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Como fazer a pesquisa híbrida

Você pode combinar queries do MongoDB Vector Search e do MongoDB Search em uma pesquisa híbrida para obter resultados unificados.

Este tutorial demonstra como executar uma pesquisa híbrida na coleção sample_mflix.embedded_movies, que contém detalhes sobre filmes. Especificamente, este tutorial orienta você pelas seguintes etapas:

  1. Crie um índice do MongoDB Vector Search no campo plot_embedding_voyage_3_large . Este campo contém incorporações vetoriais que representam o resumo da trama de um filme.

  2. Crie um índice de pesquisa do MongoDB na coleção sample_mflix.embedded_movies.

  3. Execute uma query para combinar os resultados de uma query $vectorSearch e de uma $search query utilizando as seguintes técnicas:

    • Impulsionamento semântica

    • Fusão de classificação recíproca

Por que pesquisa híbrida?

Uma pesquisa híbrida é uma agregação de diferentes métodos de pesquisa, como uma pesquisa de texto completo e semântica, para os mesmos critérios de query. Embora o texto completo seja eficaz para encontrar correspondências exatas para termos de query, a pesquisa semântica oferece o benefício adicional de identificar documentos semanticamente semelhantes, mesmo que os documentos não contenham o termo de query exato. Isso garante que correspondências sinônimas e contextualmente semelhantes também sejam incluídas nos resultados combinados de ambos os métodos de pesquisa.

Por outro lado, se você tiver tokens para nomes próprios ou palavras-chave específicas em seu conjunto de dados que não espera que sejam considerados no treinamento de um modelo de incorporação no mesmo contexto em que são usados em seu conjunto de dados, sua pesquisa vetorial poderá se beneficiar da combinação com uma pesquisa de texto completo.

Você também pode definir pesos para cada método de pesquisa por query. Com base no fato de os resultados da pesquisa de texto completo ou semântica serem mais relevantes e apropriados para uma query, você pode aumentar o peso desse método de pesquisa por query.

Você pode aplicar as seguintes técnicas para realizar uma pesquisa híbrida:

Técnica

Impulsionamento semântica

Fusão de classificação recíproca

Propósito

Melhora a precisão dos resultados da pesquisa de texto completo, aumentando os documentos que correspondem à pesquisa vetorial. Útil ao recuperar conjuntos de resultados muito grandes.

Combina resultados de diferentes métodos de pesquisa em um único conjunto de resultados classificados calculando a classificação recíproca dos documentos nos resultados.

Método de classificação

  • Execute $vectorSearch para recuperar resultados semanticamente semelhantes.

  • Execute $search para recuperar resultados de texto completo enquanto impulsiona os documentos que foram devolvidos por $vectorSearch.

  • Recupera $vectorSearch e $search resultados.

  • Mescle os dois conjuntos de resultados usando a classificação de cada documento nos respectivos conjuntos de resultados.

Método de pontuação

Aumenta a pontuação de resultados de texto completo para documentos que correspondem aos resultados da pesquisa vetorial pela pontuação ponderada da pesquisa vetorial.

Calcula a pontuação usando a seguinte fórmula:

1.0/{document position in the results + constant value}

Em seguida, para cada método de pesquisa, aplique diferentes pesos (w) para dar mais importância a esse método de pesquisa. Para cada documento, a classificação recíproca ponderada é calculada multiplicando o peso pela classificação recíproca do documento.

weighted_reciprocal_rank = w x reciprocal_rank

Recursos de pesquisa

  • Suporta facet para agrupar dados em categorias.

  • Suporta a paginação de resultados utilizando searchAfter e searchBefore.

Suporta o processamento adicional de resultados em estágios de agregação subsequentes, como $group.

Saída

Conjunto de resultados único que contém documentos de $search

Conjunto de resultados único que contém documentos de ambos os métodos de pesquisa.

Pré-requisitos

Antes de começar, você deve ter o seguinte:

  • Um Atlas cluster com MongoDB versão v8.0 ou superior.

    Observação

    Certifique-se de que seu Atlas cluster tenha memória suficiente para armazenar índices do MongoDB Search e do MongoDB Vector Search e executar queries de alto desempenho.

  • Os dados sample_mflix carregados em seu Atlas cluster.

  • mongosh para executar queries em seu Atlas cluster:

  • Project Data Access Admin acesso ao projeto para criar índices do MongoDB Vector Search e do MongoDB Search.

Crie os índices Vector Search do MongoDB e Search do MongoDB

Esta seção demonstra como criar os seguintes índices nos campos na collection sample_mflix.embedded_movies :

  • um índice MongoDB Vector Search no campo plot_embedding_voyage_3_large para executar queries vetoriais nesse campo.

  • um índice do MongoDB Search no campo title para executar a pesquisa de texto completo nesse campo.

Procedimento

1

Conecte ao cluster utilizando mongosh.

Para saber mais, consulte Conectar-se a um cluster via mongosh.

2

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

use sample_mflix
switched to db sample_mflix
3

Defina o índice do MongoDB Vector Search .

Execute o seguinte comando. Esta definição de índice indexa o campo plot_embedding_voyage_3_large como o campo Vector Search do MongoDB ao executar query da coleção.

1db.embedded_movies.createSearchIndex(
2  "hybrid-vector-search", 
3  "vectorSearch", 
4  {
5    "fields": [
6      {
7        "type": "vector",
8        "path": "plot_embedding_voyage_3_large",
9        "numDimensions": 2048,
10        "similarity": "dotProduct"
11      }
12    ]
13  }
14);
4

Defina o índice de pesquisa do MongoDB .

A seguinte definição de índice indexa automaticamente todos os campos indexáveis dinamicamente na collection.

1db.embedded_movies.createSearchIndex(
2  "hybrid-full-text-search", 
3  "search", 
4  {
5    "mappings": {
6      "dynamic": true
7    }
8  }
9);

Impulsionamento semântica

Esta seção demonstra como executar query dos dados na collection sample_mflix.embedded_movies para runas nos campos plot_embedding_voyage_3_large e plot usando os estágios de pipeline $vectorSearch e $search, respectivamente. A query aumenta a relevância das $search queries ao aumentar as pontuações de texto completo usando $vectorSearch pontuações para os mesmos documentos.

Procedimento

1

Crie o arquivo para as incorporações.

  1. Crie um arquivo denominado query-embeddings.js.

    touch query-embeddings.js

  2. Copie e cole as seguintes incorporações no arquivo.

    STAR_WARS_EMBEDDING = 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  3. Salve e feche o arquivo.

2

Conecte-se ao seu cluster usando o mongosh.

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

3

Use o banco de dados sample_mflix.

Execute o seguinte comando no prompt mongosh:

use sample_mflix
switched to db sample_mflix
4

Carregue as incorporações para usar na query.

Carregue o arquivo query-embeddings.js em mongosh.

load('<path-to-file>/query-embeddings.js');
5

Execute as seguintes queries do MongoDB Search na embedded_movies collection.

1vectorWeight = 1;  
2vectorCutoff = 0.7; 
3numResults = 20;
4numCandidates = numResults * 10;  
5
6// Execute the vector search  
7vectorResults = db.embedded_movies.aggregate([  
8    {  
9        $vectorSearch: {  
10            index: "hybrid-vector-search", 
11            path: "plot_embedding_voyage_3_large",
12            queryVector: STAR_WARS_EMBEDDING,
13            numCandidates: numCandidates,   
14            limit: numResults  
15        }  
16    },  
17    {  
18        $project: {  
19            _id: 1,  
20            title: 1,  
21            plot: 1,
22            vectorScore: { $meta: "vectorSearchScore" }  
23        }  
24    },  
25    {  
26        $match: {  
27            vectorScore: { $gte: vectorCutoff }  
28        }  
29    }  
30]).toArray();  
31
32// Create a map for fast lookup  
33vectorScoresMap = {};  
34vectorResults.forEach(result => {  
35    vectorScoresMap[result._id] = result.vectorScore * vectorWeight;  
36});  
37
38// Execute the text search with boosting based just on ID  
39textResults = db.embedded_movies.aggregate([  
40    {  
41        $search: {  
42            index: "hybrid-full-text-search",  
43            compound: {  
44                should: [  
45                    {  
46                        text: {  
47                            query: "star wars",  
48                            path: "plot"  
49                        }  
50                    },  
51                    ...Object.keys(vectorScoresMap).map(id => ({  
52                        equals: {  
53                            path: "_id",  
54                            value: id,  
55                            score: { boost: { value: vectorScoresMap[id] } }  
56                        }  
57                    }))  
58                ]  
59            }  
60        }  
61    },  
62    {
63        $limit: 20
64    },
65    {  
66        $project: {  
67            _id: 1,  
68            title: 1,  
69            plot: 1,
70            score: { $meta: "searchScore" }  
71        }  
72    }  
73]).toArray();  
[
  {
    _id: ObjectId('573a1397f29313caabce6f53'),
    plot: 'In this Star Wars take-off, the peaceful planet of Jillucia has been nearly wiped out by the Gavanas, whose leader takes orders from his mother (played a comic actor in drag) rather than ...',
    title: 'Message from Space',
    score: 4.386045455932617
  },
  {
    _id: ObjectId('573a139df29313caabcfa90b'),
    plot: 'In this Star Wars take-off, the peaceful planet of Jillucia has been nearly wiped out by the Gavanas, whose leader takes orders from his mother (played a comic actor in drag) rather than ...',
    title: 'Message from Space',
    score: 4.386045455932617
  },
  {
    _id: ObjectId('573a1398f29313caabce9851'),
    plot: 'Johnathan Cabot is a champion gymnast. In the tiny, yet savage, country of Parmistan, there is a perfect spot for a "star wars" site. For the US to get this site, they must compete in the ...',
    title: 'Gymkata',
    score: 4.269977569580078
  },
  {
    _id: ObjectId('573a13a4f29313caabd1137f'),
    plot: "A producer's film is endangered when his star walks off, so he decides to digitally create an actress to substitute for the star, becoming an overnight sensation that everyone thinks is a real person.",
    title: 'S1m0ne',
    score: 2.8472113609313965
  },
  {
    _id: ObjectId('573a1394f29313caabcdf65b'),
    plot: 'A fantastic tale of war, love, family and ambition set in the midst of the Japanese Civil Wars of the sixteenth century.',
    title: 'Ugetsu',
    score: 2.835911989212036
  },
  {
    _id: ObjectId('573a13a3f29313caabd0c992'),
    plot: 'The vampire Lestat becomes a rock star whose music wakes up the queen of all vampires.',
    title: 'Queen of the Damned',
    score: 2.664494037628174
  },
  {
    _id: ObjectId('573a139af29313caabcefe79'),
    plot: 'An all star baseball player becomes the unhealthy focus of a down on his luck salesman.',
    title: 'The Fan',
    score: 2.664494037628174
  },
  {
    _id: ObjectId('573a13a8f29313caabd1da86'),
    plot: 'During the Napoleonic Wars, a brash British captain pushes his ship and crew to their limits in pursuit of a formidable French war vessel around South America.',
    title: 'Master and Commander: The Far Side of the World',
    score: 2.6229333877563477
  },
  {
    _id: ObjectId('573a139af29313caabcf0e7d'),
    plot: 'A puppeteer discovers a portal that leads literally into the head of the movie star, John Malkovich.',
    title: 'Being John Malkovich',
    score: 2.6173996925354004
  },
  {
    _id: ObjectId('573a13b6f29313caabd4614f'),
    plot: 'A former pop star who now writes commercial jingles for a living experiences a mid-life crisis.',
    title: 'The Good Night',
    score: 2.6173996925354004
  },
  {
    _id: ObjectId('573a13cbf29313caabd7fbc9'),
    plot: 'Bertrand Tavernier is in top form with this gripping, superbly mounted drama set against the savage Catholic/Protestant wars that ripped France apart in the 16th century. Based on a novella...',
    title: 'The Princess of Montpensier',
    score: 2.4742777347564697
  },
  {
    _id: ObjectId('573a139bf29313caabcf2d8e'),
    plot: 'Updated version of the classic Rodgers and Hammerstein musical of the classic fairy-tale, with an all-star, multi-racial cast.',
    title: 'Cinderella',
    score: 2.4445695877075195
  },
  {
    _id: ObjectId('573a1399f29313caabcec853'),
    plot: 'An action movie star researching a role is allowed to tag along with a hardboiled New York policeman, who finds him superficial and irritating.',
    title: 'The Hard Way',
    score: 2.329218626022339
  },
  {
    _id: ObjectId('573a13b4f29313caabd3f550'),
    plot: 'A cop, a lawyer, and an assassin cross paths after the murder of a federal witness and the kidnapping of a famous pop star.',
    title: 'Divergence',
    score: 2.329218626022339
  },
  {
    _id: ObjectId('573a13aff29313caabd2fe20'),
    plot: 'An intimate story set during the 1860s in which a young Irish woman Sarah and her family find themselves on both sides of the turbulent wars between British and Maori during the British colonization of New Zealand.',
    title: 'River Queen',
    score: 2.2804131507873535
  },
  {
    _id: ObjectId('573a13b8f29313caabd4d8e1'),
    plot: "Thomas Michael, Paolo Mancini, Chris Klein and Joe Mantegna star in director Matthiew Klinck's holiday-themed comedy concerning two downsized Easter bunnies whose friendship starts to ...",
    title: 'Hank and Mike',
    score: 2.258181571960449
  },
  {
    _id: ObjectId('573a13aef29313caabd2df95'),
    plot: 'Story of two sisters that grew up in a small Serbian village in the beginning of the 1930s. The village is torn up by wars and years long blood oath. There are no men left in the village. ...',
    title: 'Tears for Sale',
    score: 2.2510178089141846
  },
  {
    _id: ObjectId('573a139af29313caabcf124d'),
    plot: 'As the Clone Wars near an end, the Sith Lord Darth Sidious steps out of the shadows, at which time Anakin succumbs to his emotions, becoming Darth Vader and putting his relationships with Obi-Wan and Padme at risk.',
    title: 'Star Wars: Episode III - Revenge of the Sith',
    score: 2.2223706245422363
  },
  {
    _id: ObjectId('573a13dbf29313caabdaf598'),
    plot: "Just as Clint Eastwood's star-making spaghetti Western A Fistful of Dollars was inspired by Akira Kurosawa's Yojimbo, Japanese-Korean filmmaker Sang-il Lee (Villain) has decided to ...",
    title: 'Unforgiven',
    score: 2.1913492679595947
  },
  {
    _id: ObjectId('573a13c1f29313caabd633ac'),
    plot: "Black Dynamite is the greatest African-American action star of the 1970s. When his only brother is killed by The Man it's up to him to find justice.",
    title: 'Black Dynamite',
    score: 2.1913492679595947
  }
]

Sobre a query

A query de amostra aumenta os $search resultados com base nas $vectorSearch pontuações de resultados para retornar os documentos mais relevantes. Para o aumento semântica, você executa as seguintes etapas:

  1. Execute a $vectorSearch query.

    Use um valor acima do solicitado para especificar o número de candidatos a mais do que o limite especificado a ser considerado. A solicitação over permite que você realize o seguinte:

    • Inclua um grande número e um amplo repositório de documentos a serem considerados na pesquisa inicial.

    • Refine a computação de pontuação nos estágios subsequentes.

    A query usa o seguinte cálculo para determinar o número de candidatos a considerar durante a pesquisa vetorial:

    numCandidates = limit * over request

    Os resultados são filtrados com base em um limite de pontuação mínima (vectorCutoff).

  2. Escale a pontuação da pesquisa vetorial para ajustar a influencia dos resultados da pesquisa vetorial na pontuação e na classificação de documentos.

    Use o parâmetro vectorWeight para definir o peso e multiplicar as pontuações da pesquisa vetorial pelo vectorWeight. Isso controla a importância dos resultados da pesquisa vetorial e garante que somente os documentos mais semanticamente relevantes sejam considerados.

  3. Execute a $search query e aumente a pontuação dos documentos que correspondem aos resultados da pesquisa vetorial.

Fusão de classificação recíproca

Esta seção demonstra como fazer query nos dados na coleção sample_mflix.embedded_movies para star wars nos campos plot_embedding_voyage_3_large e title usando os estágios de pipeline $vectorSearch e $search e combinar as pontuações de cada documento de ambos os estágios para reordenar os documentos nos resultados. Isso garante que os documentos que aparecem em ambas as pesquisas apareçam na parte superior dos resultados combinados.

Procedimento

1

Configure as incorporações a serem usadas na consulta.

  1. Crie um arquivo denominado query-embeddings.js.

  2. Copie e cole as seguintes incorporações no arquivo.

    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  3. Salve e feche o arquivo.

2

Conecte-se ao seu cluster usando o mongosh.

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

3

Use o banco de dados sample_mflix.

Execute o seguinte comando no prompt mongosh:

use sample_mflix
switched to db sample_mflix
4

Carregue as incorporações para usar na query.

Execute o seguinte comando para carregar as incorporações após substituir <path-to-file> pelo caminho absoluto para o arquivo embeddings.js:

load('/<path-to-file>/embeddings.js');

Para verificar se as incorporações foram carregadas com sucesso, execute o seguinte comando:

STAR_WARS_EMBEDDING.length
2048
5

Execute as seguintes queries do MongoDB Search na embedded_movies collection.

1db.embedded_movies.aggregate([
2  {
3    $rankFusion: {
4      input: {
5        pipelines: {
6          vectorPipeline: [
7            {
8              "$vectorSearch": {
9                "index": "hybrid-vector-search",
10                "path": "plot_embedding_voyage_3_large",
11                "queryVector": STAR_WARS_EMBEDDING,
12                "numCandidates": 100,
13                "limit": 20
14              }
15            }
16          ],
17          fullTextPipeline: [
18            {
19              "$search": {
20                "index": "hybrid-full-text-search",
21                "phrase": {
22                  "query": "star wars",
23                  "path": "title"
24                }
25              }
26            },
27            { "$limit": 20 }
28          ]
29        }
30      },
31      combination: {
32        weights: {
33          vectorPipeline: 0.5,
34          fullTextPipeline: 0.5
35        }
36      },
37      "scoreDetails": true
38    }
39  },
40  {
41    "$project": {
42      _id: 1,
43      title: 1,
44      plot: 1,
45      scoreDetails: {"$meta": "scoreDetails"}
46    }
47  },
48  {
49    "$limit": 10
50  }
51]);
[
  {
    _id: ObjectId('573a1397f29313caabce68f6'),
    plot: "Luke Skywalker joins forces with a Jedi Knight, a cocky pilot, a wookiee and two droids to save the universe from the Empire's world-destroying battle-station, while also attempting to rescue Princess Leia from the evil Darth Vader.",
    title: 'Star Wars: Episode IV - A New Hope',
    scoreDetails: {
      value: 0.01626123744050767,
      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: 'fullTextPipeline',
          rank: 2,
          weight: 0.5,
          value: 2.9571781158447266,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 1,
          weight: 0.5,
          value: 0.7987099885940552,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcf0f5f'),
    plot: 'Two Jedi Knights escape a hostile blockade to find allies and come across a young boy who may bring balance to the Force, but the long dormant Sith resurface to reclaim their old glory.',
    title: 'Star Wars: Episode I - The Phantom Menace',
    scoreDetails: {
      value: 0.015527202696196438,
      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: 'fullTextPipeline',
          rank: 2,
          weight: 0.5,
          value: 2.9571781158447266,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 7,
          weight: 0.5,
          value: 0.7771433591842651,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1397f29313caabce77d9'),
    plot: 'After the rebels have been brutally overpowered by the Empire on their newly established base, Luke Skywalker takes advanced Jedi training with Master Yoda, while his friends are pursued by Darth Vader as part of his plan to capture Luke.',
    title: 'Star Wars: Episode V - The Empire Strikes Back',
    scoreDetails: {
      value: 0.015504807692307693,
      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: 'fullTextPipeline',
          rank: 4,
          weight: 0.5,
          value: 2.712062358856201,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 5,
          weight: 0.5,
          value: 0.7810181975364685,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1397f29313caabce8cdb'),
    plot: 'After rescuing Han Solo from the palace of Jabba the Hutt, the rebels attempt to destroy the second Death Star, while Luke struggles to make Vader return from the dark side of the Force.',
    title: 'Star Wars: Episode VI - Return of the Jedi',
    scoreDetails: {
      value: 0.015388257575757576,
      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: 'fullTextPipeline',
          rank: 4,
          weight: 0.5,
          value: 2.712062358856201,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 6,
          weight: 0.5,
          value: 0.7782549858093262,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13c0f29313caabd62f62'),
    plot: 'Anakin Skywalker and Ahsoka Tano must rescue the kidnapped son of Jabba the Hutt, but political intrigue complicates their mission.',
    title: 'Star Wars: The Clone Wars',
    scoreDetails: {
      value: 0.015141165755919854,
      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: 'fullTextPipeline',
          rank: 1,
          weight: 0.5,
          value: 3.609658718109131,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 12,
          weight: 0.5,
          value: 0.7561323642730713,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcf124d'),
    plot: 'As the Clone Wars near an end, the Sith Lord Darth Sidious steps out of the shadows, at which time Anakin succumbs to his emotions, becoming Darth Vader and putting his relationships with Obi-Wan and Padme at risk.',
    title: 'Star Wars: Episode III - Revenge of the Sith',
    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: 'fullTextPipeline',
          rank: 4,
          weight: 0.5,
          value: 2.712062358856201,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 11,
          weight: 0.5,
          value: 0.756283700466156,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcf1258'),
    plot: 'Ten years after initially meeting, Anakin Skywalker shares a forbidden romance with Padmè, while Obi-Wan investigates an assassination attempt on the Senator and discovers a secret clone army crafted for the Jedi.',
    title: 'Star Wars: Episode II - Attack of the Clones',
    scoreDetails: {
      value: 0.014391447368421052,
      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: 'fullTextPipeline',
          rank: 4,
          weight: 0.5,
          value: 2.712062358856201,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 16,
          weight: 0.5,
          value: 0.7400004267692566,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabce9091'),
    plot: "A Duke's son leads desert warriors against the galactic emperor and his father's evil nemesis when they assassinate his father and free their desert world from the emperor's rule.",
    title: 'Dune',
    scoreDetails: {
      value: 0.008064516129032258,
      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: 'fullTextPipeline', rank: 0, weight: 0.5 },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 2,
          weight: 0.5,
          value: 0.7947750091552734,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1397f29313caabce6f53'),
    plot: 'In this Star Wars take-off, the peaceful planet of Jillucia has been nearly wiped out by the Gavanas, whose leader takes orders from his mother (played a comic actor in drag) rather than ...',
    title: 'Message from Space',
    scoreDetails: {
      value: 0.007936507936507936,
      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: 'fullTextPipeline', rank: 0, weight: 0.5 },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 3,
          weight: 0.5,
          value: 0.7913960218429565,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139df29313caabcfa90b'),
    plot: 'In this Star Wars take-off, the peaceful planet of Jillucia has been nearly wiped out by the Gavanas, whose leader takes orders from his mother (played a comic actor in drag) rather than ...',
    title: 'Message from Space',
    scoreDetails: {
      value: 0.007936507936507936,
      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: 'fullTextPipeline', rank: 0, weight: 0.5 },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 3,
          weight: 0.5,
          value: 0.7913960218429565,
          details: []
        }
      ]
    }
  }
]

Sobre a query

A query de amostra recupera os resultados de pesquisa classificados da pesquisa semântica e da pesquisa de texto completo e atribui uma pontuação de classificação recíproca aos documentos nos resultados com base na posição deles na array de resultados. A pontuação de classificação recíproca é calculada usando a seguinte fórmula:

1.0/{document position in the results + constant value}

Em seguida, a query soma as pontuações de ambas as pesquisas para cada documento, classifica os documentos com base na pontuação combinada e ordena os documentos para retornar um único resultado. O valor da constante é sempre 60.

Aprenda assistindo

Assista a uma demonstração de um aplicação que mostra queries de pesquisa híbrida que combina a pesquisa de texto completo e vetorial do MongoDB Search para retornar um único conjunto de resultados mesclados. O aplicação implementa a Fusão de Pontuação Relativa (RSF) e a Fusão de Classificação Reciproca (RRF) para retornar um conjunto mesclado criado usando um algoritmo de fusão de classificação.

Duração: 2.43 Minutos

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