Copilot commented on code in PR #403: URL: https://github.com/apache/incubator-hugegraph-doc/pull/403#discussion_r2111124690
########## content/en/docs/quickstart/hugegraph-ai.md: ########## @@ -9,92 +9,110 @@ hugegraph-ai aims to explore the integration of HugeGraph and artificial intelli with large models, integration with graph machine learning components, etc., to provide comprehensive support for developers to use HugeGraph's AI capabilities in projects. ## 2 Environment Requirements -- python 3.9+ -- hugegraph-server 1.2+ +- Python 3.9+ (better to use `3.10`) +- HugeGraph Server 1.3+ ## 3 Preparation 1. Start the HugeGraph database, you can run it via [Docker](https://hub.docker.com/r/hugegraph/hugegraph)/[Binary Package](https://hugegraph.apache.org/docs/download/download/). Refer to detailed [doc](https://hugegraph.apache.org/docs/quickstart/hugegraph-server/#31-use-docker-container-convenient-for-testdev) for more guidance -2. Clone this project +2. **Docker Deployment** + If you wish to deploy HugeGraph-AI using Docker, follow these steps: + - Ensure you have Docker installed. + - In the project root directory, use the following command to pull the remote Docker container images. We provide two container images: + - **Image 1**: [hugegraph/rag](https://hub.docker.com/r/hugegraph/rag/tags) + This image is used to build and run the RAG (Retrieval-Augmented Generation) functionality of HugeGraph-AI, suitable for users who need quick deployment and usage. + - **Image 2**: [hugegraph/rag-bin](https://hub.docker.com/r/hugegraph/rag-bin/tags) + This image provides a binary version of HugeGraph-AI, suitable for users who require more stable and efficient performance. + - Use the following command to pull the remote Docker container images: + ```bash + docker pull hugegraph/rag:latest # Pull Image 1 + docker pull hugegraph/rag-bin:latest # Pull Image 2 + ``` + - Use the following command to start the Docker container: + ```bash + docker run -it --name rag -p 8001:8001 hugegraph/rag bash Review Comment: Using the same container name 'rag' for both Docker images may lead to conflicts if the containers are run concurrently. Consider using unique container names for each Docker image to avoid potential runtime issues. ########## content/cn/docs/quickstart/hugegraph-ai.md: ########## @@ -16,40 +16,75 @@ hugegraph-ai 旨在探索 HugeGraph 与人工智能（AI）的融合，包括与 1. 启动HugeGraph数据库，可以通过 [Docker](https://hub.docker.com/r/hugegraph/hugegraph)/[Binary Package](https://hugegraph.apache.org/docs/download/download/) 运行它。 请参阅详细[文档](https://hugegraph.apache.org/docs/quickstart/hugegraph-server/#31-use-docker-container-convenient-for-testdev)以获取更多指导 -2. 克隆项目 + +2. **Docker部署** + 如果您希望使用Docker来部署HugeGraph-AI，请按照以下步骤操作： + - 确保您已安装Docker。 + - 在项目根目录下，使用以下命令拉取远端Docker容器镜像，我们提供了两种容器镜像： + - **镜像1**：[hugegraph/rag](https://hub.docker.com/r/hugegraph/rag/tags) + 该镜像用于构建和运行HugeGraph-AI的RAG（检索增强生成）功能，适合需要快速部署和使用的用户。 + - **镜像2**：[hugegraph/rag-bin](https://hub.docker.com/r/hugegraph/rag-bin/tags) + 该镜像提供了二进制版本的HugeGraph-AI，适合需要更稳定和高效性能的用户。 + - 使用以下命令拉取远端Docker容器镜像： + ```bash + docker pull hugegraph/rag:latest # 拉取镜像1 + docker pull hugegraph/rag-bin:latest # 拉取镜像2 + ``` + - 使用以下指令启动Docker容器： + ```bash + docker run -it --name rag -p 8001:8001 hugegraph/rag bash Review Comment: 使用相同的容器名称 'rag' 来启动两个 Docker 镜像可能会导致同时运行时发生冲突。建议为每个镜像使用唯一的容器名称以避免潜在的运行时问题。 -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
