Chat with your enterprise data using LLM vs ragflow
Side-by-side comparison of two AI agent tools
Short answer
- Chat with your enterprise data using LLM has had no commit in 21 months; ragflow is actively maintained (2,665 commits in the last 90 days).
- ragflow is growing faster: +2,412 GitHub stars in the last 30 days vs +-0 for Chat with your enterprise data using LLM.
- Pick Chat with your enterprise data using LLM for: open-source sample for chatting with uploaded enterprise data using Azure OpenAI and vector search. Pick ragflow for: open-source RAG engine combining knowledge retrieval and agent capabilities for LLMs.
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Chat with your enterprise data using LLMopen-source
Open-source sample for chatting with uploaded enterprise data using Azure OpenAI and vector search
ragflowopen-source
Open-source RAG engine combining knowledge retrieval and agent capabilities for LLMs
Metrics
| Chat with your enterprise data using LLM | ragflow | |
|---|---|---|
| Stars | 865 | 91.6k |
| Star velocity /mo | -0.47619047619047616 | 2.4k |
| Commits (90d) | 0 | 2.7k |
| Releases (6m) | 0 | 10 |
| Overall score | 0.12773230671695096 | 0.9150811116917444 |
Pros
- +Supports multiple vector stores (Pinecone, Redis, Azure Cognitive Search) providing flexibility in deployment options
- +Includes comprehensive evaluation framework with Prompt Flow integration and metrics like groundedness and Ada similarity
- +Active development with regular updates and refactoring to improve core functionality and remove complexity
- +结合了先进的RAG技术和Agent能力，提供比传统RAG更强大的功能
- +开源且拥有活跃社区支持，GitHub星数超过7.6万，可信度高
- +提供云服务和Docker容器化部署，支持多种部署方式
Cons
- -Designed as a sample application rather than production-ready solution, requiring additional development for enterprise deployment
- -Specifically tied to Azure OpenAI Service, limiting flexibility in LLM provider choice
- -Has undergone multiple refactoring cycles that removed features, suggesting potential instability in feature set
- -作为相对复杂的RAG系统，可能需要一定的技术背景才能充分配置和优化
- -大规模部署可能需要相当的计算资源和存储空间
Use Cases
- •Enterprise document Q&A systems where employees need to query internal knowledge bases using natural language
- •Internal chatbots for customer support teams to quickly access company policies and procedures
- •Research and development teams building custom RAG applications for proprietary data analysis
- •企业知识库问答系统，基于内部文档为员工提供智能查询服务
- •智能客服系统，结合产品文档和FAQ提供准确的客户支持
- •研究助手应用，帮助研究人员从大量学术文献中检索相关信息
FAQ
- Which is more popular, Chat with your enterprise data using LLM or ragflow?
- ragflow has more GitHub stars (91,600 vs 865).
- Which is more actively developed, Chat with your enterprise data using LLM or ragflow?
- ragflow had more commits in the last 90 days (2,665 vs 0).
- Should I use Chat with your enterprise data using LLM or ragflow?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.