您可以将MongoDB Vector Search 和MongoDB Search 查询组合成混合搜索，以获得统一结果。
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_embedding_voyage_4_large字段上创建 MongoDB 向量搜索索引。该字段包含使用 Voyage AI 的voyage-4-large嵌入模型创建的向量嵌入（2048 维），用于表示电影情节的摘要。在
sample_mflix.embedded_movies集合上创建MongoDB Search索引。Run a query to combine the results from a
$vectorSearchquery and a$searchquery by using the following techniques:语义提升
倒数秩融合
分数融合
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。
您可以应用以下技术来实现混合搜索：
技术 | 语义提升 | 倒数排名融合 | 相对分数融合 | |||
用途 | 通过提升与向量搜索匹配的文档来提高全文搜索结果的准确性。在检索非常大的结果集时很有用。 | 通过计算结果中文档的倒数排名，将不同搜索方法的结果合并到单个排名结果设立。 | 通过计算结果中文档的评分，将不同搜索方法的结果合并到单个排名结果设立。 | |||
排序方法 |
|
|
| |||
评分方法 | 通过加权向量搜索搜索分数来提高与向量搜索结果匹配的文档的全文结果分数。 | 使用以下公式计算分数： 然后，对于每种搜索方法应用不同的权重 ( | 使用以下公式计算分数： | |||
搜索功能 |
| Supports further processing of results in subsequent aggregation stages such as | Supports further processing of results in subsequent aggregation stages such as | |||
输出 | 单个结果设立，其中包含以下文档： | 包含来自两种搜索方法的文档的单个结果设立。 | 包含来自两种搜索方法的文档的单个结果设立。 |
先决条件
在开始之前，您必须具备以下条件：
MongoDB版本为 v8.0 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MongoDB Search 和MongoDB Vector Search 索引并运行高性能查询。
The sample_mflix data loaded into your Atlas cluster.
mongosh对Atlas 集群运行查询：Project Data Access Admin访问权限用于创建MongoDB Vector Search 和MongoDB Search 索引的项目。
创建MongoDB Vector Search 和MongoDB 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_embedding_voyage_4_large字段上的MongoDB Vector Search索引，用于针对该字段运行向量查询。MongoDB Search 索引，可对所有可动态索引的字段进行动态索引，以对这些字段运行全文搜索。
1
Connect to the cluster using mongosh.
To learn more, see Connect to a Cluster via mongosh.
3
定义MongoDB Vector Search索引。
运行以下命令。查询集合时，此索引定义将 plot_embedding_voyage_4_large字段作为MongoDB Vector Search字段进行索引。
1 db.embedded_movies.createSearchIndex( 2 "hybrid-vector-search", 3 "vectorSearch", 4 { 5 "fields": [ 6 { 7 "type": "vector", 8 "path": "plot_embedding_voyage_4_large", 9 "numDimensions": 2048, 10 "similarity": "dotProduct" 11 } 12 ] 13 } 14 );
运行混合搜索查询
This section demonstrates how to query the data in the sample_mflix.embedded_movies collection for charming animal in the plot_embedding_voyage_4_large and fullplot fields by using the $vectorSearch and $search pipeline stages, respectively. The query enhances relevancy of $search queries by boosting the full-text scores using $vectorSearch scores for the same documents.
语义提升查询
1
为嵌入创建文件。
创建一个名为
query-embeddings.js的文件。touch query-embeddings.js 将以下嵌入复制并粘贴到该文件中。
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2
Connect to your cluster using mongosh.
Open mongosh in a terminal window and connect to your cluster. For detailed instructions on connecting, see Connect to a Cluster via mongosh.
3
使用 sample_mflix 数据库。
Run the following command at mongosh prompt:
use sample_mflix
switched to db sample_mflix
5
针对 embedded_movies集合运行以下MongoDB搜索查询。
1 vectorWeight = 1; 2 vectorCutoff = 0.7; 3 numResults = 20; 4 numCandidates = numResults * 10; 5 6 // Execute the vector search 7 vectorResults = db.embedded_movies.aggregate([ 8 { 9 $vectorSearch: { 10 "index": "hybrid-vector-search", 11 "path": "plot_embedding_voyage_4_large", 12 "queryVector": CHARMING_ANIMAL_EMBEDDING, 13 "numCandidates": numCandidates, 14 "limit": numResults 15 } 16 }, 17 { 18 $project: { 19 "_id": 1, 20 "title": 1, 21 "fullplot": 1, 22 "vectorScore": { $meta: "vectorSearchScore" } 23 } 24 }, 25 { 26 $match: { 27 "vectorScore": { $gte: vectorCutoff } 28 } 29 } 30 ]).toArray(); 31 32 // Create a map for fast lookup 33 vectorScoresMap = {}; 34 vectorResults.forEach(result => { 35 vectorScoresMap[result._id] = result.vectorScore * vectorWeight; 36 }); 37 38 // Execute the text search with boosting based just on ID 39 textResults = db.embedded_movies.aggregate([ 40 { 41 $search: { 42 "index": "hybrid-full-text-search", 43 "compound": { 44 "should": [ 45 { 46 "text": { 47 "query": "charming animal", 48 "path": "fullplot", 49 "fuzzy": {} 50 } 51 }, 52 ...Object.keys(vectorScoresMap).map(id => ({ 53 "equals": { 54 "path": "_id", 55 "value": id, 56 "score": { boost: { value: vectorScoresMap[id] } } 57 } 58 })) 59 ] 60 } 61 } 62 }, 63 { 64 $limit: 20 65 }, 66 { 67 $project: { 68 "_id": 1, 69 "title": 1, 70 "fullplot": 1, 71 "score": { $meta: "searchScore" } 72 } 73 } 74 ]).toArray();
关于查询
The sample query boosts the $search results based on $vectorSearch result scores to return the most relevant documents. For semantic boosting, you perform the following steps:
Run the
$vectorSearchquery.使用 over 请求值指定要考虑的候选数量超过指定限制。通过over请求，您可以完成以下操作：
在初始搜索中考虑大量且广泛的文档。
完善后续阶段的评分计算。
该查询使用以下计算来确定向量搜索期间要考虑的候选数量：
numCandidates = limit * over request 结果根据最低分数阈值 (
vectorCutoff) 进行筛选。缩放向量搜索分数以调整向量搜索结果对文档评分和排名的影响。
使用
vectorWeight参数设立权重，并将向量搜索分数乘以vectorWeight。这控制了向量搜索结果的重要性，并确保只考虑语义上最相关的文档。运行
$search查询并提高与向量搜索结果匹配的文档的分数。
倒数排名融合查询
You can run a query that retrieves the sorted search results from the semantic search and the full-text search, and assigns a reciprocal rank score to the documents in the results based on their position in the results array. To learn more about the reciprocal rank fusion technique and how to run a reciprocal rank fusion query, see Perform Hybrid Vector and Full-Text Search.
相对分数融合查询
You can run a query that retrieves the scored search results from the semantic and full-text searches and applies the sigmoid expression to normalize the score before combining the results from the pipelines into a single aggregated score for each document. To learn more about the score fusion technique and how to run a score fusion query, see Perform Hybrid Vector and Full-Text Search.