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複数の `$vectorSearch` クエリを組み合わせる方法

MongoDB $rankFusion 集計ステージは、入力パイプライン内の$vectorSearchをサポートします。$rankFusionを使用して、同じパイプライン内の同じコレクションに対する複数の$vectorSearchクエリを組み合わせることができます。$rankFusionは、まずすべての入力パイプラインを個別に実行し、次に重複を除外して、入力パイプラインの結果を最終的なランク付き結果セットに組み合わせます。単一のパイプラインで複数のコレクションに対して複数の$vectorSearchクエリを実行するには、$unionWithを使用します。

ユースケースと利点

$rankFusionパイプラインを使用して、次の種類の$vectorSearchクエリを実行することができます。

同じフィールドに対して類似の用語を検索するために、$vectorSearchクエリを複数実行します。

これにより、データセット全体にわたって類似の用語を同じクエリ内で包括的に検索することができます。
複数のフィールドに対して同じ用語を検索するために、$vectorSearchクエリを複数実行します。

これにより、データセット内の複数のフィールドを検索し、どのフィールドがクエリに対して最適な結果を返すかを判断することができます。
異なる埋め込みモデルの複数の埋め込みに対して同じ用語を検索するために、$vectorSearchクエリを複数実行します。

これにより、さまざまな埋め込みモデルの埋め込みを検索し、異なるモデル間のセマンティックな解釈の違いを判断することができます。

チュートリアルについて

このチュートリアルでは、sample_mflix データベース内の embedded_movies コレクションに対してさまざまな $vectorSearch クエリを実行する方法について説明します。このコレクションには、次のフィールドが含まれています。

plot_embedding、OpenAIのtext-embedding-ada-002埋め込みモデルを使用して作成された埋め込みを含んでいます。
plot_embedding_voyage_4_largeこれには、Voyage AI の voyage-4-large 埋め込みモデルを使用して作成された埋め込みが含まれています。このチュートリアルのすべてのサンプルクエリでこのフィールドを使用します。

「ユースケースとメリット」にリストされている 2 番目のクエリに対して、別のフィールドの埋め込みを生成する必要があります。このユースケースのサンプルクエリを試すには、「埋め込みの生成」の手順を完了してください。

このチュートリアルでは、次の手順について説明します。

sample_mflix.embedded_moviesコレクションにMongoDB ベクトル検索インデックスを設定します。
$vectorSearchクエリを$rankFusionで実行して、次の検索を行います。
- plot_embedding_voyage_4_largeフィールドで、light-hearted comedy with ghostsおよびslapstick humor with paranormal eventsに類似したあらすじを持つ映画のあらすじを検索します。
- Voyage AI の埋め込みを使用して、plot_embedding_voyage_4_large および title_embedding フィールドで battle between good and evil に似たプロットを持つ映画を検索します。
- OpenAI および Voyage AI の埋め込みを使用して、plot_embedding フィールドと plot_embedding_voyage_4_large フィールドで journey across lands に似たあらすじを持つ映画を検索します。

開始する前に、クラスターが前提条件に記載されている要件を満たしていることを確認してください。

注意

$rankFusion で $vectorSearch クエリを実行するには、クラスターで MongoDB v8.0 以降を実行している必要があります。

埋め込みを生成します

複数のフィールドに対して同じ用語をクエリするには、異なるフィールドの埋め込みを含むデータセットを準備する必要があります。このセクションでは、Voyage AI の voyage-4-large 埋め込みモデルを使用して、コレクション内の title フィールドの埋め込みを生成する方法を示します。

他のサンプルユースケースのみを試したい場合は、このセクションをスキップしてください。

前提条件

VS CodeやColab. などのインタラクティブPythonノートを実行するための環境。
Voyage AI 埋め込みモデル用の API キー。

手順

このセクションでは、Voyage AI の voyage-4-large 埋め込みモデルを使用して、embedded_movies コレクション内の title フィールドの埋め込みを生成します。

環境を設定します。

.ipynb拡張機能のファイルを保存してインタラクティブ Python ノートを作成し、ノート内で次のコマンドを実行して依存関係をインストールします。

pip install --quiet voyageai pymongo

ベクトル埋め込みを生成する関数を定義して使用します。

以下のプレースホルダー値を置き換えた後、ノートブックに次のコードを貼り付けて実行してください。

`<api-key>`	Voyage AI APIキー（行6）。
`<connection-string>`	MongoDBクラスターの接続文字列（18行目）。

次のコードは、Voyage AIの独自の埋め込みモデルを使用してベクトル埋め込みを生成する関数を定義します。具体的には、このコードでは次の処理が行われます。

voyage-4-large埋め込みモデルを指定します。
モデルの API を呼び出して特定のテキスト入力に対して 2048 次元の埋め込みを生成する get_embedding() という名前の関数を作成します。
クラスターに接続し、sample_mflix.embedded_movies名前空間から 3500 ドキュメントを取得します。
get_embedding()関数を使用して、各ドキュメントのtitleフィールドから埋め込みを生成します。
MongoDB PyMongo ドライバーを使用して、埋め込み値を含む title_embedding_voyage_4_large という名前の新しいフィールドで各ドキュメントを更新します。

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-4-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_embedding_voyage_4_large": embedding}})
35         updated_doc_count += 1
36 
37 print(f"Updated {updated_doc_count} documents.")

Updated 3500 documents.

注意

操作が完了するまでに最大 15 分かかる可能性があります。

MongoDB ベクトル検索インデックスの作成

このセクションでは、sample_mflix.embedded_movies名前空間にMongoDB ベクトル検索インデックスを作成します。セクションでは、さまざまなクエリの実行中に使用できるインデックス定義が示されています。インデックスを作成する前に、埋め込みを生成する手順を完了します。

`mongosh` を使用してクラスターに接続します。

ターミナルウィンドウで mongosh を開き、Atlas クラスターに接続します。接続の詳細な手順については、 Connect via mongosh. を参照してください。

インデックスを作成するコレクションを含むデータベースに切り替えます。

例

use sample_mflix

switched to db sample_mflix

`db.collection.createSearchIndex()` メソッドを実行します。

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_4_large",
15         "numDimensions": 2048,
16         "similarity": "dotProduct"
17       },
18       {
19         "type": "vector",
20         "path": "title_embedding_voyage_4_large",
21         "numDimensions": 2048,
22         "similarity": "dotProduct"
23       }
24     ]
25   }
26 );

このインデックス定義は、次のフィールドをインデックスします。

plot_embedding フィールドをvector 型として表示する。このフィールドには、映画のプロットの概要を表すベクトル埋め込みが含まれています。インデックスの定義:
- 1536ベクトル次元を指定します。
- dotProduct類似性を使用して類似性を測定します。
このフィールドマッピングは3番目のユースケースに必要です。
plot_embedding_voyage_4_large フィールドをvector 型として表示する。このフィールドには、映画のプロットの概要を表すベクトル埋め込みが含まれています。インデックスの定義:
- 2048ベクトル次元を指定します。
- dotProduct類似性を使用して類似性を測定します。
このフィールドマッピングは、このチュートリアルのすべてのクエリで必須です。
title_embedding_voyage_4_large フィールドをvector 型として表示する。このフィールドには、映画のタイトルを表すvector embeddingsが含まれています。インデックスの定義:
- 2048ベクトル次元を指定します。
- dotProduct類似性を使用して類似性を測定します。
このフィールドマッピングは2番目のユースケースに必要です。

MongoDB ベクトル検索クエリの実行

このセクションでは、コレクション上に作成した multiple-vector-search という名前のインデックスを使用して、sample_mflix データベースの embedded_movies コレクションをクエリします。

クエリで使用する埋め込みのためのファイルを作成します。

embeddings.jsという名前のファイルを作成します。
touch embeddings.js

以下の埋め込みをコピーして、embeddings.js ファイルに貼り付けます。

このファイルには、クエリで使用される用語の埋め込みが含まれています。次の表は、クエリ用語、クエリ用語の埋め込みを含む変数、埋め込みを生成するために使用されるモデル、および次元数を示しています。

クエリフレーズ	変数	埋め込みモデル	ディメンション
light-hearted comedy with ghosts（幽霊が登場する軽妙なコメディ）	COMEDY_INVOLVING_GHOSTS	Voyage AI's `voyage-4-large`	2048
slapstick humor with paranormal events（超常現象に関するドタバタコメディ）	HUMOR_INVOLVING_PARANORMAL	Voyage AI's `voyage-4-large`	2048
battle between good and evil（善と悪の戦い）	BATTLE_GOOD_EVIL	Voyage AI's `voyage-4-large`	2048
土地を横断する旅	JOURNEY_ACROSS_LANDS_VOYAGEAI	Voyage AI's `voyage-4-large`	2048
土地を横断する旅	JOURNEY_ACROSS_LANDS_OPENAI	OpenAIの `text-embedding-ada-002`	1536

COMEDY_INVOLVING_GHOSTS=[-0.012623000890016556, -0.03503970801830292, 0.0017070976318791509, -0.08052603900432587, -0.006855594925582409, 0.0205667857080698, 0.0554976761341095, 0.034822069108486176, 0.0241578109562397, -0.012514181435108185, -0.014255285263061523, -0.0177374929189682, 0.007454099599272013, 0.036780811846256256, 0.031992778182029724, 0.002176379319280386, 0.013547961600124836, 0.015996389091014862, 0.0010133766336366534, -0.02404899150133133, 0.01294945739209652, -0.005712995771318674, 0.00924961268901825, 0.0048696487210690975, -0.03416915610432625, 0.01675812155008316, -0.01588756963610649, -0.004543192218989134, -0.00827024132013321, 0.013602371327579021, -0.008324651047587395, 0.01305827684700489, -0.014799379743635654, 0.03177513927221298, -0.009739297442138195, -0.008814336732029915, -0.001972343772649765, -0.015234655700623989, 0.018716862425208092, 0.006855594925582409, -0.007508508861064911, 0.02241670712828636, -0.0014894596533849835, -0.021654974669218063, 0.022525526583194733, 0.01588756963610649, -0.016431665048003197, 0.03525734692811966, -0.02894584648311138, -0.023831354454159737, -0.01349355187267065, 0.04178648442029953, -0.02143733762204647, 0.01686694100499153, -0.019260957837104797, -0.006746775936335325, -0.0060938624665141106, -0.01654048264026642, -0.016322845593094826, -0.03221041336655617, -0.006202681455761194, -0.022743165493011475, -0.025572458282113075, 0.013602371327579021, 0.006338704843074083, 0.0313398651778698, -0.04722743108868599, 0.03351624310016632, -0.004597601480782032, 0.008052603341639042, -0.03612789884209633, -0.015234655700623989, -0.00718205189332366, 0.017302216961979866, -0.018172768875956535, -0.009576069191098213, 0.011045125313103199, -0.017411034554243088, -0.011752448976039886, -0.010718668811023235, 0.0061754765920341015, -0.0036454354412853718, 0.01017457339912653, -0.042221758514642715, 0.045051053166389465, -0.03917482867836952, -0.029163483530282974, 0.005685791373252869, 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ファイルを保存して閉じます。

`mongosh`のクラスターに接続します。

ターミナルウィンドウで mongosh を開き、クラスターに接続します。接続の詳細な手順については、 Connect via mongosh. を参照してください。

`sample_mflix`データベースを使用します。

mongoshプロンプトで次のコマンドを実行します。

use sample_mflix

クエリで使用する埋め込みをロードします。

<path-to-file> を embeddings.js ファイルへの絶対パスに置き換えた後、次のコマンドを実行して embeddings.js ファイルに埋め込みをロードします。
load('/<path-to-file>/embeddings.js';
true
埋め込みが正常に読み込まれたことを確認してください。
次のようなコマンドを実行することで確認できます。
COMEDY_INVOLVING_GHOSTS.length
2048

コレクションに対してMongoDB`embedded_movies` ベクトル検索クエリを実行します。

単一のドキュメントベクトルに対する複数のクエリベクトル

データセットを包括的に検索し、セマンティックが類似した用語を特定して、どのクエリが最良の結果を返すかを判断します。

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_4_large",
11                 "queryVector": COMEDY_INVOLVING_GHOSTS,
12                 "numCandidates": 2000,
13                 "limit": 50
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "plot_embedding_voyage_4_large",
22                 "queryVector": HUMOR_INVOLVING_PARANORMAL,
23                 "numCandidates": 2000,
24                 "limit": 50
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$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: 0.7622715830802917,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 1,
          weight: 0.5,
          value: 0.7325888276100159,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13b3f29313caabd3e8a1'),
    plot: 'A folk tale - supernatural love story about a ghost who falls in love with a newlywed woman.',
    title: 'Paheli',
    scoreDetails: {
      value: 0.015640273704789834,
      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: 0.7293232679367065,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 6,
          weight: 0.5,
          value: 0.7031921148300171,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabce912c'),
    plot: 'Three unemployed parapsychology professors set up shop as a unique ghost removal service.',
    title: 'Ghostbusters',
    scoreDetails: {
      value: 0.015625,
      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: 4,
          weight: 0.5,
          value: 0.7225469946861267,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 4,
          weight: 0.5,
          value: 0.7111413478851318,
          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: 0.01518288474810214,
      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: 3,
          weight: 0.5,
          value: 0.7231436371803284,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 9,
          weight: 0.5,
          value: 0.6983412504196167,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabce9a71'),
    plot: 'Peanut butter is the secret ingredient for magic potions made by two friendly ghosts. Eleven-year-old Michael loses all of his hair when he gets a fright and uses the potion to get his hair...',
    title: 'The Peanut Butter Solution',
    scoreDetails: {
      value: 0.015154994259471873,
      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: 0.7036163806915283,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 5,
          weight: 0.5,
          value: 0.7067587375640869,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13d3f29313caabd96a55'),
    plot: 'A teacher with paranormal abilities helps a group of ghosts graduate high school.',
    title: 'Ghost Graduation',
    scoreDetails: {
      value: 0.014835164835164835,
      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: 5,
          weight: 0.5,
          value: 0.7210001945495605,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 10,
          weight: 0.5,
          value: 0.6979111433029175,
          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: 0.01316017316017316,
      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: 15,
          weight: 0.5,
          value: 0.687480628490448,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 17,
          weight: 0.5,
          value: 0.6881510019302368,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13a8f29313caabd1db7e'),
    plot: 'Kung-Fu Action / Comedy / Horror / Musical about the second coming.',
    title: 'Jesus Christ Vampire Hunter',
    scoreDetails: {
      value: 0.012781497261107728,
      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: 46,
          weight: 0.5,
          value: 0.6382369995117188,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 2,
          weight: 0.5,
          value: 0.720718264579773,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13b5f29313caabd41f29'),
    plot: '14-year-old Lulu moves to a small provincial town with her mother and younger brother. One night, her brother is struck by a beam of white light - actually the spirit of Herman Hartmann ...',
    title: 'Island of Lost Souls',
    scoreDetails: {
      value: 0.012579113924050634,
      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: 20,
          weight: 0.5,
          value: 0.680338978767395,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 19,
          weight: 0.5,
          value: 0.6868098974227905,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13b0f29313caabd3380e'),
    plot: 'After a failed suicide attempt, a pregnant woman gains the ability to see ghosts.',
    title: 'The Eye 2',
    scoreDetails: {
      value: 0.012361402457296973,
      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: 0.6916752457618713,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 34,
          weight: 0.5,
          value: 0.6768500208854675,
          details: []
        }
      ]
    }
  },
  {
    _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',
    scoreDetails: {
      value: 0.012352941176470587,
      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: 8,
          weight: 0.5,
          value: 0.7026016712188721,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 40,
          weight: 0.5,
          value: 0.6747320294380188,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabcebb96'),
    plot: "A mother/daughter pair of witches descend on a yuppie family's home and cause havoc, one at a time since they share one body & the other must live in a cat the rest of the time. Now it's up...",
    title: 'Wicked Stepmother',
    scoreDetails: {
      value: 0.012207602339181285,
      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: 35,
          weight: 0.5,
          value: 0.6473828554153442,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 12,
          weight: 0.5,
          value: 0.6947218179702759,
          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: 0.012196892696384493,
      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: 37,
          weight: 0.5,
          value: 0.6451717019081116,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 11,
          weight: 0.5,
          value: 0.696073055267334,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13b0f29313caabd34285'),
    plot: 'After a failed suicide attempt, a pregnant woman gains the ability to see ghosts.',
    title: 'The Eye 2',
    scoreDetails: {
      value: 0.012046485260770973,
      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: 12,
          weight: 0.5,
          value: 0.6906752586364746,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 38,
          weight: 0.5,
          value: 0.6758197546005249,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13bdf29313caabd5a0d9'),
    plot: 'A haunting ghost story spanning two worlds, two centuries apart. When 13 year old Tolly finds he can mysteriously travel between the two, he begins an adventure that unlocks family secrets laid buried for generations.',
    title: 'From Time to Time',
    scoreDetails: {
      value: 0.011611125688795592,
      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: 14,
          weight: 0.5,
          value: 0.6898269057273865,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 43,
          weight: 0.5,
          value: 0.6731695532798767,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcf0f06'),
    plot: 'The wry, comic romantic tale follows the Owens sisters, Sally and Gillian, as they struggle to use their hereditary gift for practical magic to overcome the obstacles in discovering true love.',
    title: 'Practical Magic',
    scoreDetails: {
      value: 0.011564171122994652,
      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: 25,
          weight: 0.5,
          value: 0.6609134078025818,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 28,
          weight: 0.5,
          value: 0.679317831993103,
          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: 0.011379618974555684,
      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: 19,
          weight: 0.5,
          value: 0.6816859245300293,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 39,
          weight: 0.5,
          value: 0.6753329634666443,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13a3f29313caabd0cd93'),
    plot: "A simple funeral turns a man's world topsy turvy.",
    title: 'Monday',
    scoreDetails: {
      value: 0.010752688172043012,
      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: 33,
          weight: 0.5,
          value: 0.6509981751441956,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 33,
          weight: 0.5,
          value: 0.6770291328430176,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13c0f29313caabd627c4'),
    plot: 'An adventure set in the early part of the 20th century and focused on a popular novelist and her dealings with would-be suitors, the cops, monsters, and other distractions.',
    title: 'The Extraordinary Adventures of Adèle Blanc-Sec',
    scoreDetails: {
      value: 0.009495949594959495,
      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: 50,
          weight: 0.5,
          value: 0.636377215385437,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 41,
          weight: 0.5,
          value: 0.6734551787376404,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13d9f29313caabda92a9'),
    plot: 'The misadventures of a teenager girl in her new life as zombie.',
    title: "Daddy, I'm a Zombie",
    scoreDetails: {
      value: 0.009391534391534392,
      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: 45,
          weight: 0.5,
          value: 0.6392303705215454,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 48,
          weight: 0.5,
          value: 0.6711602210998535,
          details: []
        }
      ]
    }
  }
]

このサンプルクエリは、次の入力パイプラインステージとともに $rankFusion を使用します。

$vectorSearch

plot_embedding_voyage_4_large フィールドで、クエリの queryVector フィールドに COMEDY_INVOLVING_GHOSTS 変数を使用してベクトル埋め込みとして指定されたフレーズ「light-hearted comedy with ghosts」を検索します。
最大 2000 の最近傍を検索します。
このステージからの結果を 50 件のドキュメントに制限します。
最終スコアに対するパイプラインのランクの寄与度に影響を与える重みを0.5に指定します。

$vectorSearch

HUMOR_INVOLVING_PARANORMAL 変数を使用してqueryVector フィールド内でベクトル埋め込みとして指定されたslapstick humor with paranormal eventsというフレーズの順次ベクトル検索を、plot_embedding_voyage_4_large フィールドで実行します。
最大 2000 の最近傍を検索します。
このステージからの結果を 50 件のドキュメントに制限します。
最終スコアに対するパイプラインのランクの寄与度に影響を与える重みを0.5に指定します。

このサンプルクエリでは、次のパイプラインステージも指定しています。

`$project`	結果には `plot` フィールドと `title` フィールドのみが含まれます。結果に `scoreDetails` という名前のフィールドを追加します。
`$limit`	返される結果を 20 件のドキュメントに制限します。

MongoDB ベクトル検索は、両方のクエリの結果を単一の結果セットにマージします。結果より:

scoreDetails.value は、 Reciprocal Rank Fusionを使用して重み付けおよび結合される前に、そのパイプラインの生スコアを示します。
score.details.rank は、パイプラインの結果におけるドキュメントの順位を示します。
scoreDetails.details.value は重み付けされた逆数順位のスコアを示します。

次の操作を行うことができます。

クエリ内の各パイプラインに割り当てられた重みを調整して、結果をさらに絞り込む。
不連続な結果が表示される場合は、結果内のドキュメントの数を増やす。

同じ用語のクエリベクトルを複数のドキュメントベクトルに対して実行する

データセット内の複数のフィールドを検索し、同じクエリに対してどのフィールドが最も良い結果を返すかを判断します。

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_4_large",
11                 "queryVector": BATTLE_GOOD_EVIL,
12                 "numCandidates": 2000,
13                 "limit": 200
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "title_embedding_voyage_4_large",
22                 "queryVector": BATTLE_GOOD_EVIL,
23                 "numCandidates": 2000,
24                 "limit": 200
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 20
49   }
50 ]);

[
  {
    plot: 'A team of police hunt down a villain.',
    title: 'Nowhere to Hide',
    scoreDetails: {
      value: 0.004273504273504274,
      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: 57,
          weight: 0.5,
          value: 0.6504640579223633,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'Childhood friends continue their battle against a dangerous cult.',
    title: '20th Century Boys 2: The Last Hope',
    scoreDetails: {
      value: 0.007462686567164179,
      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: 0.6790846586227417,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'The Addams Family goes on a search for their relatives.',
    title: 'Addams Family Reunion',
    scoreDetails: {
      value: 0.005813953488372093,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 26,
          weight: 0.5,
          value: 0.5182114243507385,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'An attempt to transform a Roman Western into a Greek tragedy.',
    title: 'Instructions for a Light and Sound Machine',
    scoreDetails: {
      value: 0.003968253968253968,
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  {
    plot: 'A gang of ex-cons rob a casino during Elvis convention week.',
    title: '3000 Miles to Graceland',
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  {
    plot: 'Three freedom fighters attack a large corporation to prevent a future apocalypse.',
    title: 'T2 3-D: Battle Across Time',
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  {
    plot: "Teenage twins battle dark forces hidden beneath Auckland's volcanoes.",
    title: 'Under the Mountain',
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  {
    plot: "A simple funeral turns a man's world topsy turvy.",
    title: 'Monday',
    scoreDetails: {
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  {
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    title: 'Aquamarine',
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  {
    plot: 'Seven warriors come together to protect a village from a diabolical General.',
    title: 'Seven Swords',
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  {
    plot: 'Mystical martial artist/environmental agent takes on a ruthless oil corporation.',
    title: 'On Deadly Ground',
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  {
    plot: 'A super powered vigilante defies the law using extreme violence to fight crime.',
    title: 'The Flying Man',
    scoreDetails: {
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  {
    plot: 'A rag doll fights a monster that has been stealing the souls of his people.',
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  {
    plot: 'A man receives a distressing phone call from a woman who has been kidnapped.',
    title: 'Connected',
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  {
    plot: 'The misadventures of a teenager girl in her new life as zombie.',
    title: "Daddy, I'm a Zombie",
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  {
    plot: 'A troubled writer moves into a haunted house after inheriting it from his aunt.',
    title: 'House',
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  {
    plot: 'A dramatization of the World War II Battle of Iwo Jima.',
    title: 'Sands of Iwo Jima',
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  {
    plot: 'Kung-Fu Action / Comedy / Horror / Musical about the second coming.',
    title: 'Jesus Christ Vampire Hunter',
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  {
    plot: 'An All-American trucker gets dragged into a centuries-old mystical battle in Chinatown.',
    title: 'Big Trouble in Little China',
    scoreDetails: {
      value: 0.00684931506849315,
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  {
    plot: 'A group of assassins come together for a suicide mission to kill an evil lord.',
    title: '13 Assassins',
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]

このサンプルクエリは、次の入力パイプラインステージとともに $rankFusion を使用します。

$vectorSearch

BATTLE_GOOD_EVIL 変数を使用して、クエリの queryVector フィールドでベクトル埋め込みとして指定された「善と悪の戦い」というフレーズを plot_embedding_voyage_4_large フィールドで検索します。
最大 2000 の最近傍を検索します。
結果を200 件のドキュメントに制限します。
最終スコアに対するパイプラインのランクの寄与度に影響を与える重みを0.5に指定します。

$vectorSearch

title_embedding_voyage_4_large フィールドで、queryVector フィールドにベクトル埋め込みとして指定されたstringbattle between good and evil のベクトル検索をBATTLE_GOOD_EVIL 変数を使用して実行します。
最大 2000 の最近傍を検索します。
結果を200 件のドキュメントに制限します。
最終スコアに対するパイプラインのランクの寄与度に影響を与える重みを0.5に指定します。

このサンプルクエリでは、次のパイプラインステージも指定しています。

`$project`	結果には `plot` フィールドと `title` フィールドのみが含まれます。結果に `scoreDetails` という名前のフィールドを追加します。
`$limit`	返される結果を 20 件のドキュメントに制限します。

MongoDB ベクトル検索は、両方のクエリの結果を単一の結果セットにマージします。結果より:

scoreDetails.value は、 Reciprocal Rank Fusionを使用して重み付けおよび結合される前に、そのパイプラインの生スコアを示します。
score.details.rank は、パイプラインの結果におけるドキュメントの順位を示します。
scoreDetails.details.value には、クエリ用語に対して最も関連性の高い結果を返すフィールドを示す加重逆ランクスコアが含まれています。

例として、結果の 1 番目と 4 番目のドキュメントは、plot フィールドの用語に対して有意な一致を示唆し、2 番目と 5 番目のドキュメントは title フィールドで有意な一致を示唆しています。次の操作を行うことができます。

クエリ内の各パイプラインに割り当てられた重みを調整して、結果をさらに絞り込む。
不連続な結果が表示される場合は、結果内のドキュメントの数を増やす。

複数のモデルを使用した同一用語によるクエリ

異なる埋め込みモデルから埋め込みを検索し、モデル間のセマンティック解釈の違いを特定します。

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               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "plot_embedding_voyage_4_large",
22                 "queryVector": JOURNEY_ACROSS_LANDS_VOYAGEAI,
23                 "numCandidates": 2000,
24                 "limit": 100
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$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: 'Slow West',
    scoreDetails: {
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  },
  {
    _id: ObjectId('573a13f7f29313caabde65b4'),
    plot: 'Our figurine sized supermen hero embarks on an epic surreal journey that will take him across the Ethiopian post apocalyptic landscape in search of a way to get on the hovering spacecraft that for years has become a landmark in the skies.',
    title: 'Crumbs',
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  {
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    title: 'The 13th Warrior',
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  },
  {
    _id: ObjectId('573a1392f29313caabcdaa70'),
    plot: 'Leo Vincey, told by his dying uncle of a lost land visited 500 years ago by his ancestor, heads out with family friend Horace Holly to try to discover the land and its secret of immortality...',
    title: 'She',
    scoreDetails: {
      value: 0.01198581560283688,
      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: 15,
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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: 0.011944214305440487,
      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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  },
  {
    _id: ObjectId('573a13f3f29313caabddf32e'),
    plot: 'In a dry and dusty post-apocalyptic world, two wayfarers wander aimlessly until Leif finds a copy of The Wonderful Wizard of Oz. Using the world around him to interpret what he reads, Leif ...',
    title: 'OzLand',
    scoreDetails: {
      value: 0.011777438248026482,
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  {
    _id: ObjectId('573a13d8f29313caabda5a4e'),
    plot: 'A savage warrior escapes slavery and hunted by his former masters, begins a perilous journey back to his homeland and his wife.',
    title: 'The Dragonphoenix Chronicles: Indomitable',
    scoreDetails: {
      value: 0.0116999747027574,
      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('573a139af29313caabcf02db'),
    plot: 'Chris embarks on an odyssey of self-discovery that spans the globe. Kidnapped and enslaved by gun smugglers, sold by pirates and thrust into the murky underworld of gambling and kickboxing,...',
    title: 'The Quest',
    scoreDetails: {
      value: 0.011512297226582941,
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  {
    _id: ObjectId('573a1398f29313caabceb40c'),
    plot: "Epic story about two former Texas rangers who decide to move cattle from the south to Montana. Augustus McCrae and Woodrow Call run into many problems on the way, and the journey doesn't ...",
    title: 'Lonesome Dove',
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  {
    _id: ObjectId('573a1395f29313caabce1fb7'),
    plot: 'The dying words of a thief spark a madcap cross-country rush to find some treasure.',
    title: "It's a Mad, Mad, Mad, Mad World",
    scoreDetails: {
      value: 0.010528600890046674,
      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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  {
    _id: ObjectId('573a1396f29313caabce491f'),
    plot: 'A god-fearing Ohio boy dodging the Civil War draft arrives in Jefferson City where he joins up with a hardscrabble group of like runaways heading west.',
    title: 'Bad Company',
    scoreDetails: {
      value: 0.010272536687631027,
      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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  }
]

このサンプルクエリは、次の入力パイプラインステージとともに $rankFusion を使用します。

$vectorSearch

JOURNEY_ACROSS_LANDS_OPENAI 変数を使用して、クエリの queryVector フィールドでベクトル埋め込みとして指定されたstring journey across lands を plot_embedding フィールドで検索します。
最大 2000 の最近傍を検索します。
結果を100ドキュメントに制限します。
最終スコアに対するパイプラインのランクの寄与度に影響を与える重みを0.5に指定します。

$vectorSearch

plot_embedding_voyage_4_large フィールドで、queryVector フィールドに JOURNEY_ACROSS_LANDS_VOYAGEAI 変数を使用してベクトル埋め込みとして指定されたstring journey across lands に対してベクトル検索を実行します。
最大 2000 の最近傍を検索します。
結果を100ドキュメントに制限します。
最終スコアに対するパイプラインのランクの寄与度に影響を与える重みを0.5に指定します。

このサンプルクエリでは、次のパイプラインステージも指定しています。

`$project`	結果には `plot` フィールドと `title` フィールドのみが含まれます。結果に `scoreDetails` という名前のフィールドを追加します。
`$limit`	返される結果を 20 件のドキュメントに制限します。

MongoDB ベクトル検索は、両方のクエリの結果を単一の結果セットにマージします。結果より:

scoreDetails.value は、 Reciprocal Rank Fusionを使用して重み付けおよび結合される前に、そのパイプラインの生スコアを示します。
score.details.rank は、パイプラインの結果におけるドキュメントの順位を示します。
scoreDetails.details.value には、異なる埋め込みモデルによるクエリ用語のセマンティックな解釈の強度と差異を示す加重逆ランクスコアが含まれています。

例として、結果の 1 番目と 5 番目のドキュメントは、vectorPipeline2 で使用されているモデルによってより類似した意味表現を示唆していますが、結果の 2 番目と 4 番目のドキュメントは、vectorPipeline1 で使用されているモデルによってより近い意味解釈を示唆しています。

結果の順序を変更する

このセクションでは、$rerank ステージを使用してクエリの結果を再配列します。

次のクエリを実行して結果を並べ替えます。

単一のドキュメントベクトルに対する複数のクエリベクトル

データセットの包括的な検索の結果を再配置し、セマンティックが類似した用語を検索して、どのクエリ用語が最良の結果を返すかを判断します。

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_4_large",
11                 "queryVector": COMEDY_INVOLVING_GHOSTS,
12                 "numCandidates": 2000,
13                 "limit": 50
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "plot_embedding_voyage_4_large",
22                 "queryVector": HUMOR_INVOLVING_PARANORMAL,
23                 "numCandidates": 2000,
24                 "limit": 50
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 50
49   },
50   {
51     "$rerank": {
52       "model": "rerank-2.5",
53       "query": {
54         "text": "light-hearted comedy with ghosts"
55       },
56       "path": "plot",
57       "numDocsToRerank": 50
58     }
59   },
60   {
61     "$addFields": {
62       "rerankScore": { "$meta": "score" }
63     }
64   },
65   {
66     "$limit": 20
67   },
68   {
69     "$project": {
70       _id: 0,
71       title: 1,
72       plot: 1,
73       scoreDetails: 1,
74       rerankScore: 1
75     }
76   }
77 ]);

[
  {
    plot: 'Two ghosts walk along the Camino of Santiago.',
    title: 'Finisterrae',
    scoreDetails: {
      value: 0.007142857142857143,
      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: 10,
          weight: 0.5,
          value: 0.694494366645813,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A teenage boy is cursed with periodically turning into an sheepdog.',
    title: 'The Shaggy Dog',
    scoreDetails: {
      value: 0.006172839506172839,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 21,
          weight: 0.5,
          value: 0.6834555864334106,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'Two cops are brought back to life to chase down supernatural criminals.',
    title: 'Dead Heat',
    scoreDetails: {
      value: 0.006578947368421052,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 16,
          weight: 0.5,
          value: 0.6887474060058594,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: "A simple funeral turns a man's world topsy turvy.",
    title: 'Monday',
    scoreDetails: {
      value: 0.010752688172043012,
      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: 33,
          weight: 0.5,
          value: 0.6509981751441956,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 33,
          weight: 0.5,
          value: 0.6770291328430176,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A teacher with paranormal abilities helps a group of ghosts graduate high school.',
    title: 'Ghost Graduation',
    scoreDetails: {
      value: 0.014835164835164835,
      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: 5,
          weight: 0.5,
          value: 0.7210001945495605,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 10,
          weight: 0.5,
          value: 0.6979111433029175,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A public housing tenement is plunged into a dark storm of supernatural chaos.',
    title: 'Rigor Mortis',
    scoreDetails: {
      value: 0.006666666666666667,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 15,
          weight: 0.5,
          value: 0.6895216703414917,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'Kung-Fu Action / Comedy / Horror / Musical about the second coming.',
    title: 'Jesus Christ Vampire Hunter',
    scoreDetails: {
      value: 0.012781497261107728,
      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: 46,
          weight: 0.5,
          value: 0.6382369995117188,
          details: []
        },
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          rank: 2,
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          value: 0.720718264579773,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'After a failed suicide attempt, a pregnant woman gains the ability to see ghosts.',
    title: 'The Eye 2',
    scoreDetails: {
      value: 0.012361402457296973,
      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: 0.6916752457618713,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 34,
          weight: 0.5,
          value: 0.6768500208854675,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'The misadventures of a teenager girl in her new life as zombie.',
    title: "Daddy, I'm a Zombie",
    scoreDetails: {
      value: 0.009391534391534392,
      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: 45,
          weight: 0.5,
          value: 0.6392303705215454,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 48,
          weight: 0.5,
          value: 0.6711602210998535,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'After a failed suicide attempt, a pregnant woman gains the ability to see ghosts.',
    title: 'The Eye 2',
    scoreDetails: {
      value: 0.012046485260770973,
      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: 12,
          weight: 0.5,
          value: 0.6906752586364746,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 38,
          weight: 0.5,
          value: 0.6758197546005249,
          details: []
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      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'Five bizarre stories, with no apparently connections, get intertwined resulting in a surreal situation.',
    title: 'Survive Style 5+',
    scoreDetails: {
      value: 0.005952380952380952,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 24,
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    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'Three unemployed parapsychology professors set up shop as a unique ghost removal service.',
    title: 'Ghostbusters',
    scoreDetails: {
      value: 0.015625,
      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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        {
          inputPipelineName: 'vectorPipeline1',
          rank: 4,
          weight: 0.5,
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    rerankScore: 0.5986876487731934
  },
  {
    plot: 'The funny misadventures of a dating duo of film stunt professionals in France.',
    title: 'Animal',
    scoreDetails: {
      value: 0.006756756756756757,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 14,
          weight: 0.5,
          value: 0.6896277666091919,
          details: []
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      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A folk tale - supernatural love story about a ghost who falls in love with a newlywed woman.',
    title: 'Paheli',
    scoreDetails: {
      value: 0.015640273704789834,
      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: 0.7293232679367065,
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          inputPipelineName: 'vectorPipeline2',
          rank: 6,
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      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'Bank clerk Stanley Ipkiss is transformed into a manic super-hero when he wears a mysterious mask.',
    title: 'The Mask',
    scoreDetails: {
      value: 0.00684931506849315,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 13,
          weight: 0.5,
          value: 0.6942293047904968,
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      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A lonely landscape architect falls for the spirit of the beautiful woman who used to live in his new apartment.',
    title: 'Just Like Heaven',
    scoreDetails: {
      value: 0.006172839506172839,
      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: 0.6797891855239868,
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        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: "While attending his brother's wedding, a serial womanizer is haunted by the ghosts of his past girlfriends.",
    title: 'Ghosts of Girlfriends Past',
    scoreDetails: {
      value: 0.006578947368421052,
      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: 16,
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        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'Ho Sheung Sang finds himself wrapped up in another cat-and-mouse game, this time against a tricky magician.',
    title: 'Running Out of Time 2',
    scoreDetails: {
      value: 0.005747126436781609,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
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    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A veteran of the Chechen War is helped by the ghosts of his two fallen comrades to leave the war behind.',
    title: 'Alive',
    scoreDetails: {
      value: 0.005952380952380952,
      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: 24,
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          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'Adaption of the famous Oscar Wilde tale about a young American girl that helps a British ghost find rest and forgiveness.',
    title: 'The Canterville Ghost',
    scoreDetails: {
      value: 0.007575757575757576,
      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: 0.7143866419792175,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  }
]

このサンプルクエリは、次の入力パイプラインステージとともに $rankFusion を使用します。

$vectorSearch

plot_embedding_voyage_4_large フィールドで、クエリの queryVector フィールドに COMEDY_INVOLVING_GHOSTS 変数を使用してベクトル埋め込みとして指定されたフレーズ「light-hearted comedy with ghosts」を検索します。
最大 2000 の最近傍を検索します。
このステージからの結果を 50 件のドキュメントに制限します。
最終スコアに対するパイプラインのランクの寄与度に影響を与える重みを0.5に指定します。

$vectorSearch

HUMOR_INVOLVING_PARANORMAL 変数を使用してqueryVector フィールド内でベクトル埋め込みとして指定されたslapstick humor with paranormal eventsというフレーズの順次ベクトル検索を、plot_embedding_voyage_4_large フィールドで実行します。
最大 2000 の最近傍を検索します。
このステージからの結果を 50 件のドキュメントに制限します。
最終スコアに対するパイプラインのランクの寄与度に影響を与える重みを0.5に指定します。

このサンプルクエリでは、次のパイプラインステージも指定しています。

`$project`	結果には `plot` フィールドと `title` フィールドのみが含まれます。結果に `scoreDetails` という名前のフィールドを追加します。
`$limit`	返される結果を 50 件のドキュメントに制限します。

MongoDB ベクトル検索は、両方のクエリの結果を単一の結果セットにマージします。その後、$rerank ステージを使用して結果の順序を再編成します。$rerank ステージは、クエリ期間 light-hearted comedy with ghosts に対する関連性によって結果を再配置します。結果では、$rerank ステージにより、再配置後のスコアを示す rerankScore という名前のフィールドが追加されます。再配置された結果は、クエリ期間に対してより関連性が高くなります。

同じ用語のクエリベクトルを複数のドキュメントベクトルに対して実行する

データセット内の複数のフィールドに対する検索結果の順序を変更して、同じクエリに対してどのフィールドが最も良い結果を返すかを判断します。

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_float32",
11                 "queryVector": BATTLE_GOOD_EVIL,
12                 "numCandidates": 2000,
13                 "limit": 200
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "title_embedding_voyage_4_large",
22                 "queryVector": BATTLE_GOOD_EVIL,
23                 "numCandidates": 2000,
24                 "limit": 200
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 200
49   },
50   {
51     "$match": {
52       "plot": { "$exists": true, "$type": "string" }
53     }
54   },
55   {
56     "$rerank": {
57       "model": "rerank-2.5",
58       "query": {
59         "text": "battle between good and evil"
60       },
61       "path": "plot",
62       "numDocsToRerank": 200
63     }
64   },
65   {
66     "$addFields": {
67       "rerankScore": { "$meta": "score" }
68     }
69   },
70   {
71     "$limit": 20
72   },
73   {
74     "$project": {
75       "_id": 0,
76       "title": 1,
77       "plot": 1,
78       "scoreDetails": 1,
79       "rerankScore": 1
80     }
81   }
82 ]);

[
  {
    plot: 'A team of police hunt down a villain.',
    title: 'Nowhere to Hide',
    scoreDetails: {
      value: 0.004273504273504274,
      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: 57,
          weight: 0.5,
          value: 0.6504640579223633,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'Childhood friends continue their battle against a dangerous cult.',
    title: '20th Century Boys 2: The Last Hope',
    scoreDetails: {
      value: 0.007462686567164179,
      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: 0.6790846586227417,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'The Addams Family goes on a search for their relatives.',
    title: 'Addams Family Reunion',
    scoreDetails: {
      value: 0.005813953488372093,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 26,
          weight: 0.5,
          value: 0.5182114243507385,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'An attempt to transform a Roman Western into a Greek tragedy.',
    title: 'Instructions for a Light and Sound Machine',
    scoreDetails: {
      value: 0.003968253968253968,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 66,
          weight: 0.5,
          value: 0.5156481266021729,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A gang of ex-cons rob a casino during Elvis convention week.',
    title: '3000 Miles to Graceland',
    scoreDetails: {
      value: 0.005154639175257732,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 37,
          weight: 0.5,
          value: 0.5178723335266113,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'Three freedom fighters attack a large corporation to prevent a future apocalypse.',
    title: 'T2 3-D: Battle Across Time',
    scoreDetails: {
      value: 0.003937007874015748,
      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: 67,
          weight: 0.5,
          value: 0.6470792889595032,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: "Teenage twins battle dark forces hidden beneath Auckland's volcanoes.",
    title: 'Under the Mountain',
    scoreDetails: {
      value: 0.004310344827586207,
      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: 56,
          weight: 0.5,
          value: 0.6506195068359375,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: "A simple funeral turns a man's world topsy turvy.",
    title: 'Monday',
    scoreDetails: {
      value: 0.007692307692307693,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 5,
          weight: 0.5,
          value: 0.5210264325141907,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: "Two teenage girls discover a mermaid in their beach club's swimming pool.",
    title: 'Aquamarine',
    scoreDetails: {
      value: 0.003816793893129771,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 71,
          weight: 0.5,
          value: 0.5154338479042053,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'Seven warriors come together to protect a village from a diabolical General.',
    title: 'Seven Swords',
    scoreDetails: {
      value: 0.005050505050505051,
      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: 39,
          weight: 0.5,
          value: 0.6564368009567261,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'Mystical martial artist/environmental agent takes on a ruthless oil corporation.',
    title: 'On Deadly Ground',
    scoreDetails: {
      value: 0.004807692307692308,
      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: 44,
          weight: 0.5,
          value: 0.6541689038276672,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A super powered vigilante defies the law using extreme violence to fight crime.',
    title: 'The Flying Man',
    scoreDetails: {
      value: 0.0032258064516129032,
      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: 95,
          weight: 0.5,
          value: 0.638878583908081,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A rag doll fights a monster that has been stealing the souls of his people.',
    title: '9',
    scoreDetails: {
      value: 0.005,
      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: 40,
          weight: 0.5,
          value: 0.6557563543319702,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A man receives a distressing phone call from a woman who has been kidnapped.',
    title: 'Connected',
    scoreDetails: {
      value: 0.004807692307692308,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 44,
          weight: 0.5,
          value: 0.5171101093292236,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'The misadventures of a teenager girl in her new life as zombie.',
    title: "Daddy, I'm a Zombie",
    scoreDetails: {
      value: 0.00684931506849315,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 13,
          weight: 0.5,
          value: 0.5196477770805359,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A troubled writer moves into a haunted house after inheriting it from his aunt.',
    title: 'House',
    scoreDetails: {
      value: 0.003424657534246575,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 86,
          weight: 0.5,
          value: 0.5150279998779297,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A dramatization of the World War II Battle of Iwo Jima.',
    title: 'Sands of Iwo Jima',
    scoreDetails: {
      value: 0.0031446540880503146,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 99,
          weight: 0.5,
          value: 0.5144232511520386,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'Kung-Fu Action / Comedy / Horror / Musical about the second coming.',
    title: 'Jesus Christ Vampire Hunter',
    scoreDetails: {
      value: 0.0045871559633027525,
      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: 49,
          weight: 0.5,
          value: 0.652422308921814,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'An All-American trucker gets dragged into a centuries-old mystical battle in Chinatown.',
    title: 'Big Trouble in Little China',
    scoreDetails: {
      value: 0.00684931506849315,
      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: 0.6701953411102295,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A group of assassins come together for a suicide mission to kill an evil lord.',
    title: '13 Assassins',
    scoreDetails: {
      value: 0.003703703703703704,
      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: 75,
          weight: 0.5,
          value: 0.6449810266494751,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  }
]

このサンプルクエリは、次の入力パイプラインステージとともに $rankFusion を使用します。

$vectorSearch

BATTLE_GOOD_EVIL 変数を使用して、クエリの queryVector フィールドでベクトル埋め込みとして指定された「善と悪の戦い」というフレーズを plot_embedding_voyage_4_large フィールドで検索します。
最大 2000 の最近傍を検索します。
結果を200 件のドキュメントに制限します。
最終スコアに対するパイプラインのランクの寄与度に影響を与える重みを0.5に指定します。

$vectorSearch

title_embedding_voyage_4_large フィールドで、queryVector フィールドにベクトル埋め込みとして指定されたstringbattle between good and evil のベクトル検索をBATTLE_GOOD_EVIL 変数を使用して実行します。
最大 2000 の最近傍を検索します。
結果を200 件のドキュメントに制限します。
最終スコアに対するパイプラインのランクの寄与度に影響を与える重みを0.5に指定します。

このサンプルクエリでは、次のパイプラインステージも指定しています。

`$project`	結果には `plot` フィールドと `title` フィールドのみが含まれます。結果に `scoreDetails` という名前のフィールドを追加します。
`$limit`	返される結果を 200 件のドキュメントに制限します。

MongoDB ベクトル検索は、両方のクエリの結果を単一の結果セットにマージします。その後、$rerank ステージを使用して結果の順序を再編成します。$match ステージは、ドキュメントをフィルターして、タイプ string の plot フィールドを持つドキュメントのみを含めるようにします。$rerank ステージは、クエリ期間 battle between good and evil との関連性に基づいて結果の順序を再編成します。結果では、$rerank ステージにより、再編成後のスコアを示す rerankScore という名前のフィールドが追加されます。

複数のモデルを使用した同一用語によるクエリ

異なる埋め込みモデルからの埋め込みに対する検索結果を並べ替えて、異なるモデル間のセマンティック解釈の違いを特定します。

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               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "plot_embedding_voyage_4_large",
22                 "queryVector": JOURNEY_ACROSS_LANDS_VOYAGEAI,
23                 "numCandidates": 2000,
24                 "limit": 100
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 100
49   },
50   {
51     "$rerank": {
52       "model": "rerank-2.5",
53       "query": {
54         "text": "journey across lands"
55       },
56       "path": "plot",
57       "numDocsToRerank": 100
58     }
59   },
60   {
61     "$addFields": {
62       "rerankScore": { "$meta": "score" }
63     }
64   },
65   {
66     "$limit": 20
67   },
68   {
69     "$project": {
70       _id: 0,
71       title: 1,
72       plot: 1,
73       scoreDetails: 1,
74       rerankScore: 1
75     }
76   }
77 ]);

[
  {
    plot: 'A warrior seeks his true origins in a seemingly prehistoric wasteland.',
    title: 'Yor, the Hunter from the Future',
    scoreDetails: {
      value: 0.011944214305440487,
      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: 42,
          weight: 0.5,
          value: 0.6328363418579102,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A wide variety of eccentric competitors participate in a wild and illegal cross-country car race.',
    title: 'The Cannonball Run',
    scoreDetails: {
      value: 0.0043859649122807015,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 54,
          weight: 0.5,
          value: 0.6279412508010864,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'In a mythical land, a man and a young boy investigate a series of unusual occurrences.',
    title: 'Tales from Earthsea',
    scoreDetails: {
      value: 0.015873015873015872,
      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: 3,
          weight: 0.5,
          value: 0.9192964434623718,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 3,
          weight: 0.5,
          value: 0.6772182583808899,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'The dying words of a thief spark a madcap cross-country rush to find some treasure.',
    title: "It's a Mad, Mad, Mad, Mad World",
    scoreDetails: {
      value: 0.010528600890046674,
      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: 51,
          weight: 0.5,
          value: 0.9072262048721313,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 23,
          weight: 0.5,
          value: 0.640996515750885,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'An urban husband and wife travel to the jungle and learn just how precious their relationship is.',
    title: 'Nymph',
    scoreDetails: {
      value: 0.010015634160641,
      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: 26,
          weight: 0.5,
          value: 0.909113883972168,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 59,
          weight: 0.5,
          value: 0.6264784336090088,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A Civil War veteran embarks on a journey to rescue his niece from an Indian tribe.',
    title: 'The Searchers',
    scoreDetails: {
      value: 0.006493506493506494,
      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: 17,
          weight: 0.5,
          value: 0.9103956818580627,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A man shuffles through a dream meeting various people and discussing the meanings and purposes of the universe.',
    title: 'Waking Life',
    scoreDetails: {
      value: 0.004464285714285714,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 52,
          weight: 0.5,
          value: 0.6300867795944214,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A young man crosses over North and South Korea to deliver the pain and longings of separated families.',
    title: 'Poongsan',
    scoreDetails: {
      value: 0.01482213438735178,
      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: 0.9168118238449097,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 9,
          weight: 0.5,
          value: 0.6547074913978577,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'Settlers traveling through the Oregon desert in 1845 find themselves stranded in harsh conditions.',
    title: "Meek's Cutoff",
    scoreDetails: {
      value: 0.011997226074895978,
      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: 10,
          weight: 0.5,
          value: 0.9155632257461548,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 43,
          weight: 0.5,
          value: 0.6323773860931396,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'Breck Coleman leads hundreds of settlers in covered wagons from the Mississippi River to their destiny out West.',
    title: 'The Big Trail',
    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: 4,
          weight: 0.5,
          value: 0.9187002182006836,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 11,
          weight: 0.5,
          value: 0.6496086120605469,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A young woman must journey through the seasons to rescue her boyfriend who has been kidnapped by the evil Snow Queen.',
    title: 'Snow Queen',
    scoreDetails: {
      value: 0.004672897196261682,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 47,
          weight: 0.5,
          value: 0.6311054229736328,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A young woman must journey through the seasons to rescue her boyfriend who has been kidnapped by the evil Snow Queen.',
    title: 'Snow Queen',
    scoreDetails: {
      value: 0.004672897196261682,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 47,
          weight: 0.5,
          value: 0.6311054229736328,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: "The life of an immigrant family from Sweden in the Minnesota's forests, during mid 19th century",
    title: 'The New Land',
    scoreDetails: {
      value: 0.008857968369829683,
      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: 36,
          weight: 0.5,
          value: 0.9085773825645447,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 77,
          weight: 0.5,
          value: 0.6231112480163574,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: "The life of an immigrant family from Sweden in the Minnesota's forests, during mid 19th century",
    title: 'The New Land',
    scoreDetails: {
      value: 0.008967731829573933,
      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: 36,
          weight: 0.5,
          value: 0.9085773825645447,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 73,
          weight: 0.5,
          value: 0.6232647895812988,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: "The life of an immigrant family from Sweden in the Minnesota's forests, during mid 19th century",
    title: 'The New Land',
    scoreDetails: {
      value: 0.008967731829573933,
      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: 36,
          weight: 0.5,
          value: 0.9085773825645447,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 73,
          weight: 0.5,
          value: 0.6232647895812988,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: "The life of an immigrant family from Sweden in the Minnesota's forests, during mid 19th century",
    title: 'The New Land',
    scoreDetails: {
      value: 0.008967731829573933,
      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: 36,
          weight: 0.5,
          value: 0.9085773825645447,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 73,
          weight: 0.5,
          value: 0.6232647895812988,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: "The life of an immigrant family from Sweden in the Minnesota's forests, during mid 19th century",
    title: 'The New Land',
    scoreDetails: {
      value: 0.009078547432410815,
      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: 34,
          weight: 0.5,
          value: 0.9086182713508606,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 73,
          weight: 0.5,
          value: 0.6232647895812988,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: "An account of Baron Munchausen's supposed travels and fantastical experiences with his band of misfits.",
    title: 'The Adventures of Baron Munchausen',
    scoreDetails: {
      value: 0.006493506493506494,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 17,
          weight: 0.5,
          value: 0.6459617614746094,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A young boy accidentally joins a band of dwarves as they jump from era to era looking for treasure to steal.',
    title: 'Time Bandits',
    scoreDetails: {
      value: 0.006944444444444444,
      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: 'NA' },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 12,
          weight: 0.5,
          value: 0.6489611864089966,
          details: []
        }
      ]
    },
    rerankScore: 0.5986876487731934
  },
  {
    plot: 'A troubled young man is drawn to a mythical place called Midian where a variety of monsters are hiding from humanity.',
    title: 'Nightbreed',
    scoreDetails: {
      value: 0.005,
      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: 40,
          weight: 0.5,
          value: 0.9085456132888794,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 'NA' }
      ]
    },
    rerankScore: 0.5986876487731934
  }
]

このサンプルクエリは、次の入力パイプラインステージとともに $rankFusion を使用します。

$vectorSearch

JOURNEY_ACROSS_LANDS_OPENAI 変数を使用して、クエリの queryVector フィールドでベクトル埋め込みとして指定されたstring journey across lands を plot_embedding フィールドで検索します。
最大 2000 の最近傍を検索します。
結果を100ドキュメントに制限します。
最終スコアに対するパイプラインのランクの寄与度に影響を与える重みを0.5に指定します。

$vectorSearch

plot_embedding_voyage_4_large フィールドで、queryVector フィールドに JOURNEY_ACROSS_LANDS_VOYAGEAI 変数を使用してベクトル埋め込みとして指定されたstring journey across lands に対してベクトル検索を実行します。
最大 2000 の最近傍を検索します。
結果を100ドキュメントに制限します。
最終スコアに対するパイプラインのランクの寄与度に影響を与える重みを0.5に指定します。

このサンプルクエリでは、次のパイプラインステージも指定しています。

`$project`	結果には `plot` フィールドと `title` フィールドのみが含まれます。結果に `scoreDetails` という名前のフィールドを追加します。
`$limit`	返される結果を 100 件のドキュメントに制限します。

MongoDB ベクトル検索は、両方のクエリの結果を単一の結果セットにマージします。その後、$rerank ステージを使用して結果の順序を再編成します。$rerank ステージは、クエリ期間 journey across lands に対する関連性によって結果を再配置します。結果では、$rerankステージにより、再配置後のスコアを示すrerankScoreという名前のフィールドが追加されます。

戻る

ベクトル検索と全文検索の統合

配置オプションを検討する

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-4-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_embedding_voyage_4_large": 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_4_large",
15	"numDimensions": 2048,
16	"similarity": "dotProduct"
17	},
18	{
19	"type": "vector",
20	"path": "title_embedding_voyage_4_large",
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_4_large",
11	"queryVector": COMEDY_INVOLVING_GHOSTS,
12	"numCandidates": 2000,
13	"limit": 50
14	}
15	}
16	],
17	vectorPipeline2: [
18	{
19	"$vectorSearch": {
20	"index": "multiple-vector-search",
21	"path": "plot_embedding_voyage_4_large",
22	"queryVector": HUMOR_INVOLVING_PARANORMAL,
23	"numCandidates": 2000,
24	"limit": 50
25	}
26	}
27	]
28	}
29	},
30	combination: {
31	weights: {
32	vectorPipeline1: 0.5,
33	vectorPipeline2: 0.5
34	}
35	},
36	"scoreDetails": true
37	}
38	},
39	{
40	"$project": {
41	_id: 1,
42	title: 1,
43	plot: 1,
44	scoreDetails: {"$meta": "scoreDetails"}
45	}
46	},
47	{
48	"$limit": 20
49	}
50	]);

複数の $vectorSearch クエリを組み合わせる方法

ユースケースと利点

チュートリアルについて

注意

埋め込みを生成します

前提条件

手順

環境を設定します。

ベクトル埋め込みを生成する関数を定義して使用します。

注意

MongoDB ベクトル検索インデックスの作成

mongosh を使用してクラスターに接続します。

インデックスを作成するコレクションを含むデータベースに切り替えます。

例

db.collection.createSearchIndex() メソッドを実行します。

MongoDB ベクトル検索クエリの実行

クエリで使用する埋め込みのためのファイルを作成します。

mongoshのクラスターに接続します。

sample_mflixデータベースを使用します。

クエリで使用する埋め込みをロードします。

コレクションに対してMongoDBembedded_movies ベクトル検索クエリを実行します。

単一のドキュメントベクトルに対する複数のクエリベクトル

同じ用語のクエリベクトルを複数のドキュメントベクトルに対して実行する

複数のモデルを使用した同一用語によるクエリ

結果の順序を変更する

単一のドキュメントベクトルに対する複数のクエリベクトル

同じ用語のクエリベクトルを複数のドキュメントベクトルに対して実行する

複数のモデルを使用した同一用語によるクエリ

複数の `$vectorSearch` クエリを組み合わせる方法

`mongosh` を使用してクラスターに接続します。

`db.collection.createSearchIndex()` メソッドを実行します。

`mongosh`のクラスターに接続します。

`sample_mflix`データベースを使用します。

コレクションに対してMongoDB`embedded_movies` ベクトル検索クエリを実行します。