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。
➤ 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。
1
4
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. |
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。
5
指定索引定义。
例子
此索引定义在启用自动二进制 quantization 的情况下，将 plot_embedding_voyage_3_large 字段索引为 vector 类型，并将 genres 字段索引为 Atlas Vector Search 索引中的 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。索引定义指定了 2048 个向量维度，并使用 dotProduct 相似性函数来测量距离。
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Atlas 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 连接到您的Atlasmongosh 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。