Elasticsearch:当混合搜索真正发挥作用时
作者:来自 Elastic Gustavo Llermaly
展示混合搜索何时优于单独的词汇或语义搜索。
在本文中,我们将通过示例探讨混合搜索,并展示它与单独使用词汇或语义搜索技术相比的真正优势。
什么是混合搜索?
混合搜索是一种结合了不同搜索方法(如传统词汇匹配和语义搜索)的技术。
当用户知道确切的单词时,词汇搜索非常有用。这种方法将找到相关文档,并使用 TF-IDF 以合理的方式对其进行排序,这意味着:你搜索的术语在数据集中越常见,它对分数的贡献就越小;在某个文档中越常见,它对分数的贡献就越大。
更多有关 TF-IDF 的阅读,请参阅文章 “Elasticsearch:分布式计分”。
但是,如果查询中的单词不在文档中,该怎么办?有时用户不是在寻找具体的东西,而是在寻找一个概念。他们可能不是在寻找特定的餐厅,而是在寻找 “与家人一起吃饭的好地方”。对于这种查询,语义搜索很有用,因为它会考虑搜索查询的上下文并带来类似的文档。与以前的方法相比,你可以期望获得更多的相关文档,但作为回报,这种方法在精度方面存在困难,尤其是数字方面。
混合搜索将术语匹配的精确度与语义搜索的上下文感知匹配相结合,为我们提供了两全其美的优势。
你可以在这篇文章中深入了解混合搜索,并在此文章中了解有关词汇和语义搜索差异的更多信息。
让我们使用房地产单位创建一个示例。
查询将是:quiet place in Pinewood with 2 rooms,其中 “quiet place” 是查询的语义部分,而 “Pinewood with 2 rooms” 是查询的文本或词汇部分。
配置 ELSER
我们将使用 ELSER 作为我们的模型提供者。
首先创建推理端点:
PUT _inference/sparse_embedding/my-elser-model
{
"service": "elser",
"service_settings": {
"num_allocations": 1,
"num_threads": 1
}
}
如果这是你第一次使用 ELSER,你可能会在后台加载模型时遇到 502 Bad Gateway 错误。你可以在 Kibana 中的 Machine Learning > Trained Models 中检查模型的状态。部署后,你可以继续下一步。
配置索引
对于索引,我们将使用文本字段,并使用 semantic_text 作为语义字段。我们将复制描述,因为我们想将它们用于 match 和 semantic 查询。
PUT properties-hybrid
{
"mappings": {
"properties": {
"title": {
"type": "text",
"analyzer": "english"
},
"description": {
"type": "text",
"analyzer": "english",
"copy_to": "semantic_field"
},
"neighborhood": {
"type": "keyword"
},
"bedrooms": {
"type": "integer"
},
"bathrooms": {
"type": "integer"
},
"semantic_field": {
"type": "semantic_text",
"inference_id": "my-elser-model"
}
}
}
}
索引数据
POST _bulk
{ "index" : { "_index" : "properties-hybrid" , "_id": "1"} }
{ "title": "2 Bed 2 Bath in Sunnydale", "description": "Spacious apartment with modern amenities, perfect for urban dwellers.", "neighborhood": "Sunnydale", "bedrooms": 2, "bathrooms": 2 }
{ "index" : { "_index" : "properties-hybrid", "_id": "2" } }
{ "title": "1 Bed 1 Bath in Sunnydale", "description": "Compact apartment with easy access to downtown, ideal for singles or couples.", "neighborhood": "Sunnydale", "bedrooms": 1, "bathrooms": 1 }
{ "index" : { "_index" : "properties-hybrid", "_id": "3" } }
{ "title": "2 Bed 2 Bath in Pinewood", "description": "New apartment with modern bedrooms, located in a restaurant and bar area. Suitable for active people who enjoy nightlife.", "neighborhood": "Pinewood", "bedrooms": 2, "bathrooms": 2 }
{ "index" : { "_index" : "properties-hybrid", "_id": "4" } }
{ "title": "3 Bed 2 Bath in Pinewood", "description": "Secluded and private family unit with a practical layout with three total rooms. Near schools and shops. Perfect for raising kids.", "neighborhood": "Pinewood", "bedrooms": 3, "bathrooms": 2 }
{ "index" : { "_index" : "properties-hybrid", "_id": "5" } }
{ "title": "2 Bed 2 Bath in Pinewood", "description": "Retired apartment in a serene neighborhood, perfect for those seeking a retreat. This well-maintained residence offers two bedrooms with abundant natural light and silence.", "neighborhood": "Pinewood", "bedrooms": 2, "bathrooms": 2 }
{ "index" : { "_index" : "properties-hybrid", "_id": "6" } }
{ "title": "1 Bed 1 Bath in Pinewood", "description": "Apartment with a scenic view, ideal for those seeking an energetic environment.", "neighborhood": "Pinewood", "bedrooms": 1, "bathrooms": 1 }
{ "index" : { "_index" : "properties-hybrid", "_id": "7" } }
{ "title": "2 Bed 2 Bath in Maplewood", "description": "Nice apartment with a large balcony, offering a relaxed and comfortable living experience.", "neighborhood": "Maplewood", "bedrooms": 2, "bathrooms": 2 }
{ "index" : { "_index" : "properties-hybrid", "_id": "8" } }
{ "title": "1 Bed 1 Bath in Maplewood", "description": "Charming apartment with modern interiors, situated in a peaceful neighborhood.", "neighborhood": "Maplewood", "bedrooms": 1, "bathrooms": 1 }
查询数据
让我们从经典的匹配查询开始,它将根据标题和描述的内容进行搜索:
GET properties-hybrid/_search
{
"query": {
"multi_match": {
"query": "quiet home 2 bedroom in Pinewood",
"fields": ["title", "description"]
}
}
}
这是第一个结果:
{
"description": "New apartment with modern bedrooms, located in a restaurant and bar area. Suitable for active people who enjoy nightlife.",
"title": "2 Bed 2 Bath in Pinewood"
}
还不错。它成功地吸引了附近的 Pinewood 和 2 间卧室的需求,然而,这根本不是一个 quiet place。
现在,一个纯粹的语义查询:
GET properties-hybrid/_search
{
"query": {
"semantic": {
"field": "semantic_field",
"query": "quiet home in Pinewood with 2 rooms"
}
}
}
这是第一个结果:
{
"description": "Secluded and private family unit with a practical layout with three total rooms. Near schools and shops. Perfect for raising kids.",
"title": "3 Bed 2 Bath in Pinewood"
}
现在,搜索结果考虑了 quiet home 部分,将其与 “secluded and private” 等内容联系起来,但这个是 3 间卧室,我们正在寻找 2 间卧室。
现在让我们运行混合搜索。我们将使用 RRF(Reciprocal rank fusion - 倒数排名融合)来实现此目的,并结合前两个查询。RRF 算法将为我们混合两个查询的分数。
GET properties-hybrid/_search
{
"retriever": {
"rrf": {
"retrievers": [
{
"standard": {
"query": {
"semantic": {
"field": "semantic_field",
"query": "quiet home 2 bedroom in Pinewood"
}
}
}
},
{
"standard": {
"query": {
"multi_match": {
"query": "quiet home 2 bedroom in Pinewood",
"fields": ["title", "description"]
}
}
}
}
],
"rank_window_size": 50,
"rank_constant": 20
}
}
}
这是第一个结果:
{
"description": ""Retired apartment in a serene neighborhood, perfect for those seeking a retreat. This well-maintained residence offers two bedrooms with abundant natural light and silence.",
"title": "2 Bed 2 Bath in Pinewood"
}
现在的结果考虑既是一个安静的地方,而且有 2 间卧室。
评估结果
对于评估,我们将使用排名评估 API(Ranking Evaluation API ),它允许我们自动执行运行查询的过程,然后检查相关结果的位置。你可以选择不同的评估指标。在这个例子中,我将选择平均倒数排名 MRR (Mean reciprocal ranking),它考虑了结果位置,并随着位置降低 1/位置# 而降低分数。
对于这个场景,我们将针对初始问题测试我们的 3 个查询(multi_match、semantic、hybrid):
quiet home 2 bedroom in Pinewood
预计以下公寓将排在第一位,因为它满足所有标准。
Retired apartment in a serene neighborhood, perfect for those seeking a retreat. This well-maintained residence offers two bedrooms with abundant natural light and silence."
我们可以根据需要配置任意数量的查询,并在评级中添加我们期望排在第一位的文档的 ID:
GET /properties-hybrid/_rank_eval
{
"requests": [
{
"id": "hybrid",
"request": {
"retriever": {
"rrf": {
"retrievers": [
{
"standard": {
"query": {
"semantic": {
"field": "semantic_field",
"query": "quiet home 2 bedroom in Pinewood"
}
}
}
},
{
"standard": {
"query": {
"multi_match": {
"query": "quiet home 2 bedroom in Pinewood",
"fields": [
"title",
"description"
]
}
}
}
}
],
"rank_window_size": 50,
"rank_constant": 20
}
}
},
"ratings": [
{
"_index": "properties-hybrid",
"_id": "5",
"rating": 1
}
]
},
{
"id": "lexical",
"request": {
"query": {
"multi_match": {
"query": "quiet home 2 bedroom in Pinewood",
"fields": [
"title",
"description"
]
}
}
},
"ratings": [
{
"_index": "properties-hybrid",
"_id": "5",
"rating": 1
}
]
},
{
"id": "semantic",
"request": {
"query": {
"semantic": {
"field": "semantic_field",
"query": "quiet place in Pinewood with 2 rooms"
}
}
},
"ratings": [
{
"_index": "properties-hybrid",
"_id": "5",
"rating": 1
}
]
}
],
"metric": {
"mean_reciprocal_rank": {
"k": 20,
"relevant_rating_threshold": 1
}
}
}
从图中可以看出,该查询在混合搜索(第一位)中获得了 1 分,在其他搜索中获得了 0.5 分,这意味着在第二位返回了预期结果。
结论
全文搜索技术(查找术语并按术语频率对结果进行排序)和语义搜索(按语义接近度进行搜索)在不同情况下非常有用。一方面,当用户明确他们想要搜索的内容时,文本搜索会大放异彩,例如提供文章的确切 SKU 或技术手册中的单词。另一方面,当用户正在寻找文档中未明确定义的概念或想法时,语义搜索很有用。将这两种方法与混合搜索相结合,可以为你提供全文搜索功能以及添加语义相关文档,这在需要关键字匹配和上下文理解的特定场景中非常有用。这种双重方法提高了搜索准确性和相关性,使其成为复杂查询和多样化内容类型的理想选择。
想要获得 Elastic 认证?了解下一次 Elasticsearch 工程师培训何时开始!
Elasticsearch 包含新功能,可帮助你为你的用例构建最佳搜索解决方案。深入了解我们的示例笔记本以了解更多信息,开始免费云试用,或立即在你的本地机器上试用 Elastic。
原文:When hybrid search truly shines - Elasticsearch Labs