You've already forked DataMate
feature: 修改部署文档 (#65)
This commit is contained in:
40
README-zh.md
40
README-zh.md
@@ -34,6 +34,14 @@
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- kubernetes (用于部署服务-k8s方式)
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- kubernetes (用于部署服务-k8s方式)
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- Helm (用于部署服务-k8s方式)
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- Helm (用于部署服务-k8s方式)
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本项目支持docker-compose和helm两种方式部署,请在执行命令后输入部署方式的对应编号,命令回显如下所示:
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```shell
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Choose a deployment method:
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1. Docker/Docker-Compose
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2. Kubernetes/Helm
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Enter choice:
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```
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### 拉取代码
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### 拉取代码
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```bash
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```bash
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@@ -41,22 +49,28 @@ git clone git@github.com:ModelEngine-Group/DataMate.git
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cd DataMate
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cd DataMate
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```
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```
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### 镜像构建
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### 部署基础服务
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```bash
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make install
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```
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### 构建并部署Mineru增强pdf处理
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```bash
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make build-mineru
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make install-mineru
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```
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### 部署DeerFlow服务
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1. 修改runtime/deer-flow/.env.example,添加SEARCH_API_KEY和EMBEDDING模型配置
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2. 修改runtime/deer-flow/.conf.yaml.example,添加基础模型服务配置
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3. 执行`make install-deer-flow`
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### 本地开发部署
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本地代码修改后,请执行以下命令构建镜像并使用本地镜像部署
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```bash
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```bash
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make build
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make build
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```
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make install REGISTRY=""
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### Docker安装
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```bash
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make install INSTALLER=docker
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```
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### kubernetes安装
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```bash
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make install INSTALLER=k8s
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```
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```
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## 🤝 贡献指南
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## 🤝 贡献指南
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40
README.md
40
README.md
@@ -37,6 +37,14 @@ If you like this project, please give it a Star⭐️!
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- Kubernetes (for service deployment - k8s method)
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- Kubernetes (for service deployment - k8s method)
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- Helm (for service deployment - k8s method)
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- Helm (for service deployment - k8s method)
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This project supports deployment via two methods: docker-compose and helm. After executing the command, please enter the corresponding number for the deployment method. The command echo is as follows:
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```shell
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Choose a deployment method:
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1. Docker/Docker-Compose
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2. Kubernetes/Helm
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Enter choice:
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```
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### Clone the Code
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### Clone the Code
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```bash
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```bash
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@@ -44,22 +52,28 @@ git clone git@github.com:ModelEngine-Group/DataMate.git
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cd DataMate
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cd DataMate
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```
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```
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### Build Images
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### Deploy the basic services
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```bash
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make install
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```
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### Build and deploy Mineru Enhanced PDF Processing
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```bash
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make build-mineru
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make install-mineru
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```
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### Deploy the DeerFlow service
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1. Modify `runtime/deer-flow/.env.example` and add configurations for SEARCH_API_KEY and the EMBEDDING model.
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2. Modify `runtime/deer-flow/.conf.yaml.example` and add basic model service configurations.
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3. Execute `make install-deer-flow`
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### Local Development and Deployment
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After modifying the local code, please execute the following commands to build the image and deploy using the local image.
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```bash
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```bash
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make build
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make build
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```
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make install REGISTRY=""
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### Docker Installation
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```bash
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make install INSTALLER=docker
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```
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### Kubernetes Installation
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```bash
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make install INSTALLER=k8s
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```
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```
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## 🤝 Contribution Guidelines
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## 🤝 Contribution Guidelines
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@@ -59,7 +59,6 @@ MILVUS_URI=http://milvus-standalone:19530
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MILVUS_USER=root
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MILVUS_USER=root
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MILVUS_PASSWORD=Milvus
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MILVUS_PASSWORD=Milvus
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MILVUS_AUTO_LOAD_EXAMPLES=false
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MILVUS_AUTO_LOAD_EXAMPLES=false
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# MILVUS_COLLECTION=documents
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# MILVUS_EMBEDDING_PROVIDER=openai # support openai,dashscope
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# MILVUS_EMBEDDING_PROVIDER=openai # support openai,dashscope
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# MILVUS_EMBEDDING_BASE_URL=
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# MILVUS_EMBEDDING_BASE_URL=
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# MILVUS_EMBEDDING_MODEL=
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# MILVUS_EMBEDDING_MODEL=
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