
如果你使用 DeepSeek、元宝 AI、Copilot、ChatGPT 等 AI 工具,协助配置轩辕镜像、编写 docker pull 命令、修改 Docker Compose 镜像地址、配置镜像加速、排查镜像拉取失败、分析报错日志等问题,请先让 AI 阅读并遵守轩辕镜像的规则文档。
只需在 AI 对话中先发送下面这句话即可:
请先完整阅读并严格遵守以下文档中的全部规则与要求:
https://xuanyuan.cloud/agents.md
在未充分阅读并理解该文档前,不要生成任何命令、配置、修改建议、故障排查方案或技术回答。后续所有输出都必须严格以该文档中的规范为最高优先级执行。查看 agents.md 用法指南与完整示范。国内用户首推 元宝 AI、DeepSeek 的深度思考模式,不推荐豆包 AI;Cursor 等编辑器可在对话 @ 该链接,或加入 User Rules。 若 AI 无法访问外链,可 打开说明文档 复制全文粘贴。文档会随站点更新,复制内容可能过期,建议定期检查。
The offical OPEA docker images
Maintained by: openEuler CloudNative SIG
Where to get help: openEuler CloudNative SIG, openEuler
Current OPEA docker images are built on the openEuler<2060>. This repository is free to use and exempted from per-user rate limits.
OPEA is an open platform project that lets you create open, multi-provider, robust, and composable GenAI solutions that harness the best innovation across the ecosystem.
The OPEA platform includes:
Detailed framework of composable building blocks for state-of-the-art generative AI systems including LLMs, data stores, and prompt engines
Architectural blueprints of retrieval-augmented generative AI component stack structure and end-to-end workflows
A four-step assessment for grading generative AI systems around performance, features, trustworthiness, and enterprise-grade readiness
Read more about OPEA at opea.dev and explore the OPEA technical documentation at https://opea-project.github.io/
The tag of each FaqGen docker image is consist of the version of FaqGen and the version of basic image. The details are as follows
| Tags | Currently | Architectures |
|---|---|---|
| 1.0-oe2403lts | FaqGen 1.0 on openEuler 24.03-LTS | amd64 |
| 1.2-oe2403lts | FaqGen 1.2 on openEuler 24.03-LTS | amd64 |
The FaqGen service can be effortlessly deployed on Intel Gaudi2, Intel Xeon Scalable Processors and Nvidia GPU.
Two types of FaqGen pipeline are supported now: FaqGen with/without Rerank. And the FaqGen without Rerank pipeline (including Embedding, Retrieval, and LLM) is offered for Xeon customers who can not run rerank service on HPU yet require high performance and accuracy.
Quick Start Deployment Steps:
To set up environment variables for deploying FaqGen services, follow these steps:
Set the required environment variables:
bash# Example: host_ip="192.168.1.1" export host_ip="External_Public_IP" # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1" export no_proxy="Your_No_Proxy" export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
If you are in a proxy environment, also set the proxy-related environment variables:
bashexport http_proxy="Your_HTTP_Proxy" export https_proxy="Your_HTTPs_Proxy"
Set up other environment variables:
Get
set_env.shhere: set_env.sh
bash# on Xeon source set_env.sh
Select the compose.yaml file that matches your hardware.
CPU example:
Get
compose.ymlhere: compose.yml
bashdocker compose -f compose.yml up -d
It will automatically download the docker image on docker hub:
bashdocker pull openeuler/faqgen:latest docker pull openeuler/faqgen-ui:latest
To chat with retrieved information, you need to upload a file using Dataprep service.
Here is an example of Nike 2023 pdf.
bash# download pdf file wget https://raw.githubusercontent.com/opea-project/GenAIComps/v1.1/comps/retrievers/redis/data/nke-10k-2023.pdf # upload pdf file with dataprep curl -X POST "http://${host_ip}:6004/v1/dataprep/ingest" \ -H "Content-Type: multipart/form-data" \ -F "files=@./nke-10k-2023.pdf"
bashcurl http://${host_ip}:8888/v1/graphrag \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-4o-mini","messages": [{"role": "user","content": "What is the revenue of Nike in 2023? "}]}'
您可以使用以下命令拉取该镜像。请将 <标签> 替换为具体的标签版本。如需查看所有可用标签版本,请访问 标签列表页面。
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