elvis· @omarsar0 · X·· 14 天前AI 评分50
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出现了很多 JevRAG 变体。 至少可以说很有意思。 你无法真的通过这些范围有限的测试得出任何结论，但它引发了讨论，并为在当前 RAG 和智能体系统中寻找优化带来了令人兴奋的方向。 我一直在测试自己的一个 JevRAG，用于论文探索。 到目前为止，我在将 Jev 用于重排序，以及结合语义搜索和 Jev 搜索论文的一些非常有趣的方式上取得了更多成功。 很快会有更多内容。
正文
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