Jev模型专为决策设计，适配搜索场景
Milvus推出Jev决策模型，能评估检索结果质量，比传统重排更精准，还提供9个实用教程。
Jev模型专为决策而非对话设计，与搜索场景高度契合。Milvus先通过向量搜索和元数据过滤器检索候选段落，Jev再评估这些段落是否真正有助于任务完成。该流程包含查询、检索、判断和应用四个步骤。JevRerankFunction可对查询、候选文档和top_k进行评分和重新排序。
Jev is built to decide, not to chat. That makes it a surprisingly good fit for search. Milvus first retrieves candidate passages with vector search and metadata filters. Jev then looks at those passages and judges whether they actually help with the task. In practice, the flow looks like this: Query -> Milvus retrieves -> Jev judges -> your application acts For the common reranking case, JevRerankFunction takes a query, candidate documents, and top_k, then returns them scored and reordered. We also put together nine small tutorials to show what else this pattern can do: route a query, check whether a cached answer can be reused, screen documents before indexing, or decide whether an agent has enough evidence to stop searching. Each example uses a small dataset, so it's easy to follow what Milvus retrieved, what Jev decided, and what happened next. Pick the example that feels closest to what you're building and give it a try. Try it now: milvus.io/docs/search_wi… 💬 1 🔄 0 ❤️ 0 👀 127 📊 1 ⚡
