
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。
Bibliometric experts use statistical analysis of natural language texts to provide a quantitative view of academic literature. Bibliometric techniques, such as citation analysis, are often used by funding agencies to understand and evaluate the publishing results of grant money distribution. While this field of scholarship in general, and citation analysis in particular, have been studied extensively, there still remains a lack of tools and techniques for evaluating the surrounding natural language context of individual citations on the network scale. The goal of this project is to aid in the identification of language patterns as they occur across a large corpus of academic texts, allow users to create and edit rule sets for evaluation, and then use vector space models to suggest expansions of the user defined natural language rules. Although originally developed for citation analysis, the tool can be customized for any number of categories and rules that apply to a given domain problem.
This project was built as two different servers:
Please ensure that you have docker installed on your system. Both projects are setup to run in docker containers either separately, or using docker compose. By default, these projects run on port 5432 for the frontend, and port 5000 for the backend.
Run both frontend and backend sub-folders as seperate images.
Frontend:
/frontenddocker build -t cg-frontend .docker run cg-frontendBackend:
/backenddocker build -t cg-backend .docker run cg-backendBy default, the project (frontend) should be accessible through localhost:5432
A connection to the database must be made in order for the proper functioning of this project. Both frontend and backend will require a connection. Please contact the collaborators of this project for access to the database.
Backend:
/backend sub-folder with the name database.inidatabase.ini file, edit the values as appropriate and save[postgresql] host=<host> database=citationdb user=citationdb password=<password>
#!/bin/bash echo "Starting python server.." source venv/bin/activate export FLASK_APP=server.py export DEPLOY_ENV=TEST flask run --port=5000 echo "Python listening on http://localhost:5000"
Frontend:
/frontend sub-folder in your terminalnpm install#!/bin/bash DATABASE_URL="postgresql://citationdb:<password>@<host>:<port>/citationdb" DEPLOY_ENV="PROD" node index.js
Alternatively, you may edit the Dockerfiles as you see fit for your setup. However, please DO NOT commit/push any changes to the Dockerfiles unless you know what you are doing.
Deployment for this project has been automated, and so please be aware that pushes to this repository will automatically build, run, and deploy to the VIALAB production servers at [***] Deployment will automatically handle database connections, as well as mount volumes to hold the larger files not part of the github repository (citation caching and word2vec models) through Kubernetes.
In order to ensure the appropriate database connections are made, committed versions of this project should always have ENV DEPLOY_ENV PROD in line 6 of the Dockerfile for the /backend sub folder.
This research was conducted as part of the CO.SHS project (co-shs.ca) and has received financial support from the Canada Foundation for Innovation (Cyberinfrastructure Initiative – Challenge 1 – First competition).
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。
来自真实用户的反馈,见证轩辕镜像的优质服务