使用 Atlas Vector Search 和 Atlas Search 执行混合搜索
您可以将 Atlas Vector Search 和 Atlas Search 查询组合成混合搜索以获得统一的结果。
本教程演示如何对 sample_mflix.embedded_movies 集合运行混合搜索，其中包含有关电影的详细信息。具体来说，本教程将指导您完成以下步骤：
在
plot_embeddings字段上创建 Atlas Vector Search 索引。该字段包含表示电影情节摘要的向量嵌入。在
sample_mflix.embedded_movies集合的title字段上设置 Atlas Search 索引。此字段包含文本字符串形式的电影名称。运行一个使用互易等级融合的查询，将针对
plot_embeddings字段的$vectorSearch查询结果和针对title字段的$search查询结果结合起来。
为什么选择混合搜索？
混合搜索是针对相同查询条件的不同搜索方法（如全文搜索和语义搜索）的聚合。虽然全文搜索对于查询词的精确匹配很有效，但语义搜索还提供了一个额外好处，即使文档不包含精确的查询词，也能识别语义相似的文档。这确保了同义匹配和上下文相似的匹配项也包含在两种搜索方法的组合结果中。
相反，如果数据集中有专有名词或特定关键词的词元，这些词元在数据集中使用的上下文相同，您不希望在培训嵌入模型时考虑这些词元，则向量搜索可能会受益于与全文搜索相结合。
您还可以为每个查询的每种搜索方法设立权重。根据全文或语义搜索结果是否与查询最相关且最合适，您可以增加每个查询的搜索方法的权重。
什么是倒数排名融合？
倒数排名融合是一种技术，通过执行以下操作，将语义搜索和全文搜索等不同搜索方法的结果合并为一个结果集：
计算结果中各个文档的倒数排名。
对于每个搜索结果中每个排名的文档，首先将该文档的排名 (
r) 与一个常数60相加，以平滑分数 (rank_constant)，然后将1除以r与rank_constant之和，得出该文档在搜索结果中的倒数排名。reciprocal_rank = 1 / ( r + rank_constant ) 对于每种搜索方法，应用不同的权重 （
w） 以使该搜索方法占更大重要性。对于每个文档，加权倒数排名的计算方法是将权重乘以文档的倒数排名。weighted_reciprocal_rank = w x reciprocal_rank 将结果中文档的派生排名分数和加权分数相结合。
对于所有搜索结果中的每个文档，将计算出的倒数排名相加，获得文档的单个分数。
按结果中文档的综合得分对结果进行排序。
根据结果中的综合得分对结果中的文档进行排序，以获得结果中文档的单一综合排名列表。
先决条件
在开始之前，您必须具备以下条件：
MongoDB 版本为 v 6.0.11或 v 7.0.2或更高版本的 Atlas 集群。
注意
确保您的 Atlas 集群有足够的内存来存储 Atlas Search 和 Atlas Vector Search 索引并运行高性能查询。
示例数据已加载到您的 Atlas 集群中。
用于在 Atlas 集群上运行查询的以下应用程序之一：
Project Data Access Admin访问项目以创建 Atlas Vector Search 和 Atlas Search 索引。
创建 Atlas Vector Search 和 Atlas Search 索引
本部分演示如何在sample_mflix.embedded_moviescollection中的字段上创建以下索引：
plot_embeddings字段上的 Atlas Vector Search 索引，用于针对该字段运行向量查询。title字段上的 Atlas Search 索引，用于对该字段运行全文搜索。
步骤
1
定义 Atlas Vector Search 索引。
以下索引定义在查询集合时将 plot_embeddings 字段索引为 Atlas Vector Search 字段。
要为使用 mongosh v2.1.2 或更高版本的集合创建 Atlas Search 索引，请执行以下步骤：
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。切换到包含要创建索引的集合的数据库。
use sample_mflix switched to db sample_mflix 运行
db.collection.createSearchIndex()方法。1 db.embedded_movies.createSearchIndex( 2 "rrf-vector-search", 3 "vectorSearch", 4 { 5 "fields": [ 6 { 7 "type": "vector", 8 "path": "plot_embedding", 9 "numDimensions": 1536, 10 "similarity": "dotProduct" 11 } 12 ] 13 } 14 );
2
定义 Atlas Search 索引。
以下索引定义将title字段索引为用于查询该字段的字符串类型。
要为集合使用 mongosh v2.1.2 或更高版本的数据集创建 Atlas Search 索引，请执行以下步骤：
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。切换到包含要创建索引的集合的数据库。
use sample_mflix switched to db sample_mflix 运行
db.collection.createSearchIndex()方法。1 db.embedded_movies.createSearchIndex( 2 "rrf-full-text-search", 3 "search", 4 { 5 "mappings": { 6 "dynamic": false, 7 "fields": { 8 "title": [{ 9 "type": "string" 10 }] 11 } 12 } 13 } 14 );
1
设置环境
初始化您的 Node.js 项目。
在终端中运行以下命令，创建名为
hybrid-search-mongodb的新目录并初始化项目：mkdir hybrid-search-mongodb cd hybrid-search-mongodb npm init -y 安装并导入依赖项。
运行以下命令：
npm install mongodb 更新您的
package.json文件。在项目的
package.json文件中，按以下示例所示指定type字段，然后保存文件。{ "name": "hybrid-search-mongodb", "type": "module", ... 创建
.env文件。在您的项目中，创建一个
.env文件来存储您的 Atlas 连接字符串。ATLAS_CONNECTION_STRING = "<connection-string>"; 用 Atlas 集群的SRV 连接字符串替换
<api-key>和<connection-string>占位符值。连接字符串应使用以下格式：mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net 注意
最低 Node.js 版本要求
Node.js v 20 .x 引入了
--env-file选项。如果您使用的是旧版本的 Node.js，请将dotenv包添加到项目中，或使用其他方法来管理环境变量。
2
定义 Atlas Vector Search 索引。
以下索引定义在查询集合时将 plot_embeddings 字段索引为 Atlas Vector Search 字段。
创建一个名为
avs-index.js的文件，并将以下代码粘贴到其中：import { MongoClient } from 'mongodb'; // Connect to your Atlas deployment const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING); async function run() { try { const database = client.db("sample_mflix"); const collection = database.collection("embedded_movies"); // Define your Atlas Vector Search index const index = { name: "rrf-vector-search", type: "vectorSearch", definition: { "fields": [ { "type": "vector", "numDimensions": 1536, "path": "plot_embedding", "similarity": "dotProduct" } ] } } // Call the method to create the index const result = await collection.createSearchIndex(index); console.log(result); } finally { await client.close(); } } run().catch(console.dir); 运行以下命令来执行代码：
node --env-file=.env avs-index.js Atlas 可能需要几分钟时间来创建索引。当索引可供使用时，Atlas 会发送电子邮件。在 Atlas 用户界面中，新创建的索引显示在 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。
3
定义 Atlas Search 索引。
以下索引定义将title字段索引为用于查询该字段的字符串类型。
创建一个名为
fts-index.js的文件，并将以下代码粘贴到其中：import { MongoClient } from 'mongodb'; // Connect to your Atlas deployment const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING); async function run() { try { const database = client.db("sample_mflix"); const collection = database.collection("embedded_movies"); // Define your Atlas Search index const index = { name: "rrf-full-text-search", type: "search", definition: { "mappings": { "dynamic": false, "fields": { "title": [{ "type": "string" }] } } } } // Call the method to create the index const result = await collection.createSearchIndex(index); console.log(result); } finally { await client.close(); } } run().catch(console.dir); 运行以下命令来执行代码：
node --env-file=.env fts-index.js Atlas 可能需要几分钟时间来创建索引。当索引可供使用时，Atlas 会发送电子邮件。在 Atlas 用户界面中，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。
运行组合语义搜索和全文搜索查询
本部分演示如何使用 $vectorSearch 和 $search 管道阶段查询 sample_mflix.embedded_movies 集合中 plot_embeddings 和 title 字段中的 star wars 数据，并结合两个阶段中每个文档的分数，对结果中的文档进行重新排序。这可确保同时出现在两个搜索结果中的文件都出现在组合结果的顶部。
步骤
1
使用mongosh 连接到您的集群。
在终端窗口中打开mongosh并连接到集群。 有关连接的详细说明，请参阅通过mongosh连接。
2
使用sample_mflix 数据库。
在 mongosh 提示符下运行以下命令：
use sample_mflix
switched to db sample_mflix
3
针对 集合运行以下Atlasembedded_movies Search查询。
1 var vector_weight = 0.1; 2 var full_text_weight = 0.9; 3 db.embedded_movies.aggregate([ 4 { 5 "$vectorSearch": { 6 "index": "rrf-vector-search", 7 "path": "plot_embedding", 8 "queryVector": 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9 "numCandidates": 100, 10 "limit": 20 11 } 12 }, { 13 "$group": { 14 "_id": null, 15 "docs": {"$push": "$$ROOT"} 16 } 17 }, { 18 "$unwind": { 19 "path": "$docs", 20 "includeArrayIndex": "rank" 21 } 22 }, { 23 "$addFields": { 24 "vs_score": { 25 "$multiply": [ 26 vector_weight, { 27 "$divide": [ 28 1.0, { 29 "$add": ["$rank", 60] 30 } 31 ] 32 } 33 ] 34 } 35 } 36 }, { 37 "$project": { 38 "vs_score": 1, 39 "_id": "$docs._id", 40 "title": "$docs.title" 41 } 42 }, 43 { 44 "$unionWith": { 45 "coll": "movies", 46 "pipeline": [ 47 { 48 "$search": { 49 "index": "rrf-full-text-search", 50 "phrase": { 51 "query": "star wars", 52 "path": "title" 53 } 54 } 55 }, { 56 "$limit": 20 57 }, { 58 "$group": { 59 "_id": null, 60 "docs": {"$push": "$$ROOT"} 61 } 62 }, { 63 "$unwind": { 64 "path": "$docs", 65 "includeArrayIndex": "rank" 66 } 67 }, { 68 "$addFields": { 69 "fts_score": { 70 "$multiply": [ 71 full_text_weight, { 72 "$divide": [ 73 1.0, { 74 "$add": ["$rank", 60] 75 } 76 ] 77 } 78 ] 79 } 80 } 81 }, 82 { 83 "$project": { 84 "fts_score": 1, 85 "_id": "$docs._id", 86 "title": "$docs.title" 87 } 88 } 89 ] 90 } 91 }, 92 { 93 "$group": { 94 "_id": "$title", 95 "vs_score": {"$max": "$vs_score"}, 96 "fts_score": {"$max": "$fts_score"} 97 } 98 }, 99 { 100 "$project": { 101 "_id": 1, 102 "title": 1, 103 "vs_score": {"$ifNull": ["$vs_score", 0]}, 104 "fts_score": {"$ifNull": ["$fts_score", 0]} 105 } 106 }, 107 { 108 "$project": { 109 "score": {"$add": ["$fts_score", "$vs_score"]}, 110 "_id": 1, 111 "title": 1, 112 "vs_score": 1, 113 "fts_score": 1 114 } 115 }, 116 {"$sort": {"score": -1}}, 117 {"$limit": 10} 118 ])
[ { _id: 'Star Wars: Episode IV - A New Hope', vs_score: 0.0016666666666666668, fts_score: 0, score: 0.0016666666666666668 }, { _id: 'Star Wars: Episode I - The Phantom Menace', vs_score: 0.0016393442622950822, fts_score: 0, score: 0.0016393442622950822 }, { _id: 'Star Wars: Episode V - The Empire Strikes Back', vs_score: 0.0016129032258064516, fts_score: 0, score: 0.0016129032258064516 }, { _id: 'Star Wars: Episode VI - Return of the Jedi', vs_score: 0.0015873015873015873, fts_score: 0, score: 0.0015873015873015873 }, { _id: 'Star Wars: The Clone Wars', vs_score: 0.0015625, fts_score: 0, score: 0.0015625 }, { _id: 'Message from Space', vs_score: 0.0015384615384615387, fts_score: 0, score: 0.0015384615384615387 }, { _id: 'Star Wars: Episode II - Attack of the Clones', vs_score: 0.0014925373134328358, fts_score: 0, score: 0.0014925373134328358 }, { _id: 'Guardians of the Galaxy', vs_score: 0.0014705882352941176, fts_score: 0, score: 0.0014705882352941176 }, { _id: 'Abiogenesis', vs_score: 0.0014285714285714286, fts_score: 0, score: 0.0014285714285714286 }, { _id: 'Dune', vs_score: 0.0014084507042253522, fts_score: 0, score: 0.0014084507042253522 } ]
如果您通过将第 116 行 score 的值替换为 1 来按升序对结果进行排序，Atlas Vector Search 将返回以下结果：
[ { _id: 'Cowboys & Aliens', vs_score: 0.0012658227848101266, fts_score: 0, score: 0.0012658227848101266 }, { _id: 'Planet of the Apes', vs_score: 0.001298701298701299, fts_score: 0, score: 0.001298701298701299 }, { _id: 'Starcrash', vs_score: 0.0013157894736842105, fts_score: 0, score: 0.0013157894736842105 }, { _id: 'Zathura: A Space Adventure', vs_score: 0.0013333333333333335, fts_score: 0, score: 0.0013333333333333335 }, { _id: 'Space Raiders', vs_score: 0.0013513513513513514, fts_score: 0, score: 0.0013513513513513514 }, { _id: 'Star Wars: Episode III - Revenge of the Sith', vs_score: 0.0013698630136986301, fts_score: 0, score: 0.0013698630136986301 }, { _id: 'The Ewok Adventure', vs_score: 0.001388888888888889, fts_score: 0, score: 0.001388888888888889 }, { _id: 'Dune', vs_score: 0.0014084507042253522, fts_score: 0, score: 0.0014084507042253522 }, { _id: 'Abiogenesis', vs_score: 0.0014285714285714286, fts_score: 0, score: 0.0014285714285714286 }, { _id: 'Guardians of the Galaxy', vs_score: 0.0014705882352941176, fts_score: 0, score: 0.0014705882352941176 } ]
1
定义组合查询管道。
创建一个名为 combined-query.js 的文件，并将以下代码粘贴到其中：
1 import { MongoClient } from 'mongodb'; 2 3 async function run() { 4 // Connect to your Atlas cluster 5 const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING); 6 7 try { 8 await client.connect(); 9 const database = client.db("sample_mflix"); 10 const collection = database.collection("embedded_movies"); 11 12 const vectorWeight = 0.1; 13 const fullTextWeight = 0.9; 14 const pipeline = [ 15 { 16 "$vectorSearch": { 17 "index": "rrf-vector-search", 18 "path": "plot_embedding", 19 "queryVector": 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20 "numCandidates": 100, 21 "limit": 20 22 } 23 }, { 24 "$group": { 25 "_id": null, 26 "docs": {"$push": "$$ROOT"} 27 } 28 }, { 29 "$unwind": { 30 "path": "$docs", 31 "includeArrayIndex": "rank" 32 } 33 }, { 34 "$addFields": { 35 "vs_score": { 36 "$multiply": [ 37 vectorWeight, { 38 "$divide": [ 39 1.0, { 40 "$add": ["$rank", 60] 41 } 42 ] 43 } 44 ] 45 } 46 } 47 }, { 48 "$project": { 49 "vs_score": 1, 50 "_id": "$docs._id", 51 "title": "$docs.title" 52 } 53 }, { 54 "$unionWith": { 55 "coll": "movies", 56 "pipeline": [ 57 { 58 "$search": { 59 "index": "rrf-full-text-search", 60 "phrase": { 61 "query": "star wars", 62 "path": "title" 63 } 64 } 65 }, { 66 "$limit": 20 67 }, { 68 "$group": { 69 "_id": null, 70 "docs": {"$push": "$$ROOT"} 71 } 72 }, { 73 "$unwind": { 74 "path": "$docs", 75 "includeArrayIndex": "rank" 76 } 77 }, { 78 "$addFields": { 79 "fts_score": { 80 "$multiply": [ 81 fullTextWeight, { 82 "$divide": [ 83 1.0, { 84 "$add": ["$rank", 60] 85 } 86 ] 87 } 88 ] 89 } 90 } 91 }, 92 { 93 "$project": { 94 "fts_score": 1, 95 "_id": "$docs._id", 96 "title": "$docs.title" 97 } 98 } 99 ] 100 } 101 }, { 102 "$group": { 103 "_id": "$title", 104 "vs_score": {"$max": "$vs_score"}, 105 "fts_score": {"$max": "$fts_score"} 106 } 107 }, { 108 "$project": { 109 "_id": 1, 110 "title": 1, 111 "vs_score": {"$ifNull": ["$vs_score", 0]}, 112 "fts_score": {"$ifNull": ["$fts_score", 0]} 113 } 114 }, { 115 "$project": { 116 "score": {"$add": ["$fts_score", "$vs_score"]}, 117 "_id": 1, 118 "title": 1, 119 "vs_score": 1, 120 "fts_score": 1 121 } 122 }, 123 {"$sort": {"score": -1}}, 124 {"$limit": 10} 125 ]; 126 127 // run pipeline 128 const result = collection.aggregate(pipeline); 129 130 for await (const doc of result) { 131 console.log(doc); 132 } 133 } catch (err) { 134 console.log(err.stack); 135 } 136 finally { 137 await client.close(); 138 } 139 } 140 run().catch(console.dir);
2
执行代码以对embedded_movies 集合运行查询。
node --env-file=.env combined-query.js
[ { _id: 'Star Wars: Episode IV - A New Hope', vs_score: 0.0016666666666666668, fts_score: 0, score: 0.0016666666666666668 }, { _id: 'Star Wars: Episode I - The Phantom Menace', vs_score: 0.0016393442622950822, fts_score: 0, score: 0.0016393442622950822 }, { _id: 'Star Wars: Episode V - The Empire Strikes Back', vs_score: 0.0016129032258064516, fts_score: 0, score: 0.0016129032258064516 }, { _id: 'Star Wars: Episode VI - Return of the Jedi', vs_score: 0.0015873015873015873, fts_score: 0, score: 0.0015873015873015873 }, { _id: 'Star Wars: The Clone Wars', vs_score: 0.0015625, fts_score: 0, score: 0.0015625 }, { _id: 'Message from Space', vs_score: 0.0015384615384615387, fts_score: 0, score: 0.0015384615384615387 }, { _id: 'Star Wars: Episode II - Attack of the Clones', vs_score: 0.0014925373134328358, fts_score: 0, score: 0.0014925373134328358 }, { _id: 'Guardians of the Galaxy', vs_score: 0.0014705882352941176, fts_score: 0, score: 0.0014705882352941176 }, { _id: 'Abiogenesis', vs_score: 0.0014285714285714286, fts_score: 0, score: 0.0014285714285714286 }, { _id: 'Dune', vs_score: 0.0014084507042253522, fts_score: 0, score: 0.0014084507042253522 } ]
如果您通过将第 123 行 score 的值替换为 1 来按升序对结果进行排序，Atlas Vector Search 将返回以下结果：
[ { _id: 'Cowboys & Aliens', vs_score: 0.0012658227848101266, fts_score: 0, score: 0.0012658227848101266 }, { _id: 'Planet of the Apes', vs_score: 0.001298701298701299, fts_score: 0, score: 0.001298701298701299 }, { _id: 'Starcrash', vs_score: 0.0013157894736842105, fts_score: 0, score: 0.0013157894736842105 }, { _id: 'Zathura: A Space Adventure', vs_score: 0.0013333333333333335, fts_score: 0, score: 0.0013333333333333335 }, { _id: 'Space Raiders', vs_score: 0.0013513513513513514, fts_score: 0, score: 0.0013513513513513514 }, { _id: 'Star Wars: Episode III - Revenge of the Sith', vs_score: 0.0013698630136986301, fts_score: 0, score: 0.0013698630136986301 }, { _id: 'The Ewok Adventure', vs_score: 0.001388888888888889, fts_score: 0, score: 0.001388888888888889 }, { _id: 'Dune', vs_score: 0.0014084507042253522, fts_score: 0, score: 0.0014084507042253522 }, { _id: 'Abiogenesis', vs_score: 0.0014285714285714286, fts_score: 0, score: 0.0014285714285714286 }, { _id: 'Guardians of the Galaxy', vs_score: 0.0014705882352941176, fts_score: 0, score: 0.0014705882352941176 } ]
关于查询
示例查询从语义搜索和全文搜索中检索排序后的搜索结果，并根据结果数组中的位置为结果中的文档分配倒数排名分数。倒数排名分数的计算公式如下：
1.0/{document position in the results + constant value}
然后，该查询将每个文档的两次搜索分数相加，根据组合分数对文档进行排名，并对文档进行排序以返回单个结果。
查询变量
示例查询定义了以下变量以增加分数的权重，数字越小，权重越高：
vector_weightfull_text_weight
加权倒数排名分数的计算公式如下：
weight x reciprocal rank
查询阶段
该示例查询使用以下流水线阶段对文档集进行语义搜索，并检索结果中文档的对等排序：
在 plot_embeddings 字段中搜索查询 queryVector 字段中指定为向量嵌入的 star wars 字符串。该查询使用 ada-002-text 嵌入，与 plot_embedding 字段中的向量嵌入相同。该查询还指定最多搜索 100 个最近邻，并将结果限制为仅 20 个文档。此阶段从结果中返回语义搜索的排序文档。 | |
将语义搜索结果中的所有文档分组到名为 docs 的字段中。 | |
在 docs 字段中展开文档数组，并在名为 rank 的字段中存储文档在结果数组中的位置。 | |
添加一个名为 vs_score 的新字段，其中包含结果中每个文档的倒数排名分数。在此处，倒数排名分数的计算方法是将 1.0 除以 rank 和排名常量值 60 之和。然后，通过将 vector_weight 权重乘以倒数排名分数来计算加权倒数排名。 | |
结果中仅包含以下字段：
|
该示例查询使用 $unionWith 阶段对集合执行文本搜索，并检索结果中文档的倒数排名：
搜索 title 字段中包含术语 star wars 的电影。此阶段从结果中的全文搜索中返回已排序的文档。 | |
仅将输出限制为 20 个结果。 | |
将全文搜索到的所有文档归入名为 docs 的字段中。 | |
在 docs 字段中展开文档数组，并在名为 rank 的字段中存储文档在结果数组中的位置。 | |
添加一个名为 fts_score 的新字段，其中包含结果中每个文档的倒数排名分数。在此处，倒数排名分数的计算方法是将 1.0 除以 rank 和排名常量值 60 之和。然后，通过将 full_text_weight 权重乘以倒数排名分数来计算加权倒数排名。 | |
结果中仅包含以下字段：
|
该示例查询使用以下阶段来合并语义搜索和文本搜索的结果，并在结果中返回一个排序文档列表：
通过观看学习
观看应用程序演示，该应用程序展示了混合搜索查询，该查询将 Atlas Search 全文搜索和向量搜索相结合以返回单个合并结果集。该应用程序实施相对分数融合 (RSF) 和倒数排名融合 (RRF)，以返回使用排名融合算法创建的合并集。
时长：2.43 分钟