将视图与Atlas Vector Search结合使用

您可以在视图上创建 Atlas Vector Search 索引，以转换文档和集合，从而可以部分索引集合、支持不兼容的数据类型或数据模型等。

以下示例使用 sample_mflix 示例数据库。

注意

消歧

本页讨论标准视图。要了解按需物化视图，请参阅按需物化视图。

要了解视图类型之间的差异，请参阅与按需物化视图的比较。

要求

您必须使用 MongoDB 8.0 或更高版本。

在 MongoDB v8.0+ 上：
- 使用 Atlas UI 或 Atlas Administration API 在视图上创建 Atlas Vector Search 索引。
- 针对源集合运行 Atlas Vector Search 查询。引用在视图上创建的 Atlas Vector Search 索引。这些查询会返回源集合中显示的原始文档。
在 MongoDB v8.1+ 上，您还可以：
- 使用 mongosh 和驱动程序方法，db.collection.createSearchIndex()、db.collection.updateSearchIndex()、db.collection.dropSearchIndex() 和 $listSearchIndexes，在视图上创建和管理 Atlas Vector Search 索引。
- 针对视图运行 Atlas Vector Search 查询。

要编辑视图，您必须拥有用户管理员角色，并使用 collMod 数据库命令。

限制

Atlas Vector Search 仅支持在以下阶段 $expr 的视图：
索引名称在源集合及其所有视图中必须是唯一的。
如果视图定义中包含能够生成动态结果的操作符（例如 $$USER_ROLES 系统变量和 $rand 聚合操作符），则 Atlas Vector Search 不支持该视图定义。
Atlas Vector Search 查询返回源集合中显示的原始文档。

示例：过滤文档

要创建视图，您必须拥有 createCollection 权限。

您可以使用过滤器筛选文档以对集合进行部分索引。以下示例在 sample_mflix.embedded_movies 集合上创建一个视图，以便仅对具有向量嵌入字段的文档进行索引。

使用 `mongosh` 连接到 Atlas 集群。

要了解更多信息，请参阅通过 mongosh 进行连接。

切换到 `sample_mflix` 数据库。

use sample_mflix

创建一个 `moviesWithEmbeddings` 视图。

db.createView(
  "moviesWithEmbeddings",
  "embedded_movies",
  [
    {
      "$match": {
        "$expr": {
          "$ne": [
            {
              "$type": "$plot_embedding_voyage_3_large"
            },
            "missing"
          ]
        }
      }
    }
  ]
)

AtlasGoClusters在Atlas中，Go项目的页面。

警告： 导航优化正在进行中我们目前正在逐步推出改进的全新导航体验。如果以下步骤与您在 Atlas UI 中的视图不符，请参阅预览文档。

如果尚未显示，请从导航栏上的 Organizations 菜单中选择包含所需项目的组织。
如果尚未显示，请从导航栏的Projects菜单中选择所需的项目。
如果尚未出现，请单击侧边栏中的 Clusters（集群）。
会显示集群页面。

转到集群的 Atlas Search 页面。

您可以从侧边栏、 Data Explorer 或集群详细信息页面转到 Atlas Search 页面。

在侧边栏中，单击 Services 标题下的 Atlas Search。
如果没有集群，则请单击 Create cluster 来创建一个。要了解更多信息，请参阅创建集群。
如果您的项目有多个集群，请从 Select cluster 下拉列表中选择要使用的集群，然后单击 Go to Atlas Search。
将显示 Atlas Search 页面。

单击集群的对应 Browse Collections 按钮。
展开数据库并选择集合。
单击该集合的 Search Indexes 标签页。
将显示 Atlas Search 页面。

单击集群的名称。
单击 Atlas Search 标签页。
将显示 Atlas Search 页面。

单击 Create Search Index（保存并关闭）。

开始您的索引配置。

在页面上进行以下选择，然后单击 Next。

Search Type	选择 Atlas Vector Search 索引类型。
Index Name and Data Source	指定以下信息： Index Name: `embeddingsIndex` Database and Collection: `sample_mflix` `moviesWithEmbeddings`
Configuration Method	For a guided experience, select Visual Editor. To edit the raw index definition, select JSON Editor.

注意

您的 Atlas Search 索引默认名为 default。如果保留此名称，则对于任何未在其操作符中指定不同 index选项的 Atlas Search 查询，您的搜索索引将成为默认搜索索引。如果您要创建多个索引，我们建议您在所有索引中保持一致且具有描述性的命名规则。

指定索引定义。

请指定以下索引定义：
```
{
  "fields": [
    {
      "type": "vector",
      "numDimensions": 2048,
      "path": "plot_embedding_voyage_3_large",
      "similarity": "cosine"
    }
  ]
}
```
单击 Next（连接）。

单击 Create Vector Search Index（保存并关闭）。

Atlas 会显示一个模态窗口，让您知道您的索引正在构建中。

通过单击 Close（关闭）按钮关闭 You're All Set!（一切就绪！）模态窗口。

检查状态。

新创建的索引会显示在 Atlas Search 标签页上。在构建索引期间，Status 字段显示为 Build in Progress。索引构建完成后，Status 字段将显示为 Active。

注意

较大的集合需要较长的索引时间。索引构建完成后，您将收到电子邮件通知。

在 `embeddingsIndex` 部分索引上运行查询。

注意

以下示例通过对名为 moviesWithEmbeddings 的视图运行 .aggregate 命令来查询 embeddingsIndex 索引。如果您的集群正在运行 MongoDB v8.0，则必须使用视图上的索引查询源集合（例如，embedded_movies）。升级到 MongoDB v8.1+ 以直接查询视图。

use sample_mflix

1 db.moviesWithEmbeddings.aggregate([
2   {
3     "$vectorSearch": {
4       "index": "embeddingsIndex",
5       "path": "plot_embedding_voyage_3_large",
6       "queryVector": 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7       "numCandidates": 100,
8       "limit": 10
9     }
10   },
11   {
12     "$project": {
13       "_id": 0,
14       "plot": 1,
15       "title": 1,
16     }
17   }
18 ])

[
  {
    plot: 'At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.',
    title: 'About Time'
  },
  {
    plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.',
    title: 'Retroactive'
  },
  {
    plot: 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...',
    title: 'A.P.E.X.'
  },
  {
    plot: 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.',
    title: 'Timecop'
  },
  {
    plot: 'After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.',
    title: 'Back to the Future Part II'
  },
  {
    plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
    title: 'Thrill Seekers'
  },
  {
    plot: 'Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...',
    title: 'Timerider: The Adventure of Lyle Swann'
  },
  {
    plot: 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.',
    title: 'The Time Machine'
  },
  {
    plot: 'A romantic drama about a Chicago librarian with a gene that causes him to involuntarily time travel, and the complications it creates for his marriage.',
    title: "The Time Traveler's Wife"
  },
  {
    plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.',
    title: 'The Final Countdown'
  }
]

性能考虑因素

对于在视图上创建的索引，Atlas Vector Search 在查询时和复制时应用您在视图管道中定义的转换或过滤器。虽然视图管道中的简单转换或过滤器通常不会对性能产生显著影响，但视图管道中的复杂转换或高度选择性的过滤器可能会显著降低查询或复制的速度。如果您想对文档进行大量转换或应用高度选择性的过滤器，建议创建一个包含已转换或已过滤文档的物化视图。您可以通过比较在视图上执行的查询延迟与在其源集合上执行的查询延迟来评估视图性能。

故障排除

索引更改为 `FAILED`

在以下场景中，索引会更改为 FAILED 状态：

您在与 Atlas Vector Search 不兼容的视图上创建索引。
您编辑视图的方式不符合 Atlas Vector Search 兼容性要求。
您删除或更改视图的源集合。
例如，如果一个视图是在另一个视图上创建的，并且将父视图源更改为另一个集合。
注意
如果一个视图是其他视图的后代，这一限制也同样适用。例如，您无法更改或移除所有后代所源自的源集合。

索引更改为 `STALE`

在以下场景中，索引会更改为 STALE 状态：

警告

如果在视图中定义的聚合管道与集合中的文档不兼容，搜索复制将失败。例如，如果 $toDouble 表达式对包含数组的文档字段进行操作，复制将失败。确保您的视图在处理集合中的所有文档时不会出错。

如果视图定义导致聚合失败，而索引为READY ，则索引变为STALE 。解析文档或更改视图定义以使其不再失败后，索引将返回到READY 。当为STALE 时，索引仍然可查询。如果索引脱离oplog ，则会触发索引重建。
如果视图定义在索引为 BUILDING 时导致聚合管道失败，则在修复文档之前，则索引生成将停滞。在您解析文档或更改视图定义后，索引将返回到 READY，以确保不再失败。

您可以在 Atlas 用户界面的索引状态详情页面查看索引状态。

错误：`$search` 仅作为管道中的第一阶段有效

当您使用 8.1 之前的MongoDB版本查询视图时，会出现此错误。

如果您使用 8.0 之前的MongoDB版本，我们建议您升级到 8.1+ 以直接查询视图。您可以升级到 8.0 来查询源集合。
如果使用MongoDB 8.0，则必须针对源集合查询视图索引。示例，对集合而不是视图运行.aggregate()。

索引流程

当您在视图上创建 Atlas Vector Search 索引时，mongot 进程执行的任务与在常规集合上创建 Atlas Vector Search 索引时相同。mongot 进程：

根据该集合的索引定义中的规则来创建 Atlas Vector Search 索引。
监控文档当前状态的变更流，以及已定义 Atlas Vector Search 索引的集合索引。
处理 Atlas Vector Search 查询，并将匹配文档的文档 ID 和其他搜索元数据返回给 mongod，然后执行完整的文档查找并将结果返回给客户端。

当您在视图上创建 Atlas Vector Search 索引时，视图定义将在步骤 1 和 2 期间应用，转换后的文档将存储在磁盘上的 Atlas Vector Search 索引中。

了解详情

要了解有关视图的更多信息，请参阅视图。

要在视图上创建Atlas Search索引，请参阅将视图与Atlas Search结合使用。

后退

组合向量搜索

来年

矢量量化

注意

消歧

要求

限制

示例：过滤文档

使用 mongosh 连接到 Atlas 集群。

切换到 sample_mflix 数据库。

创建一个 moviesWithEmbeddings 视图。

AtlasGo.leafygreen-ui-8n27nz{font-style:normal;font-weight:700;}Clusters在Atlas中，Go项目的 页面。

转到集群的 Atlas Search 页面。

单击 Create Search Index（保存并关闭）。

单击 Create Search Index（保存并关闭）。

开始您的索引配置。

注意

指定索引定义。

单击 Create Vector Search Index（保存并关闭）。

通过单击 Close（关闭）按钮关闭 You're All Set!（一切就绪！）模态窗口。

检查状态。

注意

在 embeddingsIndex 部分索引上运行查询。

注意

性能考虑因素

故障排除

索引更改为 FAILED

注意

索引更改为 STALE

警告

错误：$search 仅作为管道中的第一阶段有效

索引流程

了解详情

获得技能徽章

使用 `mongosh` 连接到 Atlas 集群。

切换到 `sample_mflix` 数据库。

创建一个 `moviesWithEmbeddings` 视图。

AtlasGoClusters在Atlas中，Go项目的页面。

在 `embeddingsIndex` 部分索引上运行查询。

索引更改为 `FAILED`

索引更改为 `STALE`

错误：`$search` 仅作为管道中的第一阶段有效