如何合并多个 `$vectorSearch` 查询

The $rankFusion and $scoreFusion stages are available as Preview features. To learn more, see Preview Features.

Starting in v8.1, the MongoDB $rankFusion aggregation stage supports $vectorSearch inside the input pipeline. You can use $rankFusion to combine multiple $vectorSearch queries against the same collection in the same pipeline. $rankFusion first executes all input pipelines independently, and then de-duplicates and combines the input pipeline results into a final ranked results set. To run multiple $vectorSearch queries against multiple collections in a single pipeline, use $unionWith.

用例与优势

您可以使用 $rankFusion 管道运行以下类型的 $vectorSearch 查询：

针对同一字段运行多个 $vectorSearch 查询相似术语。
这使您能够对数据集进行全面搜索，以在同一查询中查找相似术语。
针对多个字段运行多个 $vectorSearch 查询同一术语。
这使您能够在数据集中搜索多个字段，以确定哪些字段为查询返回最佳结果。
针对来自不同嵌入模型的嵌入运行多个 $vectorSearch 查询同一术语。
这使您使用不同嵌入模型生成的嵌入向量进行搜索，以确定不同模型在语义解释上的差异。

关于教程

本教程演示如何在 sample_mflix 数据库中的 embedded_movies 集合上运行不同的 $vectorSearch 查询。此集合包括以下字段：

plot_embedding，其中包含通过使用 OpenAI 的 text-embedding-ada-002 嵌入模型创建的嵌入。
plot_embedding_voyage_3_large，其中包含通过使用 Voyage AI 的 voyage-3-large 嵌入模型创建的嵌入。您在本教程中将此字段用于所有示例查询。

对于用例和优势中列出的第二个查询，您必须为另一个字段生成嵌入。要尝试此用例的示例查询，请完成生成嵌入中的步骤。

本教程将引导您完成以下步骤：

Set up an MongoDB Vector Search index on the sample_mflix.embedded_movies collection.
使用 $rankFusion 运行 $vectorSearch查询，以执行以下搜索：
- 在 plot_embedding_voyage_3_large 字段中搜索包含与 light-hearted comedy with ghosts 和 slapstick humor with paranormal events 类似情节的电影。
- 使用来自 Voyage AI 的嵌入分别在 plot_embedding_voyage_3_large 和 title_embedding 字段中搜索与 battle between good and evil 情节相似的电影。
- 使用来自 OpenAI 和 Voyage AI 的嵌入分别在 plot_embedding 和 plot_embedding_voyage_3_large 字段中搜索与 journey across lands 情节相似的电影。

开始之前，请确保 Atlas 集群满足先决条件中所述的要求。

注意

要运行带有 $rankFusion 的 $vectorSearch 查询，您的集群必须运行 MongoDB v8.1 或更高版本。

生成嵌入

要针对多个字段查询同一个术语，您必须准备数据集以包含不同字段的嵌入。本节演示如何使用 Voyage AI 的 voyage-3-large 嵌入模型为集合中的 title 字段生成嵌入。

如果您只想尝试其他示例使用案例，您可以跳过本部分。

先决条件

运行交互式Python笔记本（例如VS Code 或 Colab）的环境。
用于 Voyage AI 嵌入模型的 API 密钥。

步骤

在本节中，您将使用 Voyage AI 的 voyage-3-large 嵌入模型为 embedded_movies 集合中的 title 字段生成嵌入。

设置环境。

通过保存扩展名为 .ipynb 的文件来创建交互式 Python 笔记本，然后在笔记本中运行以下命令以安装依赖项：

pip install --quiet voyageai pymongo

定义并使用一个函数来生成向量嵌入。

替换以下占位符值后，在笔记本中粘贴并运行以下代码：

`<api-key>`	您的 Voyage AI API 密钥（第 6 行）。
`<connection-string>`	您的 MongoDB 集群连接字符串（第 18 行）。

以下代码定义了一个函数，该函数使用 Voyage AI 专有嵌入模型生成向量嵌入。具体而言，此代码执行以下操作：

指定 voyage-3-large 内嵌模型。
创建一个名为 get_embedding() 的函数，该函数调用模型的 API 来为给定的文本输入生成 2048 维嵌入。
连接到 Atlas 集群，并从 sample_mflix.embedded_movies 命名空间中获取 3500 个文档。
使用 get_embedding() 函数从每个文档的 title 字段生成嵌入。
使用 MongoDB PyMongo 驱动程序，用包含嵌入值的新字段（名为 title_voyageai_embedding）更新每个文档。

1 import os
2 import pymongo
3 import voyageai
4 
5 # Specify your Voyage API key and embedding model
6 os.environ["VOYAGE_API_KEY"] = "<api-key>"
7 
8 model = "voyage-3-large"
9 outputDimension = 2048
10 vo = voyageai.Client()
11 # Define a function to generate embeddings
12 def get_embedding(data, input_type = "document"):
13   embeddings = vo.embed(
14       data, model = model, output_dimension = outputDimension, input_type = input_type
15   ).embeddings
16   return embeddings[0]
17 
18 # Connect to your MongoDB Atlas cluster
19 mongo_client = pymongo.MongoClient("<connection-string>")
20 db = mongo_client["sample_mflix"]
21 collection = db["embedded_movies"]
22 
23 # Filter to exclude null or empty plot fields
24 filter = {"title": {"$nin": [None, ""]}}
25 
26 # Get a subset of documents in the collection
27 documents = collection.find(filter).limit(3500)
28 
29 # Update each document with a new embedding field
30 updated_doc_count = 0
31 for doc in documents:
32     embedding = get_embedding(doc["title"])
33     if embedding is not None:
34         collection.update_one({"_id": doc["_id"]}, {"$set": {"title_voyageai_embedding": embedding}})
35         updated_doc_count += 1
36 
37 print(f"Updated {updated_doc_count} documents.")

Updated 3500 documents.

注意

操作可能需要长达 15 分钟才能完成。

创建MongoDB Vector Search 索引

In this section, you create an MongoDB Vector Search index on the sample_mflix.embedded_movies namespace. The section demonstrates an index definition that can be used for running various queries. Before creating the index, complete the procedure to generate the embeddings.

使用 `mongosh` 连接到 Atlas 集群。

在终端窗口中打开 mongosh 并连接到 Atlas 集群。有关连接的详细说明，请参阅通过 mongosh 连接。

切换到包含要创建索引的集合的数据库。

例子

use sample_mflix

switched to db sample_mflix

运行 `db.collection.createSearchIndex()` 方法。

1 db.embedded_movies.createSearchIndex(
2   "multiple-vector-search", 
3   "vectorSearch", 
4   {
5     "fields": [
6       {
7         "type": "vector",
8         "path": "plot_embedding",
9         "numDimensions": 1536,
10         "similarity": "dotProduct"
11       },
12       {
13         "type": "vector",
14         "path": "plot_embedding_voyage_3_large",
15         "numDimensions": 2048,
16         "similarity": "dotProduct"
17       },
18       {
19         "type": "vector",
20         "path": "title_voyageai_embedding",
21         "numDimensions": 2048,
22         "similarity": "dotProduct"
23       }
24     ]
25   }
26 );

此索引定义对以下字段进行索引：

plot_embedding 字段为 vector 类型。该字段包含表示电影情节摘要的向量嵌入。索引定义：
- 指定 1536向量维度。
- 使用 dotProduct 相似度来衡量相似性。
此字段映射是第三个应用场景所必需的。
plot_embedding_voyage_3_large 字段为 vector 类型。该字段包含表示电影情节摘要的向量嵌入。索引定义：
- 指定 2048向量维度。
- 使用 dotProduct 相似度来衡量相似性。
本教程中的所有查询都需要此字段映射。
title_voyageai_embedding 字段为 vector 类型。该字段包含表示电影标题的向量嵌入。索引定义：
- 指定 2048向量维度。
- 使用 dotProduct 相似度来衡量相似性。
此字段映射是第二个应用场景所必需的。

Run the MongoDB Vector Search Queries

在本部分中，您将使用在集合上创建的名为 multiple-vector-search 的索引来查询 sample_mflix 数据库中的 embedded_movies 集合。

创建一个嵌入文档，以便在查询中使用。

创建一个名为 embeddings.js 的文件。
```
touch embeddings.js
```

将以下嵌入复制并粘贴到 embeddings.js 文件中。

此文件包含用于查询中的查询术语的嵌入。下表显示了查询术语、包含查询术语嵌入的变量、用于生成嵌入的嵌入模型，以及维度的数量。

查询短语	变量	嵌入模型	维度
light-hearted comedy with ghosts	COMEDY_INVOLVING_GHOSTS	Voyage AI's `voyage-3-large`	2048
包含超自然事件的滑稽幽默	HUMOR_INVOLVING_PARANORMAL	Voyage AI's `voyage-3-large`	2048
善与恶之间的斗争	BATTLE_GOOD_EVIL	Voyage AI's `voyage-3-large`	2048
journey across lands	JOURNEY_ACROSS_LANDS_VOYAGEAI	Voyage AI's `voyage-3-large`	2048
journey across lands	JOURNEY_ACROSS_LANDS_OPENAI	OpenAI 的 `text-embedding-ada-002`	1536

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保存并关闭文件。

通过 `mongosh` 连接到您的集群。

mongosh在终端窗口中打开并连接到您的集群。有关连接的详细说明，请参阅通过mongosh连接到集群。

使用 `sample_mflix` 数据库。

在 mongosh 提示符下运行以下命令：

use sample_mflix

加载要在查询中使用的嵌入。

在将 <path-to-file> 替换为 embeddings.js 文件的绝对路径后，运行以下命令以加载 embeddings.js 文件中的嵌入。
load('/<path-to-file>/embeddings.js';
true
请验证嵌入是否已成功加载。
您可以通过运行类似于以下命令的命令来进行验证：
COMEDY_INVOLVING_GHOSTS.length
2048

Run the MongoDB Vector Search queries against the `embedded_movies` collection.

多个查询向量与单个文档向量的对比

对数据集进行全面搜索，查找语义相似的术语，以确定哪个查询术语返回最佳结果。

1 db.embedded_movies.aggregate([
2   {
3     $rankFusion: {
4       input: {
5         pipelines: {
6           vectorPipeline1: [
7             {
8               "$vectorSearch": {
9                 "index": "multiple-vector-search",
10                 "path": "plot_embedding_voyage_3_large",
11                 "queryVector": COMEDY_INVOLVING_GHOSTS,
12                 "numCandidates": 2000,
13                 "limit": 50
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "plot_embedding_voyage_3_large",
22                 "queryVector": HUMOR_INVOLVING_PARANORMAL,
23                 "numCandidates": 2000,
24                 "limit": 50
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 20
49   }
50 ]);

[
  {
    _id: ObjectId('573a139af29313caabcef0a4'),
    plot: 'A paranormal expert and his daughter bunk in an abandoned house populated by 3 mischievous ghosts and one friendly one.',
    title: 'Casper',
    scoreDetails: {
      value: 0.01639344262295082,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 1,
          weight: 0.5,
          value: 0.8094863891601562,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 1,
          weight: 0.5,
          value: 0.7794423699378967,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabceaf10'),
    plot: "When Peter Plunkett's Irish castle turned hotel is about to be repossesed, he decides to spice up the attraction a bit for the 'Yanks' by having his staff pretend to haunt the castle. The ...",
    title: 'High Spirits',
    scoreDetails: {
      value: 0.01575682382133995,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 2,
          weight: 0.5,
          value: 0.7949001789093018,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 5,
          weight: 0.5,
          value: 0.7545012831687927,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabce912c'),
    plot: 'Three unemployed parapsychology professors set up shop as a unique ghost removal service.',
    title: 'Ghostbusters',
    scoreDetails: {
      value: 0.015038444142921756,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 6,
          weight: 0.5,
          value: 0.7781286239624023,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 7,
          weight: 0.5,
          value: 0.7519128918647766,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13a0f29313caabd056df'),
    plot: "A girl calls on her brother's imaginary friend to banish a mischievous boogeyman who has framed her for his pranks.",
    title: "Don't Look Under the Bed",
    scoreDetails: {
      value: 0.014913831197525408,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 13,
          weight: 0.5,
          value: 0.7534659504890442,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 2,
          weight: 0.5,
          value: 0.7626002430915833,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1399f29313caabced85b'),
    plot: 'A comical Gothic horror-movie-type family tries to rescue their beloved uncle from his gold-digging new love.',
    title: 'Addams Family Values',
    scoreDetails: {
      value: 0.014854753521126762,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 11,
          weight: 0.5,
          value: 0.7640036344528198,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 4,
          weight: 0.5,
          value: 0.7570045590400696,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13bdf29313caabd58456'),
    plot: 'Bertram Pincus is a man whose people skills leave much to be desired. When Pincus dies unexpectedly, but is miraculously revived after seven minutes, he wakes up to discover that he now has the annoying ability to see ghosts.',
    title: 'Ghost Town',
    scoreDetails: {
      value: 0.014709063378758382,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 7,
          weight: 0.5,
          value: 0.7774010300636292,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 9,
          weight: 0.5,
          value: 0.7500401735305786,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabceace9'),
    plot: 'A couple of recently deceased ghosts contract the services of a "bio-exorcist" in order to remove the obnoxious new owners of their house.',
    title: 'Beetlejuice',
    scoreDetails: {
      value: 0.014186507936507936,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
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    plot: 'Two cops are brought back to life to chase down supernatural criminals.',
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    title: 'The Peanut Butter Solution',
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    title: 'The Canterville Ghost',
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    title: 'Puppet Master II',
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    plot: 'A ghost returns back from his world to prove something. But on earth, he has something more to do for his country.',
    title: 'Bhoothnath Returns',
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  {
    _id: ObjectId('573a13bdf29313caabd58274'),
    plot: 'Banku, his mother, Anjali Sharma and father move in to their new house -- the Nath villa, unaware of the fact that the house is inhabited by a ghost. It is learnt the ghost is not too happy...',
    title: 'Bhoothnath',
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此示例查询使用 $rankFusion 及以下输入管道阶段：

$vectorSearch

在plot_embedding_voyage_3_large字段中搜索短语light-hearted comedy with ghosts，该短语在查询的queryVector字段中通过COMEDY_INVOLVING_GHOSTS变量指定为向量嵌入。
指定搜索最多 2000 个最近邻。
将此阶段的结果限制为 50 个文档。
指定权重 0.5，用于影响该管道对最终分数的排名贡献。

$vectorSearch

通过使用 HUMOR_INVOLVING_PARANORMAL 变量，在 plot_embedding_voyage_3_large 字段中对短语 slapstick humor with paranormal events 进行有序向量搜索，该短语在 queryVector 字段中指定为向量嵌入。
指定搜索最多 2000 个最近邻。
将此阶段的结果限制为 50 个文档。
指定权重 0.5，用于影响该管道对最终分数的排名贡献。

该示例查询还指定了以下管道阶段。

`$project`	在结果中仅包括 `plot` 和 `title` 字段。在结果中添加名为 `scoreDetails` 的字段。
`$limit`	将返回的结果限制为 20 个文档。

MongoDB Vector Search merges the results for both the queries into a single result set. In the results:

该 scoreDetails.value 显示管道的原始分数，然后使用倒数排名融合对其进行加权和组合。
score.details.rank 显示文档在管道结果中的排名。
scoreDetails.details.value 包含加权倒数排名分数。

您可以执行以下操作：

调整分配给查询中每个管道的权重，进一步细化结果。
如果您看到不相交的结果，请增加结果中的文档数量。

相同术语查询向量与多个文档向量的对比

在数据集中搜索多个字段，以确定哪些字段为相同查询返回最佳结果。

1 db.embedded_movies.aggregate([
2   {
3     $rankFusion: {
4       input: {
5         pipelines: {
6           vectorPipeline1: [
7             {
8               "$vectorSearch": {
9                 "index": "multiple-vector-search",
10                 "path": "plot_embedding_voyage_3_large",
11                 "queryVector": BATTLE_GOOD_EVIL,
12                 "numCandidates": 2000,
13                 "limit": 200
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "title_voyageai_embedding",
22                 "queryVector": BATTLE_GOOD_EVIL,
23                 "numCandidates": 2000,
24                 "limit": 200
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 20
49   }
50 ]);

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  },
  {
    _id: ObjectId('573a13c0f29313caabd62ff5'),
    plot: 'Wind and Cloud find themselves up against a ruthless Japanese warlord intent on invading China.',
    title: 'Storm Warriors',
    scoreDetails: {
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    plot: 'A Chinese emissary is sent to the Gobi desert to execute a renegade soldier. When a caravan transporting a Buddhist monk and a valuable treasure is threatened by thieves, however, the two warriors might unite to protect the travelers.',
    title: 'Warriors of Heaven and Earth',
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    _id: ObjectId('573a13d0f29313caabd8d3f1'),
    plot: 'A short tale about the demons that slumber deep in the human soul and have the power to push people into the abyss of blind hate, fury and rage.',
    title: 'Paths of Hate',
    scoreDetails: {
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  },
  {
    _id: ObjectId('573a1399f29313caabcee104'),
    plot: 'The Son of Satan attempts to bring his father to Earth with the aid of some mystic stones.',
    title: 'Warlock: The Armageddon',
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  },
  {
    _id: ObjectId('573a13cef29313caabd880e3'),
    plot: 'Eight classic monsters fight to the death in an explosive wrestling tournament set inside an abandoned and cursed graveyard.',
    title: 'Monster Brawl',
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    }
  },
  {
    _id: ObjectId('573a1398f29313caabce9932'),
    plot: 'A young man must stop the Lord of Darkness from both destroying daylight and marrying the woman he loves.',
    title: 'Legend',
    scoreDetails: {
      value: 0.008511658574596896,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
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  },
  {
    _id: ObjectId('573a13a5f29313caabd15bbd'),
    plot: "A desert warrior rises up against the evil army that is destroying his homeland. He captures the enemy's key sorcerer, takes her deep into the desert and prepares for a final showdown.",
    title: 'The Scorpion King',
    scoreDetails: {
      value: 0.00819672131147541,
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        { inputPipelineName: 'vectorPipeline2', rank: 0, weight: 0.5 }
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]

此示例查询使用 $rankFusion 及以下输入管道阶段：

`$vectorSearch`	在 `plot_embedding_voyage_3_large` 字段中搜索短语 battle between good and evil，该短语在查询的 `queryVector` 字段中被指定为向量嵌入，使用 `BATTLE_GOOD_EVIL` 变量。指定搜索最多 `2000` 个最近邻。仅将结果限制为 `200` 个文档。指定权重 `0.5`，用于影响该管道对最终分数的排名贡献。
`$vectorSearch`	在 `title_voyageai_embedding` 字段中对字符串 battle between good and evil 进行有序向量搜索，通过使用 `BATTLE_GOOD_EVIL` 变量将 `queryVector` 字段指定为向量嵌入。指定搜索最多 `2000` 个最近邻。仅将结果限制为 `200` 个文档。指定权重 `0.5`，用于影响该管道对最终分数的排名贡献。

该示例查询还指定了以下管道阶段。

`$project`	在结果中仅包括 `plot` 和 `title` 字段。在结果中添加名为 `scoreDetails` 的字段。
`$limit`	将返回的结果限制为 20 个文档。

MongoDB Vector Search merges the results for both the queries into a single result set. In the results:

该 scoreDetails.value 显示管道的原始分数，然后使用倒数排名融合对其进行加权和组合。
score.details.rank 显示文档在管道结果中的排名。
scoreDetails.details.value 包含加权倒数排名分数，该分数显示哪些字段返回与查询词最相关的结果。

例如，结果中的第一份和第四份文档表明该术语在 plot 字段中具有显著匹配，而第二份和第五份文档则表明该术语在 title 字段中具有显著匹配。您可以执行以下操作：

调整分配给查询中每个管道的权重，进一步细化结果。
如果您看到不相交的结果，请增加结果中的文档数量。

使用多个模型进行相同术语查询

使用不同嵌入模型生成的嵌入向量进行搜索，以确定不同模型在语义解释上的差异。

1 db.embedded_movies.aggregate([
2   {
3     $rankFusion: {
4       input: {
5         pipelines: {
6           vectorPipeline1: [
7             {
8               "$vectorSearch": {
9                 "index": "multiple-vector-search",
10                 "path": "plot_embedding",
11                 "queryVector": JOURNEY_ACROSS_LANDS_OPENAI,
12                 "numCandidates": 2000,
13                 "limit": 100
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "plot_embedding_voyage_3_large",
22                 "queryVector": JOURNEY_ACROSS_LANDS_VOYAGEAI,
23                 "numCandidates": 2000,
24                 "limit": 100
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 20
49   }
50 ]);

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    title: "Gulliver's Travels",
    scoreDetails: {
      value: 0.015549662487945998,
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        {
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  {
    _id: ObjectId('573a1397f29313caabce8828'),
    plot: 'A warrior seeks his true origins in a seemingly prehistoric wasteland.',
    title: 'Yor, the Hunter from the Future',
    scoreDetails: {
      value: 0.01117448492608544,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 11,
          weight: 0.5,
          value: 0.9153545498847961,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 61,
          weight: 0.5,
          value: 0.7023824453353882,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13aaf29313caabd218b4'),
    plot: 'An adventurous girl finds another world that is a strangely idealized version of her frustrating home, but it has sinister secrets.',
    title: 'Coraline',
    scoreDetails: {
      value: 0.010934744268077601,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 21,
          weight: 0.5,
          value: 0.9102445840835571,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 45,
          weight: 0.5,
          value: 0.7056041955947876,
          details: []
        }
      ]
    }
  }
]

此示例查询使用 $rankFusion 及以下输入管道阶段：

`$vectorSearch`	使用 `JOURNEY_ACROSS_LANDS_OPENAI` 变量在 `plot_embedding` 字段中搜索 journey across lands 字符串（该字符串在查询的 `queryVector` 字段中指定为向量嵌入）。指定搜索最多 `2000` 个最近邻。将结果限制为 `100` 个文档。指定权重 `0.5`，用于影响该管道对最终分数的排名贡献。
`$vectorSearch`	在 `plot_embedding_voyage_3_large` 字段中为字符串 journey across lands 执行顺序向量搜索，该字符串在 `queryVector` 字段中使用 `JOURNEY_ACROSS_LANDS_VOYAGEAI` 变量指定为向量嵌入。指定搜索最多 `2000` 个最近邻。将结果限制为 `100` 个文档。指定权重 `0.5`，用于影响该管道对最终分数的排名贡献。

该示例查询还指定了以下管道阶段。

`$project`	在结果中仅包括 `plot` 和 `title` 字段。在结果中添加名为 `scoreDetails` 的字段。
`$limit`	将返回的结果限制为 20 个文档。

MongoDB Vector Search merges the results for both the queries into a single result set. In the results:

该 scoreDetails.value 显示管道的原始分数，然后使用倒数排名融合对其进行加权和组合。
score.details.rank 显示文档在管道结果中的排名。
该 scoreDetails.details.value 包含加权逆秩得分，该得分显示了不同嵌入模型在查询词语义解释方面的优势与差异。

例如，结果中的第一份和第五份文档表明在 vectorPipeline2 中使用的模型提供了更相似的语义表示，而第二份和第四份文档则表明在 vectorPipeline1 中使用的模型提供了更接近的语义解释。

后退

结合向量搜索和全文搜索

来年

使用兼容视图

1	import os
2	import pymongo
3	import voyageai
4
5	# Specify your Voyage API key and embedding model
6	os.environ["VOYAGE_API_KEY"] = "<api-key>"
7
8	model = "voyage-3-large"
9	outputDimension = 2048
10	vo = voyageai.Client()
11	# Define a function to generate embeddings
12	def get_embedding(data, input_type = "document"):
13	embeddings = vo.embed(
14	data, model = model, output_dimension = outputDimension, input_type = input_type
15	).embeddings
16	return embeddings[0]
17
18	# Connect to your MongoDB Atlas cluster
19	mongo_client = pymongo.MongoClient("<connection-string>")
20	db = mongo_client["sample_mflix"]
21	collection = db["embedded_movies"]
22
23	# Filter to exclude null or empty plot fields
24	filter = {"title": {"$nin": [None, ""]}}
25
26	# Get a subset of documents in the collection
27	documents = collection.find(filter).limit(3500)
28
29	# Update each document with a new embedding field
30	updated_doc_count = 0
31	for doc in documents:
32	embedding = get_embedding(doc["title"])
33	if embedding is not None:
34	collection.update_one({"_id": doc["_id"]}, {"$set": {"title_voyageai_embedding": embedding}})
35	updated_doc_count += 1
36
37	print(f"Updated {updated_doc_count} documents.")

1	db.embedded_movies.createSearchIndex(
2	"multiple-vector-search",
3	"vectorSearch",
4	{
5	"fields": [
6	{
7	"type": "vector",
8	"path": "plot_embedding",
9	"numDimensions": 1536,
10	"similarity": "dotProduct"
11	},
12	{
13	"type": "vector",
14	"path": "plot_embedding_voyage_3_large",
15	"numDimensions": 2048,
16	"similarity": "dotProduct"
17	},
18	{
19	"type": "vector",
20	"path": "title_voyageai_embedding",
21	"numDimensions": 2048,
22	"similarity": "dotProduct"
23	}
24	]
25	}
26	);

1	db.embedded_movies.aggregate([
2	{
3	$rankFusion: {
4	input: {
5	pipelines: {
6	vectorPipeline1: [
7	{
8	"$vectorSearch": {
9	"index": "multiple-vector-search",
10	"path": "plot_embedding_voyage_3_large",
11	"queryVector": COMEDY_INVOLVING_GHOSTS,
12	"numCandidates": 2000,
13	"limit": 50
14	}
15	}
16	],
17	vectorPipeline2: [
18	{
19	"$vectorSearch": {
20	"index": "multiple-vector-search",
21	"path": "plot_embedding_voyage_3_large",
22	"queryVector": HUMOR_INVOLVING_PARANORMAL,
23	"numCandidates": 2000,
24	"limit": 50
25	}
26	}
27	]
28	}
29	},
30	combination: {
31	weights: {
32	vectorPipeline1: 0.5,
33	vectorPipeline2: 0.5
34	}
35	},
36	"scoreDetails": true
37	}
38	},
39	{
40	"$project": {
41	_id: 1,
42	title: 1,
43	plot: 1,
44	scoreDetails: {"$meta": "scoreDetails"}
45	}
46	},
47	{
48	"$limit": 20
49	}
50	]);

用例与优势

关于教程

注意

生成嵌入

先决条件

步骤

设置环境。

定义并使用一个函数来生成向量嵌入。

注意

创建MongoDB Vector Search 索引

使用 mongosh 连接到 Atlas 集群。

切换到包含要创建索引的集合的数据库。

例子

运行 db.collection.createSearchIndex() 方法。

Run the MongoDB Vector Search Queries

创建一个嵌入文档，以便在查询中使用。

通过 mongosh 连接到您的集群。

使用 sample_mflix 数据库。

加载要在查询中使用的嵌入。

Run the MongoDB Vector Search queries against the embedded_movies collection.

多个查询向量与单个文档向量的对比

相同术语查询向量与多个文档向量的对比

使用多个模型进行相同术语查询

使用 `mongosh` 连接到 Atlas 集群。

运行 `db.collection.createSearchIndex()` 方法。

通过 `mongosh` 连接到您的集群。

使用 `sample_mflix` 数据库。

Run the MongoDB Vector Search queries against the `embedded_movies` collection.