Weaviate • vector database(@weaviate_io)

Query Agent Search Mode now makes the recall-versus-precision tradeoff configurable. Search Mode re...

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Query Agent Search Mode now makes the recall-versus-precision tradeoff configurable. Search Mode re...

TL;DR · AI 摘要

Weaviate推出可配置召回率与精确率权衡的Query Agent搜索模式，工程师可按需选择多查询召回或单查询精确策略。

核心要点

  • recall模式通过多查询提升相关性结果数量，适合优先考虑覆盖率的场景。
  • precision模式强制单查询严格匹配，确保结果精准符合原始意图。
  • 电商案例显示：recall可能返回更多变体结果，precision则可能因过滤过严返回空结果。

结构提纲

按章节快速跳转。

  1. §功能发布

    宣布Query Agent Search Mode新增可配置的召回-精确率权衡参数。

  2. ·核心机制

    将自然语言请求转换为含搜索查询和元数据过滤器的Weaviate查询。

  3. ›配置选项

    filtering参数支持recall（多查询）和precision（单查询）两种策略模式。

  4. ›应用场景

    recall适合需要更多相关结果的场景，precision适合要求严格匹配的场景。

  5. ›案例演示

    电商数据集示例展示两种模式在材质/价格/防水等过滤条件下的不同表现。

  6. ›文档指引

    提供官方文档链接说明具体实现和配置方法。

思维导图

用一张图看清主题之间的关系。

查看大纲文本（无障碍 / 无 JS 友好）
  • Query Agent Search Mode
    • 配置参数
      • recall模式（多查询）
      • precision模式（单查询）
    • 应用场景
      • 需要更多相关结果的场景
      • 要求严格匹配的场景

金句 / Highlights

值得收藏与分享的关键句。

#Weaviate#AI数据库#搜索策略#召回率#精确率
打开原文

Weaviate AI Database on X: "Query Agent Search Mode now makes the recall-versus-precision tradeoff configurable. Search Mode rewrites a natural-language request into one or multiple Weaviate queries, each containing a search query, metadata filters, or both. It then returns the matching Weaviate objects https://t.co/6o1FrwGG3Z" / X

Weaviate AI Database

@weaviate_io

Query Agent Search Mode now makes the recall-versus-precision tradeoff configurable. Search Mode rewrites a natural-language request into one or multiple Weaviate queries, each containing a search query, metadata filters, or both. It then returns the matching Weaviate objects directly. The new 𝗳𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 argument controls the search strategy: - "𝘳𝘦𝘤𝘢𝘭𝘭" (default) generates multiple queries spanning different filters and interpretations. Use it when getting relevant results matters more than satisfying each criteria. - "𝘱𝘳𝘦𝘤𝘪𝘴𝘪𝘰𝘯" generates a single query targeting the most likely interpretation. Use it when every returned result should closely follow the original intent. Imagine an ecommerce dataset with 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘺, 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭, 𝘸𝘢𝘵𝘦𝘳𝘱𝘳𝘰𝘰𝘧, and 𝘱𝘳𝘪𝘤𝘦 fields. For 𝘍𝘪𝘯𝘥 𝘮𝘦 𝘸𝘢𝘵𝘦𝘳𝘱𝘳𝘰𝘰𝘧 𝘳𝘦𝘤𝘺𝘤𝘭𝘦𝘥 𝘱𝘰𝘭𝘺𝘦𝘴𝘵𝘦𝘳 𝘣𝘰𝘰𝘵𝘴 𝘧𝘰𝘳 𝘸𝘪𝘯𝘵𝘦𝘳 𝘶𝘯𝘥𝘦𝘳 $150 The "recall" method might perform a search on 𝘸𝘪𝘯𝘵𝘦𝘳 𝘣𝘰𝘰𝘵𝘴 with filters on 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭, 𝘸𝘢𝘵𝘦𝘳𝘱𝘳𝘰𝘰𝘧 and 𝘱𝘳𝘪𝘤𝘦. Then run backup searches such as 𝘸𝘢𝘵𝘦𝘳𝘱𝘳𝘰𝘰𝘧 𝘳𝘦𝘤𝘺𝘤𝘭𝘦𝘥 𝘱𝘰𝘭𝘺𝘦𝘴𝘵𝘦𝘳 𝘣𝘰𝘰𝘵𝘴 with only 𝘱𝘳𝘪𝘤𝘦<150. The "precision" method would run one query for 𝘸𝘪𝘯𝘵𝘦𝘳 𝘣𝘰𝘰𝘵𝘴 with all filters applied, returning nothing if there is no exact match. Read the documentation:

docs.weaviate.io/query-agent/gu…

2:01 PM · Aug 12, 2026

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