elvis· @omarsar0 · X·· 11 小时前AI 评分45
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大量 JevRAG 变体正在涌现。 至少可以说很有意思。 这些范围有限的测试其实得不出什么结论，但它引发了讨论，也指向了在当前 RAG 和智能体系统中寻找优化方向的激动人心的路径。 我一直在测试自己做的 JevRAG，用于论文探索。 到目前为止，我在用 Jev 做重排序上取得了更多成功，也找到了一些非常有意思的论文搜索方式，把语义搜索和 Jev 结合起来。 更多内容很快分享。
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Lots of JevRAG variants showing up.
Interesting to say the least.
You can’t really make any conclusions with these scoped tests but it raises discussions and exciting directions to find optimization in current RAG and agentic systems.
I have been testing a JevRAG of my own for paper exploration.
So far, I have had more success with Jev for reranking and some very interesting ways to search papers combining semantic search and Jev.
More on that soon.
Didn't expect this 🤯 We replaced embeddings with Jev in GPT Researcher's RAG pipeline and tested both on 28 research tasks from SimpleQA and open ended research. Jev beat embeddings on every quality measure we ran: - 59% more relevant context (73% vs 46%) - Reports preferred 15 to 3 in blind comparisons - Same cost per report GPT Researcher now runs on Jev by default, and no longer needs embeddings at all. All you need is @LangChain + @tavilyai +Jev for the perfect RAG system. Check out the repo here: https://github.com/assafelovic/gpt-researcher Research: https://docs.gptr.dev/docs/gpt-researcher/gptr/context-filter在 X 查看被引用的帖子
来源：elvis · x.com