TypeSafe 工程师用 Jev 重做 RAG 检索与重排
小语料下 Jev 直接当相似度指标，逐对判断文档是否相关并给概率，无需向量库；大语料下 embedding 召回 top-k、Jev 只做重排，能捞到零词重叠但正好答对的文档。
this is pure f*cking treasure TypeSafe AI engineers developed this themselves: Jev for RAG most agents retrieve by embedding similarity and call it meaning. Jev asks the real question for every doc: is this relevant to the query, yes or no, with a probability two regimes, one rule: small corpus: Jev is the similarity metric > score every (query, doc) pair > rank by p > no embeddings, no vector store, no index to keep > fresh, no chunking to tune > it catches the doc that shares zero words with the query but answers it exactly large corpus: Jev is the reranker > embeddings pull the top k out of millions, cheap and approximate > Jev scores only those k pairs > recall comes from the dot product, precision comes from Jev and the agent never touches the ranking: > it sends a query > Jev decides what is relevant > the top results go straight into its context your agent is only as smart as what it reads stop letting a dot product decide that
这条是引用着另一条写的：x.com/gippp69/status/2103394701063737504