如何衡量查询结果的准确性

在此页面上

用例

先决条件
步骤
创建 Atlas Vector Search 索引
运行查询
比较结果

您可以使用相同的查询条件，根据量化向量评估 ANN搜索结果与 ENN搜索结果的匹配程度，从而衡量Atlas Vector Search查询的准确性。也就是说，您可以将 ANN搜索结果与 ENN搜索结果进行比较，并衡量 ANN搜索结果在 ENN搜索结果中包含最近邻的频率。

用例

如果您有以下任一情况，则可能需要衡量结果的准确性：

量化向量
大量向量
低维向量

先决条件

要试用此页面上的示例，您需要满足以下条件：

一个Atlas 集群，其中包含可选的示例数据集。
mongosh

步骤

要评估$vectorSearch 查询结果的准确性，必须执行以下操作：

在向量字段以及要作为数据预筛选依据的任何其他字段上创建Atlas Vector Search索引。
我们建议使用量化向量来改进向量的存储和查询速度。如果您没有量化向量，则可以在为 vector 类型字段索引时启用自动量化。
构建并运行ENN查询，然后运行 ANN查询。
将 ANN查询结果与 ENN查询结果进行比较，以评估结果的异同。

本部分演示如何对 sample_mflix.embedded_movies集合中的数据执行前面的 3 步骤。如果您不希望使用示例数据集，则可以对自己的数据执行这些过程。

创建 Atlas Vector Search 索引

本部分演示如何创建Atlas Vector Search索引以运行Atlas Vector Search ANN 和 ENN 查询。

AtlasGoClusters在Atlas中，Go项目的页面。

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。
从 Select data source 下拉菜单中选择您的集群并单击 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。

创建 Atlas Vector Search 索引。

要进行创建，请单击 Create Search Index。

在 Atlas Vector Search 下面选择 JSON Editor（JSON 编辑器），然后单击 Next（下一步）。

输入 Index Name（索引名称），然后设置 Database and Collection（数据库和集合）。

在 Index Name（索引名称）字段中，输入索引的名称。
注意
无论索引类型如何，索引名称在命名空间内都必须是唯一的。
例子
输入 vector_index 作为此索引的名称。如果此集合上已有名为 vector_index 的索引，请为该索引输入其他名称。
在 Database and Collection 部分中，找到数据库，然后选择集合。
例子
在 Database and Collection（数据库和集合）部分中找到 sample_mflix 数据库，然后选择 embedded_movies 集合。

指定索引定义。

例子

此索引定义在Atlas Vector Search索引中将 plot_embedding字段索引为启用自动二进制 quantization 的 vector 类型，并将 genres字段索引为 filter 类型。 plot_embedding字段包含使用 OpenAI 的 text-embedding-ada-002 嵌入模型创建的嵌入。索引定义指定 1536 向量维度，并使用 euclidean 相似度函数测量距离。

1 {
2   "fields": [
3     {
4       "numDimensions": 1536,
5       "path": "plot_embedding",
6       "similarity": "euclidean",
7       "type": "vector",
8       "quantization": "binary"
9     },
10     {
11       "path": "genres",
12       "type": "filter"
13     }
14   ]
15 }

单击 Next（下一步）以查看索引。

单击 Create 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。

新创建的索引会出现在 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。

运行查询

本节演示如何对索引集合运行ENN 和 ANN 查询。

使用连接到您的Atlas`mongosh` 集群。

打开终端窗口并使用mongosh 连接到集群。要学习；了解更多信息，请参阅通过 mongosh连接。

切换到您的数据库。

例子

使用sample_mflix 数据库。要切换到sample_mflix 数据库，请在mongosh 提示符下运行以下命令：

use sample_mflix

switched to db sample_mflix

运行新奥查询。

例子

将以下示例查询复制并粘贴到终端，然后使用 mongosh 运行。由于向量嵌入中的字符数量，当您粘贴查询时，mongosh 可能会略有延迟。

1 db.embedded_movies.aggregate([
2   {
3     "$vectorSearch": {
4       "index": "vector_index",
5       "path": "plot_embedding",
6       "filter": {
7         "$and": [
8           {
9             "genres": { "$eq": "Action" }
10           }, 
11           {
12             "genres": { "$ne": "Western" }
13           }
14         ]
15       },
16       "queryVector": [-0.0241895392537117, -0.01683979481458664, -0.0017179687274619937, -0.03551029786467552, -0.032710395753383636, 0.026625992730259895, -0.018266668543219566, -0.010210219770669937, -0.01335337944328785, -0.022843431681394577, 0.010095800273120403, 0.008514124900102615, -0.009308327920734882, 0.0010996009223163128, 0.015332155860960484, -0.011024614796042442, 0.030825844034552574, -0.008668927475810051, 0.0013419002061709762, -0.03448725491762161, -0.0006524411146529019, 0.011031344532966614, -0.0005321328062564135, -0.004745026119053364, -0.0017970525659620762, -0.010573668405413628, 0.006505060940980911, -0.014847557991743088, 0.011482290923595428, -0.010277525521814823, -0.0018811841728165746, 0.003577278694137931, -0.010990961454808712, -0.02285689301788807, -0.012559176422655582, -0.009092951193451881, -0.006249300669878721, -0.010842889547348022, -0.013400493189692497, 0.015332155860960484, 0.007827610708773136, 0.012942816130816936, -0.008655466139316559, 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17       "exact": true,
18       "limit": 10
19     }
20   },
21   {
22     "$project": {    
23       "_id": 0,
24       "plot": 1,
25       "title": 1,
26       "genres": 1,
27       "score": { $meta: "vectorSearchScore" }    
28     }
29   }
30 ])

[
  {
    plot: "A thief with a unique code of professional ethics is double-crossed by his crew and left for dead. Assuming a new disguise and forming an unlikely alliance with a woman on the inside, he looks to hijack the score of the crew's latest heist.",
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'Parker',
    score: 0.7952193021774292
  },
  {
    plot: 'An adrenaline junkie walks away from a whirlwind romance and embraces a new life as a thief, though he soon finds himself pursued by veteran police officer and engaged in a turf war with a local gangster.',
    genres: [ 'Action', 'Comedy', 'Romance' ],
    title: 'Kick',
    score: 0.7876788377761841
  },
  {
    plot: 'The dying words of a thief spark a madcap cross-country rush to find some treasure.',
    genres: [ 'Action', 'Adventure', 'Comedy' ],
    title: "It's a Mad, Mad, Mad, Mad World",
    score: 0.7861514687538147
  },
  {
    plot: 'A group of professional bank robbers start to feel the heat from police when they unknowingly leave a clue at their latest heist.',
    genres: [ 'Action', 'Crime', 'Drama' ],
    title: 'Heat',
    score: 0.7858744859695435
  },
  {
    plot: 'A romantic and action packed story of three best friends, a group of high end art thieves, who come into trouble when a love-triangle forms between them.',
    genres: [ 'Action', 'Comedy', 'Crime' ],
    title: 'Once a Thief',
    score: 0.7815249562263489
  },
  {
    plot: 'A recently released ex-con and his loyal wife go on the run after a heist goes awry.',
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'The Getaway',
    score: 0.7795301079750061
  },
  {
    plot: "The story of what happens after a master thief achieves his last big score, when the FBI agent who promised he'd capture him is about to do just that.",
    genres: [ 'Action', 'Comedy', 'Crime' ],
    title: 'After the Sunset',
    score: 0.779007613658905
  },
  {
    plot: 'A group of bank robbers find their multi-million dollar plan interrupted by a hard-boiled detective.',
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'Takers',
    score: 0.7787627577781677
  },
  {
    plot: "After he's shot during a heist in East L.A., an armored truck driver wrestles with rehabilitation and tracking down the man who committed the crime.",
    genres: [ 'Action', 'Crime', 'Drama' ],
    title: 'The Take',
    score: 0.7776619791984558
  },
  {
    plot: 'While doing a friend a favor and searching for a runaway teenager, a police detective stumbles upon a bizarre band of criminals about to pull off a bank robbery.',
    genres: [ 'Action', 'Crime', 'Drama' ],
    title: 'No Good Deed',
    score: 0.7773410677909851
  }
]

此查询使用以下管道阶段：

`$vectorSearch`	对文档进行预过滤以搜索`Action` 类型的电影，而不是 `Western` 类型的电影。使用字符串 `thrilling heist with twists` 的向量嵌入，在 `plot_embedding`字段中搜索精确的最近邻。将输出限制为仅 10 个结果。
`$project`	从结果中的文档中排除除 `plot` 、 `title` 和 `genres` 之外的所有字段。添加名为 `score` 的字段，显示结果文档的分数。

运行 ANN查询。

例子

将以下示例查询复制并粘贴到终端，然后使用 mongosh 运行。由于向量嵌入中的字符数量，当您粘贴查询时，mongosh 可能会略有延迟。

1 db.embedded_movies.aggregate([
2   {
3     "$vectorSearch": {
4       "index": "vector_index",
5       "path": "plot_embedding",
6       "filter": {
7         "$and": [
8           {
9             "genres": { "$eq": "Action" }
10           }, 
11           {
12             "genres": { "$ne": "Western" }
13           }
14         ]
15       },
16       "queryVector": [-0.0241895392537117, -0.01683979481458664, -0.0017179687274619937, -0.03551029786467552, -0.032710395753383636, 0.026625992730259895, -0.018266668543219566, -0.010210219770669937, -0.01335337944328785, -0.022843431681394577, 0.010095800273120403, 0.008514124900102615, -0.009308327920734882, 0.0010996009223163128, 0.015332155860960484, -0.011024614796042442, 0.030825844034552574, -0.008668927475810051, 0.0013419002061709762, -0.03448725491762161, -0.0006524411146529019, 0.011031344532966614, -0.0005321328062564135, -0.004745026119053364, -0.0017970525659620762, -0.010573668405413628, 0.006505060940980911, -0.014847557991743088, 0.011482290923595428, -0.010277525521814823, -0.0018811841728165746, 0.003577278694137931, -0.010990961454808712, -0.02285689301788807, -0.012559176422655582, -0.009092951193451881, -0.006249300669878721, -0.010842889547348022, -0.013400493189692497, 0.015332155860960484, 0.007827610708773136, 0.012942816130816936, -0.008655466139316559, 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17       "numCandidates": 100,
18       "limit": 10
19     }
20   },
21   {
22     "$project": {    
23       "_id": 0,
24       "plot": 1,
25       "title": 1,
26       "genres": 1,
27       "score": { $meta: "vectorSearchScore" }    
28     }
29   }
30 ])

[
  {
    plot: "A thief with a unique code of professional ethics is double-crossed by his crew and left for dead. Assuming a new disguise and forming an unlikely alliance with a woman on the inside, he looks to hijack the score of the crew's latest heist.",
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'Parker',
    score: 0.7952193021774292
  },
  {
    plot: 'An adrenaline junkie walks away from a whirlwind romance and embraces a new life as a thief, though he soon finds himself pursued by veteran police officer and engaged in a turf war with a local gangster.',
    genres: [ 'Action', 'Comedy', 'Romance' ],
    title: 'Kick',
    score: 0.7876788377761841
  },
  {
    plot: 'The dying words of a thief spark a madcap cross-country rush to find some treasure.',
    genres: [ 'Action', 'Adventure', 'Comedy' ],
    title: "It's a Mad, Mad, Mad, Mad World",
    score: 0.7861514687538147
  },
  {
    plot: 'A group of professional bank robbers start to feel the heat from police when they unknowingly leave a clue at their latest heist.',
    genres: [ 'Action', 'Crime', 'Drama' ],
    title: 'Heat',
    score: 0.7858744859695435
  },
  {
    plot: 'A romantic and action packed story of three best friends, a group of high end art thieves, who come into trouble when a love-triangle forms between them.',
    genres: [ 'Action', 'Comedy', 'Crime' ],
    title: 'Once a Thief',
    score: 0.7815249562263489
  },
  {
    plot: 'A recently released ex-con and his loyal wife go on the run after a heist goes awry.',
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'The Getaway',
    score: 0.7795301079750061
  },
  {
    plot: "The story of what happens after a master thief achieves his last big score, when the FBI agent who promised he'd capture him is about to do just that.",
    genres: [ 'Action', 'Comedy', 'Crime' ],
    title: 'After the Sunset',
    score: 0.779007613658905
  },
  {
    plot: 'A group of bank robbers find their multi-million dollar plan interrupted by a hard-boiled detective.',
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'Takers',
    score: 0.7787627577781677
  },
  {
    plot: "After he's shot during a heist in East L.A., an armored truck driver wrestles with rehabilitation and tracking down the man who committed the crime.",
    genres: [ 'Action', 'Crime', 'Drama' ],
    title: 'The Take',
    score: 0.7776619791984558
  },
  {
    plot: 'A high-tech police surveillance team attempts to take down a gang of ruthless bank robbers.',
    genres: [ 'Action', 'Crime' ],
    title: 'Cold Eyes',
    score: 0.775409996509552
  }
]

此查询使用以下管道阶段：

`$vectorSearch`	按 `Action` 类型的电影而不是 `Western` 类型的电影对要搜索的文档进行预过滤。使用字符串 `thrilling heist with twists` 的向量嵌入，在 `plot_embedding`字段中搜索近似最近邻。最多考虑 `100` 个最近邻，但将输出限制为仅 10 个结果。
`$project`	从结果中的文档中排除除 `plot` 、 `title` 和 `genres` 之外的所有字段。添加名为 `score` 的字段，显示结果文档的分数。

比较结果

示例ENN 和 ANN查询结果中的前 9 个文档相同，并且具有相同的分数。这表明查询的热门结果具有高度相似性。但是，ENN 和 ANN查询结果中的第 10 个文档不同，这反映了精确最近邻搜索和近似最近邻搜索中的细微差别。

新奥搜索检查所有可能的候选，并根据相似度分数返回与查询最接近的匹配项。 ANN搜索使用近似值来加快搜索速度，这可能会改变文档的分数。如果增加numCandidates ANN查询中的值，则结果将与 ENN查询结果更加匹配。但是，这会消耗额外的计算资源，并可能降低查询速度。结果中的第十个文档反映了准确性和速度之间的权衡。

在根据 ENN 参考标准对结果进行定量评估后，我们建议以相同的方式测试一设立100 查询，并计算结果集之间的“jaccard 相似度”。 Jaccard 相似度可以通过将两个集合之间的交集（即重叠项目）除以设立的总大小来计算。这可以了解 ANN 查询的召回性能，包括针对量化向量执行的召回性能。

如果您发现 ENN 和 ANN查询结果之间存在较大差异，我们建议您调整 numCandidates值，以便在应用程序的准确性和速度之间达到理想的平衡。

后退

API 资源

来年

提高性能(Improve Performance)

1	{
2	"fields": [
3	{
4	"numDimensions": 1536,
5	"path": "plot_embedding",
6	"similarity": "euclidean",
7	"type": "vector",
8	"quantization": "binary"
9	},
10	{
11	"path": "genres",
12	"type": "filter"
13	}
14	]
15	}

用例