIDC 配置和域名配置是否填写正确，确保配置无误后更新服务。 - - ```bash - cat /home/euler-copilot-framework/deploy/chart/euler_copilot - ``` - - ```bash - vim values.yaml | grep oidc - ``` - - ```bash - helm upgrade euler-copilot -n euler-copilot . - ``` diff --git 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b3f6abde54923c2b2dfb767a8cb03eb9281cd22c..0000000000000000000000000000000000000000 --- a/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/introduction.md +++ /dev/null @@ -1,68 +0,0 @@ -# 前言 - -## 概述 - -本文档介绍了 EulerCopilot 的使用方法，对 EulerCopilot 线上服务的 Web 界面的各项功能做了详细介绍，同时提供了常见的问题解答，详细请参考对应手册。 - -## 读者对象 - -本文档主要适用于 EulerCopilot 的使用人员。使用人员必须具备以下经验和技能： - -- 熟悉 openEuler 操作系统相关情况。 -- 有 AI 对话使用经验。 - -## 修改记录 - -| 文档版本 | 发布日期 | 修改说明 | -|--------|------------|----------------| -| 03 | 2024-09-19 | 更新新版界面。 | -| 02 | 2024-05-13 | 优化智能对话操作指引。 | -| 01 | 2024-01-28 | 第一次正式发布。 | - -## 介绍 - -### 免责声明 - -- 使用过程中涉及的非工具本身验证功能所用的用户名和密码，不作他用，且不会被保存在系统环境中。 -- 在您进行对话或操作前应当确认您为应用程序的所有者或已获得所有者的充足授权同意。 -- 对话结果中可能包含您所分析应用的内部信息和相关数据，请妥善管理。 -- 除非法律法规或双方合同另有规定，openEuler 社区对分析结果不做任何明示或暗示的声明和保证，不对分析结果的适销性、满意度、非侵权性或特定用途适用性等作出任何保证或者承诺。 -- 您根据分析记录所采取的任何行为均应符合法律法规的要求，并由您自行承担风险。 -- 未经所有者授权，任何个人或组织均不得使用应用程序及相关分析记录从事任何活动。openEuler 社区不对由此造成的一切后果负责，亦不承担任何法律责任。必要时，将追究其法律责任。 - -### EulerCopilot 简介 - -EulerCopilot 是一个基于 openEuler 操作系统的人工智能助手，可以帮助用户解决各种技术问题，提供技术支持和咨询服务。它使用了最先进的自然语言处理技术和机器学习算法，能够理解用户的问题并提供相应的解决方案。 - -### 场景内容 - -1. OS 领域通用知识：EulerCopilot 可以咨询 Linux 常规知识、上游信息和工具链介绍和指导。 -2. openEuler 专业知识：EulerCopilot 可以咨询 openEuler 社区信息、技术原理和使用指导。 -3. openEuler 扩展知识：EulerCopilot 可以咨询 openEuler 周边硬件特性知识和ISV、OSV相关信息。 -4. openEuler 应用案例：EulerCopilot 可以提供 openEuler 技术案例、行业应用案例。 -5. shell 命令生成：EulerCopilot 可以帮助用户生成单条 shell 命令或者复杂命令。 - -总之，EulerCopilot 可以应用于各种场景，帮助用户提高工作效率和了解 Linux、openEuler 等的相关知识。 - -### 访问和使用 - -EulerCopilot 通过网址访问 Web 网页进行使用。账号注册与登录请参考[注册与登录](./registration_and_login.md)。使用方法请参考[智能问答使用指南](./qa_guide.md)。 - -### 界面说明 - -#### 界面分区 - -EulerCopilot 界面主要由如图 1 所示的区域组成，各个区域的作用如表 1 所示。 - -- 图 1 EulerCopilot 界面 - -![Copilot 界面](./pictures/main-page-sections.png) - -- 表 1 EulerCopilot 首页界面分区说明 - -| 区域 | 名称 | 说明 | -|-----|------------|----------------------------------------------------------------| -| 1 | 设置管理区 | 提供账号登录和退出操作入口和明亮/黑暗模式切换功能 | -| 2 | 对话管理区 | 用于用户新建对话、对话历史记录管理和对话历史记录批量删除操作 | -| 3 | 对话区 | 用于用户和 EulerCopilot 的对话聊天 | -| 4 | 服务协议和隐私政策区 | 提供查看服务协议和隐私政策入口 | diff --git a/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/qa_guide.md b/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/qa_guide.md deleted file mode 100644 index 73f25bfce21a08d3eef048aa74e29ed0cb1f65d4..0000000000000000000000000000000000000000 --- a/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/qa_guide.md +++ /dev/null @@ -1,179 +0,0 @@ -# 智能问答使用指南 - -## 开始对话 - -在对话区下侧输入框即可输入对话想要提问的内容，输入 `Shift + Enter` 可进行换行，输入 `Enter` 即可发送对话提问内容，或者单击“发送”也可发送对话提问内容。 - -> **说明** -> -> 对话区位于页面的主体部分，如图 1 所示。 - -- 图 1 对话区 - ![对话区](./pictures/chat-area.png) - -### 多轮连续对话 - -EulerCopilot 智能问答支持多轮连续对话。只需要在同一个对话中继续追问即可使用，如图 2 所示。 - -- 图 2 多轮对话 - ![多轮对话](./pictures/context-support.png) - -### 重新生成 - -如遇到 AI 生成的内容有误或不完整的特殊情况，可以要求 AI 重新回答问题。单击对话回答左下侧的“重新生成”文字，可让 EulerCopilot 重新回答用户问题，重新回答后，在对话回答右下侧，会出现回答翻页的图标![向前翻页](./pictures/icon-arrow-prev.png)和![向后翻页](./pictures/icon-arrow-next.png)，单击![向前翻页](./pictures/icon-arrow-prev.png)或![向后翻页](./pictures/icon-arrow-next.png)可查看不同的回答，如图 3 所示。 - -- 图 3 重新生成 - ![重新生成](./pictures/regenerate.png) - -### 推荐问题 - -在 AI 回答的下方，会展示一些推荐的问题，单击即可进行提问，如图 4 所示。 - -- 图 4 推荐问题 - ![推荐问题](./pictures/recommend-questions.png) - -## 自定义背景知识 - -EulerCopilot 支持上传文件功能。上传文件后，AI 会将上传的文件内容作为背景知识，在回答问题时，会结合背景知识进行回答。上传的背景知识只会作用于当前对话，不会影响其他对话。 - -### 上传文件 - -**步骤1** 单击对话区左下角的“上传文件”按钮，如图 5 所示。 - -- 图 5 上传文件按钮 - ![上传文件](./pictures/file-upload-btn.png) - -> **说明** -> -> 鼠标悬停到“上传文件”按钮上，会显示提示允许上传文件的规格和格式，如图 6 所示。 - -- 图 6 鼠标悬停显示上传文件规格提示 - ![上传文件提示](./pictures/file-upload-btn-prompt.png) - -**步骤2** 在弹出的文件选择框中，选择需要上传的文件，单击“打开”，即可上传文件。最多上传10个文件，总大小限制为64MB。接受 PDF、docx、doc、txt、md、xlsx。 - -开始上传后，对话区下方会显示上传进度，如图 7 所示。 - -- 图 7 同时上传的所有文件排列在问答输入框下方 - ![上传文件](./pictures/file-upload-uploading.png) - -文件上传完成后会自动解析，如图 8 所示，解析完成后，对话区下方会显示每个文件的大小信息。 - -- 图 8 文件上传至服务器后将显示“正在解析” - ![文件解析](./pictures/file-upload-parsing.png) - -文件上传成功后，左侧历史记录区会显示上传的文件数量，如图 9 所示。 - -- 图 9 对话历史记录标签上会展示上传文件数量 - ![历史记录标记](./pictures/file-upload-history-tag.png) - -### 针对文件提问 - -文件上传完成后，即可针对文件进行提问，提问方式同普通对话模式，如图 10 所示。 -回答结果如图 11 所示。 - -- 图 10 询问与上传的文件相关的问题 - ![针对文件提问](./pictures/file-upload-ask-against-file.png) - -- 图 11 AI 以上传的为背景知识进行回答 - ![根据自定义背景知识回答](./pictures/file-upload-showcase.png) - -## 管理对话 - -> **说明** -> -> 对话管理区在页面左侧。 - -### 新建对话 - -单击“新建对话”按钮即可新建对话，如图 12 所示。 - -- 图 12 “新建对话”按钮在页面左上方 - ![新建对话](./pictures/new-chat.png) - -### 对话历史记录搜索 - -在页面左侧历史记录搜索输入框输入关键词，然后单击![搜索](./pictures/icon-search.png)即可进行对话历史记录搜索如图 13 所示。 - -- 图 13 对话历史记录搜索框 - ![对话历史记录搜索](./pictures/search-history.png) - -### 对话历史记录单条管理 - -历史记录的列表位于历史记录搜索栏的下方，在每条对话历史记录的右侧，单击![编辑](./pictures/icon-edit.png)即可编辑对话历史记录的名字，如图 14 所示。 - -- 图 14 点击“编辑”图标重命名历史记录 - ![重命名历史记录](./pictures/rename-session.png) - -在对话历史记录名字重新书写完成后，单击右侧![确认](./pictures/icon-confirm.png)即可完成重命名，或者单击右侧![取消](./pictures/icon-cancel.png)放弃本次重命名，如图 15 所示。 - -- 图 15 完成/取消重命名历史记录 - ![完成/取消重命名历史记录](./pictures/rename-session-confirmation.png) - -另外，单击对话历史记录右侧的删除图标，如图 16 所示，即可对删除单条对话历史记录进行二次确认，在二次确认弹出框，如图 17 所示，单击“确认”，可确认删除单条对话历史记录，或者单击“取消”，取消本次删除。 - -- 图 16 点击“垃圾箱”图标删除单条历史记录 - ![删除单条历史记录](./pictures/delete-session.png) - -- 图 17 二次确认后删除历史记录 - ![删除单条历史记录二次确认](./pictures/delete-session-confirmation.png) - -### 对话历史记录批量删除 - -首先单击“批量删除”，如图 18 所示。 - -- 图 18 批量删除功能在历史记录搜索框右上方 - ![批量删除](./pictures/bulk-delete.png) - -然后可对历史记录进行选择删除，如图 19 所示。单击“全选”，即对所有历史记录选中，单击单条历史记录或历史记录左侧的选择框，可对单条历史记录进行选中。 - -- 图 19 在左侧勾选要批量删除历史记录 - ![批量删除历史记录选择](./pictures/bulk-delete-multi-select.png) - -最后需要对批量删除历史记录进行二次确认，如图 20 所示，单击“确认”，即删除，单击“取消”，即取消本次删除。 - -- 图 20 二次确认后删除所选的历史记录 - ![批量删除二次确认](./pictures/bulk-delete-confirmation.png) - -## 反馈与举报 - -在对话记录区，对话回答的右下侧，可进行对话回答反馈，如图 21 所示，单击![满意](./pictures/icon-thumb-up.png)，可给对话回答点赞；单击![不满意](./pictures/icon-thumb-down.png)，可以给对话回答反馈不满意的原因。 - -- 图 21 点赞和不满意反馈 - ![点赞和不满意反馈](./pictures/feedback.png) - -对于反馈不满意原因，如图 22 所示，在单击![不满意](./pictures/icon-thumb-down.png)之后，对话机器人会展示反馈内容填写的对话框，可选择相关的不满意原因的选项。 - -- 图 22 回答不满意反馈 - ![回答不满意反馈](./pictures/feedback-illegal.png) - -其中单击选择“存在错误信息”，需要填写参考答案链接和描述，如图 23 所示。 - -- 图 23 回答不满意反馈——存在错误信息 - ![回答不满意反馈——存在错误信息](./pictures/feedback-misinfo.png) - -### 举报 - -如果发现 AI 返回的内容中有违规信息，可以点击右下角按钮举报，如图 24 所示。点击举报后选择举报类型并提交，若没有合适的选项，请选择“其他”并输入原因，如图 25 所示。 - -- 图 24 举报按钮在对话块的右下角 - ![举报1](./pictures/report.png) - -- 图 25 点击后可选择举报类型 - ![举报2](./pictures/report-options.png) - -## 查看服务协议和隐私政策 - -单击文字“服务协议”，即可查看服务协议，单击文字“隐私政策”，即可查看隐私政策，如图 26、图 27 所示。 - -- 图 26 服务协议和隐私政策入口在页面底部信息栏 - ![服务协议和隐私政策入口](./pictures/privacy-policy-entry.png) - -- 图 27 点击后显示服务协议或隐私政策弹窗 - ![服务协议和隐私政策](./pictures/privacy-policy.png) - -## 附录 - -### 用户信息导出说明 - -EulerCopilot 后台存在用户信息导出功能，如用户需要，需主动通过 邮箱联系我们，运维会将导出的用户信息通过邮箱回送给用户。 diff --git a/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/registration_and_login.md b/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/registration_and_login.md deleted file mode 100644 index 8f5cd63781e1f320af33ffe4baf276f65f4696fa..0000000000000000000000000000000000000000 --- a/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/registration_and_login.md +++ /dev/null @@ -1,60 +0,0 @@ -# 登录 EulerCopilot - -本章节介绍如何登录已部署的 EulerCopilot 网页端的具体步骤。 - -## 浏览器要求 - -浏览器要求如表 1 所示。 - -- 表 1 浏览器要求 - -| 浏览器类型 | 最低版本 | 推荐版本 | -| ----- | ----- | ----- | -| Google Chrome | 72 | 121 或更高版本 | -| Mozilla Firefox | 89 | 122 或更高版本 | -| Apple Safari | 11.0 | 16.3 或更高版本 | - -## 操作步骤 - -**步骤1** 打开本地 PC 机的浏览器，在地址栏输入部署指南中配置好的域名，按下 `Enter`。在未登录状态，进入 EulerCopilot，会出现登录提示弹出框，如图 1 所示。 - -- 图 1 未登录 - -![未登录](./pictures/login-popup.png) - -**步骤2** 登录 EulerCopilot（已注册账号）。 - -打开登录界面，如图 2 所示。 - -- 图 2 登录 EulerCopilot - -![登录 EulerCopilot](./pictures/authhub-login.png) - -## 注册 EulerCopilot 账号 - -**步骤1** 在登录信息输入框右下角单击“立即注册”，如图 3 所示。 - -- 图 3 立即注册 - -![立即注册](./pictures/authhub-login-click2signup.png) - -**步骤2** 进入账号注册页，根据页面提示填写相关内容，如图 4 所示。 - -- 图 4 账号注册 - -![账号注册](./pictures/authhub-signup.png) - -**步骤3** 按页面要求填写账号信息后，单击“注册”，即可注册成功。注册后即可返回登录。 - -## 退出登录 - -**步骤1** 单击![退出登录](./pictures/icon-user.png)后，会出现“退出登录”下拉框，如图 5 所示。 - -> **说明** -> 账号管理区位于页面的右上角部分，如图 5 所示。 - -- 图 5 账号管理区 - -![账号管理区](./pictures/logout.png) - -**步骤2** 单击“退出登录”即可退出登录，如图 5 所示。 diff --git a/docs/en/openEuler_intelligence/intelligent_vulnerability_patching/intelligent-vulnerability-patching-user-guide.md b/docs/en/openEuler_intelligence/intelligent_vulnerability_patching/intelligent-vulnerability-patching-user-guide.md deleted file mode 100644 index b19e2c0025cf962506a663c72c6c484b67944a69..0000000000000000000000000000000000000000 --- a/docs/en/openEuler_intelligence/intelligent_vulnerability_patching/intelligent-vulnerability-patching-user-guide.md +++ /dev/null @@ -1,58 +0,0 @@ -# openEuler 智能化漏洞修补用户指南 - -## 简介 - -智能化漏洞修补提供了对openEuler的kernel仓库（[https://gitee.com/openeuler/kernel](https://gitee.com/openeuler/kernel)）进行智能化修补的能力，当前提供了CVE影响范围分析和补丁PR创建的功能。在代表CVE的issue下面评论/analyze和/create_pr命令来执行功能。 - -## 功能入口 - -在src-openEuler的kernel仓库（[https://gitee.com/src-openeuler/kernel.git](https://gitee.com/src-openeuler/kernel.git)）中，对代表CVE的issue下面进行评论。 - -![CVE截图](pictures/代表CVE的issue.png) - -## /analyze命令 - -`/analyze`命令提供了对CVE影响范围进行分析的能力。通过在issue下面评论`/analyze`，即可自动对当前维护范围内的openEuler版本进行分析，判断每一个openEuler版本是否引入该CVE，是否修复该CVE。 - -![/analyze命令](pictures/analyze命令.png) - -> [!NOTE]说明 -> /analyze命令无参数 - -CVE是否引入存在如下几种情况： - -* 无影响 -* 受影响 - -CVE是否修复存在如下几种情况： - -* 未修复 -* 已修复 - -在评论的最后，会贴上引入补丁链接与修复补丁链接。 - -## /create_pr命令 - -`/create_pr`命令提供了对CVE的补丁进行智能化修复的能力。通过在issue下面评论`/create_pr `，即可自动获得漏洞补丁，并通过创建PR来合入openEuler下的linux仓库（[https://gitee.com/openeuler/kernel.git](https://gitee.com/openeuler/kernel.git)）中。 -![/create_pr命令](pictures/create_pr命令.png) - -`/create_pr`命令存在参数，包括如下几种情况： - -```shell -# 对OLK-5.10分支创建补丁PR -/create_pr OLK-5.10 - -# 对OLK-5.10、OLK-6.6分支创建PR -/create_pr OLK-5.10 OLK-6.6 - -# 对当前所有的上游分支，包括openEuler-1.0-LTS、OLK-5.10、OLK-6.6三个分支 -/create_pr -``` - -返回结果如下： - -* pr创建成功 -* 没有修复补丁 -* 无法修复，存在冲突 - -如果补丁代码与修复分支存在冲突的话，会提示`无法修复，存在冲突`。该能力将会在后续的版本中进行迭代强化。 diff --git a/docs/en/openEuler_intelligence/mcp_agent/_toc.yaml b/docs/en/openEuler_intelligence/mcp_agent/_toc.yaml deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/docs/en/openEuler_intelligence/intelligent_assistant/_toc.yaml b/docs/en/openeuler_intelligence/intelligent_assistant/_toc.yaml similarity index 47% rename from docs/en/openEuler_intelligence/intelligent_assistant/_toc.yaml rename to docs/en/openeuler_intelligence/intelligent_assistant/_toc.yaml index 4eeff60477fc964bb62b1ebbe711c71035b1e13a..cf1d20f531dabc437a294f7e38fb8cb64ff70b45 100644 --- a/docs/en/openEuler_intelligence/intelligent_assistant/_toc.yaml +++ b/docs/en/openeuler_intelligence/intelligent_assistant/_toc.yaml @@ -1,56 +1,56 @@ -label: openEuler智能助手 +label: openEuler Intelligent Assistant isManual: true -description: 部署和使用openEuler智能助手 +description: Deploy and use openEuler Intelligent Assistant sections: - - label: 快速上手 + - label: Quick Start sections: - - label: web问答 + - label: Web Q&A sections: - - label: 部署指南 + - label: Deployment Guide sections: - - label: 无网络环境下部署指南 + - label: Offline Deployment Guide href: ./quick_start/smart_web/deploy_guide/offline.md - - label: 网络环境下部署指南 + - label: Online Deployment Guide href: ./quick_start/smart_web/deploy_guide/online.md - - label: 使用指导 + - label: User Guide sections: - - label: 前言 + - label: Introduction href: ./quick_start/smart_web/user_guide/introduction.md - - label: 注册与登录 + - label: Registration and Login href: ./quick_start/smart_web/user_guide/registration_and_login.md - - label: 智能问答使用 + - label: Intelligent Q&A Usage href: ./quick_start/smart_web/user_guide/qa_guide.md - - label: shell问答 + - label: Shell Q&A sections: - - label: 使用指导 + - label: User Guide sections: - - label: API Key 获取 - href: ./quick_start/smart_shell/user_guide/API_key.md - - label: 命令行助手使用 + - label: API Key Acquisition + href: ./quick_start/smart_shell/user_guide/api_key.md + - label: Command Line Assistant Usage href: ./quick_start/smart_shell/user_guide/shell.md - - label: 进阶使用 + - label: Advanced Usage sections: - - label: 智能诊断 + - label: Intelligent Diagnosis sections: - - label: 部署指南 + - label: Deployment Guide href: ./advance/smart_diagnosis/deploy_guide/diagnosis_deployment.md - - label: 使用指导 + - label: User Guide href: ./advance/smart_diagnosis/user_guide/diagnosis_guidance.md - - label: 智能调优 + - label: Intelligent Tuning sections: - - label: 部署指南 + - label: Deployment Guide href: ./advance/smart_tuning/deploy_guide/tune_deployment.md - - label: 使用指导 + - label: User Guide href: ./advance/smart_tuning/user_guide/tune_guidance.md - - label: 知识库管理 + - label: Knowledge Base Management sections: - - label: 部署指南 - href: ./advance/knowledge_base/deploy_guide/witChainD_deployment.md - - label: 使用指导 - href: ./advance/knowledge_base/user_guide/witChainD_guidance.md - - label: 工作流编排 + - label: Deployment Guide + href: ./advance/knowledge_base/deploy_guide/witchaind_deployment.md + - label: User Guide + href: ./advance/knowledge_base/user_guide/witchaind_guidance.md + - label: Workflow Orchestration sections: - - label: 部署指南 + - label: Deployment Guide href: ./advance/work_flow/deploy_guide/workflow_deployment.md - - label: 使用指导 + - label: User Guide href: ./advance/work_flow/user_guide/workflow_guidance.md diff --git 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docs/en/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/pictures/witchaind/witchaind_login_interface.png diff --git a/docs/en/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/witchaind_deployment.md b/docs/en/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/witchaind_deployment.md new file mode 100644 index 0000000000000000000000000000000000000000..2a374d5737b925d7e4c5ad12e09ac0074ae925cc --- /dev/null +++ b/docs/en/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/witchaind_deployment.md @@ -0,0 +1,122 @@ +# Local Asset Library Construction Guide + +This platform is designed to provide enterprises (users) with a visual entry point to manage local document assets. The functionality is divided into the following sections: + +- User Management: + + You can log into the witchainD platform with your account and configure large models on the platform to provide capabilities for certain document parsing modes. +- Asset Management + + Create, delete, import assets, edit assets or asset information by specifying asset name, asset description, asset default parsing method, asset default chunk size, and embedding model. +- Document Management + - Support users to upload files with limited size and quantity, and also allow users to download their uploaded files. Users can enter assets by clicking on asset cards, where documents are displayed as entries. + - Support document parsing for txt, md, xlsx, docx, doc, and pdf documents + - There are three document parsing methods: general, ocr, and enhanced modes. In general mode, only text and tables are extracted. In ocr mode, not only text and tables are extracted, but also image content from some documents. Enhanced mode summarizes the extracted content from images based on ocr. + +- Document Parsing Result Management: + + Evaluate or eliminate the impact of chunks on retrieval by specifying chunk categories and keywords to filter target chunks, or by specifying whether chunks are enabled. +- Task Management + + View the status of current import/export asset and document parsing tasks, cancel or remove import/export asset libraries and document parsing tasks +- Retrieval Enhancement: + + The final parsing results of this platform are presented externally in the form of vectorization and keywords. It also provides technologies such as token compression and question completion to enhance the probability of result hits, and uses random contextual association to complete retrieval results. + +## Login Management Platform + +Please enter `https://$(wichaind domain)` in your browser to access EulerCopilot's WitChainD web page. +The login interface is as follows. Enter your account (admin) and password (123456), then click the login button to access the system. + +![witchaind login interface](./pictures/witchaind/witchaind_login_interface.png) + +## Create New Asset + +### 1. View Asset Library + +Enter the asset card display page. The cards show the asset name, description, number of documents, creation time, and asset ID. + +![View asset library interface](./pictures/witchaind/view_asset_library_interface.png) + +You can convert the assets displayed as cards to entry display through the button in the red box. + +### 2. Create New Asset Library + +Click "Create New Asset" to bring up the asset information configuration dialog. + +![Create new asset library interface](./pictures/witchaind/create_new_asset_library_interface.png) + +Fill in the asset library name, asset library description (optional), language, embedding model, default parsing method, and default file chunk size, then click OK. +![Create new asset library fill display interface](./pictures/witchaind/create_new_asset_library_fill_display_interface.png) + +After the asset library is created, it will ask whether to add documents. Click OK. + +![Asset library creation completed interface](./pictures/witchaind/asset_library_creation_completed_interface.png) + +Click OK to complete and enter the asset library. + +![Asset library creation completed interface](./pictures/witchaind/enter_asset_library_interface.png) + +## Import Documents + +### Single Import + +Click the "Import Document" button to bring up the document upload dialog. Select local files and check them for upload. + +![Import document interface](./pictures/witchaind/import_document_interface.png) + +![Select file](./pictures/witchaind/select_file.png) + +### Batch Import + +Click 1 "Batch Import Assets", 2 "Select Local Assets", 3 "Check Local Assets", and finally click OK to import assets. + +![Batch import interface](./pictures/witchaind/batch_import_interface.png) + +Assets importing. + +![Batch importing interface](./pictures/witchaind/batch_importing_interface.png) + +Asset import successful. + +![Batch import successful interface](./pictures/witchaind/batch_import_successful_interface.png) + +## Parse Documents + +Waiting for parsing. Click cancel to stop document parsing. + +![Parsing interface](./pictures/witchaind/parsing_interface.png) + +After parsing is completed, the parsing status will show "Parse Successful". + +![Parse successful interface](./pictures/witchaind/parse_successful_interface.png) + +There are three document parsing methods: general, ocr, and enhanced modes. Please choose the appropriate document parsing method according to your needs. + +![Parsing method selection interface](./pictures/witchaind/parsing_method_selection_interface.png) + +After parsing is complete, you can click on the filename to enter the document parsing result display details. You can view the document parsing results as shown in the figure below: + +![Parse details interface](./pictures/witchaind/parse_details_interface.png) + +You can filter document parsing fragments, tables, and images through 1, search for corresponding fragments through keywords with fuzzy search through 2, and set whether to enable fragments in retrieval through 3, as shown in the figure below: + +![Parse result filter interface](./pictures/witchaind/parse_result_filter_interface.png) + +## Export Assets + +Click "Return to Homepage". + +![Return to homepage](./pictures/witchaind/return_to_homepage.png) + +Then click "Export Assets". + +![Export assets interface](./pictures/witchaind/export_assets_interface.png) + +Shows that assets are being exported, as shown in the figure below: + +![Exporting interface](./pictures/witchaind/exporting_interface.png) + +Export successful. 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"a/docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/pictures/\346\226\207\346\234\254\345\235\227\347\273\223\346\236\234\351\242\204\350\247\210.png" b/docs/en/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/pictures/text_block_result_preview.png similarity index 100% rename from "docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/pictures/\346\226\207\346\234\254\345\235\227\347\273\223\346\236\234\351\242\204\350\247\210.png" rename to docs/en/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/pictures/text_block_result_preview.png diff --git a/docs/en/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/witchaind_guidance.md b/docs/en/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/witchaind_guidance.md new file mode 100644 index 0000000000000000000000000000000000000000..d7294f578aac43a72699c1bda711a5f543d57291 --- /dev/null +++ b/docs/en/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/witchaind_guidance.md @@ -0,0 +1,87 @@ +# witChainD User Guide - Knowledge Base Management + +After completing the witChainD deployment, you can use witChainD for knowledge base management. + +The following sections will introduce witChainD's functionality from a page perspective. + +## 1. Asset Library Management Interface + +This page is the asset library management interface, which users will enter after logging in. + +![Asset Library Management Interface](./pictures/asset_library_management_interface.png) + +**Supported Operations:** + +- Configure Models: Click the settings button in the upper right corner to modify model-related configurations. + + ![Model Configuration](./pictures/model_configuration.png) + +- Create New Asset Library: Click the "New Asset Library" button to create a new one, supporting custom name, description, language, embedding model, parsing method, file chunk size, and document category. Note: Duplicate names will automatically be modified to the asset library ID. + + ![Create New Asset Library](./pictures/create_new_asset_library.png) + +- Edit Asset Library: Click the edit button of the asset library to edit, supporting modification of name, description, language, parsing method, file chunk size, and document category. Note: Cannot be modified to duplicate names. + + ![Edit Asset Library](./pictures/edit_asset_library_0.png) + + ![Edit Asset Library](./pictures/edit_asset_library.png) + +- Export Asset Library: Click the export button of the asset library to export. After export completion, you need to download the corresponding asset library to local according to the download task in the task list. + + ![Export Asset Library](./pictures/export_asset_library.png) + +- Batch Import Asset Libraries: Click "Batch Import", upload local files and select them to import. + + ![Batch Import Asset Libraries](./pictures/batch_import_asset_libraries.png) + +- Search Asset Libraries: Type text in the search bar to search for asset libraries whose names contain the corresponding text. + +## 2. Document Management Interface + +Click on the corresponding asset library in the asset management interface to enter the document management interface. + +![Document Management Interface](./pictures/document_management_interface.png) + +**Supported Operations:** + +- Import Documents: Click "Import Documents" to upload files from local for import. After import, parsing will automatically start with the default configuration of that asset library. + + ![Import Documents](./pictures/import_documents.png) + +- Parse Documents: Click "Parse" in the operations to parse the document. You can also select multiple documents for batch parsing. + + ![Document Parsing](./pictures/document_parsing.png) + + ![Document Parsing 2](./pictures/document_parsing_2.png) + + ![Parsing Complete](./pictures/parsing_complete.png) + +- Edit Document Configuration: Click "Edit" to edit document configuration, supporting editing of document name, parsing method, category, and file chunk size. + + ![Edit Document Configuration](./pictures/edit_document_configuration.png) + +- Download Documents: Click download to download documents to local, or select multiple documents for batch download. + +- Delete Documents: Click delete to remove documents from the asset library, or select multiple documents for batch deletion. + +- Search Documents: Click the search key next to the document name, type the search text in the popup search box to search for documents whose names contain that text. + + ![Search Documents](./pictures/search_documents.png) + +- Edit Asset Library Configuration: Supports editing asset library name, description, language, default parsing method, file chunk size, and document information category. + + ![Edit Asset Library Configuration](./pictures/edit_asset_library_configuration.png) + +## 3. Parsing Result Management Interface + +Click on a parsed document to enter the document's parsing result management interface. The interface displays text block content previews after document parsing in order, with each text block accompanied by a tag indicating whether the information in that text block comes from paragraphs, lists, or images in the document. The switch on the right indicates whether that text block is enabled. + +![Text Block Result Preview](./pictures/text_block_result_preview.png) + +**Supported Operations:** + +- Disable/Enable Text Blocks: Click the switch on the right side of the text block to disable/enable the corresponding text block, or select multiple text blocks for batch disable/enable. + + ![Batch Enable](./pictures/batch_enable.png) + +- Search Text Blocks: Type content in the search box to find text blocks containing the corresponding content. diff --git a/docs/zh/openEuler_intelligence/intelligent_assistant/advance/smart_diagnosis/deploy_guide/diagnosis_deployment.md b/docs/en/openeuler_intelligence/intelligent_assistant/advance/smart_diagnosis/deploy_guide/diagnosis_deployment.md similarity index 31% rename from docs/zh/openEuler_intelligence/intelligent_assistant/advance/smart_diagnosis/deploy_guide/diagnosis_deployment.md rename to docs/en/openeuler_intelligence/intelligent_assistant/advance/smart_diagnosis/deploy_guide/diagnosis_deployment.md index 7581b1bd17e804f72923d56b5e4f7a036ca2bd93..db1319efb33e460083c695244a738a6721f06a29 100644 --- a/docs/zh/openEuler_intelligence/intelligent_assistant/advance/smart_diagnosis/deploy_guide/diagnosis_deployment.md +++ b/docs/en/openeuler_intelligence/intelligent_assistant/advance/smart_diagnosis/deploy_guide/diagnosis_deployment.md @@ -1,93 +1,93 @@ -# 智能诊断部署指南 +# Smart Diagnosis Deployment Guide -## 准备工作 +## Preparation -+ 提前安装 [EulerCopilot 命令行（智能 Shell）客户端](../../../quick_start/smart_shell/user_guide/shell.md) ++ Install [EulerCopilot Command Line (Smart Shell) Client](../../../quick_start/smart_shell/user_guide/shell.md) in advance -+ 被诊断机器不能安装 crictl 和 isula，只能有 docker 一个容器管理工具 ++ The machine to be diagnosed cannot install crictl and isula, only docker as the container management tool -+ 在需要被诊断的机器上安装 gala-gopher 和 gala-anteater ++ Install gala-gopher and gala-anteater on the machine to be diagnosed -## 部署 gala-gopher +## Deploying gala-gopher -### 1. 准备 BTF 文件 +### 1. Prepare BTF File -**如果Linux内核支持 BTF，则不需要准备 BTF 文件。**可以通过以下命令来查看Linux内核是否已经支持 BTF： +**If the Linux kernel supports BTF, then there is no need to prepare a BTF file.** You can check whether the Linux kernel already supports BTF by the following command: ```bash cat /boot/config-$(uname -r) | grep CONFIG_DEBUG_INFO_BTF ``` -如果输出结果为`CONFIG_DEBUG_INFO_BTF=y`，则表示内核支持BTF。否则表示内核不支持BTF。 -如果内核不支持BTF，需要手动制作BTF文件。步骤如下： +If the output result is `CONFIG_DEBUG_INFO_BTF=y`, then it means the kernel supports BTF. Otherwise it means the kernel does not support BTF. +If the kernel does not support BTF, you need to manually create a BTF file. The steps are as follows: -1. 获取当前Linux内核版本的 vmlinux 文件 +1. Obtain the vmlinux file of the current Linux kernel version - vmlinux 文件存放在 `kernel-debuginfo` 包里面，存放路径为 `/usr/lib/debug/lib/modules/$(uname -r)/vmlinux`。 + The vmlinux file is stored in the `kernel-debuginfo` package, and the storage path is `/usr/lib/debug/lib/modules/$(uname -r)/vmlinux`. - 例如，对于 `kernel-debuginfo-5.10.0-136.65.0.145.oe2203sp1.aarch64`，对应的vmlinux路径为`/usr/lib/debug/lib/modules/5.10.0-136.65.0.145.oe2203sp1.aarch64/vmlinux`。 + For example, for `kernel-debuginfo-5.10.0-136.65.0.145.oe2203sp1.aarch64`, the corresponding vmlinux path is `/usr/lib/debug/lib/modules/5.10.0-136.65.0.145.oe2203sp1.aarch64/vmlinux`. -2. 制作 BTF 文件 +2. Create BTF File - 基于获取到 vmlinux 文件来制作 BTF 文件。这一步可以在自己的环境里操作。首先，需要安装相关的依赖包： + Based on the obtained vmlinux file to create a BTF file. This step can be performed in your own environment. First, install the relevant dependency packages: ```bash - # 说明：dwarves 包中包含 pahole 命令，llvm 包中包含 llvm-objcopy 命令 + # Note: the dwarves package contains the pahole command, and the llvm package contains the llvm-objcopy command yum install -y llvm dwarves ``` - 执行下面的命令行，生成 BTF 文件。 + Execute the following command line to generate the BTF file. ```bash - kernel_version=4.19.90-2112.8.0.0131.oe1.aarch64 # 说明：这里需要替换成目标内核版本，可通过 uname -r 命令获取 + kernel_version=4.19.90-2112.8.0.0131.oe1.aarch64 # Note: replace this with the target kernel version, which can be obtained by the uname -r command pahole -J vmlinux llvm-objcopy --only-section=.BTF --set-section-flags .BTF=alloc,readonly --strip-all vmlinux ${kernel_version}.btf strip -x ${kernel_version}.btf ``` - 生成的 BTF 文件名称为`.btf`格式，其中 ``为目标机器的内核版本，可通过 `uname -r` 命令获取。 + The generated BTF file name is in `.btf` format, where `` is the kernel version of the target machine, which can be obtained by the `uname -r` command. -### 2. 下载 gala-gopher 容器镜像 +### 2. Download gala-gopher Container Image -#### 在线下载 +#### Online Download -gala-gopher 容器镜像已归档到 仓库中，可通过如下命令获取。 +The gala-gopher container image has been archived to the repository, and can be obtained by the following command. ```bash -# 获取 aarch64 架构的镜像 +# Get the aarch64 architecture image docker pull hub.oepkgs.net/a-ops/gala-gopher-profiling-aarch64:latest -# 获取 x86_64 架构的镜像 +# Get the x86_64 architecture image docker pull hub.oepkgs.net/a-ops/gala-gopher-profiling-x86_64:latest ``` -#### 离线下载 +#### Offline Download -若无法通过在线下载的方式下载容器镜像，可联系我（何秀军 00465007）获取压缩包。 +If you cannot download the container image through the online download method, contact me (He XiuJun 00465007) to obtain the compressed package. -拿到压缩包后，放到目标机器上，解压并加载容器镜像，命令行如下： +After obtaining the compressed package, place it on the target machine, decompress it and load the container image, the command line is as follows: ```bash tar -zxvf gala-gopher-profiling-aarch64.tar.gz docker load < gala-gopher-profiling-aarch64.tar ``` -### 3. 启动 gala-gopher 容器 +### 3. Start gala-gopher Container -容器启动命令： +Container startup command: ```shell docker run -d --name gala-gopher-profiling --privileged --pid=host --network=host -v /:/host -v /etc/localtime:/etc/localtime:ro -v /sys:/sys -v /usr/lib/debug:/usr/lib/debug -v /var/lib/docker:/var/lib/docker -v /tmp/$(uname -r).btf:/opt/gala-gopher/btf/$(uname -r).btf -e GOPHER_HOST_PATH=/host gala-gopher-profiling-aarch64:latest ``` -启动配置参数说明： +Startup configuration parameter description: -+ `-v /tmp/$(uname -r).btf:/opt/gala-gopher/btf/$(uname -r).btf` ：如果内核支持 BTF，则删除该配置即可。如果内核不支持 BTF，则需要将前面准备好的 BTF 文件拷贝到目标机器上，并将 `/tmp/$(uname -r).btf` 替换为对应的路径。 -+ `gala-gopher-profiling-aarch64-0426` ：gala-gopher容器对应的tag，替换成实际下载的tag。 ++ `-v /tmp/$(uname -r).btf:/opt/gala-gopher/btf/$(uname -r).btf` : If the kernel supports BTF, then you can simply remove this configuration. If the kernel does not support BTF, you need to copy the previously prepared BTF file to the target machine and replace `/tmp/$(uname -r).btf` with the corresponding path. ++ `gala-gopher-profiling-aarch64-0426` : The tag corresponding to the gala-gopher container, replace with the actual downloaded tag. -探针启动： +Probe Startup: -+ `container_id` 为需要观测的容器 id -+ 分别启动 sli 和 container 探针 ++ `container_id` is the ID of the container to be observed ++ Start the sli and container probes separately ```bash curl -X PUT http://localhost:9999/sli -d json='{"cmd":{"check_cmd":""},"snoopers":{"container_id":[""]},"params":{"report_period":5},"state":"running"}' @@ -107,33 +107,33 @@ curl -X PUT http://localhost:9999/sli -d json='{"state": "stopped"}' curl -X PUT http://localhost:9999/container -d json='{"state": "stopped"}' ``` -## 部署 gala-anteater +## Deploying gala-anteater -源码部署： +Source code deployment: ```bash -# 请指定分支为 930eulercopilot +# Please specify the branch as 930eulercopilot git clone https://gitee.com/GS-Stephen_Curry/gala-anteater.git ``` -安装部署请参考 -(请留意python版本导致执行setup.sh install报错) +For installation and deployment, please refer to +(please note that python version may cause setup.sh install to fail) -镜像部署： +Image deployment: ```bash docker pull hub.oepkgs.net/a-ops/gala-anteater:2.0.2 ``` -`/etc/gala-anteater/config/gala-anteater.yaml` 中 Kafka 和 Prometheus 的 `server` 和 `port` 需要按照实际部署修改，`model_topic`、`meta_topic`、`group_id` 自定义 +The `server` and `port` of Kafka and Prometheus in `/etc/gala-anteater/config/gala-anteater.yaml` need to be modified according to the actual deployment, and `model_topic`, `meta_topic`, `group_id` are customizable ```yaml Kafka: server: "xxxx" port: "xxxx" - model_topic: "xxxx" # 自定义，与rca配置中保持一致 - meta_topic: "xxxx" # 自定义，与rca配置中保持一致 - group_id: "xxxx" # 自定义，与rca配置中保持一致 + model_topic: "xxxx" # customizable, keep consistent with rca configuration + meta_topic: "xxxx" # customizable, keep consistent with rca configuration + group_id: "xxxx" # customizable, keep consistent with rca configuration # auth_type: plaintext/sasl_plaintext, please set "" for no auth auth_type: "" username: "" @@ -145,33 +145,33 @@ Prometheus: steps: "5" ``` -gala-anteater 中模型的训练依赖于 gala-gopher 采集的数据，因此请保证 gala-gopher 探针正常运行至少24小时，在运行 gala-anteater。 +The model training in gala-anteater depends on the data collected by gala-gopher, so please ensure that the gala-gopher probe runs normally for at least 24 hours before running gala-anteater. -## 部署 gala-ops +## Deploying gala-ops -每个中间件的大致介绍： +Introduction to each middleware: -kafka ： 一个数据库中间件， 分布式数据分流作用， 可以配置为当前的管理节点。 +kafka: A database middleware with distributed data分流 functionality, which can be configured as the current management node. -prometheus：性能监控， 配置需要监控的生产节点 ip list。 +prometheus: Performance monitoring, configure the ip list of production nodes to be monitored. -直接通过yum install安装kafka和prometheus，可参照安装脚本 +Install kafka and prometheus directly through yum install, refer to the installation script -只需要参照其中 kafka 和 prometheus 的安装即可 +Only need to refer to the installation of kafka and prometheus in it -## 部署 euler-copilot-rca +## Deploying euler-copilot-rca -镜像拉取 +Image pull ```bash docker pull hub.oepkgs.net/a-ops/euler-copilot-rca:0.9.1 ``` -+ 修改 `config/config.json` 文件，配置 gala-gopher 镜像的 `container_id` 以及 `ip`，Kafka 和 Prometheus 的 `ip` 和 `port`（与上述 gala-anteater 配置保持一致） ++ Modify the `config/config.json` file to configure gala-gopher image's `container_id` and `ip`, Kafka and Prometheus's `ip` and `port` (keep consistent with the above gala-anteater configuration) ```yaml -"gopher_container_id": "xxxx", # gala-gopher的容器id - "remote_host": "xxxx" # gala-gopher的部署机器ip +"gopher_container_id": "xxxx", # Container ID of gala-gopher + "remote_host": "xxxx" # Machine IP address of gala-gopher }, "kafka": { "server": "xxxx", diff --git a/docs/en/openeuler_intelligence/intelligent_assistant/advance/smart_diagnosis/user_guide/diagnosis_guidance.md b/docs/en/openeuler_intelligence/intelligent_assistant/advance/smart_diagnosis/user_guide/diagnosis_guidance.md new file mode 100644 index 0000000000000000000000000000000000000000..8a7bf783c1cf1686258c00f313bcc17b7d67ef91 --- /dev/null +++ b/docs/en/openeuler_intelligence/intelligent_assistant/advance/smart_diagnosis/user_guide/diagnosis_guidance.md @@ -0,0 +1,50 @@ +# Smart Plugin: Intelligent Diagnosis + +After deploying the intelligent diagnosis tool, you can use the EulerCopilot intelligent agent framework to perform diagnostics on your local machine. +In intelligent diagnosis mode, the intelligent agent framework service can call local diagnostic tools to diagnose abnormal conditions, analyze them, and generate reports. + +## Operation Steps + +**Step 1** Switch to "Smart Plugin" mode + +```bash +copilot -p +``` + +![Switch to Smart Plugin Mode](./pictures/shell-plugin-diagnose-switch-mode.png) + +**Step 2** Abnormal Event Detection + +```bash +Help me perform abnormal event detection +``` + +Press `Ctrl + O` to ask a question, then select "Intelligent Diagnosis" from the plugin list. + +![Abnormal Event Detection](./pictures/shell-plugin-diagnose-detect.png) + +**Step 3** View Abnormal Event Details + +```bash +View abnormal event details for XXX container +``` + +![View Abnormal Event Details](./pictures/shell-plugin-diagnose-detail.png) + +**Step 4** Execute Abnormal Event Analysis + +```bash +Please perform profiling analysis on XXX metrics for XXX container +``` + +![Abnormal Event Analysis](./pictures/shell-plugin-diagnose-profiling.png) + +**Step 5** View Abnormal Event Analysis Report + +Wait 5 to 10 minutes, then view the analysis report. + +```bash +View the profiling report corresponding to +``` + +![Execute Optimization Script](./pictures/shell-plugin-diagnose-report.png) diff --git a/docs/en/openEuler_intelligence/intelligent_assistant/advance/smart_diagnosis/user_guide/pictures/shell-chat-ask.png 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a/docs/en/openEuler_intelligence/intelligent_assistant/advance/smart_tuning/deploy_guide/tune_deployment.md +++ b/docs/en/openeuler_intelligence/intelligent_assistant/advance/smart_tuning/deploy_guide/tune_deployment.md @@ -1,23 +1,23 @@ -# 智能调优部署指南 +# Smart Tuning Deployment Guide -## 准备工作 +## Prerequisites -+ 提前安装 [EulerCopilot 命令行（智能 Shell）客户端](../../../quick_start/smart_shell/user_guide/shell.md)) ++ Install [EulerCopilot Command Line (Smart Shell) Client](../../../quick_start/smart_shell/user_guide/shell.md) in advance -+ 被调优机器需要为 openEuler 22.03 LTS-SP3 ++ The machine to be tuned must be openEuler 22.03 LTS-SP3 -+ 在需要被调优的机器上安装依赖 ++ Install dependencies on the machine that needs to be tuned ```bash yum install -y sysstat perf ``` -+ 被调优机器需要开启 SSH 22端口 ++ The machine to be tuned must have SSH port 22 enabled -## 编辑配置文件 +## Edit Configuration File -修改values.yaml文件的tune部分，将 `enable` 字段改为 `True` ，并配置大模型设置、 -Embedding模型文件地址、以及需要调优的机器和对应机器上的 mysql 的账号名以及密码 +Modify the tune section in the values.yaml file, change the `enable` field to `True`, and configure the large language model settings, +Embedding model file address, as well as the machines that need tuning and their corresponding MySQL account names and passwords ```bash vim /home/euler-copilot-framework/deploy/chart/agents/values.yaml @@ -25,75 +25,75 @@ vim /home/euler-copilot-framework/deploy/chart/agents/values.yaml ```yaml tune: - # 【必填】是否启用智能调优Agent + # [Required] Whether to enable Smart Tuning Agent enabled: true - # 镜像设置 + # Image settings image: - # 镜像仓库。留空则使用全局设置。 + # Image registry. Leave empty to use global settings. registry: "" - # 【必填】镜像名称 + # [Required] Image name name: euler-copilot-tune - # 【必填】镜像标签 + # [Required] Image tag tag: "0.9.1" - # 拉取策略。留空则使用全局设置。 + # Pull policy. Leave empty to use global settings. imagePullPolicy: "" - # 【必填】容器根目录只读 + # [Required] Container root directory read-only readOnly: false - # 性能限制设置 + # Performance limit settings resources: {} - # Service设置 + # Service settings service: - # 【必填】Service类型，ClusterIP或NodePort + # [Required] Service type, ClusterIP or NodePort type: ClusterIP nodePort: - # 大模型设置 + # Large language model settings llm: - # 【必填】模型地址（需要包含v1后缀） + # [Required] Model address (must include v1 suffix) url: - # 【必填】模型名称 + # [Required] Model name name: "" - # 【必填】模型API Key + # [Required] Model API Key key: "" - # 【必填】模型最大Token数 + # [Required] Maximum tokens for the model max_tokens: 8096 - # 【必填】Embedding模型文件地址 + # [Required] Embedding model file address embedding: "" - # 待优化机器信息 + # Target optimization machine information machine: - # 【必填】IP地址 + # [Required] IP address ip: "" - # 【必填】Root用户密码 - # 注意：必需启用Root用户以密码形式SSH登录 + # [Required] Root user password + # Note: Root user must be enabled for SSH login with password password: "" - # 待优化应用设置 + # Target optimization application settings mysql: - # 【必填】数据库用户名 + # [Required] Database username user: "root" - # 【必填】数据库密码 + # [Required] Database password password: "" ``` -## 安装智能调优插件 +## Install Smart Tuning Plugin ```bash helm install -n euler-copilot agents . ``` -如果之前有执行过安装，则按下面指令更新插件服务 +If you have installed before, update the plugin service with the following command ```bash helm upgrade-n euler-copilot agents . ``` -如果 framework未重启，则需要重启framework配置 +If the framework has not been restarted, you need to restart the framework configuration ```bash kubectl delete pod framework-deploy-service-bb5b58678-jxzqr -n eulercopilot ``` -## 测试 +## Testing -+ 查看 tune 的 pod 状态 ++ Check the tune pod status ```bash NAME READY STATUS RESTARTS AGE @@ -108,19 +108,19 @@ kubectl delete pod framework-deploy-service-bb5b58678-jxzqr -n eulercopilot tune-deploy-agents-5d46bfdbd4-xph7b 1/1 Running 0 2d ``` -+ pod启动失败排查办法 - + 检查 euler-copilot-tune 目录下的 openapi.yaml 中 `servers.url` 字段，确保调优服务的启动地址被正确设置 - + 检查 `$plugin_dir` 插件文件夹的路径是否配置正确，该变量位于 `deploy/chart/euler_copilot/values.yaml` 中的 `framework`模块，如果插件目录不存在，需新建该目录，并需要将该目录下的 euler-copilot-tune 文件夹放到 `$plugin_dir` 中。 - + 检查sglang的地址和key填写是否正确，该变量位于 `vim /home/euler-copilot-framework/deploy/chart/euler_copilot/values.yaml` ++ Pod startup failure troubleshooting methods + + Check the `servers.url` field in the openapi.yaml under the euler-copilot-tune directory to ensure the tuning service startup address is correctly set + + Check if the `$plugin_dir` plugin folder path is correctly configured. This variable is located in the `framework` module in `deploy/chart/euler_copilot/values.yaml`. If the plugin directory doesn't exist, create it, and place the euler-copilot-tune folder from that directory into `$plugin_dir`. + + Check if the sglang address and key are correctly filled. This variable is located in `vim /home/euler-copilot-framework/deploy/chart/euler_copilot/values.yaml` ```yaml - # 用于Function Call的模型 + # Model for Function Call scheduler: - # 推理框架类型 + # Inference framework type backend: sglang - # 模型地址 + # Model address url: "" - # 模型 API Key + # Model API Key key: "" - # 数据库设置 + # Database settings ``` diff --git a/docs/en/openEuler_intelligence/intelligent_assistant/advance/smart_tuning/user_guide/pictures/shell-chat-ask.png b/docs/en/openeuler_intelligence/intelligent_assistant/advance/smart_tuning/user_guide/pictures/shell-chat-ask.png similarity index 100% rename from docs/en/openEuler_intelligence/intelligent_assistant/advance/smart_tuning/user_guide/pictures/shell-chat-ask.png rename to docs/en/openeuler_intelligence/intelligent_assistant/advance/smart_tuning/user_guide/pictures/shell-chat-ask.png diff --git a/docs/en/openEuler_intelligence/intelligent_assistant/advance/smart_tuning/user_guide/pictures/shell-chat-continue-result.png 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b/docs/en/openeuler_intelligence/intelligent_assistant/advance/smart_tuning/user_guide/pictures/shell-plugin.png similarity index 100% rename from docs/en/openEuler_intelligence/intelligent_assistant/advance/smart_tuning/user_guide/pictures/shell-plugin.png rename to docs/en/openeuler_intelligence/intelligent_assistant/advance/smart_tuning/user_guide/pictures/shell-plugin.png diff --git a/docs/en/openeuler_intelligence/intelligent_assistant/advance/smart_tuning/user_guide/tune_guidance.md b/docs/en/openeuler_intelligence/intelligent_assistant/advance/smart_tuning/user_guide/tune_guidance.md new file mode 100644 index 0000000000000000000000000000000000000000..fe4dd5f849827e460914d7db5378bc4aef68d994 --- /dev/null +++ b/docs/en/openeuler_intelligence/intelligent_assistant/advance/smart_tuning/user_guide/tune_guidance.md @@ -0,0 +1,53 @@ +# Smart Plugin: Intelligent Tuning + +After deploying the intelligent tuning tool, you can use the EulerCopilot agent framework to perform tuning on the local machine. +In intelligent tuning mode, the agent framework service can call the local tuning tool to collect performance metrics and generate performance analysis reports and performance optimization recommendations. + +## Operation Steps + +**Step 1** Switch to "Intelligent Tuning" mode + +```bash +copilot -t +``` + +![Switch to Intelligent Tuning Mode](./pictures/shell-plugin-tuning-switch-mode.png) + +**Step 2** Collect Performance Metrics + +```bash +Help me collect performance metrics +``` + +![Performance Metrics Collection](./pictures/shell-plugin-tuning-metrics-collect.png) + +**Step 3** Generate Performance Analysis Report + +```bash +Help me generate a performance analysis report +``` + +![Performance Analysis Report](./pictures/shell-plugin-tuning-report.png) + +**Step 4** Generate Performance Optimization Recommendations + +```bash +Please generate a performance optimization script +``` + +![Performance Optimization Script](./pictures/shell-plugin-tuning-script-gen.png) + +**Step 5** Select "Execute Command" to run the optimization script + +![Execute Optimization Script](./pictures/shell-plugin-tuning-script-exec.png) + +- Script content as shown in the figure: + ![Optimization Script Content](./pictures/shell-plugin-tuning-script-view.png) + +## Remote Tuning + +If you need to perform remote tuning on other machines, please add the corresponding machine's IP address before the questions in the examples above. + +For example: `Please perform performance metrics collection on the machine 192.168.1.100.` + +Before performing remote tuning, please ensure that the target machine has deployed the intelligent tuning tool, and also ensure that the EulerCopilot agent framework can access the target machine. diff --git a/docs/en/openeuler_intelligence/intelligent_assistant/advance/work_flow/deploy_guide/workflow_deployment.md b/docs/en/openeuler_intelligence/intelligent_assistant/advance/work_flow/deploy_guide/workflow_deployment.md new file mode 100644 index 0000000000000000000000000000000000000000..f3f584960e9b4c5f8d03ddaa7cdcff6bfa5c22f0 --- /dev/null +++ b/docs/en/openeuler_intelligence/intelligent_assistant/advance/work_flow/deploy_guide/workflow_deployment.md @@ -0,0 +1,3 @@ +# Workflow Orchestration Deployment Guide + +(Current content is being updated, please wait.) diff --git a/docs/en/openeuler_intelligence/intelligent_assistant/advance/work_flow/user_guide/workflow_guidance.md b/docs/en/openeuler_intelligence/intelligent_assistant/advance/work_flow/user_guide/workflow_guidance.md new file mode 100644 index 0000000000000000000000000000000000000000..75f04c38f56d3a3ed6d6c3b696ffdf9cc0c3896a --- /dev/null +++ b/docs/en/openeuler_intelligence/intelligent_assistant/advance/work_flow/user_guide/workflow_guidance.md @@ -0,0 +1,3 @@ +# Workflow Orchestration User Guide + +(Current content is being updated, please wait.) diff --git a/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/api_key.md b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/api_key.md new file mode 100644 index 0000000000000000000000000000000000000000..9b7025519cbecc39ec79cf991cbf1ff7c1f1a2e7 --- /dev/null +++ b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/api_key.md @@ -0,0 +1,29 @@ +# Getting API Key + +## Introduction + +EulerCopilot command-line assistant uses API Key to verify user identity and obtain API access permissions. +Therefore, before you start using it, you need to get an API Key first. + +## Important Notes + +- Please keep your API Key secure and do not share it with others. +- API Key is only used for command-line assistant and DevStation desktop client, not for other purposes. +- Each user can only have one API Key. Creating a duplicate API Key will invalidate the old key. +- API Key is only displayed once when created. Please save it immediately. If the key is lost, you need to recreate it. +- If you encounter a "request too frequent" error during use, your API Key may have been used by others. Please go to the official website to refresh or revoke the API Key immediately. + +## How to Get + +1. Log in to the EulerCopilot web interface. +2. Click on the avatar in the upper right corner and select "API KEY". +3. Click the "New" button. +4. **Please save the API Key immediately. It is only displayed once when created. Do not share it with others.** + +## Managing API Key + +1. Log in to the EulerCopilot web interface. +2. Click on the avatar in the upper right corner and select "API KEY". +3. Click the "Refresh" button to refresh the API Key; click the "Revoke" button to revoke the API Key. + +- After refreshing the API Key, the old key becomes invalid. 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new file mode 100644 index 0000000000000000000000000000000000000000..dcf6a97bb57ad4f66125330565d412315e653147 --- /dev/null +++ b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/shell.md @@ -0,0 +1,157 @@ +# Command Line Assistant User Guide + +## Introduction + +EulerCopilot Command Line Assistant is a command line (Shell) AI assistant that allows you to quickly generate and execute Shell commands, thereby improving your work efficiency. Additionally, the standard version based on Gitee AI online service comes with built-in knowledge about openEuler, helping you learn and use the openEuler operating system. + +## System Requirements + +- Operating System: openEuler 22.03 LTS SP3, or openEuler 24.03 LTS and above +- Command Line Software: + - Linux Desktop Environment: Supports built-in terminals in desktop environments such as GNOME, KDE, DDE, etc. + - Remote SSH Connection: Supports terminals compatible with xterm-256 and UTF-8 character sets + +## Installation + +EulerCopilot Command Line Assistant can be installed through the OEPKGS repository. + +### Configure OEPKGS Repository + +```bash +sudo dnf config-manager --add-repo https://repo.oepkgs.net/openeuler/rpm/`sed 's/release //;s/[()]//g;s/ /-/g' /etc/openEuler-release`/extras/`uname -m` +``` + +```bash +sudo dnf clean all +``` + +```bash +sudo dnf makecache +``` + +### Install Command Line Assistant + +```bash +sudo dnf install eulercopilot-cli +``` + +If you encounter `Error: GPG check FAILED`, use `--nogpgcheck` to skip the check. + +```bash +sudo dnf install --nogpgcheck eulercopilot-cli +``` + +## Initialization + +```bash +copilot --init +``` + +Then follow the prompts to enter your API Key to complete the configuration. + +![shell-init](./pictures/shell-init.png) + +Please exit the terminal or reconnect your SSH session before first use to make the configuration effective. + +- **View Assistant Help Page** + + ```bash + copilot --help + ``` + + ![shell-help](./pictures/shell-help.png) + +## Usage + +Enter your question in the terminal and press `Ctrl + O` to ask. + +### Shortcuts + +- After entering a natural language question, press `Ctrl + O` to directly ask the AI. +- Pressing `Ctrl + O` directly will automatically fill in the command prefix `copilot`, and you can press `Enter` after entering parameters to execute. + +### Smart Q&A + +After the command line assistant is initialized, it defaults to smart Q&A mode. +The current mode will be displayed in the **top-left corner** of the command prompt. +If the current mode is not "Smart Q&A", execute `copilot -c` (`copilot --chat`) to switch to smart Q&A mode. + +![chat-ask](./pictures/shell-chat-ask.png) + +After the AI answers, it will generate recommended questions based on the Q&A history. You can copy and paste them into the command line for follow-up questions. After entering a follow-up question, press `Enter` to ask. + +![chat-next](./pictures/shell-chat-continue.png) + +![chat-next-result](./pictures/shell-chat-continue-result.png) + +Smart Q&A mode supports continuous follow-up questions, with each follow-up question able to associate with up to 3 historical Q&A contexts. + +Enter `exit` to exit smart Q&A mode and return to the Linux command line. + +![chat-exit](./pictures/shell-chat-exit.png) + +- If you encounter program errors during Q&A, you can press `Ctrl + C` to immediately exit the current Q&A, then try asking again. + +### Shell Commands + +The AI will return Shell commands based on your question. EulerCopilot Command Line Assistant can explain, edit, or execute these commands and display the command execution results. + +![shell-cmd](./pictures/shell-cmd.png) + +The command line assistant will automatically extract commands from the AI's response and display related operations. You can use the up and down arrow keys to select operations and press `Enter` to confirm. + +![shell-cmd-interact](./pictures/shell-cmd-interact.png) + +#### Explanation + +If the AI only returns one command, selecting explanation will directly request the AI to explain the command and display the answer. +If the AI answers with multiple commands, selecting will display a command list, and you can choose **one** command at a time to request AI explanation. + +![shell-cmd-explain-select](./pictures/shell-cmd-explain-select.png) + +After completing the explanation, you can continue to select other operations. + +![shell-cmd-explain-result](./pictures/shell-cmd-explain-result.png) + +#### Edit + +![shell-cmd-edit](./pictures/shell-cmd-edit.png) + +Select a command to edit, and press `Enter` to confirm after editing. + +![shell-cmd-edit-result](./pictures/shell-cmd-edit-result.png) + +After completing the edit, you can continue editing other commands or select other operations. + +#### Execute + +If the AI only returns one command, selecting execute will directly execute the command and display the execution result. +If the AI answers with multiple commands, selecting will display a command list, and you can choose **multiple** commands to execute. + +You can use the up and down arrow keys to move the cursor, press the `Spacebar` to select commands, and press `Enter` to execute the selected commands. +Selected commands will display with **blue highlighting**, as shown in the figure. + +![shell-cmd-exec-multi-select](./pictures/shell-cmd-exec-multi-select.png) + +If no commands are selected and you press `Enter` directly, it will skip executing commands and proceed directly to the next round of Q&A. + +After pressing `Enter`, the selected commands will be executed from top to bottom in sequence. + +![shell-cmd-exec-result](./pictures/shell-cmd-exec-result.png) + +If errors occur during execution, the command line assistant will display error messages and **terminate command execution**, entering the next round of Q&A. +You can prompt the AI to correct commands or ask the AI to regenerate commands in the next round of Q&A. + +## Uninstallation + +```bash +sudo dnf remove eulercopilot-cli +``` + +Then use the following command to delete the configuration file. + +```bash +rm ~/.config/eulercopilot/config.json +``` + +After uninstallation is complete, please restart the terminal or reconnect your SSH session to restore the configuration. diff --git a/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/offline.md b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/offline.md new file mode 100644 index 0000000000000000000000000000000000000000..a21733c0336d65f8181e1656529e3ce77985654c --- /dev/null +++ b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/offline.md @@ -0,0 +1,620 @@ +# **EulerCopilot Intelligent Assistant Deployment Guide** + +Version Information +Current Version: v0.9.5 +Update Date: April 1, 2025 + +## Product Overview + +EulerCopilot is an intelligent Q&A tool that solves the convenience of acquiring operating system knowledge and empowers developers and operations personnel in the OS domain model. As a knowledge acquisition tool for operating systems that enables OS productivity (such as A-Ops / A-Tune / x2openEuler / EulerMaker / EulerDevOps / StratoVirt / iSulad, etc.), it transforms the traditional command delivery method to a natural semantic approach and combines with the intelligent agent task planning capabilities, lowering the threshold for developing and using operating system features. + +This guide provides system deployment instructions for the EulerCopilot intelligent assistant based on automated scripts, supporting both one-click automatic deployment and manual step-by-step deployment methods. + +### Component Introduction + +| Component | Port | Description | +| ----------------------------- | --------------- | ----------------------- | +| euler-copilot-framework | 8002 (internal port) | Intelligent agent framework service | +| euler-copilot-web | 8080 | Intelligent agent frontend interface | +| euler-copilot-rag | 9988 (internal port) | Retrieval-Augmented Generation service | +| authhub-backend-service | 11120 (internal port) | Authentication service backend | +| authhub-web-service | 8000 | Authentication service frontend | +| mysql | 3306 (internal port) | MySQL database | +| redis | 6379 (internal port) | Redis database | +| minio | 9000 (internal port) 9001 (external port) | minio database | +| mongo | 27017 (internal port) | mongo database | +| postgres | 5432 (internal port) | Vector database | +| secret_inject | None | Secure configuration file replication tool | + +### Software Requirements + +| Type | Version Requirements | Description | +|------|----------------------|-------------| +| Operating System | openEuler 22.03 LTS and above | None | +| K3s | >= v1.30.2, with Traefik Ingress tool | K3s provides a lightweight Kubernetes cluster, easy to deploy and manage | +| Helm | >= v3.15.3 | Helm is a Kubernetes package management tool, aimed at quickly installing, upgrading, and uninstalling EulerCopilot services | +| python | >=3.9.9 | Python 3.9.9 and above provides the runtime environment for model download and installation | + +--- + +### Hardware Specifications + +| Hardware Resources | Minimum Configuration | Recommended Configuration | +|--------------------|-----------------------|-----------------------------| +| CPU | 4 cores | 16 cores or above | +| RAM | 4 GB | 64 GB | +| Storage | 32 GB | 64 GB | +| Large Model Name | deepseek-llm-7b-chat | DeepSeek-R1-Llama-8B | +| GPU Memory | NVIDIA RTX A4000 8GB | NVIDIA A100 80GB * 2 | + +**Key Notes**: + +- For pure CPU environments, it is recommended to implement functionality by calling the OpenAI interface or using the built-in model deployment method. +- If k8s cluster environment exists, there is no need to install k3s separately, requiring version >= 1.28 + +--- + +### Deployment View + +![Deployment Diagram](./pictures/deployment-overview.png) + +--- + +### Domain Name Configuration + +Two service domain names need to be prepared: + +- Authhub authentication service: `authhub.eulercopilot.local` +- EulerCopilot web service: `www.eulercopilot.local` + +```bash +# Configure on local Windows host +# Open C:\Windows\System32\drivers\etc\hosts to add records +# Replace 127.0.0.1 with the target server IP +127.0.0.1 authhub.eulercopilot.local +127.0.0.1 www.eulercopilot.local +``` + +## Quick Start + +### 1. Resource Acquisition + +- **Obtain Deployment Scripts** + [Official Git repository of EulerCopilot](https://gitee.com/openeuler/euler-copilot-framework) + Switch to the dev branch, click to download ZIP, and upload to the target server + + ```bash + unzip euler-copilot-framework.tar -d /home + ``` + +- **Resource List** + + - **Download Address** + [EulerCopilot Resource Repository](https://repo.oepkgs.net/openEuler/rpm/openEuler-22.03-LTS/contrib/eulercopilot) + - **Image Files** + + ```bash + # Use script to automatically download all images (execute in network-connected environment) + cd /home/euler-copilot-framework/deploy/scripts/9-other-script/ + # Execute image download (version number can be replaced) + ./save_images.sh 0.9.5 # Automatically saved to directory /home/eulercopilot/images + # Upload to target server + scp /home/eulercopilot/images/* root@target-server:/home/eulercopilot/images/ + ``` + + - **Model deployment files**: `bge-m3-Q4_K_M.gguf`, `deepseek-llm-7b-chat-Q4_K_M.gguf`, `ollama-linux-arm64.tgz/ollama-linux-amd64.tgz` + - **Tool packages**: `helm-v3.15.0-linux-arm64.tar.gz/helm-v3.15.0-linux-amd64.tar.gz`, `k3s-airgap-images-arm64.tar.zst/k3s-airgap-images-amd64.tar.zst`, `k3s-arm64/k3s-amd64`, `k3s-install.sh` + +- **Key Notes** + + - **Network Requirements** + - Manual download requires ensuring there is a Windows environment with access to external network files. All downloads must be completed and then transferred to the offline environment. + - Script download requires execution on a network-connected server, only completing image downloads, then transfer to the offline environment. + - **Ensure the target server has the following directories** + + ```bash + /home/eulercopilot/ + ├── images/ # Store image files + ├── models/ # Store model files + └── tools/ # Store tool packages + ``` + +### 2. Deploy EulerCopilot + +#### One-click Deployment + +```bash +cd /home/euler-copilot-framework/deploy/scripts +``` + +```bash +bash deploy.sh +``` + +```bash +# Enter 0 for one-click automatic deployment +============================== + Main Deployment Menu +============================== +0) One-click automatic deployment +1) Manual step-by-step deployment +2) Restart services +3) Uninstall all components and clear data +4) Exit program +============================== +Please enter option number (0-3): 0 +``` + +--- + +#### Manual Step-by-step Deployment + +```bash +# Choose 1 -> 1 to enter manual step-by-step deployment +============================== + Main Deployment Menu +============================== +0) One-click automatic deployment +1) Manual step-by-step deployment +2) Restart services +3) Uninstall all components and clear data +4) Exit program +============================== +Please enter option number (0-3): 1 +``` + +```bash +# Enter option number (0-9) for step-by-step deployment +============================== + Manual Step-by-step Deployment Menu +============================== +1) Execute environment check script +2) Install k3s and helm +3) Install Ollama +4) Deploy Deepseek model +5) Deploy Embedding model +6) Install databases +7) Install AuthHub +8) Install EulerCopilot +9) Return to main menu +============================== +Please enter option number (0-9): +``` + +--- + +**Key Notes**: + +1. Resource preparation is required before installing and deploying +2. During the deployment process, you need to input the Authhub domain and EulerCopilot domain. If not input, default domains `authhub.eulercopilot.local`, `www.eulercopilot.local` will be used. + +#### Restart Services + +```bash +# Enter option to restart services +============================== + Service Restart Menu +============================== +Restartable service list: +1) authhub-backend +2) authhub +3) framework +4) minio +5) mongo +6) mysql +7) pgsql +8) rag +9) rag-web +10) redis +11) web +12) Return to main menu +============================== +Please enter the number of the service to restart (1-12): +``` + +#### Uninstall All Components + +```bash +sudo ./deploy.sh +# Choose 2 for complete uninstallation +============================== + Main Deployment Menu +============================== +0) One-click automatic deployment +1) Manual step-by-step deployment +2) Uninstall all components and clear data +3) Exit program +============================== +Please enter option number (0-3): 2 +``` + +--- + +**Key Notes**: + +- During the deployment process, you need to input the Authhub domain and EulerCopilot domain. If not input, default domains `authhub.eulercopilot.local`, `www.eulercopilot.local` will be used. +- When resources are insufficient, refer to FAQ to evaluate resource availability +- View component logs + +```bash +kubectl logs -n euler-copilot +``` + +- View service status + +```bash +kubectl get pod -n euler-copilot +``` + +- Modify large model configuration and update EulerCopilot + +```bash +cd /home/euler-copilot-framework/deploy/chart/euler-copilot +``` + +```bash +vim values.yaml +``` + +```bash +helm upgrade euler-copilot -n euler-copilot . +``` + +## Installation Verification + +Congratulations, **EulerCopilot** has been successfully deployed! To begin your usage, enter the `https://Your EulerCopilot domain` link in your browser to access the EulerCopilot web interface: + +When visiting for the first time, you need to click the **Register Now** button on the page to create a new account and complete the login process. + +![Web Login Interface](./pictures/web-login.png) +![Web Interface](./pictures/web.png) + +## Build Custom Domain-Specific Intelligent Q&A + +Click on the knowledge base to log into the local knowledge base management page. For detailed information, please refer to the documentation [Local Asset Repository Construction Guide](../../../advance/knowledge_base/deploy_guide/witchaind_deployment.md) +**Default account for knowledge base login `admin`, password `123456`** + +--- + +## Appendix + +### Large Model Preparation + +#### GPU Environment + +The deployed deepseek large model can be used directly. Refer to the following deployment method: + +1. Download model files + + ```bash + huggingface-cli download --resume-download Qwen/Qwen1.5-14B-Chat --local-dir Qwen1.5-14B-Chat + ``` + +2. Create terminal control + + ```bash + screen -S control + ``` + + ```bash + python3 -m fastchat.serve.controller + ``` + + Press Ctrl A+D to put it in the background + +3. Create new terminal api + + ```bash + screen -S api + ``` + + ```bash + python3 -m fastchat.serve.openai_api_server --host 0.0.0.0 --port 30000 --api-keys sk-123456 + ``` + + Press Ctrl A+D to put it in the background + If the current environment's Python version is 3.12 or 3.9, a conda virtual environment with Python 3.10 can be created + + ```bash + mkdir -p /root/py310 + ``` + + ```bash + conda create --prefix=/root/py310 python==3.10.14 + ``` + + ```bash + conda activate /root/py310 + ``` + +4. Create new terminal worker + + ```bash + screen -S worker + ``` + + ```bash + screen -r worker + ``` + + Install fastchat and vllm + + ```bash + pip install fschat vllm + ``` + + Install dependencies: + + ```bash + pip install fschat[model_worker] + ``` + + ```bash + 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 + ``` + + Press Ctrl A+D to put it in the background + +5. Modify the large model parameters in the configuration as follows, and update the service. + + ```bash + vim /home/euler-copilot-framework/deploy/chart/euler_copilot/values.yaml + ``` + + Modify the following parts + + ```yaml + # Model settings + models: + # Large model for Q&A; must be OpenAI-compatible interface + answer: + # [Required] Interface URL (without "v1" suffix) + url: http://172.168.178.107:11434 + # [Required] Interface API Key; defaults to empty + key: sk-123456 + # [Required] Model name + name: deepseek-llm-7b-chat:latest + # [Required] Maximum context length for the model; recommended >= 8192 + ctx_length: 8192 + # Maximum output length for the model, recommended >= 2048 + max_tokens: 2048 + # Model for Function Call; recommended to use a specific inference framework + functioncall: + # Inference framework type, default is ollama + # Available framework types: ["vllm", "sglang", "ollama", "openai"] + backend: + # Model address; if not filled, same as Q&A model + url: ollama + # API Key; if not filled, same as Q&A model + key: + # Model name; if not filled, same as Q&A model + name: + # Maximum context length; if not filled, same as Q&A model + ctx_length: + # Maximum output length; if not filled, same as Q&A model + max_tokens: + # Model for data vectorization (Embedding) + embedding: + # Inference framework type, default is openai + # [Required] Embedding interface type: ["openai", "mindie"] + type: openai + # [Required] Embedding URL (with "v1" suffix) + url: http://172.168.178.107:11434 + # [Required] Embedding model API Key + key: sk-123456 + # [Required] Embedding model name + name: bge-m3:latest + ``` + + ```bash + # Update the service + helm upgrade -n euler-copilot euler-copilot . + # Restart framework service + kubectl get pod -n euler-copilot + kubectl delete pod framework-deploy-65b669fc58-q9bw7 -n euler-copilot + ``` + +#### NPU Environment + +NPU environment deployment can refer to the link [MindIE Installation Guide](https://www.hiascend.com/document/detail/zh/mindie/10RC2/whatismindie/mindie_what_0001.html) + +### FAQ + +#### 1. Resolve Hugging Face Connection Error + +If encountering connection errors like the following: + +```text +urllib3.exceptions.NewConnectionError: : Failed to establish a new connection: [Errno 101] Network is unreachable +``` + +Try the following solutions: + +- Update the `huggingface_hub` package to the latest version. + + ```bash + pip3 install -U huggingface_hub + ``` + +- If network issues persist, try using a mirror site as the endpoint. + + ```bash + export HF_ENDPOINT=https://hf-mirror.com + ``` + +#### 2. Invoke Q&A Interface in RAG Container + +After entering the corresponding RAG Pod, you can send POST requests via the `curl` command to get Q&A results. Please ensure that the specific question text is provided in the request body. + +```bash +curl -k -X POST "http://localhost:9988/kb/get_answer" \ + -H "Content-Type: application/json" \ + -d '{ + "question": "Your question", + "kb_sn": "default_test", + "fetch_source": true + }' +``` + +#### 3. Resolve `helm upgrade` Error + +When the Kubernetes cluster is unreachable, you might encounter error messages similar to the following: + +```text +Error: UPGRADE FAILED: Kubernetes cluster unreachable +``` + +Ensure the correct KUBECONFIG environment variable is set to point to a valid configuration file. + +```bash +echo "export KUBECONFIG=/etc/rancher/k3s/k3s.yaml" >> /root/.bashrc +source /root/.bashrc +``` + +#### 4. Failed to View Pod Logs + +If you encounter permission denied issues when viewing Pod logs, check if the proxy settings are correctly configured and add the local IP address to the `no_proxy` environment variable. + +```bash +cat /etc/systemd/system/k3s.service.env +``` + +Edit the file to ensure it includes: + +```bash +no_proxy=XXX.XXX.XXX.XXX +``` + +#### 5. Large Model Streaming Response Issue in GPU Environment + +For some services where curl to the large model fails to stream responses, try changing the `"stream"` parameter in the request to `false`. Also, confirm that the compatible version of the Pydantic library is installed. + +```bash +pip install pydantic==1.10.13 +``` + +#### 6. sglang Model Deployment Guide + +Deploy the sglang-based model following the steps below: + +```bash +# 1. 激活名为 `myenv` 的 Conda 环境，该环境基于 Python 3.10 创建： +conda activate myenv + +# 2. 安装 sglang 及其所有依赖项，指定版本为 0.3.0 +pip install "sglang[all]==0.3.0" + +# 3. 从特定索引安装 flashinfer，确保与您的 CUDA 和 PyTorch 版本兼容 +pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/ + +# 4. Start the server using sglang with the following configuration: +python -m sglang.launch_server \ + --served-model-name Qwen2.5-32B \ + --model-path Qwen2.5-32B-Instruct-AWQ \ + --host 0.0.0.0 \ + --port 8001 \ + --api-key "sk-12345" \ + --mem-fraction-static 0.5 \ + --tp 8 +``` + +- Verify installation + + ```bash + pip show sglang + pip show flashinfer + ``` + +**Important Notes:** + +- API Key: Please ensure the API key in the `--api-key` parameter is correct +- Model Path: Ensure the path in the `--model-path` parameter is correct and the model files exist at that path +- CUDA Version: Ensure your system has CUDA 12.1 and PyTorch 2.4 installed, as the `flashinfer` package depends on these specific versions +- Thread Pool Size: Adjust the thread pool size based on your GPU resources and expected load. If you have 8 GPUs, you can choose --tp 8 to fully utilize these resources + +#### 7. Get Embedding + +Use curl to send a POST request to get embedding results: + +```bash +curl -k -X POST http://localhost:11434/v1/embeddings \ + -H "Content-Type: application/json" \ + -d {"input": "The food was delicious and the waiter...", "model": "bge-m3", "encoding_format": "float"} +``` + +#### 8. Generate Certificates + +To generate self-signed certificates, first download the [mkcert](https://github.com/FiloSottile/mkcert/releases) tool, then run the following commands: + +```bash +mkcert -install +mkcert example.com +``` + +Finally, copy the generated certificate and private key to values.yaml and apply it to the Kubernetes Secret. + +```bash +vim /home/euler-copilot-framework_openeuler/deploy/chart_ssl/traefik-secret.yaml +``` + +```bash +kubectl apply -f traefik-secret.yaml +``` + +#### 9. Troubleshooting Methods + +1. **Get Cluster Event Information** + + To better identify the cause of Pod failures, first check the events (Events) in the Kubernetes cluster. This can provide contextual information about Pod state changes. + + ```bash + kubectl get events -n euler-copilot + ``` + +2. **Verify Image Pull Status** + + Confirm whether the container image was successfully pulled. If the image fails to load correctly, it may be due to network issues or incorrect image repository configuration. + + ```bash + k3s crictl images + ``` + +3. **Review Pod Logs** + + Check the logs of related Pods to look for possible error messages or abnormal behavior. This is particularly useful for diagnosing application-level issues. + + ```bash + kubectl logs rag-deploy-service-5b7887644c-sm58z -n euler-copilot + ``` + +4. **Assess Resource Availability** + + Ensure the Kubernetes cluster has sufficient resources (such as CPU, memory, and storage) to support Pod operation. Insufficient resources may cause image pull failures or other performance issues, or cause Pod status to change from Running to Pending or Completed. Check disk space and ensure at least 30% is available. This helps maintain stable Pod operation. Refer to this link to mount a disk with larger space [How to move k3s data to another location](https://mrkandreev.name/snippets/how_to_move_k3s_data_to_another_location/) + + ```bash + kubectl top nodes + ``` + +5. **Confirm k3s Version Compatibility** + + If you encounter image pull failures with image size 0, please check if your k3s version meets the minimum requirements (v1.30.2 or higher). Lower versions may have compatibility issues. + + ```bash + k3s -v + ``` + +6. **Check Configuration** + + Check if the OIDC configuration and domain configuration in the `values.yaml` file are filled in correctly, and update the service after ensuring the configuration is correct. + + ```bash + cat /home/euler-copilot-framework/deploy/chart/euler_copilot + ``` + + ```bash + vim values.yaml | grep oidc + ``` + + ```bash + helm upgrade euler-copilot -n euler-copilot . + ``` diff --git a/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/online.md b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/online.md new file mode 100644 index 0000000000000000000000000000000000000000..304f9ca3122c950a25e5f891b765509a2e16852e --- /dev/null +++ b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/online.md @@ -0,0 +1,596 @@ +# **EulerCopilot Intelligent Assistant Deployment Guide** + +Version Information +Current Version: v0.9.5 +Update Date: April 1, 2025 + +## Product Overview + +EulerCopilot is an intelligent Q&A tool that uses EulerCopilot to solve the convenience of operating system knowledge acquisition and empowers developers and operations personnel with OS domain models. As a tool for acquiring operating system knowledge and enabling operating system productivity tools (such as A-Ops / A-Tune / x2openEuler / EulerMaker / EulerDevOps / StratoVirt / iSulad, etc.), it subverts traditional command delivery methods, evolving from traditional command delivery methods to natural semantics, and combines intelligent agent task planning capabilities to lower the threshold for developing and using operating system features. + +This guide provides deployment instructions for the EulerCopilot intelligent assistant system based on automated scripts, supporting both one-click automatic deployment and manual step-by-step deployment. + +### Component Introduction + +| Component | Port | Description | +| ----------------------------- | --------------- | -------------------- | +| euler-copilot-framework | 8002 (internal port) | Intelligent agent framework service | +| euler-copilot-web | 8080 | Intelligent agent frontend interface | +| euler-copilot-rag | 9988 (internal port) | Retrieval enhancement service | +| authhub-backend-service | 11120 (internal port) | Authentication service backend | +| authhub-web-service | 8000 | Authentication service frontend | +| mysql | 3306 (internal port) | MySQL database | +| redis | 6379 (internal port) | Redis database | +| minio | 9000 (internal port) 9001(external port) | MinIO database | +| mongo | 27017 (internal port) | MongoDB database | +| postgres | 5432 (internal port) | Vector database | +| secret_inject | None | Configuration file secure copy tool | + +### Software Requirements + +| Type | Version Requirements | Description | +|----------------| -------------------------------------|--------------------------------------| +| Operating System | openEuler 22.03 LTS and above | None | +| K3s | >= v1.30.2, with Traefik Ingress tools | K3s provides a lightweight Kubernetes cluster that is easy to deploy and manage | +| Helm | >= v3.15.3 | Helm is a Kubernetes package management tool designed to quickly install, upgrade, and uninstall EulerCopilot services | +| Python | >=3.9.9 | Python 3.9.9 and above provides the runtime environment for model download and installation | + +--- + +### Hardware Specifications + +| Hardware Resource | Minimum Configuration | Recommended Configuration | +|--------------|----------------------------|------------------------------| +| CPU | 4 cores | 16 cores and above | +| RAM | 4 GB | 64 GB | +| Storage | 32 GB | 64G | +| Large Model Name | deepseek-llm-7b-chat | DeepSeek-R1-Llama-8B | +| VRAM (GPU) | NVIDIA RTX A4000 8GB | NVIDIA A100 80GB * 2 | + +**Key Notes**: + +- For pure CPU environments, it is recommended to implement functionality by calling OpenAI interfaces or using the built-in model deployment method. +- If it's a k8s cluster environment, there's no need to install k3s separately, requiring version >= 1.28 + +--- + +### Deployment View + +![Deployment Diagram](./pictures/deployment-overview.png) + +--- + +### Domain Configuration + +Prepare the following two service domains: + +- AuthHub authentication service: `authhub.eulercopilot.local` +- EulerCopilot web service: `www.eulercopilot.local` + +```bash +# Configure in local Windows host +# Open C:\Windows\System32\drivers\etc\hosts to add records +# Replace 127.0.0.1 with the target server's IP +127.0.0.1 authhub.eulercopilot.local +127.0.0.1 www.eulercopilot.local +``` + +## Quick Start + +### 1. Get Deployment Scripts + +- Download the latest deployment repository from EulerCopilot's official Git repository [euler-copilot-framework](https://gitee.com/openeuler/euler-copilot-framework) +- If you are using Kubernetes, there's no need to install the k3s tool. + +```bash +# Use home directory as download example +cd /home +``` + +```bash +git clone https://gitee.com/openeuler/euler-copilot-framework.git -b dev +``` + +```bash +cd euler-copilot-framework/deploy/scripts +``` + +```bash +# Add executable permissions to script files +chmod -R +x ./* +``` + +### 2. Deploy EulerCopilot + +#### **One-Click Deployment** + +```bash +cd /home/euler-copilot-framework/deploy/scripts +``` + +```bash +bash deploy.sh +``` + +```bash +# Enter 0 for one-click automatic deployment +============================== + Main Deployment Menu +============================== +0) One-Click Automatic Deployment +1) Manual Step-by-Step Deployment +2) Restart Services +3) Uninstall All Components and Clear Data +4) Exit Program +============================== +Please enter option number (0-3): 0 +``` + +--- + +#### **Step-by-Step Deployment** + +```bash +# Select 1 -> 1 to enter manual step-by-step deployment +============================== + Main Deployment Menu +============================== +0) One-Click Automatic Deployment +1) Manual Step-by-Step Deployment +2) Restart Services +3) Uninstall All Components and Clear Data +4) Exit Program +============================== +Please enter option number (0-3): 1 +``` + +```bash +# Enter option number (0-9) to deploy step by step +============================== + Manual Step-by-Step Deployment Menu +============================== +1) Execute Environment Check Script +2) Install k3s and helm +3) Install Ollama +4) Deploy Deepseek Model +5) Deploy Embedding Model +6) Install Databases +7) Install AuthHub +8) Install EulerCopilot +9) Return to Main Menu +============================== +Please enter option number (0-9): +``` + +--- + +#### **Restart Services** + +```bash +# Enter option to restart services +============================== + Service Restart Menu +============================== +List of services that can be restarted: +1) authhub-backend +2) authhub +3) framework +4) minio +5) mongo +6) mysql +7) pgsql +8) rag +9) rag-web +10) redis +11) web +12) Return to Main Menu +============================== +Please enter the service number to restart (1-12): +``` + +#### **Uninstall All Components** + +```bash +sudo ./deploy.sh +# Select 2 for complete uninstallation +============================== + Main Deployment Menu +============================== +0) One-Click Automatic Deployment +1) Manual Step-by-Step Deployment +2) Uninstall All Components and Clear Data +3) Exit Program +============================== +Please enter option number (0-3): 2 +``` + +--- + +**Key Notes**: + +- During deployment, you need to enter the AuthHub domain and EulerCopilot domain. If not entered, the default domains `authhub.eulercopilot.local` and `www.eulercopilot.local` will be used. +- When resources are insufficient, refer to the FAQ section for solutions on evaluating resource availability +- View component logs + +```bash +kubectl logs -n euler-copilot +``` + +- View service status + +```bash +kubectl get pod -n euler-copilot +``` + +- Modify large model configuration and update EulerCopilot + +```bash +cd /home/euler-copilot-framework/deploy/chart/euler-copilot +``` + +```bash +vim values.yaml +``` + +```bash +helm upgrade euler-copilot -n euler-copilot . +``` + +## Verify Installation + +Congratulations! **EulerCopilot** has been successfully deployed! To start your experience, please enter the link `https://your-eulercopilot-domain` in your browser to access the EulerCopilot web interface: + +On your first visit, you need to click the **Register Now** button on the page to create a new account and complete the login process. + +![Web Login Interface](./pictures/web-login.png) +![Web Interface](./pictures/web.png) + +## Build Domain-Specific Intelligent Q&A + +Click on Knowledge Base to log into the local knowledge base management page. For detailed information, please refer to the document [Local Asset Library Construction Guide](../../../advance/knowledge_base/deploy_guide/witchaind_deployment.md) +**Knowledge Base default login account: `admin`, password: `123456`** + +--- + +## Appendix + +### Large Model Preparation + +#### GPU Environment + +You can directly use the deployed deepseek large model. Refer to the following deployment method: + +1. Download model files: + + ```bash + huggingface-cli download --resume-download Qwen/Qwen1.5-14B-Chat --local-dir Qwen1.5-14B-Chat + ``` + +2. Create terminal control + + ```bash + screen -S control + ``` + + ```bash + python3 -m fastchat.serve.controller + ``` + + Press Ctrl A+D to put it in the background + +3. Create new terminal api + + ```bash + screen -S api + ``` + + ```bash + python3 -m fastchat.serve.openai_api_server --host 0.0.0.0 --port 30000 --api-keys sk-123456 + ``` + + Press Ctrl A+D to put it in the background + If the current environment's Python version is 3.12 or 3.9, you can create a conda virtual environment with Python 3.10 + + ```bash + mkdir -p /root/py310 + ``` + + ```bash + conda create --prefix=/root/py310 python==3.10.14 + ``` + + ```bash + conda activate /root/py310 + ``` + +4. Create new terminal worker + + ```bash + screen -S worker + ``` + + ```bash + screen -r worker + ``` + + Install fastchat and vllm + + ```bash + pip install fschat vllm + ``` + + Install dependencies: + + ```bash + pip install fschat[model_worker] + ``` + + ```bash + 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 + ``` + + Press Ctrl A+D to put it in the background + +5. Modify the configured large model parameters as follows and update the service. + + ```bash + vim /home/euler-copilot-framework/deploy/chart/euler_copilot/values.yaml + ``` + + Modify the following section + + ```yaml + # Model Settings + models: + # Large model for Q&A; needs to be OpenAI-compatible interface + answer: + # [Required] Interface URL (no need to include "v1" suffix) + url: http://172.168.178.107:11434 + # [Required] Interface API Key; default is empty + key: sk-123456 + # [Required] Model name + name: deepseek-llm-7b-chat:latest + # [Required] Model maximum context length; recommended >=8192 + ctx_length: 8192 + # Model maximum output length, recommended >=2048 + max_tokens: 2048 + # Model for Function Call; recommended to use specific inference framework + functioncall: + # Inference framework type, default is ollama + # Available framework types: ["vllm", "sglang", "ollama", "openai"] + backend: + # Model address; if not filled, same as Q&A model + url: ollama + # API Key; if not filled, same as Q&A model + key: + # Model name; if not filled, same as Q&A model + name: + # Model maximum context length; if not filled, same as Q&A model + ctx_length: + # Model maximum output length; if not filled, same as Q&A model + max_tokens: + # Model for data vectorization (Embedding) + embedding: + # Inference framework type, default is openai + # [Required] Embedding interface type: ["openai", "mindie"] + type: openai + # [Required] Embedding URL (needs to include "v1" suffix) + url: http://172.168.178.107:11434 + # [Required] Embedding model API Key + key: sk-123456 + # [Required] Embedding model name + name: bge-m3:latest + ``` + + ```bash + # Update service + helm upgrade -n euler-copilot euler-copilot . + # Restart framework service + kubectl get pod -n euler-copilot + kubectl delete pod framework-deploy-65b669fc58-q9bw7 -n euler-copilot + ``` + +#### NPU Environment + +For NPU environment deployment, refer to the link [MindIE Installation Guide](https://www.hiascend.com/document/detail/zh/mindie/10RC2/whatismindie/mindie_what_0001.html) + +### FAQ + +#### 1. Resolve Hugging Face Connection Errors + +If you encounter the following connection error: + +```text +urllib3.exceptions.NewConnectionError: : Failed to establish a new connection: [Errno 101] Network is unreachable +``` + +Try the following solutions: + +- Update the `huggingface_hub` package to the latest version. + + ```bash + pip3 install -U huggingface_hub + ``` + +- If network issues persist, try using mirror sites as endpoints. + + ```bash + export HF_ENDPOINT=https://hf-mirror.com + ``` + +#### 2. Call Q&A Interface in RAG Container + +After entering the corresponding RAG Pod, you can send POST requests via `curl` commands to get Q&A results. Please ensure to provide specific question text in the request body. + +```bash +curl -k -X POST "http://localhost:9988/kb/get_answer" \ + -H "Content-Type: application/json" \ + -d '{ + "question": "Your question", + "kb_sn": "default_test", + "fetch_source": true + }' +``` + +#### 3. Resolve `helm upgrade` Errors + +When the Kubernetes cluster is unreachable, you may encounter error messages similar to the following: + +```text +Error: UPGRADE FAILED: Kubernetes cluster unreachable +``` + +Ensure that the correct KUBECONFIG environment variable is set to point to a valid configuration file. + +```bash +echo "export KUBECONFIG=/etc/rancher/k3s/k3s.yaml" >> /root/.bashrc +source /root/.bashrc +``` + +#### 4. Failed to View Pod Logs + +If you encounter permission denied issues when viewing Pod logs, check if proxy settings are correctly configured and add your local IP address to the `no_proxy` environment variable. + +```bash +cat /etc/systemd/system/k3s.service.env +``` + +Edit the file and ensure it contains: + +```bash +no_proxy=XXX.XXX.XXX.XXX +``` + +#### 5. Large Model Streaming Response Issues in GPU Environment + +For cases where certain services cannot perform streaming responses when executing curl with large models, try modifying the `"stream"` parameter in the request to `false`. Additionally, confirm that a compatible version of the Pydantic library is installed. + +```bash +pip install pydantic==1.10.13 +``` + +#### 6. sglang Model Deployment Guide + +Follow these steps to deploy models based on sglang: + +```bash +# 1. Activate the conda environment named `myenv`, which is created based on Python 3.10: +conda activate myenv + +# 2. Install sglang and all its dependencies, specifying version 0.3.0 +pip install "sglang[all]==0.3.0" + +# 3. Install flashinfer from a specific index to ensure compatibility with your CUDA and PyTorch versions +pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/ + +# 4. Start the server using sglang with the following configuration: +python -m sglang.launch_server \ + --served-model-name Qwen2.5-32B \ + --model-path Qwen2.5-32B-Instruct-AWQ \ + --host 0.0.0.0 \ + --port 8001 \ + --api-key "sk-12345" \ + --mem-fraction-static 0.5 \ + --tp 8 +``` + +- Verify installation + + ```bash + pip show sglang + pip show flashinfer + ``` + +**Important Notes:** + +- API Key: Please ensure the API key in the `--api-key` parameter is correct +- Model Path: Ensure the path in the `--model-path` parameter is correct and that the model files exist at that path. +- CUDA Version: Ensure you have CUDA 12.1 and PyTorch 2.4 installed on your system, as the `flashinfer` package depends on these specific versions. +- Thread Pool Size: Adjust the thread pool size based on your GPU resources and expected load. If you have 8 GPUs, you can choose --tp 8 to fully utilize these resources. + +#### 7. Get Embedding + +Use curl to send POST requests to get embedding results: + +```bash +curl -k -X POST http://localhost:11434/v1/embeddings \ + -H "Content-Type: application/json" \ + -d {"input": "The food was delicious and the waiter...", "model": "bge-m3", "encoding_format": "float"} +``` + +#### 8. Generate Certificates + +To generate self-signed certificates, first download the [mkcert](https://github.com/FiloSottile/mkcert/releases) tool, then run the following commands: + +```bash +mkcert -install +mkcert example.com +``` + +Finally, copy the generated certificate and private key to values.yaml and apply them to the Kubernetes Secret. + +```bash +vim /home/euler-copilot-framework_openeuler/deploy/chart_ssl/traefik-secret.yaml +``` + +```bash +kubectl apply -f traefik-secret.yaml +``` + +#### 9. Troubleshooting Methods + +1. **Get Cluster Event Information** + + To better locate the cause of Pod failures, first check the events in the Kubernetes cluster. This can provide contextual information about Pod state changes. + + ```bash + kubectl get events -n euler-copilot + ``` + +2. **Verify Image Pull Status** + + Confirm whether container images are successfully pulled. If images fail to load correctly, it may be due to network issues or incorrect image repository configuration. + + ```bash + k3s crictl images + ``` + +3. **Review Pod Logs** + + Check the logs of relevant Pods to look for possible error messages or abnormal behavior. This is particularly useful for diagnosing application-level issues. + + ```bash + kubectl logs rag-deploy-service-5b7887644c-sm58z -n euler-copilot + ``` + +4. **Evaluate Resource Availability** + + Ensure the Kubernetes cluster has sufficient resources (such as CPU, memory, and storage) to support Pod operation. Insufficient resources may cause image pull failures or other performance issues, or cause Pod status to change from Running to Pending or Completed. Check disk space and ensure at least 30% free space. This helps maintain stable Pod operation. Refer to this link to mount disks with larger space [How to move k3s data to another location](https://mrkandreev.name/snippets/how_to_move_k3s_data_to_another_location/) + + ```bash + kubectl top nodes + ``` + +5. **Confirm k3s Version Compatibility** + + If you encounter image pull failures with image size 0, please check if your k3s version meets the minimum requirements (v1.30.2 or higher). Lower versions may have compatibility issues. + + ```bash + k3s -v + ``` + +6. **Check Configuration** + + Check whether the OIDC configuration and domain configuration in the `values.yaml` file are filled in correctly, and update the service after ensuring the configuration is correct. + + ```bash + cat /home/euler-copilot-framework/deploy/chart/euler_copilot + ``` + + ```bash + vim values.yaml | grep oidc + ``` + + ```bash + helm upgrade euler-copilot -n euler-copilot . + ``` diff --git "a/docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/pictures/\351\203\250\347\275\262\350\247\206\345\233\276.png" b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/pictures/deployment-overview.png similarity index 100% rename from "docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/pictures/\351\203\250\347\275\262\350\247\206\345\233\276.png" rename to docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/pictures/deployment-overview.png diff --git "a/docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/pictures/WEB\347\231\273\345\275\225\347\225\214\351\235\242.png" b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/pictures/web-login.png similarity index 100% rename from "docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/pictures/WEB\347\231\273\345\275\225\347\225\214\351\235\242.png" rename to docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/pictures/web-login.png diff --git "a/docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/pictures/WEB\347\225\214\351\235\242.png" b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/pictures/web.png similarity index 100% rename from "docs/en/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/pictures/WEB\347\225\214\351\235\242.png" rename to docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/pictures/web.png diff --git a/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/_toc.yaml b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/_toc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..594dec7f4ea81089052df0c0a83d9bb0aa002cc0 --- /dev/null +++ b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/_toc.yaml @@ -0,0 +1,17 @@ +# User Guide for Intelligent Assistant +label: Intelligent Assistant User Guide +isManual: true +description: Guide for using the openEuler Intelligent Assistant +sections: + - label: Quick Start + sections: + - label: Web Q&A + sections: + - label: User Guide + sections: + - label: Introduction + href: introduction.md + - label: Registration and Login + href: registration_and_login.md + - label: Q&A Usage + href: qa_guide.md \ No newline at end of file diff --git a/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/introduction.md b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/introduction.md new file mode 100644 index 0000000000000000000000000000000000000000..c43fec83f822b106579e6ee36b6df4096d266fa0 --- /dev/null +++ b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/introduction.md @@ -0,0 +1,68 @@ +# Introduction + +## Overview + +This document introduces the usage of EulerCopilot, providing detailed descriptions of various features of the EulerCopilot online service web interface, along with common Q&A. For detailed information, please refer to the corresponding manuals. + +## Target Audience + +This document is primarily intended for EulerCopilot users. Users should possess the following experience and skills: + +- Familiarity with openEuler operating system related information. +- Experience with AI dialogue systems. + +## Revision History + +| Document Version | Release Date | Revision Description | +|------------------|--------------|---------------------| +| 03 | 2024-09-19 | Updated new interface. | +| 02 | 2024-05-13 | Optimized intelligent dialogue operation guidance. | +| 01 | 2024-01-28 | First official release. | + +## Introductions + +### Disclaimer + +- Usernames and passwords used during the process that are not part of the tool's own verification functionality are not used for other purposes and will not be saved in the system environment. +- Before engaging in dialogue or operations, you should confirm that you are the owner of the application or have obtained sufficient authorization consent from the owner. +- Dialogue results may contain internal information and related data of the applications you analyze. Please manage them appropriately. +- Unless otherwise stipulated by laws and regulations or bilateral contracts, the openEuler community makes no express or implied statements and warranties regarding analysis results, and makes no guarantees or commitments regarding the merchantability, satisfaction, non-infringement, or fitness for a particular purpose of analysis results. +- Any actions you take based on analysis records should comply with legal and regulatory requirements, and you bear the risks yourself. +- Without owner authorization, no individual or organization may use applications and related analysis records for any activities. The openEuler community is not responsible for any consequences arising there from, nor does it assume any legal liability. Legal liability will be pursued when necessary. + +### EulerCopilot Introduction + +EulerCopilot is an artificial intelligence assistant based on the openEuler operating system that helps users solve various technical problems and provides technical support and consulting services. It uses the most advanced natural language processing technology and machine learning algorithms to understand user questions and provide corresponding solutions. + +### Application Scenarios + +1. **OS Domain General Knowledge**: EulerCopilot can provide consultation on Linux general knowledge, upstream information, and toolchain introduction and guidance. +2. **openEuler Professional Knowledge**: EulerCopilot can provide consultation on openEuler community information, technical principles, and usage guidance. +3. **openEuler Extended Knowledge**: EulerCopilot can provide consultation on openEuler peripheral hardware feature knowledge and ISV, OSV related information. +4. **openEuler Application Cases**: EulerCopilot can provide openEuler technical cases and industry application cases. +5. **Shell Command Generation**: EulerCopilot can help users generate single shell commands or complex commands. + +In summary, EulerCopilot can be applied to various scenarios, helping users improve work efficiency and understand related knowledge about Linux, openEuler, and more. + +### Access and Usage + +EulerCopilot is accessed and used through a web page via URL. For account registration and login, please refer to [Registration and Login](./registration_and_login.md). For usage methods, please refer to [Intelligent Q&A User Guide](./qa_guide.md). + +### Interface Description + +#### Interface Layout + +The EulerCopilot interface mainly consists of the areas shown in Figure 1, with the functions of each area described in Table 1. + +- Figure 1: EulerCopilot Interface + +![Copilot Interface](./pictures/main-page-sections.png) + +- Table 1: EulerCopilot Homepage Interface Area Description + +| Area | Name | Description | +|------|------|-------------| +| 1 | Settings Management Area | Provides account login and logout operation entries and light/dark mode toggle functionality | +| 2 | Dialogue Management Area | Used for users to create new dialogues, manage dialogue history, and perform batch deletion of dialogue history | +| 3 | Dialogue Area | Used for dialogue chat between users and EulerCopilot | +| 4 | Service Agreement and Privacy Policy Area | Provides access to view service agreements and privacy policies | diff --git 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b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/pictures/search-history.png similarity index 100% rename from docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/pictures/search-history.png rename to docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/pictures/search-history.png diff --git a/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/qa_guide.md b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/qa_guide.md new file mode 100644 index 0000000000000000000000000000000000000000..6ed27a63caa6e1fb7561df611a29e0b489287b03 --- /dev/null +++ b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/qa_guide.md @@ -0,0 +1,179 @@ +# Intelligent Q&A User Guide + +## Start a Conversation + +In the input box below the conversation area, you can enter the content you want to ask. Press `Shift + Enter` for line breaks, press `Enter` to send your question, or click "Send" to submit your question. + +> **Note** +> +> The conversation area is located in the main part of the page, as shown in Figure 1. + +- Figure 1 Conversation Area + ![Conversation Area](./pictures/chat-area.png) + +### Multi-turn Continuous Conversation + +EulerCopilot intelligent Q&A supports multi-turn continuous conversations. You can simply continue asking follow-up questions in the same conversation, as shown in Figure 2. + +- Figure 2 Multi-turn Conversation + ![Multi-turn Conversation](./pictures/context-support.png) + +### Regenerate Response + +If you encounter situations where AI-generated content is incorrect or incomplete, you can ask AI to regenerate the answer. Click the "Regenerate" text on the bottom left of the AI response to have EulerCopilot answer your question again. After regenerating, pagination icons ![Previous Page](./pictures/icon-arrow-prev.png) and ![Next Page](./pictures/icon-arrow-next.png) will appear on the bottom right of the response. Click ![Previous Page](./pictures/icon-arrow-prev.png) or ![Next Page](./pictures/icon-arrow-next.png) to view different responses, as shown in Figure 3. + +- Figure 3 Regenerate Response + ![Regenerate Response](./pictures/regenerate.png) + +### Recommended Questions + +Below the AI response, some recommended questions will be displayed. Click on them to ask questions, as shown in Figure 4. + +- Figure 4 Recommended Questions + ![Recommended Questions](./pictures/recommend-questions.png) + +## Custom Background Knowledge + +EulerCopilot supports file upload functionality. After uploading files, AI will use the uploaded file content as background knowledge and incorporate it when answering questions. The uploaded background knowledge only applies to the current conversation and will not affect other conversations. + +### Upload Files + +**Step 1** Click the "Upload File" button in the bottom left corner of the conversation area, as shown in Figure 5. + +- Figure 5 Upload File Button + ![Upload File](./pictures/file-upload-btn.png) + +> **Note** +> +> Hover your mouse over the "Upload File" button to see prompts about the allowed file specifications and formats, as shown in Figure 6. + +- Figure 6 Mouse Hover Shows Upload File Specification Prompts + ![Upload File Prompts](./pictures/file-upload-btn-prompt.png) + +**Step 2** In the popup file selection dialog, select the file you want to upload and click "Open" to upload. You can upload up to 10 files with a total size limit of 64MB. Accepted formats include PDF, docx, doc, txt, md, and xlsx. + +After starting the upload, the upload progress will be displayed below the conversation area, as shown in Figure 7. + +- Figure 7 All Files Being Uploaded Simultaneously Are Arranged Below the Q&A Input Box + ![Upload File](./pictures/file-upload-uploading.png) + +After file upload is complete, it will be automatically parsed, as shown in Figure 8. After parsing is complete, the file size information for each file will be displayed below the conversation area. + +- Figure 8 After File Upload to Server, "Parsing" Will Be Displayed + ![File Parsing](./pictures/file-upload-parsing.png) + +After successful file upload, the left history record area will display the number of uploaded files, as shown in Figure 9. + +- Figure 9 The Uploaded File Count Will Be Displayed on the Conversation History Record Tab + ![History Record Mark](./pictures/file-upload-history-tag.png) + +### Ask Questions About Files + +After file upload is complete, you can ask questions about the files. The questioning method is the same as the regular conversation mode, as shown in Figure 10. +The answer result is shown in Figure 11. + +- Figure 10 Ask Questions Related to the Uploaded Files + ![Ask Questions About Files](./pictures/file-upload-ask-against-file.png) + +- Figure 11 AI Answers Based on Uploaded Background Knowledge + ![Answer Based on Custom Background Knowledge](./pictures/file-upload-showcase.png) + +## Manage Conversations + +> **Note** +> +> The conversation management area is on the left side of the page. + +### Create New Conversation + +Click the "New Conversation" button to create a new conversation, as shown in Figure 12. + +- Figure 12 "New Conversation" Button is Located in the Upper Left of the Page + ![New Conversation](./pictures/new-chat.png) + +### Search Conversation History + +Enter keywords in the history search input box on the left side of the page, then click ![Search](./pictures/icon-search.png) to search conversation history, as shown in Figure 13. + +- Figure 13 Conversation History Search Box + ![Conversation History Search](./pictures/search-history.png) + +### Manage Individual Conversation History Records + +The history record list is located below the history search bar. On the right side of each conversation history record, click ![Edit](./pictures/icon-edit.png) to edit the name of the conversation history record, as shown in Figure 14. + +- Figure 14 Click "Edit" Icon to Rename History Record + ![Rename History Record](./pictures/rename-session.png) + +After rewriting the conversation history record name, click ![Confirm](./pictures/icon-confirm.png) on the right to complete the renaming, or click ![Cancel](./pictures/icon-cancel.png) on the right to abandon this renaming, as shown in Figure 15. + +- Figure 15 Complete/Cancel Renaming History Record + ![Complete/Cancel Renaming History Record](./pictures/rename-session-confirmation.png) + +Additionally, click the delete icon on the right side of the conversation history record, as shown in Figure 16, to perform secondary confirmation for deleting a single conversation history record. In the secondary confirmation popup, as shown in Figure 17, click "Confirm" to confirm deletion of the single conversation history record, or click "Cancel" to cancel this deletion. + +- Figure 16 Click "Trash" Icon to Delete Single History Record + ![Delete Single History Record](./pictures/delete-session.png) + +- Figure 17 Secondary Confirmation Before Deleting History Record + ![Secondary Confirmation for Deleting Single History Record](./pictures/delete-session-confirmation.png) + +### Batch Delete Conversation History Records + +First, click "Batch Delete", as shown in Figure 18. + +- Figure 18 Batch Delete Function is Located Above the Right Side of the History Search Box + ![Batch Delete](./pictures/bulk-delete.png) + +Then you can select history records for deletion, as shown in Figure 19. Click "Select All" to select all history records, or click on a single history record or the selection box on the left side of the history record to select individual history records. + +- Figure 19 Check the Box on the Left to Select History Records for Batch Deletion + ![Batch Delete History Record Selection](./pictures/bulk-delete-multi-select.png) + +Finally, secondary confirmation is required for batch deleting history records, as shown in Figure 20. Click "Confirm" to delete, or click "Cancel" to cancel this deletion. + +- Figure 20 Secondary Confirmation Before Deleting Selected History Records + ![Batch Delete Secondary Confirmation](./pictures/bulk-delete-confirmation.png) + +## Feedback and Report + +In the conversation record area, on the bottom right side of the AI response, you can provide feedback on the conversation response, as shown in Figure 21. Click ![Thumbs Up](./pictures/icon-thumb-up.png) to give the conversation response a thumbs up; click ![Thumbs Down](./pictures/icon-thumb-down.png) to provide feedback on why you're dissatisfied with the response. + +- Figure 21 Thumbs Up and Dissatisfaction Feedback + ![Thumbs Up and Dissatisfaction Feedback](./pictures/feedback.png) + +For feedback on dissatisfaction reasons, as shown in Figure 22, after clicking ![Thumbs Down](./pictures/icon-thumb-down.png), the chatbot will display a dialog box for filling in feedback content, where you can choose relevant options for dissatisfaction reasons. + +- Figure 22 Dissatisfaction Feedback for Response + ![Dissatisfaction Feedback for Response](./pictures/feedback-illegal.png) + +Among them, clicking "Contains Incorrect Information" requires filling in reference answer links and descriptions, as shown in Figure 23. + +- Figure 23 Dissatisfaction Feedback for Response - Contains Incorrect Information + ![Dissatisfaction Feedback for Response - Contains Incorrect Information](./pictures/feedback-misinfo.png) + +### Report + +If you find that AI-generated content contains inappropriate information, you can click the report button in the bottom right corner, as shown in Figure 24. After clicking report, select the report type and submit. If there are no suitable options, please select "Other" and enter the reason, as shown in Figure 25. + +- Figure 24 Report Button is Located in the Bottom Right Corner of the Conversation Block + ![Report 1](./pictures/report.png) + +- Figure 25 After Clicking, You Can Select Report Type + ![Report 2](./pictures/report-options.png) + +## View Service Agreement and Privacy Policy + +Click on the text "Service Agreement" to view the service agreement, and click on the text "Privacy Policy" to view the privacy policy, as shown in Figures 26 and 27. + +- Figure 26 Service Agreement and Privacy Policy Entry Points are Located in the Bottom Information Bar of the Page + ![Service Agreement and Privacy Policy Entry](./pictures/privacy-policy-entry.png) + +- Figure 27 After Clicking, Service Agreement or Privacy Policy Popup Will Be Displayed + ![Service Agreement and Privacy Policy](./pictures/privacy-policy.png) + +## Appendix + +### User Information Export Instructions + +EulerCopilot backend has user information export functionality. If users need it, they must actively contact us through the email. Operations staff will send the exported user information back to users via email. diff --git a/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/registration_and_login.md b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/registration_and_login.md new file mode 100644 index 0000000000000000000000000000000000000000..4bf9fffa5f0df1bf238c4c5b5637fccd99ec2cfd --- /dev/null +++ b/docs/en/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/registration_and_login.md @@ -0,0 +1,60 @@ +# Logging into EulerCopilot + +This section introduces the specific steps to log into the deployed EulerCopilot web client. + +## Browser Requirements + +The browser requirements are shown in Table 1. + +- Table 1 Browser Requirements + +| Browser Type | Minimum Version | Recommended Version | +| ----- | ----- | ----- | +| Google Chrome | 72 | 121 or higher | +| Mozilla Firefox | 89 | 122 or higher | +| Apple Safari | 11.0 | 16.3 or higher | + +## Procedure + +**Step 1** Open the browser on your local PC, enter the configured domain name from the deployment guide in the address bar, and press `Enter`. When not logged in, accessing EulerCopilot will display a login prompt pop-up box, as shown in Figure 1. + +- Figure 1 Not Logged In + +![Not Logged In](./pictures/login-popup.png) + +**Step 2** Log into EulerCopilot (with an existing account). + +Open the login interface, as shown in Figure 2. + +- Figure 2 Logging into EulerCopilot + +![Logging into EulerCopilot](./pictures/authhub-login.png) + +## Registering an EulerCopilot Account + +**Step 1** Click "Register Now" in the bottom right corner of the login information input box, as shown in Figure 3. + +- Figure 3 Register Now + +![Register Now](./pictures/authhub-login-click2signup.png) + +**Step 2** Go to the account registration page and fill in the relevant information according to the page prompts, as shown in Figure 4. + +- Figure 4 Account Registration + +![Account Registration](./pictures/authhub-signup.png) + +**Step 3** After filling in the account information as required by the page, click "Register" to successfully register. After registration, you can return to the login page. + +## Logging Out + +**Step 1** Click ![Logout](./pictures/icon-user.png) to display the logout dropdown menu, as shown in Figure 5. + +> **Note** +> The account management area is located at the top right of the page, as shown in Figure 5. + +- Figure 5 Account Management Area + +![Account Management Area](./pictures/logout.png) + +**Step 2** Click "Logout" to log out, as shown in Figure 5. diff --git a/docs/en/openeuler_intelligence/intelligent_vulnerability_patching/_toc.yaml b/docs/en/openeuler_intelligence/intelligent_vulnerability_patching/_toc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2b8a86cf7894955fc8e5c8bba34b1deda2aacf24 --- /dev/null +++ b/docs/en/openeuler_intelligence/intelligent_vulnerability_patching/_toc.yaml @@ -0,0 +1,6 @@ +label: Intelligent Vulnerability Patching User Guide +isManual: true +description: Supports vulnerability patching for the kernel repository +sections: + - label: openEuler Intelligent Vulnerability Patching User Guide + href: ./intelligent_vulnerability_patching_user_guide.md diff --git a/docs/en/openeuler_intelligence/intelligent_vulnerability_patching/intelligent_vulnerability_patching_user_guide.md b/docs/en/openeuler_intelligence/intelligent_vulnerability_patching/intelligent_vulnerability_patching_user_guide.md new file mode 100644 index 0000000000000000000000000000000000000000..0ee977c12da96fb47aa83ea0fe2a990dfe4c136d --- /dev/null +++ b/docs/en/openeuler_intelligence/intelligent_vulnerability_patching/intelligent_vulnerability_patching_user_guide.md @@ -0,0 +1,59 @@ +# openEuler Intelligent Vulnerability Patching User Guide + +## Introduction + +Intelligent vulnerability patching provides the capability to intelligently patch the openEuler kernel repository ([https://gitee.com/openeuler/kernel](https://gitee.com/openeuler/kernel)). Currently, it offers CVE impact scope analysis and patch PR creation functionality. Use the `/analyze` and `/create_pr` commands in the comments below CVE-representing issues to execute these features. + +## Feature Entry + +In the src-openEuler kernel repository ([https://gitee.com/src-openeuler/kernel.git](https://gitee.com/src-openeuler/kernel.git)), comment below the CVE-representing issues. + +![CVE Screenshot](pictures/cve.png) + +## /analyze Command + +The `/analyze` command provides the capability to analyze the impact scope of CVEs. By commenting `/analyze` below an issue, it will automatically analyze the current maintenance scope of openEuler versions, determining whether each openEuler version introduces the CVE and whether it has been patched. + +![/analyze Command](pictures/analyze.png) + +> [!NOTE]Note +> The /analyze command takes no parameters + +CVE introduction status includes the following scenarios: + +* No impact +* Affected + +CVE patching status includes the following scenarios: + +* Not patched +* Patched + +At the end of the comment, links to the introduction patch and the fix patch will be attached. + +## /create_pr Command + +The `/create_pr` command provides the capability to intelligently fix CVE patches. By commenting `/create_pr ` below an issue, it will automatically obtain vulnerability patches and merge them into the openEuler linux repository ([https://gitee.com/openeuler/kernel.git](https://gitee.com/openeuler/kernel.git)) by creating PRs. + +![/create_pr Command](pictures/create_pr.png) + +The `/create_pr` command accepts parameters, including the following scenarios: + +```shell +# Create a patch PR for the OLK-5.10 branch +/create_pr OLK-5.10 + +# Create PRs for OLK-5.10 and OLK-6.6 branches +/create_pr OLK-5.10 OLK-6.6 + +# For all current upstream branches, including openEuler-1.0-LTS, OLK-5.10, and OLK-6.6 branches +/create_pr +``` + +Return results include: + +* PR creation successful +* No fix patch available +* Cannot fix, conflicts exist + +If there are conflicts between the patch code and the fix branch, it will prompt `Cannot fix, conflicts exist`. This capability will be iteratively enhanced in future versions. diff --git "a/docs/en/openEuler_intelligence/intelligent_vulnerability_patching/pictures/analyze\345\221\275\344\273\244.png" b/docs/en/openeuler_intelligence/intelligent_vulnerability_patching/pictures/analyze.png similarity index 100% rename from "docs/en/openEuler_intelligence/intelligent_vulnerability_patching/pictures/analyze\345\221\275\344\273\244.png" rename to docs/en/openeuler_intelligence/intelligent_vulnerability_patching/pictures/analyze.png diff --git "a/docs/en/openEuler_intelligence/intelligent_vulnerability_patching/pictures/create_pr\345\221\275\344\273\244.png" b/docs/en/openeuler_intelligence/intelligent_vulnerability_patching/pictures/create_pr.png similarity index 100% rename from "docs/en/openEuler_intelligence/intelligent_vulnerability_patching/pictures/create_pr\345\221\275\344\273\244.png" rename to docs/en/openeuler_intelligence/intelligent_vulnerability_patching/pictures/create_pr.png diff --git "a/docs/en/openEuler_intelligence/intelligent_vulnerability_patching/pictures/\344\273\243\350\241\250CVE\347\232\204issue.png" b/docs/en/openeuler_intelligence/intelligent_vulnerability_patching/pictures/cve.png similarity index 100% rename from "docs/en/openEuler_intelligence/intelligent_vulnerability_patching/pictures/\344\273\243\350\241\250CVE\347\232\204issue.png" rename to docs/en/openeuler_intelligence/intelligent_vulnerability_patching/pictures/cve.png diff --git a/docs/en/openeuler_intelligence/mcp_agent/_toc.yaml b/docs/en/openeuler_intelligence/mcp_agent/_toc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7af448ed9960b64b9cf3359549c785b3ca155d09 --- /dev/null +++ b/docs/en/openeuler_intelligence/mcp_agent/_toc.yaml @@ -0,0 +1,6 @@ +label: openEuler MCP Service Guide +isManual: true +description: openEuler Intelligent MCP +sections: + - label: openEuler MCP Service Guide + href: ./mcp_guide.md \ No newline at end of file diff --git a/docs/en/openeuler_intelligence/mcp_agent/mcp_guide.md b/docs/en/openeuler_intelligence/mcp_agent/mcp_guide.md new file mode 100644 index 0000000000000000000000000000000000000000..68b35325d03ddabed51005746b9008b6fb89a0fc --- /dev/null +++ b/docs/en/openeuler_intelligence/mcp_agent/mcp_guide.md @@ -0,0 +1,3 @@ +# MCP Service Guide + +(Current content is being updated, please wait.) diff --git a/docs/variable_configuration.md b/docs/variable_configuration.md deleted file mode 100644 index b105c4af1fb43d785e8fadb0af84f5965a6783af..0000000000000000000000000000000000000000 --- a/docs/variable_configuration.md +++ /dev/null @@ -1,133 +0,0 @@ -# 变量存储格式配置说明 - -## 概述 - -节点执行完成后，系统会根据节点的`output_parameters`配置自动将输出数据保存到对话变量池中。变量的存储格式有两种： - -1. **直接格式**: `conversation.key` -2. **带前缀格式**: `conversation.step_id.key` - -## 配置方式 - -### 1. 通过节点类型配置 - -在 `apps/scheduler/executor/step_config.py` 中的 `DIRECT_CONVERSATION_VARIABLE_NODE_TYPES` 集合中添加节点类型： - -```python -DIRECT_CONVERSATION_VARIABLE_NODE_TYPES: Set[str] = { - "Start", # Start节点 - "Input", # 输入节点 - "YourNewNode", # 新增的节点类型 -} -``` - -### 2. 通过名称模式配置 - -在 `DIRECT_CONVERSATION_VARIABLE_NAME_PATTERNS` 集合中添加匹配模式： - -```python -DIRECT_CONVERSATION_VARIABLE_NAME_PATTERNS: Set[str] = { - "start", # 匹配以"start"开头的节点名称 - "init", # 匹配以"init"开头的节点名称 - "config", # 新增：匹配以"config"开头的节点名称 -} -``` - -## 判断逻辑 - -系统会按以下顺序判断是否使用直接格式： - -1. 检查节点的 `call_id` 是否在 `DIRECT_CONVERSATION_VARIABLE_NODE_TYPES` 中 -2. 检查节点的 `step_name` 是否在 `DIRECT_CONVERSATION_VARIABLE_NODE_TYPES` 中 -3. 检查节点名称（小写）是否以 `DIRECT_CONVERSATION_VARIABLE_NAME_PATTERNS` 中的模式开头 -4. 检查 `step_id`（小写）是否以 `DIRECT_CONVERSATION_VARIABLE_NAME_PATTERNS` 中的模式开头 - -如果任一条件满足，则使用直接格式 `conversation.key`，否则使用带前缀格式 `conversation.step_id.key`。 - -## 使用示例 - -### 示例1：Start节点 - -```json -// 节点配置 -{ - "call_id": "Start", - "step_name": "start", - "step_id": "start_001", - "output_parameters": { - "user_name": {"type": "string"}, - "session_id": {"type": "string"} - } -} - -// 保存的变量格式 -conversation.user_name = "张三" -conversation.session_id = "sess_123" -``` - -### 示例2：普通处理节点 - -```json -// 节点配置 -{ - "call_id": "Code", - "step_name": "数据处理", - "step_id": "process_001", - "output_parameters": { - "result": {"type": "object"}, - "status": {"type": "string"} - } -} - -// 保存的变量格式 -conversation.process_001.result = {...} -conversation.process_001.status = "success" -``` - -### 示例3：配置节点（新增类型） - -```python -# 在step_config.py中添加 -DIRECT_CONVERSATION_VARIABLE_NODE_TYPES.add("GlobalConfig") -``` - -```json -// 节点配置 -{ - "call_id": "GlobalConfig", - "step_name": "全局配置", - "step_id": "config_001", - "output_parameters": { - "api_key": {"type": "secret"}, - "timeout": {"type": "number"} - } -} - -// 保存的变量格式（使用直接格式） -conversation.api_key = "xxx" -conversation.timeout = 30 -``` - -## 变量引用 - -在其他节点中可以通过以下方式引用这些变量： - -```json -{ - "input_parameters": { - "user": { - "reference": "{{conversation.user_name}}" // 直接格式变量 - }, - "data": { - "reference": "{{conversation.process_001.result}}" // 带前缀格式变量 - } - } -} -``` - -## 注意事项 - -1. **一致性**: 建议同时添加大小写版本以确保兼容性 -2. **命名冲突**: 使用直接格式时需要注意变量名冲突问题 -3. **可追溯性**: 带前缀格式便于追踪变量来源，直接格式便于全局访问 -4. **配置变更**: 修改配置后需要重启服务才能生效 \ No newline at end of file diff --git a/docs/zh/ai_full_stack/ai_container_image_userguide/_toc.yaml b/docs/zh/ai_full_stack/ai_container_image_userguide/_toc.yaml index 8782a2915974178dadfa7b97efcb19df99069a72..6cec8dda55332335d1fe0c70bce89728f9cb1694 100644 --- a/docs/zh/ai_full_stack/ai_container_image_userguide/_toc.yaml +++ b/docs/zh/ai_full_stack/ai_container_image_userguide/_toc.yaml @@ -3,4 +3,4 @@ isManual: true description: openEuler AI 容器镜像封装了 AI 框架等软件，提高 AI 应用开发或使用效率 sections: - label: AI容器镜像用户指南 - href: ./ai-container-image-user-guide.md + href: ./ai_container_image_user_guide.md diff --git a/docs/en/ai_full_stack/ai_container_image_userguide/ai-container-image-user-guide.md b/docs/zh/ai_full_stack/ai_container_image_userguide/ai_container_image_user_guide.md similarity index 74% rename from docs/en/ai_full_stack/ai_container_image_userguide/ai-container-image-user-guide.md rename to docs/zh/ai_full_stack/ai_container_image_userguide/ai_container_image_user_guide.md index b433f2f4dcb8d609d014e8ecf3f77d37d5fe5773..c28f36bbe86b9156f3c35974a93c3746110fee57 100644 --- a/docs/en/ai_full_stack/ai_container_image_userguide/ai-container-image-user-guide.md +++ b/docs/zh/ai_full_stack/ai_container_image_userguide/ai_container_image_user_guide.md @@ -8,11 +8,11 @@ openEuler AI 容器镜像封装了不同硬件算力的 SDK 以及 AI 框架、 目前，openEuler 已发布支持 Ascend 和 NVIDIA 平台的容器镜像，获取路径如下： -- [openeuler/cann](https://hub.docker.com/r/openeuler/cann) 存放 SDK 类镜像，在 openEuler 基础镜像之上安装 CANN 系列软件，适用于 Ascend 环境。 -- [openeuler/cuda](https://hub.docker.com/r/openeuler/cuda) 存放 SDK 类镜像，在 openEuler 基础镜像之上安装 CUDA 系列软件，适用于 NVIDIA 环境。 -- [openeuler/pytorch](https://hub.docker.com/r/openeuler/pytorch) 存放 AI 框架类镜像，在 SDK 镜像基础之上安装 PyTorch，根据安装的 SDK 软件内容区分适用平台。 -- [openeuler/tensorflow](https://hub.docker.com/r/openeuler/tensorflow) 存放 AI 框架类镜像，在 SDK 镜像基础之上安装 TensorFlow，根据安装的 SDK 软件内容区分适用平台。 -- [openeuler/llm](https://hub.docker.com/r/openeuler/tensorrt) 存放模型应用类镜像，在 AI 框架镜像之上包含特定大模型及工具链，根据安装的 SDK 软件内容区分适用平台。 +- `docker.io/openeuler/cann` 存放 SDK 类镜像，在 openEuler 基础镜像之上安装 CANN 系列软件，适用于 Ascend 环境。 +- `docker.io/openeuler/cuda` 存放 SDK 类镜像，在 openEuler 基础镜像之上安装 CUDA 系列软件，适用于 NVIDIA 环境。 +- `docker.io/openeuler/pytorch` 存放 AI 框架类镜像，在 SDK 镜像基础之上安装 PyTorch，根据安装的 SDK 软件内容区分适用平台。 +- `docker.io/openeuler/tensorflow` 存放 AI 框架类镜像，在 SDK 镜像基础之上安装 TensorFlow，根据安装的 SDK 软件内容区分适用平台。 +- `docker.io/openeuler/llm` 存放模型应用类镜像，在 AI 框架镜像之上包含特定大模型及工具链，根据安装的 SDK 软件内容区分适用平台。 详细的 AI 容器镜像分类和镜像 tag 的规范说明见[oEEP-0014](https://gitee.com/openeuler/TC/blob/master/oEEP/oEEP-0014%20openEuler%20AI容器镜像软件栈规范.md)。 @@ -26,7 +26,7 @@ docker pull image:tag ## 启动容器 -1. 在环境中安装`docker`，官方安装方法见[Install Docker Engine](https://docs.docker.com/engine/install/)，也可直接通过如下命令进行安装。 +1. 在环境中安装`docker`，官方安装方法见 `https://docs.docker.com/engine/install/`，也可直接通过如下命令进行安装。 ```sh yum install -y docker diff --git a/docs/zh/ai_full_stack/ai_large_model_service_images_userguide/_toc.yaml b/docs/zh/ai_full_stack/ai_large_model_service_images_userguide/_toc.yaml index bed3b23bcec452195f1b6a830c6c7e3a017413b0..e4765568ccc2d1478573f457bf3fe636ff583949 100644 --- a/docs/zh/ai_full_stack/ai_large_model_service_images_userguide/_toc.yaml +++ b/docs/zh/ai_full_stack/ai_large_model_service_images_userguide/_toc.yaml @@ -3,4 +3,4 @@ isManual: true description: 支持百川、chatglm、星火等AI大模型的容器化封装 sections: - label: AI大模型服务镜像使用指南 - href: ./llm-service-image-user-guide.md + href: ./llm_service_image_user_guide.md diff --git a/docs/zh/ai_full_stack/ai_large_model_service_images_userguide/llm-service-image-user-guide.md b/docs/zh/ai_full_stack/ai_large_model_service_images_userguide/llm_service_image_user_guide.md similarity index 100% rename from docs/zh/ai_full_stack/ai_large_model_service_images_userguide/llm-service-image-user-guide.md rename to docs/zh/ai_full_stack/ai_large_model_service_images_userguide/llm_service_image_user_guide.md diff --git a/docs/zh/intelligent_foundation/sysHAX/deploy_guide/_toc.yaml b/docs/zh/intelligent_foundation/syshax/deploy_guide/_toc.yaml similarity index 75% rename from docs/zh/intelligent_foundation/sysHAX/deploy_guide/_toc.yaml rename to docs/zh/intelligent_foundation/syshax/deploy_guide/_toc.yaml index 6bf313f0c1c4a0b7b05b5f1c2814c8f083f6d4db..92a32e90ccf0368a8970930a40d85609c9631b03 100644 --- a/docs/zh/intelligent_foundation/sysHAX/deploy_guide/_toc.yaml +++ b/docs/zh/intelligent_foundation/syshax/deploy_guide/_toc.yaml @@ -3,4 +3,4 @@ isManual: true description: 异构协同加速运行 sections: - label: sysHAX部署指南 - href: ./sysHax-deployment-guide.md + href: ./syshax_deployment_guide.md diff --git a/docs/zh/intelligent_foundation/syshax/deploy_guide/pictures/syshax-deploy.png b/docs/zh/intelligent_foundation/syshax/deploy_guide/pictures/syshax-deploy.png new file mode 100644 index 0000000000000000000000000000000000000000..466c24fca4227dd00fc450cf9e405debe60811a5 Binary files /dev/null and b/docs/zh/intelligent_foundation/syshax/deploy_guide/pictures/syshax-deploy.png differ diff --git a/docs/zh/intelligent_foundation/sysHAX/deploy_guide/sysHax-deployment-guide.md b/docs/zh/intelligent_foundation/syshax/deploy_guide/syshax_deployment_guide.md similarity index 99% rename from docs/zh/intelligent_foundation/sysHAX/deploy_guide/sysHax-deployment-guide.md rename to docs/zh/intelligent_foundation/syshax/deploy_guide/syshax_deployment_guide.md index a8573f7b4368a2d3a0a2e85928507b1d6425e516..452ccd2f4885015bcbf12a0087d6409e3f4f3b8d 100644 --- a/docs/zh/intelligent_foundation/sysHAX/deploy_guide/sysHax-deployment-guide.md +++ b/docs/zh/intelligent_foundation/syshax/deploy_guide/syshax_deployment_guide.md @@ -12,7 +12,7 @@ sysHAX功能定位为K+X异构融合推理加速，主要包含两部分功能 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"a/docs/zh/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/pictures/\351\203\250\347\275\262\350\247\206\345\233\276.png" and /dev/null differ diff --git a/docs/zh/openEuler_intelligence/intelligent_assistant/advance/smart_diagnosis/user_guide/diagnosis_guidance.md b/docs/zh/openEuler_intelligence/intelligent_assistant/advance/smart_diagnosis/user_guide/diagnosis_guidance.md deleted file mode 100644 index eb428ada7017effc1e0d58c18c71eae7ffe88050..0000000000000000000000000000000000000000 --- a/docs/zh/openEuler_intelligence/intelligent_assistant/advance/smart_diagnosis/user_guide/diagnosis_guidance.md +++ /dev/null @@ -1,50 +0,0 @@ -# 智能插件：智能诊断 - -部署智能诊断工具后，可以通过 EulerCopilot 智能体框架实现对本机进行诊断。 -在智能诊断模式提问，智能体框架服务可以调用本机的诊断工具诊断异常状况、分析并生成报告。 - -## 操作步骤 - -**步骤1** 切换到“智能插件”模式 - -```bash -copilot -p -``` - -![切换到智能插件模式](./pictures/shell-plugin-diagnose-switch-mode.png) - -**步骤2** 异常事件检测 - -```bash -帮我进行异常事件检测 -``` - -按下 `Ctrl + O` 键提问，然后在插件列表中选择“智能诊断”。 - -![异常事件检测](./pictures/shell-plugin-diagnose-detect.png) - -**步骤3** 查看异常事件详情 - -```bash -查看 XXX 容器的异常事件详情 -``` - -![查看异常事件详情](./pictures/shell-plugin-diagnose-detail.png) - -**步骤4** 执行异常事件分析 - -```bash -请对 XXX 容器的 XXX 指标执行 profiling 分析 -``` - -![异常事件分析](./pictures/shell-plugin-diagnose-profiling.png) - -**步骤5** 查看异常事件分析报告 - -等待 5 至 10 分钟，然后查看分析报告。 - -```bash -查看 对应的 profiling 报告 -``` - -![执行优化脚本](./pictures/shell-plugin-diagnose-report.png) diff --git a/docs/zh/openEuler_intelligence/intelligent_assistant/advance/smart_tuning/user_guide/tune_guidance.md b/docs/zh/openEuler_intelligence/intelligent_assistant/advance/smart_tuning/user_guide/tune_guidance.md deleted file mode 100644 index a9e915e1d80a36e8483d84729d319be5c8e873ec..0000000000000000000000000000000000000000 --- a/docs/zh/openEuler_intelligence/intelligent_assistant/advance/smart_tuning/user_guide/tune_guidance.md +++ /dev/null @@ -1,53 +0,0 @@ -# 智能插件：智能调优 - -部署智能调优工具后，可以通过 EulerCopilot 智能体框架实现对本机进行调优。 -在智能调优模式提问，智能体框架服务可以调用本机的调优工具采集性能指标，并生成性能分析报告和性能优化建议。 - -## 操作步骤 - -**步骤1** 切换到“智能调优”模式 - -```bash -copilot -t -``` - -![切换到智能调优模式](./pictures/shell-plugin-tuning-switch-mode.png) - -**步骤2** 采集性能指标 - -```bash -帮我进行性能指标采集 -``` - -![性能指标采集](./pictures/shell-plugin-tuning-metrics-collect.png) - -**步骤3** 生成性能分析报告 - -```bash -帮我生成性能分析报告 -``` - -![性能分析报告](./pictures/shell-plugin-tuning-report.png) - -**步骤4** 生成性能优化建议 - -```bash -请生成性能优化脚本 -``` - -![性能优化脚本](./pictures/shell-plugin-tuning-script-gen.png) - -**步骤5** 选择“执行命令”，运行优化脚本 - -![执行优化脚本](./pictures/shell-plugin-tuning-script-exec.png) - -- 脚本内容如图： - ![优化脚本内容](./pictures/shell-plugin-tuning-script-view.png) - -## 远程调优 - -如果需要对其他机器进行远程调优，请在上文示例的问题前面加上对应机器的 IP 地址。 - -例如：`请对 192.168.1.100 这台机器进行性能指标采集。` - -进行远程调优前请确保目标机器已部署智能调优工具，同时请确保 EulerCopilot 智能体框架能够访问目标机器。 diff --git a/docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/API_key.md b/docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/API_key.md deleted file mode 100644 index 5fc8699eb097e6a7dcb17409519343c46b9801ca..0000000000000000000000000000000000000000 --- a/docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/API_key.md +++ /dev/null @@ -1,29 +0,0 @@ -# 获取 API Key - -## 前言 - -EulerCopilot 命令行助手使用 API Key 来验证用户身份，并获取 API 访问权限。 -因此，开始使用前，您需要先获取 API Key。 - -## 注意事项 - -- 请妥善保管您的 API Key，不要泄露给他人。 -- API Key 仅用于命令行助手与 DevStation 桌面端，不用于其他用途。 -- 每位用户仅可拥有一个 API Key，重复创建 API Key 将导致旧密钥失效。 -- API Key 仅在创建时显示一次，请务必及时保存。若密钥丢失，您需要重新创建。 -- 若您在使用过程中遇到“请求过于频繁”的错误，您的 API Key 可能已被他人使用，请及时前往官网刷新或撤销 API Key。 - -## 获取方法 - -1. 登录 EulerCopilot 网页端。 -2. 点击右上角头像，选择“API KEY”。 -3. 点击“新建”按钮。 -4. **请立即保存 API Key，它仅在创建时显示一次，请勿泄露给他人。** - -## 管理 API Key - -1. 登录 EulerCopilot 网页端。 -2. 点击右上角头像，选择“API KEY”。 -3. 点击“刷新”按钮，刷新 API Key；点击“撤销”按钮，撤销 API Key。 - -- 刷新 API Key 后，旧密钥失效，请立即保存新生成的 API Key。 diff --git a/docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/online.md b/docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/online.md deleted file mode 100644 index 3e946173658dffbc296f616d94bfbd92a3d70890..0000000000000000000000000000000000000000 --- a/docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/online.md +++ /dev/null @@ -1,596 +0,0 @@ -# **EulerCopilot 智能助手部署指南** - -版本信息 -当前版本：v0.9.5 -更新日期：2025年4月1日 - -## 产品概述 - -EulerCopilot 是一款智能问答工具，使用 EulerCopilot 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 / A-Tune / x2openEuler / EulerMaker / 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。 - -本指南提供基于自动化脚本的EulerCopilot智能助手系统部署说明，支持一键自动部署和手动分步部署两种方式。 - -### 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 | 9988 (内部端口) | 检索增强服务 | -| authhub-backend-service | 11120 (内部端口) | 鉴权服务后端 | -| authhub-web-service | 8000 | 鉴权服务前端 | -| mysql | 3306 (内部端口) | MySQL数据库 | -| redis | 6379 (内部端口) | Redis数据库 | -| minio | 9000 (内部端口) 9001(外部部端口) | minio数据库 | -| mongo | 27017 (内部端口) | mongo数据库 | -| postgres | 5432 (内部端口) | 向量数据库 | -| secret_inject | 无 | 配置文件安全复制工具 | - -### 软件要求 - -| 类型 | 版本要求 | 说明 | -|----------------| -------------------------------------|--------------------------------------| -| 操作系统 | openEuler 22.03 LTS 及以上版本 | 无 | -| K3s | >= v1.30.2，带有 Traefik Ingress 工具 | K3s 提供轻量级的 Kubernetes 集群，易于部署和管理 | -| Helm | >= v3.15.3 | 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 | -| python | >=3.9.9 | python3.9.9 以上版本为模型的下载和安装提供运行环境 | - ---- - -### 硬件规格 - -| 硬件资源 | 最小配置 | 推荐配置 | -|--------------|----------------------------|------------------------------| -| CPU | 4 核心 | 16 核心及以上 | -| RAM | 4 GB | 64 GB | -| 存储 | 32 GB | 64G | -| 大模型名称 | deepseek-llm-7b-chat | DeepSeek-R1-Llama-8B | -| 显存 (GPU) | NVIDIA RTX A4000 8GB | NVIDIA A100 80GB * 2 | - -**关键说明**： - -- 纯CPU环境，建议通过调用 OpenAI 接口或使用自带的模型部署方式来实现功能。 -- 如果k8s集群环境，则不需要单独安装k3s，要求version >= 1.28 - ---- - -### 部署视图 - -![部署图](./pictures/部署视图.png) - ---- - -### 域名配置 - -需准备以下两个服务域名： - -- authhub认证服务：authhub.eulercopilot.local -- EulerCopilot web服务：www.eulercopilot.local - -```bash -# 本地Windows主机中进行配置 -# 打开 C:\Windows\System32\drivers\etc\hosts 添加记录 -# 替换127.0.0.1为目标服务器的IP -127.0.0.1 authhub.eulercopilot.local -127.0.0.1 www.eulercopilot.local -``` - -## 快速开始 - -### 1. 获取部署脚本 - -- 从 EulerCopilot 的官方Git仓库 [euler-copilot-framework](https://gitee.com/openeuler/euler-copilot-framework) 下载最新的部署仓库 -- 如果您正在使用 Kubernetes，则不需要安装 k3s 工具。 - -```bash -# 下载目录以 home 为例 -cd /home -``` - -```bash -git clone https://gitee.com/openeuler/euler-copilot-framework.git -b dev -``` - -```bash -cd euler-copilot-framework/deploy/scripts -``` - -```bash -# 为脚本文件添加可执行权限 -chmod -R +x ./* -``` - -### 2. 部署EulerCopilot - -#### **一键部署** - -```bash -cd /home/euler-copilot-framework/deploy/scripts -``` - -```bash -bash deploy.sh -``` - -```bash -# 输入0进行一键自动部署 -============================== - 主部署菜单 -============================== -0) 一键自动部署 -1) 手动分步部署 -2) 重启服务 -3) 卸载所有组件并清除数据 -4) 退出程序 -============================== -请输入选项编号（0-3）: 0 -``` - ---- - -#### **分步部署** - -```bash -# 选择1 -> 1 进入手动分步部署 -============================== - 主部署菜单 -============================== -0) 一键自动部署 -1) 手动分步部署 -2) 重启服务 -3) 卸载所有组件并清除数据 -4) 退出程序 -============================== -请输入选项编号（0-3）: 1 -``` - -```bash -# 输入选项编号（0-9），逐步部署 -============================== - 手动分步部署菜单 -============================== -1) 执行环境检查脚本 -2) 安装k3s和helm -3) 安装Ollama -4) 部署Deepseek模型 -5) 部署Embedding模型 -6) 安装数据库 -7) 安装AuthHub -8) 安装EulerCopilot -9) 返回主菜单 -============================== -请输入选项编号（0-9）: -``` - ---- - -#### **重启服务** - -```bash -# 输入选项重启服务 -============================== - 服务重启菜单 -============================== -可重启的服务列表： -1) authhub-backend -2) authhub -3) framework -4) minio -5) mongo -6) mysql -7) pgsql -8) rag -9) rag-web -10) redis -11) web -12) 返回主菜单 -============================== -请输入要重启的服务编号（1-12）: -``` - -#### **卸载所有组件** - -```bash -sudo ./deploy.sh -# 选择2进行完全卸载 -============================== - 主部署菜单 -============================== -0) 一键自动部署 -1) 手动分步部署 -2) 卸载所有组件并清除数据 -3) 退出程序 -============================== -请输入选项编号（0-3）: 2 -``` - ---- - -**关键说明**： - -- 在部署过程中，您需要输入 Authhub 域名和 EulerCopilot 域名, 不输入则使用默认域名`authhub.eulercopilot.local`, `www.eulercopilot.local`。 -- 资源不足时可参考 FAQ 中的评估资源可用性解决 -- 查看组件日志 - -```bash -kubectl logs -n euler-copilot -``` - -- 查看服务状态 - -```bash -kubectl get pod -n euler-copilot -``` - -- 大模型配置修改并更新EulerCopilot - -```bash -cd /home/euler-copilot-framework/deploy/chart/euler-copilot -``` - -```bash -vim values.yaml -``` - -```bash -helm upgrade euler-copilot -n euler-copilot . -``` - -## 验证安装 - -恭喜您，**EulerCopilot** 已成功部署！为了开始您的体验，请在浏览器中输入 `https://您的EulerCopilot域名` 链接访问 EulerCopilot 的网页界面： - -首次访问时，您需要点击页面上的 **立即注册** 按钮来创建一个新的账号，并完成登录过程。 - -![Web登录界面](./pictures/WEB登录界面.png) -![Web 界面](./pictures/WEB界面.png) - -## 构建专有领域智能问答 - -点击知识库，可登录本地知识库管理页面，详细信息请参考文档 [本地资产库构建指南](../../../advance/knowledge_base/deploy_guide/witChainD_deployment.md) -**知识库登录默认账号 `admin`, 密码 `123456`** - ---- - -## 附录 - -### 大模型准备 - -#### GPU 环境 - -可直接使用部署的deepseek大模型参考以下方式进行部署 - -1. 下载模型文件： - - ```bash - huggingface-cli download --resume-download Qwen/Qwen1.5-14B-Chat --local-dir Qwen1.5-14B-Chat - ``` - -2. 创建终端 control - - ```bash - screen -S control - ``` - - ```bash - python3 -m fastchat.serve.controller - ``` - - 按 Ctrl A+D 置于后台 - -3. 创建新终端 api - - ```bash - screen -S api - ``` - - ```bash - python3 -m fastchat.serve.openai_api_server --host 0.0.0.0 --port 30000 --api-keys sk-123456 - ``` - - 按 Ctrl A+D 置于后台 - 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 - - ```bash - mkdir -p /root/py310 - ``` - - ```bash - conda create --prefix=/root/py310 python==3.10.14 - ``` - - ```bash - conda activate /root/py310 - ``` - -4. 创建新终端 worker - - ```bash - screen -S worker - ``` - - ```bash - screen -r worker - ``` - - 安装 fastchat 和 vllm - - ```bash - pip install fschat vllm - ``` - - 安装依赖： - - ```bash - pip install fschat[model_worker] - ``` - - ```bash - 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 - ``` - - 按 Ctrl A+D 置于后台 - -5. 按照如下方式修改配置的大模型参数，并更新服务。 - - ```bash - vim /home/euler-copilot-framework/deploy/chart/euler_copilot/values.yaml - ``` - - 修改如下部分 - - ```yaml - # 模型设置 - models: - # 用于问答的大模型；需要为OpenAI兼容接口 - answer: - # [必填] 接口URL（无需带上“v1”后缀） - url: http://172.168.178.107:11434 - # [必填] 接口API Key；默认置空 - key: sk-123456 - # [必填] 模型名称 - name: deepseek-llm-7b-chat:latest - # [必填] 模型最大上下文数；建议>=8192 - ctx_length: 8192 - # 模型最大输出长度，建议>=2048 - max_tokens: 2048 - # 用于Function Call的模型；建议使用特定推理框架 - functioncall: - # 推理框架类型，默认为ollama - # 可用的框架类型：["vllm", "sglang", "ollama", "openai"] - backend: - # 模型地址；不填则与问答模型一致 - url: ollama - # API Key；不填则与问答模型一致 - key: - # 模型名称；不填则与问答模型一致 - name: - # 模型最大上下文数；不填则与问答模型一致 - ctx_length: - # 模型最大输出长度；不填则与问答模型一致 - max_tokens: - # 用于数据向量化（Embedding）的模型 - embedding: - # 推理框架类型，默认为openai - # [必填] Embedding接口类型：["openai", "mindie"] - type: openai - # [必填] Embedding URL（需要带上“v1”后缀） - url: http://172.168.178.107:11434 - # [必填] Embedding 模型API Key - key: sk-123456 - # [必填] Embedding 模型名称 - name: bge-m3:latest - ``` - - ```bash - # 更新服务 - helm upgrade -n euler-copilot euler-copilot . - # 重启framework服务 - kubectl get pod -n euler-copilot - kubectl delete pod framework-deploy-65b669fc58-q9bw7 -n euler-copilot - ``` - -#### NPU 环境 - -NPU 环境部署可参考链接 [MindIE安装指南](https://www.hiascend.com/document/detail/zh/mindie/10RC2/whatismindie/mindie_what_0001.html) - -### FAQ - -#### 1. 解决 Hugging Face 连接错误 - -如果遇到如下连接错误： - -```text -urllib3.exceptions.NewConnectionError: : Failed to establish a new connection: [Errno 101] Network is unreachable -``` - -尝试以下解决方案： - -- 更新 `huggingface_hub` 包到最新版本。 - - ```bash - pip3 install -U huggingface_hub - ``` - -- 如果网络问题依旧存在，可以尝试使用镜像站点作为端点。 - - ```bash - export HF_ENDPOINT=https://hf-mirror.com - ``` - -#### 2. 在 RAG 容器中调用问答接口 - -进入对应的 RAG Pod 后，可以通过 `curl` 命令发送 POST 请求来获取问答结果。请确保在请求体中提供具体的问题文本。 - -```bash -curl -k -X POST "http://localhost:9988/kb/get_answer" \ - -H "Content-Type: application/json" \ - -d '{ - "question": "您的问题", - "kb_sn": "default_test", - "fetch_source": true - }' -``` - -#### 3. 解决 `helm upgrade` 错误 - -当 Kubernetes 集群不可达时，您可能会遇到类似下面的错误信息： - -```text -Error: UPGRADE FAILED: Kubernetes cluster unreachable -``` - -确保设置了正确的 KUBECONFIG 环境变量指向有效的配置文件。 - -```bash -echo "export KUBECONFIG=/etc/rancher/k3s/k3s.yaml" >> /root/.bashrc -source /root/.bashrc -``` - -#### 4. 查看 Pod 日志失败 - -如果您遇到查看 Pod 日志时权限被拒绝的问题，检查是否正确配置了代理设置，并将本机 IP 地址添加到 `no_proxy` 环境变量中。 - -```bash -cat /etc/systemd/system/k3s.service.env -``` - -编辑文件并确保包含： - -```bash -no_proxy=XXX.XXX.XXX.XXX -``` - -#### 5. GPU环境中大模型流式回复问题 - -对于某些服务执行 curl 大模型时无法进行流式回复的情况，尝试修改请求中的 `"stream"` 参数为 `false`。此外，确认已安装兼容版本的 Pydantic 库。 - -```bash -pip install pydantic==1.10.13 -``` - -#### 6. sglang 模型部署指南 - -按照以下步骤部署基于 sglang 的模型： - -```bash -# 1. 激活名为 `myenv` 的 Conda 环境，该环境基于 Python 3.10 创建： -conda activate myenv - -# 2. 安装 sglang 及其所有依赖项，指定版本为 0.3.0 -pip install "sglang[all]==0.3.0" - -# 3. 从特定索引安装 flashinfer，确保与您的 CUDA 和 PyTorch 版本兼容 -pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/ - -# 4. 使用 sglang 启动服务器，配置如下： -python -m sglang.launch_server \ - --served-model-name Qwen2.5-32B \ - --model-path Qwen2.5-32B-Instruct-AWQ \ - --host 0.0.0.0 \ - --port 8001 \ - --api-key "sk-12345" \ - --mem-fraction-static 0.5 \ - --tp 8 -``` - -- 验证安装 - - ```bash - pip show sglang - pip show flashinfer - ``` - -**注意事项：** - -- API Key：请确保 `--api-key` 参数中的 API 密钥是正确的 -- 模型路径： 确保 `--model-path` 参数中的路径是正确的，并且模型文件存在于该路径下。 -- CUDA 版本：确保你的系统上安装了 CUDA 12.1 和 PyTorch 2.4，因为 `flashinfer` 包依赖于这些特定版本。 -- 线程池大小：根据你的GPU资源和预期负载调整线程池大小。如果你有 8 个 GPU，那么可以选择 --tp 8 来充分利用这些资源。 - -#### 7. 获取 Embedding - -使用 curl 发送 POST 请求以获取 embedding 结果： - -```bash -curl -k -X POST http://localhost:11434/v1/embeddings \ - -H "Content-Type: application/json" \ - -d {"input": "The food was delicious and the waiter...", "model": "bge-m3", "encoding_format": "float"} -``` - -#### 8. 生成证书 - -为了生成自签名证书，首先下载 [mkcert](https://github.com/FiloSottile/mkcert/releases)工具，然后运行以下命令： - -```bash -mkcert -install -mkcert example.com -``` - -最后，将生成的证书和私钥拷贝到 values.yaml 中, 并应用至 Kubernetes Secret。 - -```bash -vim /home/euler-copilot-framework_openeuler/deploy/chart_ssl/traefik-secret.yaml -``` - -```bash -kubectl apply -f traefik-secret.yaml -``` - -#### 9. 问题排查方法 - -1. **获取集群事件信息** - - 为了更好地定位 Pod 失败的原因，请首先检查 Kubernetes 集群中的事件 (Events)。这可以提供有关 Pod 状态变化的上下文信息。 - - ```bash - kubectl get events -n euler-copilot - ``` - -2. **验证镜像拉取状态** - - 确认容器镜像是否成功拉取。如果镜像未能正确加载，可能是由于网络问题或镜像仓库配置错误。 - - ```bash - k3s crictl images - ``` - -3. **审查 Pod 日志** - - 检查相关 Pod 的日志，以寻找可能的错误信息或异常行为。这对于诊断应用程序级别的问题特别有用。 - - ```bash - kubectl logs rag-deploy-service-5b7887644c-sm58z -n euler-copilot - ``` - -4. **评估资源可用性** - - 确保 Kubernetes 集群有足够的资源（如 CPU、内存和存储）来支持 Pod 的运行。资源不足可能导致镜像拉取失败或其他性能问题，或使得 Pod 状态从 Running 变为 Pending 或 Completed。可查看磁盘空间并保证至少有 30% 的可用空间。这有助于维持 Pod 的稳定运行状态。参考该链接挂载空间较大的磁盘[How to move k3s data to another location](https://mrkandreev.name/snippets/how_to_move_k3s_data_to_another_location/) - - ```bash - kubectl top nodes - ``` - -5. **确认 k3s 版本兼容性** - - 如果遇到镜像拉取失败且镜像大小为 0 的问题，请检查您的 k3s 版本是否符合最低要求（v1.30.2 或更高）。较低版本可能存在不兼容的问题。 - - ```bash - k3s -v - ``` - -6. **检查配置** - - 检查 `values.yaml` 文件中关于 OIDC 配置和域名配置是否填写正确，确保配置无误后更新服务。 - - ```bash - cat /home/euler-copilot-framework/deploy/chart/euler_copilot - ``` - - ```bash - vim values.yaml | grep oidc - ``` - - ```bash - helm upgrade euler-copilot -n euler-copilot . - ``` diff --git 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"b/docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/pictures/\351\203\250\347\275\262\350\247\206\345\233\276.png" deleted file mode 100644 index 181bf1d2ddbe15cfd296c27df27d865bdbce8d69..0000000000000000000000000000000000000000 Binary files "a/docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/pictures/\351\203\250\347\275\262\350\247\206\345\233\276.png" and /dev/null differ diff --git a/docs/zh/openEuler_intelligence/intelligent_vulnerability_patching/_toc.yaml b/docs/zh/openEuler_intelligence/intelligent_vulnerability_patching/_toc.yaml deleted file mode 100644 index b1a4ea7f0bc6c9169f6c9cc1ab084ad03074091b..0000000000000000000000000000000000000000 --- a/docs/zh/openEuler_intelligence/intelligent_vulnerability_patching/_toc.yaml +++ /dev/null @@ -1,6 +0,0 @@ -label: 智能化漏洞修补用户指南 -isManual: true -description: 支持kernel仓库的漏洞修补 -sections: - - label: 智能化漏洞修补用户指南 - href: ./intelligent-vulnerability-patching-user-guide.md diff --git a/docs/zh/openEuler_intelligence/intelligent_assistant/_toc.yaml b/docs/zh/openeuler_intelligence/intelligent_assistant/_toc.yaml similarity index 90% rename from docs/zh/openEuler_intelligence/intelligent_assistant/_toc.yaml rename to docs/zh/openeuler_intelligence/intelligent_assistant/_toc.yaml index f6172785fb448f5ae708916e5bcb01a6d652e91e..9bf30db6740b15d1cab02614f9ae50581d328f80 100644 --- a/docs/zh/openEuler_intelligence/intelligent_assistant/_toc.yaml +++ b/docs/zh/openeuler_intelligence/intelligent_assistant/_toc.yaml @@ -25,7 +25,7 @@ sections: - label: 使用指导 sections: - label: API Key 获取 - href: ./quick_start/smart_shell/user_guide/API_key.md + href: ./quick_start/smart_shell/user_guide/api_key.md - label: 命令行助手使用 href: ./quick_start/smart_shell/user_guide/shell.md - label: 进阶使用 @@ -45,9 +45,9 @@ sections: - label: 知识库管理 sections: - label: 部署指南 - href: ./advance/knowledge_base/deploy_guide/witChainD_deployment.md + href: ./advance/knowledge_base/deploy_guide/witchaind_deployment.md - label: 使用指导 - href: ./advance/knowledge_base/user_guide/witChainD_guidance.md + href: ./advance/knowledge_base/user_guide/witchaind_guidance.md - label: 工作流编排 sections: - label: 部署指南 diff --git "a/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/pictures/witChainD/\350\265\204\344\272\247\345\272\223\345\210\233\345\273\272\345\256\214\346\210\220\347\225\214\351\235\242.png" b/docs/zh/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/pictures/witchaind/asset_library_creation_completed_interface.png similarity index 100% rename from "docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/pictures/witChainD/\350\265\204\344\272\247\345\272\223\345\210\233\345\273\272\345\256\214\346\210\220\347\225\214\351\235\242.png" rename to 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"docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/pictures/witChainD/\346\237\245\347\234\213\350\265\204\344\272\247\345\272\223\347\225\214\351\235\242.png" rename to docs/zh/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/pictures/witchaind/view_asset_library_interface.png diff --git "a/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/pictures/witChainD/witchaind\347\231\273\345\275\225\347\225\214\351\235\242.png" b/docs/zh/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/pictures/witchaind/witchaind_login_interface.png similarity index 100% rename from "docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/pictures/witChainD/witchaind\347\231\273\345\275\225\347\225\214\351\235\242.png" rename to docs/zh/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/pictures/witchaind/witchaind_login_interface.png diff --git a/docs/zh/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/witChainD_deployment.md b/docs/zh/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/witchaind_deployment.md similarity index 58% rename from docs/zh/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/witChainD_deployment.md rename to docs/zh/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/witchaind_deployment.md index 7aa0d750ae4421365b42ed38e45d3e06d88767ff..cfb912bb95777b0473afe6d84e0020e0360ff19c 100644 --- a/docs/zh/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/witChainD_deployment.md +++ b/docs/zh/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/deploy_guide/witchaind_deployment.md @@ -9,9 +9,9 @@ 通过指定资产名称、资产简介、资产默认解析方法、资产默认分块大小和embedding模型等条目创建、删除、导入资产、编辑资产或资产信息 - 文档管理 - - 支持用户上传限定大小和限定数量的文件，也允许用户下载自己上传的文件，用户可以通过点击资产卡片的形式进入资产，此时文档以条目的形式展示。 - - 支持txt、md、xlsx、docx和doc以及pdf文档的文档解析 - - 文档解析方式有三种general、ocr和enhanced模式，general模式下只提取文字和表格，ocr模式下不仅提取文字和表格嗨提取部分文档的图片内容，enhanced在ocr的基础上对图片中提取的内容进行总结。 + - 支持用户上传限定大小和限定数量的文件，也允许用户下载自己上传的文件，用户可以通过点击资产卡片的形式进入资产，此时文档以条目的形式展示。 + - 支持txt、md、xlsx、docx和doc以及pdf文档的文档解析 + - 文档解析方式有三种general、ocr和enhanced模式，general模式下只提取文字和表格，ocr模式下不仅提取文字和表格嗨提取部分文档的图片内容，enhanced在ocr的基础上对图片中提取的内容进行总结。 - 文档解析结果管理： @@ -28,7 +28,7 @@ 请在浏览器中输入 `https://$(wichaind域名)`访问 EulerCopilot 的 WitChainD 网页, 登入界面如下，输入账号(admin)、密码(123456)点击登录按钮登录系统。 -![witchaind登录界面](./pictures/witChainD/witchaind登录界面.png) +![witchaind登录界面](./pictures/witchaind/witchaind_login_interface.png) ## 新建资产 @@ -36,7 +36,7 @@ 进入资产卡片显示页面，卡片展示了资产的名称、简介、文档篇数、创建时间和资产ID。 -![查看资产库界面](./pictures/witChainD/查看资产库界面.png) +![查看资产库界面](./pictures/witchaind/view_asset_library_interface.png) 可通过红框中的按钮将卡片展示的资产转换为条目显示。 @@ -44,18 +44,18 @@ 点击新建资产，会跳出资产信息配置的框图 -![新建资产库界面](./pictures/witChainD/新建资产库界面.png) +![新建资产库界面](./pictures/witchaind/create_new_asset_library_interface.png) 填写资产库名称、资产库描述（可选）、语言、嵌入模型、默认解析方法和默认文件分块大小后，点击确定。 -![新建资产库填写展示界面](./pictures/witChainD/新建资产库填写展示界面.png) +![新建资产库填写展示界面](./pictures/witchaind/create_new_asset_library_fill_display_interface.png) 资产库建立完毕之后会显示是否添加文档，点击确定 -![资产库创建完成界面](./pictures/witChainD/资产库创建完成界面.png) +![资产库创建完成界面](./pictures/witchaind/asset_library_creation_completed_interface.png) 点击确定完成后进入资产库 -![资产库创建完成界面](./pictures/witChainD/进入资产库界面.png) +![资产库创建完成界面](./pictures/witchaind/enter_asset_library_interface.png) ## 导入文档 @@ -63,60 +63,60 @@ 点击导入文档按钮跳出文档上传框，选择本地文件并勾选进行上传 -![导入文档界面](./pictures/witChainD/导入文档界面.png) +![导入文档界面](./pictures/witchaind/import_document_interface.png) -![选择文件](./pictures/witChainD/选择文件.png) +![选择文件](./pictures/witchaind/select_file.png) ### 批量导入 点击1批量导入资产，2选择本地资产，3勾选本地资产，最后点击确定进行资产导入。 -![批量导入界面](./pictures/witChainD/批量导入界面.png) +![批量导入界面](./pictures/witchaind/batch_import_interface.png) 资产导入中 -![批量正在导入界面](./pictures/witChainD/批量正在导入界面.png) +![批量正在导入界面](./pictures/witchaind/batch_importing_interface.png) 资产导入成功 -![批量导入成功界面](./pictures/witChainD/批量导入成功界面.png) +![批量导入成功界面](./pictures/witchaind/batch_import_successful_interface.png) ## 解析文档 等待解析中，点击取消可以停止文档解析。 -![解析中界面](./pictures/witChainD/解析中界面.png) +![解析中界面](./pictures/witchaind/parsing_interface.png) 解析完成后，解析状态会显示解析成功。 -![解析成功界面](./pictures/witChainD/解析成功界面.png) +![解析成功界面](./pictures/witchaind/parse_successful_interface.png) 文档解析方式有三种general、ocr和enhanced模式，请根据需要选择合适的文档解析方法 -![解析方法选择界面](./pictures/witChainD/解析方法选择界面.png) +![解析方法选择界面](./pictures/witchaind/parsing_method_selection_interface.png) 解析完毕可以通过点击文件名进入文档解析结果展示详情，可以查看文档解析结果，如下图所示： -![解析详情界面](./pictures/witChainD/解析详情界面.png) +![解析详情界面](./pictures/witchaind/parse_details_interface.png) 可以通过1过滤文档解析的片段、表格和图片等内容，通过2可以通过关键字检索模糊检索对应的片段，通过3可以设定是否在检索中是否启用片段，如下图所示： -![解析结果过滤界面](./pictures/witChainD/解析结果过滤界面.png) +![解析结果过滤界面](./pictures/witchaind/parse_result_filter_interface.png) ## 导出资产 点击回到首页 -![回到首页](./pictures/witChainD/回到首页.png) +![回到首页](./pictures/witchaind/return_to_homepage.png) 再点击导出资产 -![导出资产界面](./pictures/witChainD/导出资产界面.png) +![导出资产界面](./pictures/witchaind/export_assets_interface.png) 显示资产正在导出中，如下图所示： -![正在导出界面](./pictures/witChainD/正在导出界面.png) +![正在导出界面](./pictures/witchaind/exporting_interface.png) 导出成功点击下载，显示下载成功 -![导出成功](./pictures/witChainD/导出成功.png) +![导出成功](./pictures/witchaind/export_successful.png) diff --git "a/docs/zh/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/pictures/\350\265\204\344\272\247\345\272\223\347\256\241\347\220\206\347\225\214\351\235\242.png" b/docs/zh/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/pictures/asset_library_management_interface.png similarity index 100% rename from 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a/docs/zh/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/witChainD_guidance.md b/docs/zh/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/witchaind_guidance.md similarity index 73% rename from docs/zh/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/witChainD_guidance.md rename to docs/zh/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/witchaind_guidance.md index 4759a57baa4e35ee529e9f4da70e1d1405612e6e..9caf3aaebfe2eef9f9645ba5127778ff8c8a9c44 100644 --- a/docs/zh/openEuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/witChainD_guidance.md +++ b/docs/zh/openeuler_intelligence/intelligent_assistant/advance/knowledge_base/user_guide/witchaind_guidance.md @@ -8,31 +8,31 @@ 该页面为资产库管理界面，用户登录后将会进入该界面。 -![资产库管理界面](./pictures/资产库管理界面.png) +![资产库管理界面](./pictures/asset_library_management_interface.png) **支持操作：** - 配置模型：点击右上角的设置按键，可以修改模型相关的配置。 - ![模型配置](./pictures/模型配置.png) + ![模型配置](./pictures/model_configuration.png) - 新增资产库：点击新建资产库按钮新建，支持自定义名称、描述、语言、嵌入模型、解析方法、文件分块大小、文档类别。注意：重复名称会自动将名称修改成资产库id。 - ![新增资产库](./pictures/新增资产库.png) + ![新增资产库](./pictures/create_new_asset_library.png) - 编辑资产库：点击资产库的编辑按钮编辑，支持修改名称、描述、语言、解析方法、文件分块大小、文档类别。注意：不能修改成重复名称。 - ![编辑资产库](./pictures/编辑资产库0.png) + ![编辑资产库](./pictures/edit_asset_library_0.png) - ![编辑资产库](./pictures/编辑资产库.png) + ![编辑资产库](./pictures/edit_asset_library.png) - 导出资产库：点击资产库的导出按钮导出，导出完成后需要按任务列表中的下载任务下载对应资产库到本地。 - ![导出资产库](./pictures/导出资产库.png) + ![导出资产库](./pictures/export_asset_library.png) - 批量导入资产库：点击批量导入，上传本地文件后选中即可导入。 - ![批量导入资产库](./pictures/批量导入资产库.png) + ![批量导入资产库](./pictures/batch_import_asset_libraries.png) - 搜索资产库：在搜索栏中键入文本，可以搜索得到名称包含对应文本的资产库。 @@ -40,25 +40,25 @@ 在资产管理界面点击对应资产库，可以进入文档管理界面。 -![文档管理界面](./pictures/文档管理界面.png) +![文档管理界面](./pictures/document_management_interface.png) **支持操作：** - 导入文档：点击导入文档，从本地上传文件导入，导入后会自动以该资产库默认配置开始解析。 - ![导入文档](./pictures/导入文档.png) + ![导入文档](./pictures/import_documents.png) - 解析文档：点击操作中的解析，对文档进行解析。也可以选中多个文档批量解析。 - ![文档解析](./pictures/文档解析.png) + ![文档解析](./pictures/document_parsing.png) - ![文档解析2](./pictures/文档解析2.png) + ![文档解析2](./pictures/document_parsing_2.png) - ![解析完成](./pictures/解析完成.png) + ![解析完成](./pictures/parsing_complete.png) - 编辑文档配置：点击编辑对文档配置进行编辑，支持编辑文档名称、解析方法、类别、文件分块大小。 - ![编辑文档配置](./pictures/编辑文档配置.png) + ![编辑文档配置](./pictures/edit_document_configuration.png) - 下载文档：点击下载即可将文档下载至本地，也可以选中多个文档批量下载。 @@ -66,22 +66,22 @@ - 搜索文档：点击文档名称旁的搜索键，在弹出的搜索框中键入搜索的文本，可以搜索得到名称包含这些文本的文档。 - ![搜索文档](./pictures/搜索文档.png) + ![搜索文档](./pictures/search_documents.png) - 编辑资产库配置：支持编辑资产库名称、描述、语言、默认解析方法、文件分块大小、文档信息类别。 - ![编辑资产库配置](./pictures/编辑资产库配置.png) + ![编辑资产库配置](./pictures/edit_asset_library_configuration.png) ## 3. 解析结果管理界面 点击解析完成的文档，可以进入文档的解析结果管理界面。界面中会按照顺序显示文档解析后的文本块内容预览，每个文本块会附带一个标签，表示该文本块中的信息来源于文档中的段落、列表或者是图片。右侧的开关表示该文本块是否被启用。 -![文本块结果预览](./pictures/文本块结果预览.png) +![文本块结果预览](./pictures/text_block_result_preview.png) 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b/docs/zh/openeuler_intelligence/intelligent_assistant/advance/smart_tuning/deploy_guide/tune_deployment.md similarity index 85% rename from docs/zh/openEuler_intelligence/intelligent_assistant/advance/smart_tuning/deploy_guide/tune_deployment.md rename to docs/zh/openeuler_intelligence/intelligent_assistant/advance/smart_tuning/deploy_guide/tune_deployment.md index a770bc81e7efd9ad9f3910d92396a97c62be7ea2..5f986a6e7d612d5637ccd3b0bba50ffbafb6d6f2 100644 --- a/docs/zh/openEuler_intelligence/intelligent_assistant/advance/smart_tuning/deploy_guide/tune_deployment.md +++ b/docs/zh/openeuler_intelligence/intelligent_assistant/advance/smart_tuning/deploy_guide/tune_deployment.md @@ -109,9 +109,9 @@ kubectl delete pod framework-deploy-service-bb5b58678-jxzqr -n eulercopilot ``` + pod启动失败排查办法 - + 检查 euler-copilot-tune 目录下的 openapi.yaml 中 `servers.url` 字段，确保调优服务的启动地址被正确设置 - + 检查 `$plugin_dir` 插件文件夹的路径是否配置正确，该变量位于 `deploy/chart/euler_copilot/values.yaml` 中的 `framework`模块，如果插件目录不存在，需新建该目录，并需要将该目录下的 euler-copilot-tune 文件夹放到 `$plugin_dir` 中。 - + 检查sglang的地址和key填写是否正确，该变量位于 `vim /home/euler-copilot-framework/deploy/chart/euler_copilot/values.yaml` + + 检查 euler-copilot-tune 目录下的 openapi.yaml 中 `servers.url` 字段，确保调优服务的启动地址被正确设置 + + 检查 `$plugin_dir` 插件文件夹的路径是否配置正确，该变量位于 `deploy/chart/euler_copilot/values.yaml` 中的 `framework`模块，如果插件目录不存在，需新建该目录，并需要将该目录下的 euler-copilot-tune 文件夹放到 `$plugin_dir` 中。 + + 检查sglang的地址和key填写是否正确，该变量位于 `vim /home/euler-copilot-framework/deploy/chart/euler_copilot/values.yaml` ```yaml # 用于Function Call的模型 diff --git a/docs/zh/openEuler_intelligence/intelligent_assistant/advance/smart_tuning/user_guide/pictures/shell-chat-ask.png b/docs/zh/openeuler_intelligence/intelligent_assistant/advance/smart_tuning/user_guide/pictures/shell-chat-ask.png similarity index 100% rename from docs/zh/openEuler_intelligence/intelligent_assistant/advance/smart_tuning/user_guide/pictures/shell-chat-ask.png rename to 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b/docs/zh/openeuler_intelligence/intelligent_assistant/advance/work_flow/deploy_guide/workflow_deployment.md similarity index 43% rename from docs/zh/openEuler_intelligence/intelligent_assistant/advance/work_flow/deploy_guide/workflow_deployment.md rename to docs/zh/openeuler_intelligence/intelligent_assistant/advance/work_flow/deploy_guide/workflow_deployment.md index 28a85e9089b05bd769a8e08e2aa5b3b66fdfc10e..6c72ebd1a0adf0f25172ca4e8bdb40698cb2715d 100644 --- a/docs/zh/openEuler_intelligence/intelligent_assistant/advance/work_flow/deploy_guide/workflow_deployment.md +++ b/docs/zh/openeuler_intelligence/intelligent_assistant/advance/work_flow/deploy_guide/workflow_deployment.md @@ -1,3 +1,3 @@ # 工作流编排部署指导 -（当前内容待更新，请等待） \ No newline at end of file +（当前内容待更新，请等待） diff --git a/docs/zh/openEuler_intelligence/intelligent_assistant/advance/work_flow/user_guide/workflow_guidance.md b/docs/zh/openeuler_intelligence/intelligent_assistant/advance/work_flow/user_guide/workflow_guidance.md similarity index 43% rename from docs/zh/openEuler_intelligence/intelligent_assistant/advance/work_flow/user_guide/workflow_guidance.md rename to docs/zh/openeuler_intelligence/intelligent_assistant/advance/work_flow/user_guide/workflow_guidance.md index cbb83f3811d35d76e2b115f7a7b82dc2f74760aa..bc4d8e02e5f546947b165b6efee7288998dcbac2 100644 --- a/docs/zh/openEuler_intelligence/intelligent_assistant/advance/work_flow/user_guide/workflow_guidance.md +++ b/docs/zh/openeuler_intelligence/intelligent_assistant/advance/work_flow/user_guide/workflow_guidance.md @@ -1,3 +1,3 @@ # 工作流编排使用指导 -（当前内容待更新，请等待） \ No newline at end of file +（当前内容待更新，请等待） diff --git a/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/API_key.md b/docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/api_key.md similarity index 100% rename from docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/API_key.md rename to docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/api_key.md diff --git a/docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/pictures/shell-chat-ask.png b/docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/pictures/shell-chat-ask.png similarity index 100% rename from docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/pictures/shell-chat-ask.png rename to docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/pictures/shell-chat-ask.png diff --git a/docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/pictures/shell-chat-continue-result.png b/docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/pictures/shell-chat-continue-result.png similarity index 100% rename from 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similarity index 96% rename from docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/shell.md rename to docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/shell.md index bb88c52f55a984d8115227454e67ed1970423583..b0f8dde5ed888536483cd2c08519d80a4a30981a 100644 --- a/docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/shell.md +++ b/docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_shell/user_guide/shell.md @@ -8,8 +8,8 @@ EulerCopilot 命令行助手是一个命令行（Shell）AI 助手，您可以 - 操作系统：openEuler 22.03 LTS SP3，或者 openEuler 24.03 LTS 及以上版本 - 命令行软件： - - Linux 桌面环境：支持 GNOME、KDE、DDE 等桌面环境的内置终端 - - 远程 SSH 链接：支持兼容 xterm-256 与 UTF-8 字符集的终端 + - Linux 桌面环境：支持 GNOME、KDE、DDE 等桌面环境的内置终端 + - 远程 SSH 链接：支持兼容 xterm-256 与 UTF-8 字符集的终端 ## 安装 diff --git a/docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/offline.md b/docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/offline.md similarity index 97% rename from docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/offline.md rename to docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/offline.md index 8167907479569db279a838dc9dcf3e17597abbfd..c79e5f562f408d50828876c54f5e466830706434 100644 --- a/docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/offline.md +++ b/docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/offline.md @@ -56,7 +56,7 @@ EulerCopilot 是一款智能问答工具，使用 EulerCopilot 可以解决操 ### 部署视图 -![部署图](./pictures/部署视图.png) +![部署图](./pictures/deployment-overview.png) --- @@ -89,9 +89,9 @@ EulerCopilot 是一款智能问答工具，使用 EulerCopilot 可以解决操 - **资源清单** - - **下载地址** + - **下载地址** [EulerCopilot 资源仓库](https://repo.oepkgs.net/openEuler/rpm/openEuler-22.03-LTS/contrib/eulercopilot) - - **镜像文件** + - **镜像文件** ```bash # 使用脚本自动下载所有镜像（需在联网环境执行） @@ -107,10 +107,10 @@ EulerCopilot 是一款智能问答工具，使用 EulerCopilot 可以解决操 - **关键说明** - - **网络要求** - - 手动下载需确保存在可访问外网文件的Windows环境，全部下载完成后传输至离线环境 - - 脚本下载需在联网服务器执行，仅完成镜像下载，完成传输至离线环境 - - **确保目标服务器存在以下目录** + - **网络要求** + - 手动下载需确保存在可访问外网文件的Windows环境，全部下载完成后传输至离线环境 + - 脚本下载需在联网服务器执行，仅完成镜像下载，完成传输至离线环境 + - **确保目标服务器存在以下目录** ```bash /home/eulercopilot/ @@ -266,12 +266,12 @@ helm upgrade euler-copilot -n euler-copilot . 首次访问时，您需要点击页面上的 **立即注册** 按钮来创建一个新的账号，并完成登录过程。 -![Web登录界面](./pictures/WEB登录界面.png) -![Web 界面](./pictures/WEB界面.png) +![Web登录界面](./pictures/web-login.png) +![Web 界面](./pictures/web.png) ## 构建专有领域智能问答 -点击知识库，可登录本地知识库管理页面，详细信息请参考文档 [本地资产库构建指南](../../../advance/knowledge_base/deploy_guide/witChainD_deployment.md) +点击知识库，可登录本地知识库管理页面，详细信息请参考文档 [本地资产库构建指南](../../../advance/knowledge_base/deploy_guide/witchaind_deployment.md) **知识库登录默认账号 `admin`, 密码 `123456`** --- diff --git a/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/online.md b/docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/online.md similarity index 98% rename from docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/online.md rename to docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/online.md index 3e946173658dffbc296f616d94bfbd92a3d70890..59527296199c2a1f06b81575815e481cbf54c6c2 100644 --- a/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/online.md +++ b/docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/online.md @@ -56,7 +56,7 @@ EulerCopilot 是一款智能问答工具，使用 EulerCopilot 可以解决操 ### 部署视图 -![部署图](./pictures/部署视图.png) +![部署图](./pictures/deployment-overview.png) --- @@ -242,12 +242,12 @@ helm upgrade euler-copilot -n euler-copilot . 首次访问时，您需要点击页面上的 **立即注册** 按钮来创建一个新的账号，并完成登录过程。 -![Web登录界面](./pictures/WEB登录界面.png) -![Web 界面](./pictures/WEB界面.png) +![Web登录界面](./pictures/web-login.png) +![Web 界面](./pictures/web.png) ## 构建专有领域智能问答 -点击知识库，可登录本地知识库管理页面，详细信息请参考文档 [本地资产库构建指南](../../../advance/knowledge_base/deploy_guide/witChainD_deployment.md) +点击知识库，可登录本地知识库管理页面，详细信息请参考文档 [本地资产库构建指南](../../../advance/knowledge_base/deploy_guide/witchaind_deployment.md) **知识库登录默认账号 `admin`, 密码 `123456`** --- diff --git "a/docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/pictures/\351\203\250\347\275\262\350\247\206\345\233\276.png" b/docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/pictures/deployment-overview.png similarity index 100% rename from "docs/en/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/deploy_guide/pictures/\351\203\250\347\275\262\350\247\206\345\233\276.png" rename to 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b/docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/pictures/rename-session.png similarity index 100% rename from docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/pictures/rename-session.png rename to docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/pictures/rename-session.png diff --git a/docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/pictures/search-history.png b/docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/pictures/search-history.png similarity index 100% rename from docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/pictures/search-history.png rename to docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/pictures/search-history.png diff --git a/docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/qa_guide.md b/docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/qa_guide.md similarity index 99% rename from docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/qa_guide.md rename to docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/qa_guide.md index 5c6bc55ea795b0aa2d9ba576904fdd3855998566..ec525ab7e9c16680cc462cbf68af87a3c0b7db82 100644 --- a/docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/qa_guide.md +++ b/docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/qa_guide.md @@ -9,6 +9,7 @@ > 对话区位于页面的主体部分，如图 1 所示。 - 图 1 对话区 + ![对话区](./pictures/chat-area.png) > **说明** @@ -18,6 +19,7 @@ - 图二 知识库配置 ![知识库设置](./pictures/knowledge-base-setting.png) + > **说明** > > 用户可以点击左下角的设置按钮进入模型配置页面，现提供8中供应商的基础模板用于大模型创建 diff --git a/docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/registration_and_login.md b/docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/registration_and_login.md similarity index 100% rename from docs/zh/openEuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/registration_and_login.md rename to docs/zh/openeuler_intelligence/intelligent_assistant/quick_start/smart_web/user_guide/registration_and_login.md diff --git a/docs/en/openEuler_intelligence/intelligent_vulnerability_patching/_toc.yaml b/docs/zh/openeuler_intelligence/intelligent_vulnerability_patching/_toc.yaml similarity index 72% rename from docs/en/openEuler_intelligence/intelligent_vulnerability_patching/_toc.yaml rename to docs/zh/openeuler_intelligence/intelligent_vulnerability_patching/_toc.yaml index b1a4ea7f0bc6c9169f6c9cc1ab084ad03074091b..dd80caddc875c8a9e4fd510c148ed27bae11063a 100644 --- a/docs/en/openEuler_intelligence/intelligent_vulnerability_patching/_toc.yaml +++ b/docs/zh/openeuler_intelligence/intelligent_vulnerability_patching/_toc.yaml @@ -3,4 +3,4 @@ isManual: true description: 支持kernel仓库的漏洞修补 sections: - label: 智能化漏洞修补用户指南 - href: ./intelligent-vulnerability-patching-user-guide.md + href: ./intelligent_vulnerability_patching_user_guide.md diff --git a/docs/zh/openEuler_intelligence/intelligent_vulnerability_patching/intelligent-vulnerability-patching-user-guide.md b/docs/zh/openeuler_intelligence/intelligent_vulnerability_patching/intelligent_vulnerability_patching_user_guide.md similarity index 93% rename from docs/zh/openEuler_intelligence/intelligent_vulnerability_patching/intelligent-vulnerability-patching-user-guide.md rename to docs/zh/openeuler_intelligence/intelligent_vulnerability_patching/intelligent_vulnerability_patching_user_guide.md index b19e2c0025cf962506a663c72c6c484b67944a69..3c14b68913e6bd6b3ed1b534a55fc6720983fb58 100644 --- a/docs/zh/openEuler_intelligence/intelligent_vulnerability_patching/intelligent-vulnerability-patching-user-guide.md +++ b/docs/zh/openeuler_intelligence/intelligent_vulnerability_patching/intelligent_vulnerability_patching_user_guide.md @@ -8,13 +8,13 @@ 在src-openEuler的kernel仓库（[https://gitee.com/src-openeuler/kernel.git](https://gitee.com/src-openeuler/kernel.git)）中，对代表CVE的issue下面进行评论。 -![CVE截图](pictures/代表CVE的issue.png) +![CVE截图](pictures/cve.png) ## /analyze命令 `/analyze`命令提供了对CVE影响范围进行分析的能力。通过在issue下面评论`/analyze`，即可自动对当前维护范围内的openEuler版本进行分析，判断每一个openEuler版本是否引入该CVE，是否修复该CVE。 -![/analyze命令](pictures/analyze命令.png) +![/analyze命令](pictures/analyze.png) > [!NOTE]说明 > /analyze命令无参数 @@ -34,7 +34,7 @@ CVE是否修复存在如下几种情况： ## /create_pr命令 `/create_pr`命令提供了对CVE的补丁进行智能化修复的能力。通过在issue下面评论`/create_pr `，即可自动获得漏洞补丁，并通过创建PR来合入openEuler下的linux仓库（[https://gitee.com/openeuler/kernel.git](https://gitee.com/openeuler/kernel.git)）中。 -![/create_pr命令](pictures/create_pr命令.png) +![/create_pr命令](pictures/create_pr.png) `/create_pr`命令存在参数，包括如下几种情况： diff --git "a/docs/zh/openEuler_intelligence/intelligent_vulnerability_patching/pictures/analyze\345\221\275\344\273\244.png" b/docs/zh/openeuler_intelligence/intelligent_vulnerability_patching/pictures/analyze.png similarity index 100% rename from "docs/zh/openEuler_intelligence/intelligent_vulnerability_patching/pictures/analyze\345\221\275\344\273\244.png" rename to docs/zh/openeuler_intelligence/intelligent_vulnerability_patching/pictures/analyze.png diff --git "a/docs/zh/openEuler_intelligence/intelligent_vulnerability_patching/pictures/create_pr\345\221\275\344\273\244.png" b/docs/zh/openeuler_intelligence/intelligent_vulnerability_patching/pictures/create_pr.png similarity index 100% rename from "docs/zh/openEuler_intelligence/intelligent_vulnerability_patching/pictures/create_pr\345\221\275\344\273\244.png" rename to docs/zh/openeuler_intelligence/intelligent_vulnerability_patching/pictures/create_pr.png diff --git "a/docs/zh/openEuler_intelligence/intelligent_vulnerability_patching/pictures/\344\273\243\350\241\250CVE\347\232\204issue.png" b/docs/zh/openeuler_intelligence/intelligent_vulnerability_patching/pictures/cve.png similarity index 100% rename from "docs/zh/openEuler_intelligence/intelligent_vulnerability_patching/pictures/\344\273\243\350\241\250CVE\347\232\204issue.png" rename to docs/zh/openeuler_intelligence/intelligent_vulnerability_patching/pictures/cve.png diff --git a/docs/zh/openEuler_intelligence/mcp_agent/_toc.yaml b/docs/zh/openeuler_intelligence/mcp_agent/_toc.yaml similarity index 100% rename from docs/zh/openEuler_intelligence/mcp_agent/_toc.yaml rename to docs/zh/openeuler_intelligence/mcp_agent/_toc.yaml diff --git a/docs/zh/openEuler_intelligence/mcp_agent/mcp_guide.md b/docs/zh/openeuler_intelligence/mcp_agent/mcp_guide.md similarity index 33% rename from docs/zh/openEuler_intelligence/mcp_agent/mcp_guide.md rename to docs/zh/openeuler_intelligence/mcp_agent/mcp_guide.md index c30fbf395d7b81d0df69b9d7cb5565e3cbee7e18..1518c6b91c4729d8fc0b7d4234b62a6cb78df5b2 100644 --- a/docs/zh/openEuler_intelligence/mcp_agent/mcp_guide.md +++ b/docs/zh/openeuler_intelligence/mcp_agent/mcp_guide.md @@ -1,3 +1,3 @@ # MCP 服务指南 -（当前内容待更新，请等待） \ No newline at end of file +（当前内容待更新，请等待）