複数の`$vectorSearch`クエリを組み合わせる方法

$rankFusion ステージと $scoreFusion ステージはプレビュー機能として利用できます。詳しくは、プレビュー機能を参照してください。

v8.1 以降、 MongoDB $rankFusion集計ステージは入力パイプライン内で $vectorSearch をサポートします。$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、次に入力パイプラインの結果を重複を排除して最終順位の結果セットに結合します。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_3_largeこれには、Voyage AI の voyage-3-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_3_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_3_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_3_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 v8.1 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-3-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-3-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>`	お客様の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-3-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_voyageai_embedding 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-3-large"
9 outputDimension = 2048
10 vo = voyageai.Client()
11 # Define a function to generate embeddings
12 def get_embedding(data, input_type = "document"):
13   embeddings = vo.embed(
14       data, model = model, output_dimension = outputDimension, input_type = input_type
15   ).embeddings
16   return embeddings[0]
17 
18 # Connect to your MongoDB Atlas cluster
19 mongo_client = pymongo.MongoClient("<connection-string>")
20 db = mongo_client["sample_mflix"]
21 collection = db["embedded_movies"]
22 
23 # Filter to exclude null or empty plot fields
24 filter = {"title": {"$nin": [None, ""]}}
25 
26 # Get a subset of documents in the collection
27 documents = collection.find(filter).limit(3500)
28 
29 # Update each document with a new embedding field
30 updated_doc_count = 0
31 for doc in documents:
32     embedding = get_embedding(doc["title"])
33     if embedding is not None:
34         collection.update_one({"_id": doc["_id"]}, {"$set": {"title_voyageai_embedding": embedding}})
35         updated_doc_count += 1
36 
37 print(f"Updated {updated_doc_count} documents.")

Updated 3500 documents.

注意

操作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、「 mongosh 経由での接続」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_3_large",
15         "numDimensions": 2048,
16         "similarity": "dotProduct"
17       },
18       {
19         "type": "vector",
20         "path": "title_voyageai_embedding",
21         "numDimensions": 2048,
22         "similarity": "dotProduct"
23       }
24     ]
25   }
26 );

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：vector タイプ。このフィールドには、映画のあらすじの要約を表現するベクトル埋め込みが含まれています。インデックス定義：
- 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_3_large フィールド：vector タイプ。このフィールドには、映画のあらすじの要約を表現するベクトル埋め込みが含まれています。インデックス定義：
- 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_voyageai_embedding フィールド：vector タイプ。このフィールドには、映画のタイトル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-3-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-3-large`	2048
battle between good and evil（善と悪の戦い）	BATTLE_GOOD_EVIL	Voyage AI's `voyage-3-large`	2048
土地を横断する旅	JOURNEY_ACROSS_LANDS_VOYAGEAI	Voyage AI's `voyage-3-large`	2048
土地を横断する旅	JOURNEY_ACROSS_LANDS_OPENAI	OpenAIの `text-embedding-ada-002`	1536

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ファイルを保存して閉じます。

`mongosh` 内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、 mongosh経由でクラスターに接続する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

`embedded_movies`コレクションに対してMongoDB 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_3_large",
11                 "queryVector": COMEDY_INVOLVING_GHOSTS,
12                 "numCandidates": 2000,
13                 "limit": 50
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "plot_embedding_voyage_3_large",
22                 "queryVector": HUMOR_INVOLVING_PARANORMAL,
23                 "numCandidates": 2000,
24                 "limit": 50
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 20
49   }
50 ]);

[
  {
    _id: ObjectId('573a139af29313caabcef0a4'),
    plot: 'A paranormal expert and his daughter bunk in an abandoned house populated by 3 mischievous ghosts and one friendly one.',
    title: 'Casper',
    scoreDetails: {
      value: 0.01639344262295082,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 1,
          weight: 0.5,
          value: 0.8094863891601562,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 1,
          weight: 0.5,
          value: 0.7794423699378967,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabceaf10'),
    plot: "When Peter Plunkett's Irish castle turned hotel is about to be repossesed, he decides to spice up the attraction a bit for the 'Yanks' by having his staff pretend to haunt the castle. The ...",
    title: 'High Spirits',
    scoreDetails: {
      value: 0.01575682382133995,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 2,
          weight: 0.5,
          value: 0.7949001789093018,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 5,
          weight: 0.5,
          value: 0.7545012831687927,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabce912c'),
    plot: 'Three unemployed parapsychology professors set up shop as a unique ghost removal service.',
    title: 'Ghostbusters',
    scoreDetails: {
      value: 0.015038444142921756,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 6,
          weight: 0.5,
          value: 0.7781286239624023,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 7,
          weight: 0.5,
          value: 0.7519128918647766,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13a0f29313caabd056df'),
    plot: "A girl calls on her brother's imaginary friend to banish a mischievous boogeyman who has framed her for his pranks.",
    title: "Don't Look Under the Bed",
    scoreDetails: {
      value: 0.014913831197525408,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 13,
          weight: 0.5,
          value: 0.7534659504890442,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 2,
          weight: 0.5,
          value: 0.7626002430915833,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1399f29313caabced85b'),
    plot: 'A comical Gothic horror-movie-type family tries to rescue their beloved uncle from his gold-digging new love.',
    title: 'Addams Family Values',
    scoreDetails: {
      value: 0.014854753521126762,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 11,
          weight: 0.5,
          value: 0.7640036344528198,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 4,
          weight: 0.5,
          value: 0.7570045590400696,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13bdf29313caabd58456'),
    plot: 'Bertram Pincus is a man whose people skills leave much to be desired. When Pincus dies unexpectedly, but is miraculously revived after seven minutes, he wakes up to discover that he now has the annoying ability to see ghosts.',
    title: 'Ghost Town',
    scoreDetails: {
      value: 0.014709063378758382,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 7,
          weight: 0.5,
          value: 0.7774010300636292,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 9,
          weight: 0.5,
          value: 0.7500401735305786,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabceace9'),
    plot: 'A couple of recently deceased ghosts contract the services of a "bio-exorcist" in order to remove the obnoxious new owners of their house.',
    title: 'Beetlejuice',
    scoreDetails: {
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    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',
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    title: 'Loose in London',
    scoreDetails: {
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    plot: 'Two cops are brought back to life to chase down supernatural criminals.',
    title: 'Dead Heat',
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    title: 'The Peanut Butter Solution',
    scoreDetails: {
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    title: 'Corpse Bride',
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  {
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    plot: 'Adaption of the famous Oscar Wilde tale about a young American girl that helps a British ghost find rest and forgiveness.',
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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

plot_embedding_voyage_3_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_3_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_3_large",
11                 "queryVector": BATTLE_GOOD_EVIL,
12                 "numCandidates": 2000,
13                 "limit": 200
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "title_voyageai_embedding",
22                 "queryVector": BATTLE_GOOD_EVIL,
23                 "numCandidates": 2000,
24                 "limit": 200
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 20
49   }
50 ]);

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    title: 'The Great Yokai War',
    scoreDetails: {
      value: 0.01278772378516624,
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      details: [
        {
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    _id: ObjectId('573a13bbf29313caabd54a66'),
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    title: 'Demon Empire',
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    plot: 'A raging god of battle and a master samurai duke it out in a series of sword fights in a remote temple.',
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  },
  {
    _id: ObjectId('573a13b6f29313caabd467f5'),
    plot: "Story centers on a battle during China's Warring States Period, a series of civil wars, which spanned from the 5th to the 3rd century B.C. Based on a popular Japanese manga, which was in turn based a Japanese novel inspired by Warring States history in China.",
    title: 'Battle of the Warriors',
    scoreDetails: {
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    _id: ObjectId('573a1398f29313caabceb2ff'),
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  },
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    _id: ObjectId('573a13c2f29313caabd67b22'),
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    title: 'Knights of Bloodsteel',
    scoreDetails: {
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  },
  {
    _id: ObjectId('573a139af29313caabcf00f7'),
    plot: 'A shape-shifting mountain man and a group of children team up to protect an enchanted forest from evil lumberjacks.',
    title: 'Forest Warrior',
    scoreDetails: {
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      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('573a13c0f29313caabd62ff5'),
    plot: 'Wind and Cloud find themselves up against a ruthless Japanese warlord intent on invading China.',
    title: 'Storm Warriors',
    scoreDetails: {
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      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('573a13aef29313caabd2c923'),
    plot: 'A Chinese emissary is sent to the Gobi desert to execute a renegade soldier. When a caravan transporting a Buddhist monk and a valuable treasure is threatened by thieves, however, the two warriors might unite to protect the travelers.',
    title: 'Warriors of Heaven and Earth',
    scoreDetails: {
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    _id: ObjectId('573a13d0f29313caabd8d3f1'),
    plot: 'A short tale about the demons that slumber deep in the human soul and have the power to push people into the abyss of blind hate, fury and rage.',
    title: 'Paths of Hate',
    scoreDetails: {
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  {
    _id: ObjectId('573a1399f29313caabcee104'),
    plot: 'The Son of Satan attempts to bring his father to Earth with the aid of some mystic stones.',
    title: 'Warlock: The Armageddon',
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    title: 'Monster Brawl',
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    _id: ObjectId('573a1398f29313caabce9932'),
    plot: 'A young man must stop the Lord of Darkness from both destroying daylight and marrying the woman he loves.',
    title: 'Legend',
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      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
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  {
    _id: ObjectId('573a13a5f29313caabd15bbd'),
    plot: "A desert warrior rises up against the evil army that is destroying his homeland. He captures the enemy's key sorcerer, takes her deep into the desert and prepares for a final showdown.",
    title: 'The Scorpion King',
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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_3_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_voyageai_embedding 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               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "plot_embedding_voyage_3_large",
22                 "queryVector": JOURNEY_ACROSS_LANDS_VOYAGEAI,
23                 "numCandidates": 2000,
24                 "limit": 100
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 20
49   }
50 ]);

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    title: "Gulliver's Travels",
    scoreDetails: {
      value: 0.015549662487945998,
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        {
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    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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    title: 'She',
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  {
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        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 50,
          weight: 0.5,
          value: 0.7051275968551636,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13b6f29313caabd46ae0'),
    plot: "In a countryside town bordering on a magical land, a young man makes a promise to his beloved that he'll retrieve a fallen star by venturing into the magical realm.",
    title: 'Stardust',
    scoreDetails: {
      value: 0.011742820602255552,
      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: 0.9228402376174927,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 81,
          weight: 0.5,
          value: 0.6994539499282837,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1397f29313caabce8828'),
    plot: 'A warrior seeks his true origins in a seemingly prehistoric wasteland.',
    title: 'Yor, the Hunter from the Future',
    scoreDetails: {
      value: 0.01117448492608544,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 11,
          weight: 0.5,
          value: 0.9153545498847961,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 61,
          weight: 0.5,
          value: 0.7023824453353882,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13aaf29313caabd218b4'),
    plot: 'An adventurous girl finds another world that is a strangely idealized version of her frustrating home, but it has sinister secrets.',
    title: 'Coraline',
    scoreDetails: {
      value: 0.010934744268077601,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 21,
          weight: 0.5,
          value: 0.9102445840835571,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 45,
          weight: 0.5,
          value: 0.7056041955947876,
          details: []
        }
      ]
    }
  }
]

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_3_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。

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

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

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

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 内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。

embedded_moviesコレクションに対してMongoDB 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

`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` 内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。

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