{"skill":{"slug":"open-ragflow","displayName":"RAGFlow open-source Retrieval-Augmented Generation (RAG) engine — deployment, configuration, management, and troubleshooting.","summary":"RAGFlow open-source Retrieval-Augmented Generation (RAG) engine — deployment, configuration, management, and troubleshooting.","tags":{"latest":"1.0.0"},"stats":{"comments":0,"downloads":73,"installsAllTime":0,"installsCurrent":0,"stars":0,"versions":1},"createdAt":1778548445680,"updatedAt":1778548909518},"latestVersion":{"version":"1.0.0","createdAt":1778548445680,"changelog":"- Initial release of open-ragflow: open-source RAG engine with agent capabilities.\n- Full-stack deployment: Python (Flask) backend, React/TypeScript frontend, Docker-based microservices.\n- Supports deployment via Docker Compose and self-hosting from source.\n- CLI and REST API for managing knowledge bases, datasets, agents, and chats.\n- Comprehensive documentation and troubleshooting included for deployment and configuration.\n- Compatible with multiple LLM providers, embedding models, and document engines (Elasticsearch/Infinity).","license":"MIT-0"},"metadata":null,"owner":{"handle":"openlark","userId":"s1727wv2g20pc729snzcm4nf8183hy72","displayName":"OpenLark","image":"https://avatars.githubusercontent.com/u/260858787?v=4"},"moderation":null}