Milvus推出的这个功能，能帮你看看检索结果是不是集中在少数文档，和之前没聚合前不一样，现在能更清楚判断怎么调整检索啦。
Milvus 3.0版本中，RAG系统检索20个数据块时，若多数块来自同一文档与分散在不同文档的情况不同；该功能聚合搜索结果展示来源分布；从文档维度聚合后可分析匹配是否集中以优化检索。
Top‑K tells you which chunks matched. It doesn’t tell you how concentrated those matches are. Say a...
Top‑K tells you which chunks matched. It doesn’t tell you how concentrated those matches are. Say a RAG system retrieves 20 chunks for a question. If 18 come from the same document, that’s a very different evidence set versus 20 chunks spread across 12 documents. A flat ranked list holds the raw data, but does not summarize result distribution for you. 𝗠𝗶𝗹𝘃𝘂𝘀 𝟯.𝟬 𝗮𝗱𝗱𝘀 𝗦𝗲𝗮𝗿𝗰𝗵 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝘀𝗼𝗹𝘃𝗲 𝘁𝗵𝗶𝘀. It organizes retained ANN candidates into buckets by doc_id, tenant, product line or content type, returning bucket counts, metrics and representative hits. For RAG pipelines, this bucketed view delivers a new retrieval signal: • Are candidates concentrated in a small set of sources? • Or spread across many different documents? Apps can use this to decide: broaden retrieval, check more sources, or proceed with existing evidence. Key distinction: this is a coverage signal, not a confidence score. Search Aggregation ≠ Grouping Search • Want fewer duplicate doc_id in your final ranked list → use Grouping Search • Want to inspect group‑level counts & metrics → use Search Aggregation Important limit: Search Aggregation works only over retained ANN candidates, not the full collection. Metrics depend on candidates kept d milvus.io/blog/from-retr… the full guide: https://t.co/WCWW9oLyHw 💬 0 🔄 1 ❤️ 1 👀 124 📊 1 ⚡