运行MongoDB Vector Search 查询

自动嵌入作为预览功能仅适用于MongoDB Community Edition v8.2 及更高版本。在预览期间，功能和相应的文档可能随时更改。要学习；了解详情，请参阅预览功能。

Overview

在本指南中，您可以学习如何在 Go 驱动程序中使用 MongoDB 向量搜索功能，并通过 $vectorSearch 管道阶段实现。此管道阶段允许您对文档进行语义搜索。语义搜索可以查找与您提供的搜索词或短语在含义上相似但不完全相同的信息。

重要

功能兼容性

要了解哪些版本的 MongoDB Atlas 支持此功能，请参阅 MongoDB Atlas 文档中的限制。

样本数据

本页上的示例查询plot_embedding embedded_movies集合中的字段，该集合可在Atlas示例数据集的 sample_mflix数据库中找到。

plot_embedding字段包含使用 OpenAI 的 text-embedding-ada-002 嵌入模型创建的 1536 维度的向量嵌入。

要学习如何创建免费的MongoDB Atlas 集群并加载示例数据集，请参阅Atlas入门。

运行MongoDB Vector Search 查询

要使用此功能，您必须创建向量搜索索引并为向量索引。要学习；了解如何以编程方式创建向量搜索索引，请参阅 MongoDB搜索和MongoDB Vector Search 索引。要学习；了解有关向量嵌入的更多信息，请参阅Atlas文档中的如何为向量搜索编制向量嵌入索引。

在向量嵌入上创建向量搜索索引后，您可以在聚合管道中引用此索引来运行向量搜索查询。

以下部分演示如何为查询向量创建BSON二进制向量，以及如何使用向量搜索索引通过 plot_embedding字段运行向量搜索查询。

创建BSON二进制向量

在此示例中，您可以创建一个 1536 维度向量，用作针对 plot_embedding字段进行向量搜索查询的查询向量。该查询使用向量嵌入来搜索 plot_embedding字段。

以下示例展示了如何将此向量嵌入转换为可用作查询向量的BSON二进制向量：

// Converts a float32 array to a BSON vector
vectorFloat := bson.NewVector([]float32{-0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 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-0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136})
// Converts the BSON vector to a BSON binary vector
queryVector := vectorFloat.Binary()

如果需要访问权限原始向量的切片，还可以将查询向量反序列化回BSON向量。

以下示例展示了如何使用 NewVectorFromBinary() 方法将查询向量从BSON二进制向量转换为BSON向量：

// Converts the BSON binary vector back to a BSON vector
backToVectorFloat, err := bson.NewVectorFromBinary(queryVector)
if err != nil {
	log.Fatal(err)
}

提示

查询向量类型

前面的示例创建了一个BSON二进制向量实例提供服务查询向量，但您也可以使用BSON double 值的大量。但是，我们建议您使用BSON二进制向量以提高存储效率。

运行向量搜索查询

以下示例展示了如何构建一个聚合管道，该管道使用 $vectorSearch 和 $project 方法来执行符合以下规范的近似最近邻 (ANN)向量搜索：

使用BSON二进制 queryVector 查询 plot_embedding字段
使用 numCandidates 选项将搜索中使用的最近邻数量设置为 150
使用在 plot_embedding字段上创建的 vector_search索引
返回具有指定 plot、title 和 score 字段的 5 文档

// Creates the aggregation pipeline
vectorSearchStage := bson.D{
	{"$vectorSearch", bson.D{
		{"index", "vector_index"},
		{"path", "plot_embedding"},
		{"queryVector", queryVector},
		{"numCandidates", 150},
		{"limit", 5},
	}}}
projectStage := bson.D{
	{"$project", bson.D{
		{"_id", 0},
		{"plot", 1},
		{"title", 1},
		{"score", bson.D{{"$meta", "vectorSearchScore"}}},
	}}}
// Runs the aggregation pipeline
cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
if err != nil {
	log.Fatalf("failed to retrieve data from the server: %v", err)
}
// Displays the results
type ProjectedMovieResult struct {
	Title string  `bson:"title"`
	Plot  string  `bson:"plot"`
	Score float64 `bson:"score"`
}
var results []ProjectedMovieResult
if err = cursor.All(ctx, &results); err != nil {
	log.Fatalf("failed to unmarshal retrieved docs to ProjectedMovieResult objects: %v", err)
}
for _, result := range results {
	fmt.Printf("Title: %v \nPlot: %v \nScore: %v \n\n", result.Title, result.Plot, result.Score)
}

Title: Thrill Seekers
Plot: A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.
Score: 0.92730712890625
Title: About Time
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.
Score: 0.926605224609375
Title: The Time Machine
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.
Score: 0.9239959716796875
Title: Timecop
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.
Score: 0.923583984375
Title: Crusade in Jeans
Plot: After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...
Score: 0.9222412109375

查询自动嵌入索引

您可以通过查询自动嵌入的MongoDB 向量搜索索引，自动生成文本搜索的向量。要学习如何使用 Go 驱动程序创建自动嵌入索引，请参阅索引指南中的 MongoDB 自动嵌入搜索索引模型部分。

以下示例构建了一个聚合管道，用于搜索与 plot字段中的短语 "time travel" 的语义相似性。该查询在名为 auto_embedding_index 的 plot字段上使用自动嵌入的MongoDB 向量搜索索引：

// Creates the $vectorSearch stage that queries the
// auto-embedding index named "auto_embedding_index"
vectorSearchStage := bson.D{
	{"$vectorSearch", bson.D{
		{"index", "auto_embedding_index"},
		{"path", "plot"},
		{"query", bson.D{{"text", "time travel"}}},
		{"model", "voyage-4"},
		{"numCandidates", 150},
		{"limit", 10},
	}},
}
// Projects only the title and plot fields
projectStage := bson.D{
	{"$project", bson.D{
		{"_id", 0},
		{"title", 1},
		{"plot", 1},
	}},
}
// Runs the aggregation pipeline
cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
if err != nil {
	log.Fatalf("failed to retrieve data from the server: %v", err)
}
defer cursor.Close(ctx)
type ProjectedMovieResult struct {
	Title string `bson:"title"`
	Plot  string `bson:"plot"`
}
var results []ProjectedMovieResult
if err := cursor.All(ctx, &results); err != nil {
	log.Fatalf("failed to unmarshal retrieved docs: %v", err)
}
for _, result := range results {
	fmt.Printf("Title: %v\nPlot: %v\n---\n\n", result.Title, result.Plot)
}

Title: Manuel on the Island of Wonders
Plot: Manuel's fantasy travel through Time goes from Long Ago (Episode 1 - O jardim proibido / Le Jardin interdit) through Now (Episode 2 - O pique-nique dos sonhos / Le Pique-nique des rèves), ...
---
Title: 11 Minutes Ago
Plot: Traveling in 11-minute increments, a time-tumbler from 48-years in the future spends two years of his life weaving through a two-hour wedding reception.
---
Title: Time Freak
Plot: A neurotic inventor creates a time machine and gets lost traveling around yesterday.
---
Title: Timecrimes
Plot: A man accidentally gets into a time machine and travels back in time nearly an hour. Finding himself will be the first of a series of disasters of unforeseeable consequences.
---
Title: The Little Girl Who Conquered Time
Plot: A high-school girl acquires the ability to time travel.
---
Title: The Little Girl Who Conquered Time
Plot: A high-school girl acquires the ability to time travel.
---
Title: Time Traveller
Plot: A high-school girl acquires the ability to time travel.
---
Title: Je t'aime je t'aime
Plot: Recovering from an attempted suicide, a man is selected to participate in a time travel experiment that has only been tested on mice. A malfunction in the experiment causes the man to ...
---
Title: A.P.E.X.
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: The Ah of Life
Plot: Theoretical mathematician, Nigel Kline finds himself the subject of his own vertical time study. Entering into Einstein's relativity, three versions of Nigel face off with each other, weaving time and space in a world of fluid moments.
---
Title: About Time
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.
---

注意

当您查询自动嵌入的MongoDB Vector Search索引时，请在 query字段中提供要搜索的文本（示例{"text", "time travel"}），并在 model字段中提供兼容模型名称。支持的模型包括 voyage-4-lite、voyage-4、voyage-4-large 和 voyage-code-3。您无需计算或传递 queryVector 值。

Overview

重要

功能兼容性

样本数据

运行MongoDB Vector Search 查询

创建BSON二进制向量

提示

查询向量类型

运行向量搜索查询

查询自动嵌入索引

注意

更多信息

API 文档