Milvus 把 Jev 接进三个开源项目做实测
Zilliz 在 DeepSearcher、MemSearch、Vector Graph RAG 三个项目里用 Jev 做停止判断、重排和关系过滤，给出延迟、召回与成本对比数据。
𝗪𝗲 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗝𝗲𝘃 𝗶𝗻𝘁𝗼 𝘁𝗵𝗿𝗲𝗲 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝘁𝗲𝘀𝘁 𝘁𝗵𝗮𝘁 𝗶𝗱𝗲𝗮. First, in 𝗗𝗲𝗲𝗽𝗦𝗲𝗮𝗿𝗰𝗵𝗲𝗿, we used Jev to decide whether an Agentic Search workflow had gathered enough evidence to stop. Across 100 multi-hop questions, Jev matched DeepSeek V4 Flash at 93.25% Recall@5. Yet median decision latency fell from 2.23s to 0.55s, with estimated API cost at roughly one-seventh. Second, in 𝗠𝗲𝗺𝗦𝗲𝗮𝗿𝗰𝗵, we tested coding-agent memory reranking on 2,172 private Chinese and English queries. Jev improved Recall@5 from 74.71% to 79.41%, but remained behind the specialist Voyage rerank-3 at 81.87%. Third, in 𝗩𝗲𝗰𝘁𝗼𝗿 𝗚𝗿𝗮𝗽𝗵 𝗥𝗔𝗚, Jev reranked and filtered candidate relationships for multi-hop retrieval. It outperformed GPT-4o-mini, but trailed GPT-5-mini—by just 1 point on HotpotQA and 4.13 points on the harder MuSiQue benchmark. Jev is compelling for frequent, bounded judgments such as search stopping, filtering, routing, and simpler reranking. But as tasks become more domain-specific or reasoning-heavy, stronger generative models and specialist rerankers still earn their place. Want to dig into the experiments? • DeepSearcher — search-stopping experiment, code, and evaluation: https://github.com/zilliztech/deep-searcher/blob/master/evaluation/jev_stopping/README.md • MemSearch — Jev reranking integration and evaluation: https://github.com/zilliztech/memsearch/blob/main/evaluation/reranking-evaluation.md • Vector Graph RAG — Jev relationship filtering, evaluation, and cost analysis: https://github.com/zilliztech/vector-graph-rag/blob/main/evaluation/jev/README.md