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Pesquisa híbrida

Página inicial do Docs

Desenvolvimento

Vector Search

Pesquisa híbrida

Página inicial do Docs

Desenvolvimento

Vector Search

Pesquisa híbrida

Como combinar múltiplas `$vectorSearch` queries

A partir da v8.1, o estágio de agregação do MongoDB $rankFusion suporta $vectorSearch dentro do pipeline de entrada. Você pode usar $rankFusion para combinar várias $vectorSearch queries da mesma collection no mesmo pipeline. $rankFusion primeiro executa todos os pipelines de entrada de forma independente e, em seguida, deduplica e combina os resultados do pipeline de entrada em um conjunto final de resultados classificados. Para executar várias $vectorSearch queries em várias collections em um único pipeline, use $unionWith.

Casos de uso e benefícios

Você pode executar os seguintes tipos de query $vectorSearch usando o pipeline $rankFusion:

Execute várias queries $vectorSearch para termos semelhantes no mesmo campo.

Isso permite realizar uma pesquisa abrangente no seu conjunto de dados para termos semelhantes na mesma query.
Execute várias queries $vectorSearch para o mesmo termo em vários campos.

Isso permite pesquisar vários campos do seu conjunto de dados para determinar quais retornam os melhores resultados para a query.
Execute várias queries $vectorSearch para o mesmo termo em incorporações de diferentes modelos de incorporação.

Isso permite pesquisar incorporações de diferentes modelos de incorporação para determinar as diferenças de interpretação semântica entre os diferentes modelos.

Sobre o tutorial

Este tutorial demonstra como executar diferentes queries $vectorSearch na coleção embedded_movies no banco de dados sample_mflix. Esta coleção inclui os seguintes campos:

plot_embedding, que contém incorporações criadas com o modelo de incorporação text-embedding-ada-002 da OpenAI.
plot_embedding_voyage_3_large, que contém incorporações criadas com o modelo de incorporação voyage-3-large da Voyage AI. Você utiliza este campo para todas as queries de exemplo neste tutorial.

Para a segunda query listada em Casos de uso e benefícios, você deve gerar incorporações para outro campo. Para experimentar a query de amostra para este caso de uso, conclua as etapas em Gerar as Incorporações.

Este tutorial orienta você pelas seguintes etapas:

Configure um índice do MongoDB Vector Search na coleção sample_mflix.embedded_movies.
Execute a query $vectorSearch com $rankFusion para realizar as seguintes pesquisas:
- Pesquise no campo plot_embedding_voyage_3_large em busca de filmes com enredos semelhantes a light-hearted comedy with ghosts e slapstick humor with paranormal events.
- Pesquise nos campos plot_embedding_voyage_3_large e title_embedding por filmes com enredos semelhantes a battle between good and evil usando incorporações da Voyage AI.
- Pesquise nos campos plot_embedding e plot_embedding_voyage_3_large filmes com enredos semelhantes a journey across lands usando incorporações da OpenAI e da Voyage AI, respectivamente.

Antes de começar, certifique-se de que seu cluster atenda aos requisitos descritos nos pré-requisitos.

Observação

Para fazer uma query $vectorSearch com $rankFusion, seu cluster deve executar o MongoDB v8.1 ou superior.

Gerar as incorporações

Para consultar o mesmo termo em vários campos, é necessário preparar o conjunto de dados para incluir incorporações para diferentes campos. Esta seção demonstra como gerar incorporações para o campo title na coleção usando o modelo de incorporação voyage-3-large da Voyage AI.

Pule esta seção se quiser apenas experimentar os outros casos de uso de exemplo.

Pré-requisitos

Um ambiente para executar blocos de anotações interativos do Python, como VS Code ou CoLab.
Chave de API para modelos de incorporação da Voyage AI.

Procedimento

Nesta seção, você gera incorporações para o campo title na coleção embedded_movies usando o modelo de incorporação voyage-3-large da Voyage AI.

Configure o ambiente.

Crie um notebook Python interativo salvando um arquivo com a extensão .ipynb e, em seguida, execute o seguinte comando no notebook para instalar as dependências:

pip install --quiet voyageai pymongo

Defina e use uma função para gerar incorporações vetoriais.

Cole e execute o seguinte código no seu notebook depois de substituir os seguintes valores de espaço reservado:

`<api-key>`	Sua chave de API da Voyage AI (linha 6).
`<connection-string>`	Sua string de conexão do cluster do MongoDB (linha 18).

O código a seguir define uma função que gera embeddings vetoriais usando um modelo de embedding proprietário da Voyage AI. Especificamente, esse código faz o seguinte:

Especifica o modelo de incorporação voyage-3-large.
Cria uma função chamada get_embedding() que chama a API do modelo para gerar uma incorporação de dimensão 2048 para uma determinada entrada de texto.
Conecta-se ao cluster e obtém documentos do 3500 do namespace do sample_mflix.embedded_movies.
Gera incorporações do campo title de cada documento usando a função get_embedding().
Atualiza cada documento com um novo campo chamado title_voyageai_embedding que contém o valor de incorporação usando o driver MongoDB PyMongo.

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.

Observação

A operação pode levar até 15 minutos para ser concluída.

Crie o índice de Vector Search do MongoDB

Nesta seção, você cria um índice do MongoDB Vector Search no namespace sample_mflix.embedded_movies . A seção demonstra uma definição de índice que pode ser utilizada para executar várias consultas. Antes de criar o índice, conclua o procedimento para gerar as incorporações.

Conecte ao cluster utilizando `mongosh`.

Abra o mongosh em uma janela do terminal e conecte ao seu Atlas cluster. Para obter instruções detalhadas sobre a conexão, consulte Conectar via mongosh.

Alterne para o banco de dados que contém a collection para a qual você deseja criar o índice.

Exemplo

use sample_mflix

switched to db sample_mflix

Execute o método `db.collection.createSearchIndex()`.

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 );

Esta definição de índice indexa os seguintes campos:

plot_embedding campo como o vector tipo. Este campo contém vector embeddings que representam o resumo da trama de um filme. A definição do índice:
- Especifica 1536 dimensões do vetor.
- Mede a similaridade usando a similaridade dotProduct.
Esse mapeamento de campo é necessário para o terceiro caso de uso.
plot_embedding_voyage_3_large campo como o vector tipo. Este campo contém vector embeddings que representam o resumo da trama de um filme. A definição do índice:
- Especifica 2048 dimensões do vetor.
- Mede a similaridade usando a similaridade dotProduct.
Este mapeamento de campo é necessário para todas as queries neste tutorial.
title_voyageai_embedding campo como o vector tipo. Este campo contém vector embeddings que representam o título do filme. A definição do índice:
- Especifica 2048 dimensões do vetor.
- Mede a similaridade usando a similaridade dotProduct.
Esse mapeamento de campo é necessário para o segundo caso de uso.

Execute as queries do MongoDB Vector Search

Nesta seção, você consulta a coleção embedded_movies no banco de dados sample_mflix utilizando o índice chamado multiple-vector-search que você criou na coleção.

Crie um arquivo para as incorporações a serem usadas na query.

Crie um arquivo denominado embeddings.js.
touch embeddings.js

Copie e cole as seguintes incorporações no arquivo embeddings.js.

Este arquivo contém incorporações para os termos de query utilizados nas queries. A tabela a seguir mostra o termo da query, a variável que contém as incorporações para o termo da query, o modelo de incorporação usado para gerar as incorporações e o número de dimensões.

Frase de query	Variável	Modelo de incorporação	Dimensões
comédia leve com fantasmas	COMEDY_INVOLVING_GHOSTS	Voyage AI's `voyage-3-large`	2048
humor pastelão com eventos paranormais	HUMOR_INVOLVING_PARANORMAL	Voyage AI's `voyage-3-large`	2048
conflito entre o bem e o mal	BATTLE_GOOD_EVIL	Voyage AI's `voyage-3-large`	2048
jornada por territórios	JOURNEY_ACROSS_LANDS_VOYAGEAI	Voyage AI's `voyage-3-large`	2048
jornada por territórios	JOURNEY_ACROSS_LANDS_OPENAI	OpenAI: `text-embedding-ada-002`	1536

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Salve e feche o arquivo.

Conecte-se ao seu cluster no `mongosh`.

Abra o mongosh em uma janela do terminal e conecte ao seu cluster. Para obter instruções detalhadas sobre a conexão, consulte Conectar via mongosh.

Use o banco de dados `sample_mflix`.

Execute o seguinte comando no prompt mongosh:

use sample_mflix

Carregue as incorporações para usar na query.

Execute o seguinte comando para carregar as incorporações no arquivo embeddings.js após substituir <path-to-file> pelo caminho absoluto para o seu arquivo embeddings.js.
load('/<path-to-file>/embeddings.js';
true
Verifique se as incorporações foram carregadas com sucesso.
Você pode verificar executando um comando semelhante ao seguinte:
COMEDY_INVOLVING_GHOSTS.length
2048

Execute as consultas do MongoDB Vector Search na coleção `embedded_movies`.

Vários vetores de query contra um único vetor de documento

Realize uma pesquisa abrangente no conjunto de dados por termos semanticamente semelhantes para determinar qual termo de query oferece os melhores resultados.

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

[
  {
    _id: ObjectId('573a139af29313caabcef0a4'),
    plot: 'A paranormal expert and his daughter bunk in an abandoned house populated by 3 mischievous ghosts and one friendly one.',
    title: 'Casper',
    scoreDetails: {
      value: 0.01639344262295082,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 1,
          weight: 0.5,
          value: 0.8094863891601562,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 1,
          weight: 0.5,
          value: 0.7794423699378967,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabceaf10'),
    plot: "When Peter Plunkett's Irish castle turned hotel is about to be repossesed, he decides to spice up the attraction a bit for the 'Yanks' by having his staff pretend to haunt the castle. The ...",
    title: 'High Spirits',
    scoreDetails: {
      value: 0.01575682382133995,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 2,
          weight: 0.5,
          value: 0.7949001789093018,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 5,
          weight: 0.5,
          value: 0.7545012831687927,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabce912c'),
    plot: 'Three unemployed parapsychology professors set up shop as a unique ghost removal service.',
    title: 'Ghostbusters',
    scoreDetails: {
      value: 0.015038444142921756,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 6,
          weight: 0.5,
          value: 0.7781286239624023,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 7,
          weight: 0.5,
          value: 0.7519128918647766,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13a0f29313caabd056df'),
    plot: "A girl calls on her brother's imaginary friend to banish a mischievous boogeyman who has framed her for his pranks.",
    title: "Don't Look Under the Bed",
    scoreDetails: {
      value: 0.014913831197525408,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 13,
          weight: 0.5,
          value: 0.7534659504890442,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 2,
          weight: 0.5,
          value: 0.7626002430915833,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1399f29313caabced85b'),
    plot: 'A comical Gothic horror-movie-type family tries to rescue their beloved uncle from his gold-digging new love.',
    title: 'Addams Family Values',
    scoreDetails: {
      value: 0.014854753521126762,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 11,
          weight: 0.5,
          value: 0.7640036344528198,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 4,
          weight: 0.5,
          value: 0.7570045590400696,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13bdf29313caabd58456'),
    plot: 'Bertram Pincus is a man whose people skills leave much to be desired. When Pincus dies unexpectedly, but is miraculously revived after seven minutes, he wakes up to discover that he now has the annoying ability to see ghosts.',
    title: 'Ghost Town',
    scoreDetails: {
      value: 0.014709063378758382,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 7,
          weight: 0.5,
          value: 0.7774010300636292,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 9,
          weight: 0.5,
          value: 0.7500401735305786,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabceace9'),
    plot: 'A couple of recently deceased ghosts contract the services of a "bio-exorcist" in order to remove the obnoxious new owners of their house.',
    title: 'Beetlejuice',
    scoreDetails: {
      value: 0.014186507936507936,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 3,
          weight: 0.5,
          value: 0.785067617893219,
          details: []
        },
        {
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    title: 'Wicked Stepmother',
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    plot: 'The Bowery Boys find themselves in London, in an old mansion complete with a dungeon, an ominous bell tower and the ghost of an old hangman.',
    title: 'Loose in London',
    scoreDetails: {
      value: 0.013235294117647059,
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  },
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    plot: 'Two cops are brought back to life to chase down supernatural criminals.',
    title: 'Dead Heat',
    scoreDetails: {
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    plot: 'Con artists plan to fleece the eccentric family using an accomplice who claims to be their long lost Uncle Fester.',
    title: 'The Addams Family',
    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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    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: {
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    title: 'Bubba Ho-Tep',
    scoreDetails: {
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    title: 'Corpse Bride',
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    title: 'Puppetmaster',
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    plot: 'A teacher with paranormal abilities helps a group of ghosts graduate high school.',
    title: 'Ghost Graduation',
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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.',
    title: 'The Canterville Ghost',
    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:',
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  {
    _id: ObjectId('573a1399f29313caabcec258'),
    plot: 'The puppets return, this time they hunt some Paranormal Researchers to take their brain fluid for the dead/living puppet master, Andre Toulon.',
    title: 'Puppet Master II',
    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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        {
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    _id: ObjectId('573a13edf29313caabdd4424'),
    plot: 'A ghost returns back from his world to prove something. But on earth, he has something more to do for his country.',
    title: 'Bhoothnath Returns',
    scoreDetails: {
      value: 0.007246376811594203,
      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('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: {
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]

Esse exemplo de consulta usa o $rankFusion com as seguintes etapas do pipeline de entrada:

$vectorSearch

Pesquisa o campo plot_embedding_voyage_3_large pela frase comédia leve com fantasmas, especificada no campo queryVector da query como incorporações de vetores usando a variável COMEDY_INVOLVING_GHOSTS.
Especifica uma pesquisa por até 2000 vizinhos mais próximos.
Limita os resultados do estágio a 50 documentos.
Especifica um peso de 0.5 para influenciar a contribuição de classificação desse pipeline para a pontuação final.

$vectorSearch

Realiza uma pesquisa vetorial sequencial no campo plot_embedding_voyage_3_large para a expressão humor pastelão com eventos paranormais, especificada no campo queryVector como incorporações vetoriais usando a variável HUMOR_INVOLVING_PARANORMAL.
Especifica uma pesquisa por até 2000 vizinhos mais próximos.
Limita os resultados do estágio a 50 documentos.
Especifica um peso de 0.5 para influenciar a contribuição de classificação desse pipeline para a pontuação final.

O exemplo de query também especifica os seguintes estágios de pipeline.

`$project`	Inclui somente os campos `plot` e `title` nos resultados. Adiciona um campo chamado `scoreDetails` nos resultados.
`$limit`	Limita os resultados retornados a 20 documentos.

A Vector Search do MongoDB mescla os resultados de ambas as queries em um único conjunto de resultados. Nos resultados:

O scoreDetails.value exibe a pontuação bruta desse pipeline antes de ser ponderado e combinado usando a fusão de classificação recíproca.
O score.details.rank indica a posição do documento nos resultados do pipeline.
O scoreDetails.details.value contém a pontuação de classificação recíproca ponderada.

Você pode fazer o seguinte:

Ajuste os pesos atribuídos a cada pipeline na query para aperfeiçoar ainda mais os resultados.
Aumente o número de documentos nos resultados se você observar resultados desconexos.

Vetor de query do mesmo termo em relação a vários vetores de documentos

Pesquise vários campos no conjunto de dados para determinar quais campos retornam os melhores resultados para a mesma query.

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,
      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,
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    title: 'Demon Empire',
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    title: 'Aragami',
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    _id: ObjectId('573a13b6f29313caabd467f5'),
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    title: 'Battle of the Warriors',
    scoreDetails: {
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    _id: ObjectId('573a1398f29313caabceb2ff'),
    plot: 'Dragon, a bloody dictator, is challenged by Lancelot, who comes to save the girl, and to liberate the people.',
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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:',
      details: [
        {
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    plot: 'A Chinese emissary is sent to the Gobi desert to execute a renegade soldier. When a caravan transporting a Buddhist monk and a valuable treasure is threatened by thieves, however, the two warriors might unite to protect the travelers.',
    title: 'Warriors of Heaven and Earth',
    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: {
      value: 0.009109311740890687,
      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('573a1399f29313caabcee104'),
    plot: 'The Son of Satan attempts to bring his father to Earth with the aid of some mystic stones.',
    title: 'Warlock: The Armageddon',
    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('573a13cef29313caabd880e3'),
    plot: 'Eight classic monsters fight to the death in an explosive wrestling tournament set inside an abandoned and cursed graveyard.',
    title: 'Monster Brawl',
    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('573a1398f29313caabce9932'),
    plot: 'A young man must stop the Lord of Darkness from both destroying daylight and marrying the woman he loves.',
    title: 'Legend',
    scoreDetails: {
      value: 0.008511658574596896,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
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        {
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  },
  {
    _id: ObjectId('573a13a5f29313caabd15bbd'),
    plot: "A desert warrior rises up against the evil army that is destroying his homeland. He captures the enemy's key sorcerer, takes her deep into the desert and prepares for a final showdown.",
    title: 'The Scorpion King',
    scoreDetails: {
      value: 0.00819672131147541,
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]

Esse exemplo de consulta usa o $rankFusion com as seguintes etapas do pipeline de entrada:

$vectorSearch

Pesquisa o campo plot_embedding_voyage_3_large para a frase batalha entre o bem e o mal, especificada no campo queryVector da query como incorporações vetoriais usando a variável BATTLE_GOOD_EVIL.
Especifica uma pesquisa por até 2000 vizinhos mais próximos.
Limita os resultados a apenas 200 documentos.
Especifica um peso de 0.5 para influenciar a contribuição de classificação desse pipeline para a pontuação final.

$vectorSearch

Executa uma pesquisa vetorial sequencial no campo title_voyageai_embedding para a string batalha entre o bem e o mal, especificada no campo queryVector como incorporações vetoriais usando a variável BATTLE_GOOD_EVIL.
Especifica uma pesquisa por até 2000 vizinhos mais próximos.
Limita os resultados a apenas 200 documentos.
Especifica um peso de 0.5 para influenciar a contribuição de classificação desse pipeline para a pontuação final.

O exemplo de query também especifica os seguintes estágios de pipeline.

`$project`	Inclui somente os campos `plot` e `title` nos resultados. Adiciona um campo chamado `scoreDetails` nos resultados.
`$limit`	Limita os resultados retornados a 20 documentos.

A Vector Search do MongoDB mescla os resultados de ambas as queries em um único conjunto de resultados. Nos resultados:

O scoreDetails.value exibe a pontuação bruta desse pipeline antes de ser ponderado e combinado usando a fusão de classificação recíproca.
O score.details.rank indica a posição do documento nos resultados do pipeline.
O scoreDetails.details.value contém a pontuação de classificação recíproca ponderada, que indica quais campos retornam o resultado mais relevante para o termo da query.

Por exemplo, o primeiro e o quarto documento nos resultados sugerem uma correspondência significativa para o termo no campo plot, enquanto o segundo e o quinto documento sugerem uma correspondência significativa no campo title. Você pode fazer o seguinte:

Ajuste os pesos atribuídos a cada pipeline na query para aperfeiçoar ainda mais os resultados.
Aumente o número de documentos nos resultados se você observar resultados desconexos.

Query de mesmo termo usando vários modelos

Pesquise incorporações de diferentes modelos de incorporação para determinar as diferenças de interpretação semântica entre os diferentes modelos.

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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    plot: 'A Englishman returns after a long time abroad and tells his strange stories about the lands he visited which are allegories about the real world.',
    title: "Gulliver's Travels",
    scoreDetails: {
      value: 0.015549662487945998,
      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: 0.9156127572059631,
          details: []
        },
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    title: 'Crumbs',
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    title: 'Foreign Land',
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    title: 'The Young and Prodigious T.S. Spivet',
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    }
  },
  {
    _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: []
        }
      ]
    }
  }
]

Esse exemplo de consulta usa o $rankFusion com as seguintes etapas do pipeline de entrada:

$vectorSearch

Pesquisa no campo plot_embedding a string jornada por territórios especificada no campo queryVector da query como incorporações vetoriais usando a variável JOURNEY_ACROSS_LANDS_OPENAI.
Especifica uma pesquisa por até 2000 vizinhos mais próximos.
Limita os resultados a 100 documentos.
Especifica um peso de 0.5 para influenciar a contribuição de classificação desse pipeline para a pontuação final.

$vectorSearch

Executa uma pesquisa vetorial sequencial no campo plot_embedding_voyage_3_large para a string jornada por territórios, especificada no campo queryVector como incorporações vetoriais usando a variável JOURNEY_ACROSS_LANDS_VOYAGEAI.
Especifica uma pesquisa por até 2000 vizinhos mais próximos.
Limita os resultados a 100 documentos.
Especifica um peso de 0.5 para influenciar a contribuição de classificação desse pipeline para a pontuação final.

O exemplo de query também especifica os seguintes estágios de pipeline.

`$project`	Inclui somente os campos `plot` e `title` nos resultados. Adiciona um campo chamado `scoreDetails` nos resultados.
`$limit`	Limita os resultados retornados a 20 documentos.

A Vector Search do MongoDB mescla os resultados de ambas as queries em um único conjunto de resultados. Nos resultados:

O scoreDetails.value exibe a pontuação bruta desse pipeline antes de ser ponderado e combinado usando a fusão de classificação recíproca.
O score.details.rank indica a posição do documento nos resultados do pipeline.
O scoreDetails.details.value contém a pontuação de classificação recíproca ponderada, que mostra as forças e diferenças na interpretação semântica do termo de query pelos diferentes modelos de incorporações.

Por exemplo, o primeiro e o quinto documento nos resultados sugerem uma representação semântica mais semelhante do modelo usado no vectorPipeline2, enquanto o segundo e o quarto documento nos resultados sugerem uma interpretação semântica mais próxima do modelo usado no vectorPipeline1.

Voltar

Pesquisa vetorial combinada e pesquisa de texto completo

Revisar as opções de implantação

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	]);

Como combinar múltiplas $vectorSearch queries

Casos de uso e benefícios

Sobre o tutorial

Observação

Gerar as incorporações

Pré-requisitos

Procedimento

Configure o ambiente.

Defina e use uma função para gerar incorporações vetoriais.

Observação

Crie o índice de Vector Search do MongoDB

Conecte ao cluster utilizando mongosh.

Alterne para o banco de dados que contém a collection para a qual você deseja criar o índice.

Exemplo

Execute o método db.collection.createSearchIndex().

Execute as queries do MongoDB Vector Search

Crie um arquivo para as incorporações a serem usadas na query.

Conecte-se ao seu cluster no mongosh.

Use o banco de dados sample_mflix.

Carregue as incorporações para usar na query.

Execute as consultas do MongoDB Vector Search na coleção embedded_movies.

Vários vetores de query contra um único vetor de documento

Vetor de query do mesmo termo em relação a vários vetores de documentos

Query de mesmo termo usando vários modelos

Como combinar múltiplas `$vectorSearch` queries

Conecte ao cluster utilizando `mongosh`.

Execute o método `db.collection.createSearchIndex()`.

Conecte-se ao seu cluster no `mongosh`.

Use o banco de dados `sample_mflix`.

Execute as consultas do MongoDB Vector Search na coleção `embedded_movies`.