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MongoDB 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：
一个包含示例数据集（可选）的集群。
➤ 使用选择语言下拉菜单选择要用于创建索引的界面。
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MongoDB 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MongoDB Vector Search 索引
本部分演示如何创建MongoDB Vector Search索引以运行MongoDB Vector Search ANN 和 ENN 查询。
1
在Atlas中，转到集群的Search & Vector Search 页面。
您可以从 Search & Vector Search 选项或 Data Explorer 转到MongoDB搜索页面。
如果尚未显示，请从导航栏上的 Organizations 菜单中选择包含项目的组织。
如果尚未显示，请从导航栏的 Projects 菜单中选择您的项目。
在侧边栏中，单击 Database 标题下的 Search & Vector 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。
如果尚未显示，请从导航栏上的 Organizations 菜单中选择包含项目的组织。
如果尚未显示，请从导航栏的 Projects 菜单中选择您的项目。
在侧边栏中，单击 Database 标题下的 Data Explorer。
展开数据库并选择集合。
单击该集合的 Indexes 标签页。
将显示 Atlas Search 页面。
3
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 Vector Search 索引类型。 |
Index Name and Data Source | 指定以下信息：
|
Configuration Method | For a guided experience, select Visual Editor. To edit the raw index definition, select JSON Editor. |
重要提示：
默认下， MongoDB Search索引名为 default。如果保留此名称，则该索引将是任何未在运算符中指定其他 index 选项的MongoDB搜索查询的默认搜索索引。如果您要创建多个索引，我们建议您在所有索引之间保持一致的描述性命名约定。
4
指定索引定义。
例子
此索引定义在MongoDB Vector Search索引中将 plot_embedding_voyage_3_large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_voyage_3_large字段包含使用 Voyage AI 的 voyage-3-large 嵌入模型创建的嵌入。索引定义指定 2048 向量维度，并使用 dotProduct 相似度函数测量距离。
Atlas 会自动检测包含向量嵌入的字段及其对应的维度。对于 sample_mflix.embedded_movies 集合，选择 plot_embedding_voyage_3_large 字段。
要配置该索引，请执行以下操作：
从 Similarity Method 下拉列表中选择 Dot Product。
单击 Advanced，然后从下拉菜单中选择 Binary 量化。
在 Filter Field 部分，指定用于筛选该数据的
genres字段。
在 JSON 编辑器中粘贴以下索引定义：
1 { 2 "fields": [ 3 { 4 "numDimensions": 2048, 5 "path": "plot_embedding_voyage_3_large", 6 "similarity": "dotProduct", 7 "type": "vector", 8 "quantization": "binary" 9 }, 10 { 11 "path": "genres", 12 "type": "filter" 13 } 14 ] 15 }
3
运行db.collection.createSearchIndex() 方法以创建索引。
db.embedded_movies.createSearchIndex( "vector_index", "vectorSearch", { "fields": [ { "numDimensions": 2048, "path": "plot_embedding_voyage_3_large", "similarity": "dotProduct", "type": "vector", "quantization": "binary" }, { "path": "genres", "type": "filter" } ] } } )
vector_index
运行查询
您无法在Atlas用户界面Search Tester 中运行MongoDB Vector Search 查询。使用 mongosh 或支持的驾驶员运行查询。
本节演示如何对索引集合运行ENN 和 ANN 查询。
1
准备您的查询嵌入。
将以下嵌入保存在名为 query-embeddings.js 的文件中：
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2
使用 mongosh 连接到集群。
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连接到集群。
4
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
5
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_voyage_3_large", 6 "filter": { 7 "$and": [ 8 { 9 "genres": { "$eq": "Action" } 10 }, 11 { 12 "genres": { "$ne": "Comedy" } 13 } 14 ] 15 }, 16 "queryVector": TIME_TRAVEL_EMBEDDING, 17 "exact": true, 18 "limit": 10 19 } 20 }, 21 { 22 "$project": { 23 "_id": 0, 24 "plot": 1, 25 "title": 1, 26 "genres": 1, 27 "score": { $meta: "vectorSearchScore" } 28 } 29 } 30 ])
[ { plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.', genres: [ 'Action', 'Crime', 'Drama' ], title: 'Retroactive', score: 0.760047972202301 }, { 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...', genres: [ 'Action', 'Sci-Fi' ], title: 'A.P.E.X.', score: 0.7576861381530762 }, { 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.', genres: [ 'Action', 'Crime', 'Sci-Fi' ], title: 'Timecop', score: 0.7576561570167542 }, { plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.', genres: [ 'Action', 'Sci-Fi', 'Thriller' ], title: 'Thrill Seekers', score: 0.7509932518005371 }, { 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, ...', genres: [ 'Action', 'Adventure', 'Sci-Fi' ], title: 'Timerider: The Adventure of Lyle Swann', score: 0.7502642869949341 }, { 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.', genres: [ 'Sci-Fi', 'Adventure', 'Action' ], title: 'The Time Machine', score: 0.7502503395080566 }, { plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.', genres: [ 'Action', 'Sci-Fi' ], title: 'The Final Countdown', score: 0.7469133734703064 }, { plot: "Ba'al travels back in time and prevents the Stargate program from being started. SG-1 must somehow restore history.", genres: [ 'Action', 'Adventure', 'Drama' ], title: 'Stargate: Continuum', score: 0.7468316555023193 }, { plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', genres: [ 'Action', 'Adventure', 'Drama' ], title: 'Love Story 2050', score: 0.7420939207077026 }, { plot: "Captain Picard and his crew pursue the Borg back in time to stop them from preventing Earth's first contact with an alien species. They also make sure that Zefram Cochrane makes his famous maiden flight at warp speed.", genres: [ 'Action', 'Adventure', 'Sci-Fi' ], title: 'Star Trek: First Contact', score: 0.7356286644935608 } ]
此查询使用以下管道阶段：
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6
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_voyage_3_large", 6 "filter": { 7 "$and": [ 8 { 9 "genres": { "$eq": "Action" } 10 }, 11 { 12 "genres": { "$ne": "Comedy" } 13 } 14 ] 15 }, 16 "queryVector": TIME_TRAVEL_EMBEDDING, 17 "numCandidates": 100, 18 "limit": 10 19 } 20 }, 21 { 22 "$project": { 23 "_id": 0, 24 "plot": 1, 25 "title": 1, 26 "genres": 1, 27 "score": { $meta: "vectorSearchScore" } 28 } 29 } 30 ])
[ { plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.', genres: [ 'Action', 'Crime', 'Drama' ], title: 'Retroactive', score: 0.760047972202301 }, { 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...', genres: [ 'Action', 'Sci-Fi' ], title: 'A.P.E.X.', score: 0.7576861381530762 }, { 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.', genres: [ 'Action', 'Crime', 'Sci-Fi' ], title: 'Timecop', score: 0.7576561570167542 }, { plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.', genres: [ 'Action', 'Sci-Fi', 'Thriller' ], title: 'Thrill Seekers', score: 0.7509932518005371 }, { 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, ...', genres: [ 'Action', 'Adventure', 'Sci-Fi' ], title: 'Timerider: The Adventure of Lyle Swann', score: 0.7502642869949341 }, { 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.', genres: [ 'Sci-Fi', 'Adventure', 'Action' ], title: 'The Time Machine', score: 0.7502503395080566 }, { plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.', genres: [ 'Action', 'Sci-Fi' ], title: 'The Final Countdown', score: 0.7469133734703064 }, { plot: "Ba'al travels back in time and prevents the Stargate program from being started. SG-1 must somehow restore history.", genres: [ 'Action', 'Adventure', 'Drama' ], title: 'Stargate: Continuum', score: 0.7468316555023193 }, { plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', genres: [ 'Action', 'Adventure', 'Drama' ], title: 'Love Story 2050', score: 0.7420939207077026 }, { plot: "Captain Picard and his crew pursue the Borg back in time to stop them from preventing Earth's first contact with an alien species. They also make sure that Zefram Cochrane makes his famous maiden flight at warp speed.", genres: [ 'Action', 'Adventure', 'Sci-Fi' ], title: 'Star Trek: First Contact', score: 0.7356286644935608 } ]
此查询使用以下管道阶段：
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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，以便在应用应用程序的准确性和速度之间达到理想的平衡。
我们建议您使用判断列表，以结构化的形式列出查询及其理想结果，用于近似最近邻 (ANN) 查询或精确最近邻 (ENN) 真实值。您可以将 ENN 查询结果作为基准判断列表，然后将 ANN 查询结果与该判断列表进行对比，以评估召回率、重叠度和性能。判断列表提供了一种方法来评估 ANN 查询是否达到与 ENN 基线相比的预期准确率或召回率阈值。使用 LLM 生成示例查询。