文本+稠密向量混合查询最佳实践_智能开放搜索 OpenSearch(Open Search)-阿里云帮助中心

本文将介绍如何使用OpenSearch-向量检索版，实现文本+向量混合检索。

背景介绍

在RAG和语义搜索场景中，文本倒排索引和稠密向量的结合使用是一种常见的方法，用于高效地进行文本检索和相似性搜索，这种组合方式结合了倒排索引的精准匹配能力和稠密向量的语义表示能力。目前，许多搜索应用也正在采用混合搜索方法，提升检索效果。

常见场景

文档检索：从海量文档中快速定位最相关结果，混合检索提升召回率和精度，支持复杂、多维查询。
知识问答：快速匹配知识库中最相关内容，结合生成模型提供更准确、高效的回答，更好理解用户意图。
电商搜索：精准理解用户需求，混合检索优化商品搜索，提高搜索召回率和准确性，减少关键词不匹配问题。

方案介绍

向量检索版的文本+向量混合查询方案，其核心在于向量查询的同时，也能实现传统的分词倒排查询和可调节的权重配置，因此向量检索版能够适应多样化的搜索需求，显著提升搜索质量和用户体验。

数据准备
- 向量数据：
  - 方案一：需要自行准备向量类型数据，直接将向量数据导入OpenSearch向量检索版。
  - 方案二：如果只有文本类型数据，可在OpenSearch向量检索版的配置中，调用OpenSearch内置的文本embedding模型或AI搜索开放平台提供的文本embedding模型，在OpenSearch向量检索版内部将文本内容转换为稠密向量，用于后续语义检索。
- 文本数据：自行准备文本类型数据
文本+向量混合检索
- 多路查询能力结合：
  - 传统分词倒排查询：精准匹配
  - 向量查询：语义理解
- 多路查询优化：
  - 结果权重
    - 手动设置各路结果权重
    - RRF自动分配权重
  - 结果个数：支持配置各路召回个数与总结果返回个数

使用向量检索版实现文本+向量混合检索，包含以下步骤：

步骤一：购买向量检索版实例
步骤二：配置实例
步骤三：添加数据
步骤四：查询

方案演示视频：

操作步骤

步骤一：购买向量检索版实例

参考购买OpenSearch向量检索版实例。

步骤二：配置实例

在实例列表页面，找到新购买的实例，其状态为 待配置 ，在左侧操作栏选择配置，进入实例详情页。

1.表基础信息

输入表名称，设置数据分片数和数据更新资源数，场景模板选择通用模板，完成后点击下一步。

2.数据同步

配置全量数据来源，可以参考文档添加表，这里以API方式为例，点击下一步。

3.字段配置

但由于选择的是API方式和通用模板，所以不会预设任何字段，需要手动添加字段：

1.主键（id）、2.稠密向量（vector）、3.文本倒排字段（title）。

字段配置说明：

必选字段：主键字段和向量字段。
- 主键字段：int或string类型并且需要勾选主键
- 向量字段：
  - 准备了向量类型的数据，字段类型需要选择float类型并且需要勾选向量字段。
  - 只有文本类型数据，可以使用OpenSearch提供的多种模型来实现文本向量化，通过需数据预处理 - 去配置 - 字段vector数据预处理配置进行设置，其中内置模型可以免费调用，而AI搜索开放平台模型需要付费，可参考文档调用AI搜索开放平台模型服务了解。完成字段source_text数据预处理配置页设置后，返回到字段配置页，会自动增加1个新的预设字段。
文本倒排索引字段的字段类型选择TEXT。

4.索引结构

配置完成向量索引后点击下一步，其他参数保持默认即可，更多高级配置按需填写，可参考向量索引通用配置。

演示中采用的是「AI平台 OpenSearch通用文本向量服务-001-1536维」模型，所以向量维度的设置1536维。

5.确认创建

配置完成后，点击确认创建。

步骤三：添加数据

向量检索版分别支持的单条数据添加的表单模式，多条数据添加的开发者模式，添加方式可参考添加数据。

测试数据参考：

[
  {
    "id": 1,
    "vector": "男子法式毛圈套头连帽衫",
    "title":"男子法式毛圈套头连帽衫"
  },
  {
    "id": 2,
    "vector": "男子速干印花篮球短裤",
    "title":"男子速干印花篮球短裤"
  },
  {
    "id": 3,
    "vector": "男子速干舒爽篮球长裤",
    "title":"男子速干舒爽篮球长裤"
  },
  {
    "id": 4,
    "vector": "速干中筒运动袜",
    "title":"速干中筒运动袜"
  },
  {
    "id": 5,
    "vector": "速干渔夫运动帽",
    "title":"速干渔夫运动帽"
  },
  {
    "id": 6,
    "vector": "亚洲版型太阳镜",
    "title":"亚洲版型太阳镜"
  },
  {
    "id": 7,
    "vector": "足球守门员手套",
    "title":"足球守门员手套"
  },
  {
    "id": 8,
    "vector": "男子速干球衣",
    "title":"男子速干球衣"
  },
  {
    "id": 9,
    "vector": "男子实战杜兰特篮球鞋",
    "title":"男子实战杜兰特篮球鞋"
  },
  {
    "id": 10,
    "vector": "男子印花紧身泳裤",
    "title":"男子印花紧身泳裤"
  }
]

步骤四：查询

配置说明：

以表单模式为例，在查询测试页面选择向量文本混合查询方式。
设置表名，查询方式选择稠密向量+文本，排序方式有加权分数排序和RRF两种方式，选择加权分数排序的方式，就能对向量和文本的查询权重比例进行调整。
分别在对向量查询配置和文本查询配置项中输入查询权重、查询内容后，点击搜索就能查看到搜索结果。

向量查询配置示例参考：

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文本查询配置示例参考：

title:'连帽衫'

说明

查询条件的格式要求为 索引名：查询词，查询的内容需要使用单引号''引起来，否则将会造成查询失败。
多个条件之间用AND / OR等连接。如 indexName：value1 AND indexName2：value2。

结果分数：

在本文索引结构页面，我们选择的距离类型是欧式距离（SquareEuclidean），所以结果分数越小表示相似度越高。但在稠密向量+文本的检索方式中，文本部分的得分是基于关键词匹配度的，匹配度越高得分越高，为了将向量分数与文本分数做融合，所以将向量的距离分数做转换。

# 欧式距离
score = 1.0 / (1.0 + l2_distance^2)

# 内积距离
score = (1.0 + ip_distance) / 2.0

SDK（语法说明）：

向量文本混合查询