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Execute a pesquisa híbrida com o Atlas Vector Search e o Atlas Search

Nesta página

  • Por que pesquisa híbrida?
  • O que é a fusão de classificação recíproca?
  • Pré-requisitos
  • Crie o Atlas Vector Search e o índice de pesquisa do Atlas Search
  • Procedimento
  • Executar uma pesquisar combinada de query de pesquisa semântica e pesquisar de texto completo
  • Procedimento
  • Sobre a query
  • Aprenda assistindo

Você pode combinar as queries do Atlas Vector Search e do Atlas Search em uma pesquisa híbrida para ter 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 Atlas Vector Search no campo plot_embedding. Esse campo contém embeddings de vetor que representam o resumo do enredo de um filme.

  2. Crie um índice do Atlas Search no campo title na coleção sample_mflix.embedded_movies. Esse campo contém o nome do filme como uma string de texto.

  3. Execute uma consulta que use fusão de classificação recíproca para combinar os resultados de uma consulta $vectorSearch no campo plot_embedding e uma consulta { $search } no campo title .

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.

O que é a fusão de classificação recíproca?

A fusão de classificação recíproca é uma técnica para combinar resultados de diferentes métodos de pesquisa, como uma pesquisa semântica e uma pesquisa de texto completo, em um único conjunto de resultados, executando as seguintes ações:

  1. Calcula a classificação recíproca dos documentos nos resultados.

    Para cada documento classificado em cada resultado da pesquisa, primeiro adicione a classificação (r) do documento com um número constante, 60, para suavizar a pontuação (rank_constant) e, em seguida, divida 1 pela soma de r e rank_constant para a classificação recíproca do documento nos resultados.

    reciprocal_rank = 1 / ( r + rank_constant )

    Para cada método de pesquisa, aplique pesos diferentes (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

  2. Combine as pontuações ponderadas e derivadas da classificação dos documentos nos resultados.

    Para cada documento em todos os resultados de pesquisa, adicione as classificações recíprocas calculadas para uma única pontuação para o documento.

  3. Classifique os resultados pela pontuação combinada dos documentos nos resultados.

    Classifique os documentos nos resultados com base na pontuação combinada entre os resultados para obter uma lista única e combinada de documentos classificados nos resultados.

Pré-requisitos

Antes de começar, você deve ter o seguinte:

  • Um Atlas cluster com versão do MongoDB v6.0.11 ou v7.0.2 ou posterior.

    Observação

    Certifique-se de que seu cluster do Atlas tenha memória suficiente para armazenar os índice de pesquisa do Atlas Search e do Atlas Vector Search e executar query de alto desempenho.

  • Os dados de amostra carregados no seu Atlas cluster.

  • Um dos seguintes clientes para executar queries no seu Atlas cluster:

    • Testador de pesquisa

    • mongosh

    • Compass

    • C#

    • Java

    • Driver de nó do MongoDB

    • PyMongo

  • Project Data Access Admin acesso ao projeto para criar os índices Atlas Vector Search e Atlas Search.

Crie o Atlas Vector Search e o índice de pesquisa do Atlas Search

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

  • Um índice do Atlas Vector Search no campo plot_embedding para executar query vetoriais nesse campo.

  • Um índice de pesquisa do Atlas Search no campo title para executar a pesquisar de texto completo nesse campo.

Procedimento

1

Defina o índice do Atlas Vector Search.

A seguinte definição de índice indexa o campo plot_embedding como o campo do Atlas Vector Search ao consultar a coleção.

Para criar um índice Atlas Vector Search para uma coleção usando mongosh v2.1.2 ou posterior, execute as seguintes etapas:

  1. Conecte-se ao cluster do Atlas usando mongosh.

    Para saber mais, consulte Conectar 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. Execute o método db.collection.createSearchIndex().

    1db.embedded_movies.createSearchIndex(
    2  "rrf-vector-search", 
    3  "vectorSearch", 
    4  {
    5    "fields": [
    6      {
    7        "type": "vector",
    8        "path": "plot_embedding",
    9        "numDimensions": 1536,
    10        "similarity": "dotProduct"
    11      }
    12    ]
    13  }
    14);
2

Defina o Atlas Search índice de pesquisa.

A seguinte definição de índice indexa o campo title como o tipo de string para consultar o campo.

Para criar um índice do Atlas Search para uma coleção usando o mongosh v2.1.2 ou posterior, execute as seguintes etapas:

  1. Conecte-se ao cluster do Atlas usando mongosh.

    Para saber mais, consulte Conectar 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. Execute o método db.collection.createSearchIndex().

    1db.embedded_movies.createSearchIndex(
    2  "rrf-full-text-search", 
    3  "search", 
    4  {
    5    "mappings": {
    6      "dynamic": false,
    7      "fields": {
    8        "title": [{
    9          "type": "string"
    10        }]
    11      }
    12    }
    13  }
    14);
1

Configuração do ambiente

  1. Inicialize seu projeto Node.js.

    Execute os seguintes comandos em seu terminal para criar um novo diretório chamado hybrid-search-mongodb e inicializar seu projeto:

    mkdir hybrid-search-mongodb
    cd hybrid-search-mongodb
    npm init -y

  2. Instalar e importar dependências.

    Execute o seguinte comando:

    npm install mongodb

  3. Atualize seu arquivo package.json.

    No arquivo package.json do seu projeto, especifique o campo type como mostrado no exemplo a seguir e salve o arquivo.

    {
       "name": "hybrid-search-mongodb",
       "type": "module",
       ...

  4. Crie um arquivo .env.

    1. No seu projeto, crie um arquivo .env para armazenar sua string de conexão do Atlas.

      ATLAS_CONNECTION_STRING = "<connection-string>";

    2. Substitua os valores de placeholder <api-key> e <connection-string> reservado e pela SRV string de conexão do seu cluster Atlas. Sua string de conexão deve usar o seguinte formato:

      mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

      Observação

      Requisitos mínimos de versão do Node.js

      O Node.js v20.x introduziu a opção --env-file. Se você estiver usando uma versão mais antiga do Node.js, adicione o pacote dotenv ao seu projeto ou use um método diferente para gerenciar suas variáveis de ambiente.

2

Defina o índice do Atlas Vector Search.

A seguinte definição de índice indexa o campo plot_embedding como o campo do Atlas Vector Search ao consultar a coleção.

  1. Crie um arquivo chamado avs-index.js, e cole o seguinte código nele:

    import { MongoClient } from 'mongodb';
    // Connect to your Atlas deployment
    const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING);
    async function run() {
        try {
          const database = client.db("sample_mflix");
          const collection = database.collection("embedded_movies");
          // Define your Atlas Vector Search index
          const index = {
              name: "rrf-vector-search",
              type: "vectorSearch",
              definition: {
                "fields": [
                  {
                    "type": "vector",
                    "numDimensions": 1536,
                    "path": "plot_embedding",
                    "similarity": "dotProduct"
                  }
                ]
              }
          }
          // Call the method to create the index
          const result = await collection.createSearchIndex(index);
          console.log(result);
        } finally {
          await client.close();
        }
    }
    run().catch(console.dir);

  2. Execute o seguinte comando para executar o código:

    node --env-file=.env avs-index.js

    O Atlas pode levar alguns minutos para criar o índice. O Atlas envia um e-mail quando o índice fica pronto para uso. Na IU do Atlas, o índice recém-criado aparece na aba Atlas Search. Enquanto o índice está sendo criado, o campo Status exibe Build in Progress. Quando o índice termina de ser criado, o campo Status exibe Active.

3

Defina o Atlas Search índice de pesquisa.

A seguinte definição de índice indexa o campo title como o tipo de string para consultar o campo.

  1. Crie um arquivo chamado fts-index.js, e cole o seguinte código nele:

    import { MongoClient } from 'mongodb';
    // Connect to your Atlas deployment
    const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING);
    async function run() {
        try {
          const database = client.db("sample_mflix");
          const collection = database.collection("embedded_movies");
          // Define your Atlas Search index
          const index = {
              name: "rrf-full-text-search",
              type: "search",
              definition: {
                "mappings": {
                  "dynamic": false,
                  "fields": {
                    "title": [{
                      "type": "string"
                    }]
                  }
                }
              }
          }
          // Call the method to create the index
          const result = await collection.createSearchIndex(index);
          console.log(result);
        } finally {
          await client.close();
        }
    }
    run().catch(console.dir);

  2. Execute o seguinte comando para executar o código:

    node --env-file=.env fts-index.js

    O Atlas pode levar alguns minutos para criar o índice. O Atlas envia um e-mail quando o índice fica pronto para uso. Na IU do Atlas, o índice recém-criado aparece na aba Atlas Search. Enquanto o índice está sendo criado, o campo Status exibe Build in Progress. Quando o índice termina de ser criado, o campo Status exibe Active.

Executar uma pesquisar combinada de query de pesquisa semântica e pesquisar de texto completo

Esta seção demonstra como fazer query nos dados na coleção sample_mflix.embedded_movies para star wars nos campos plot_embedding 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

Conecte-se ao seu cluster mongosh usando.

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

2

Use o sample_mflix banco de dados do.

Execute o seguinte comando no prompt mongosh:

use sample_mflix
switched to db sample_mflix
3

Execute as seguintes consultas do Atlas Search na coleção.<a class=\" \" href=\" \" title=\" \"><svg xmlns=\" \" width=\" \"embedded_movies height=\" \" fill=\" \" viewbox=\" \" class=\" \" role=\" \" aria-label=\" \"><path fill=\" \" d=\" \"> <path fill=\" \" d=\" \">

1var vector_weight = 0.1;
2var full_text_weight = 0.9;
3db.embedded_movies.aggregate([
4  {
5    "$vectorSearch": {
6      "index": "rrf-vector-search",
7      "path": "plot_embedding",
8      "queryVector": 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67        }, {
68          "$addFields": {
69            "fts_score": {
70              "$multiply": [
71                full_text_weight, {
72                  "$divide": [
73                    1.0, {
74                      "$add": ["$rank", 60]
75                    }
76                  ]
77                }
78              ]
79            }
80          } 
81        },
82        {
83          "$project": {
84            "fts_score": 1,
85            "_id": "$docs._id",
86            "title": "$docs.title"
87          }
88        }
89      ]
90    }
91  },
92  {
93    "$group": {
94      "_id": "$title",
95      "vs_score": {"$max": "$vs_score"},
96      "fts_score": {"$max": "$fts_score"}
97    }
98  },
99  {
100    "$project": {
101      "_id": 1,
102      "title": 1,
103      "vs_score": {"$ifNull": ["$vs_score", 0]},
104      "fts_score": {"$ifNull": ["$fts_score", 0]}
105    }
106  },
107  {
108    "$project": {
109      "score": {"$add": ["$fts_score", "$vs_score"]},
110      "_id": 1,
111      "title": 1,
112      "vs_score": 1,
113      "fts_score": 1
114    }
115  },
116  {"$sort": {"score": -1}},
117  {"$limit": 10}
118])
[
  {
    _id: 'Star Wars: Episode IV - A New Hope',
    vs_score: 0.0016666666666666668,
    fts_score: 0,
    score: 0.0016666666666666668
  },
  {
    _id: 'Star Wars: Episode I - The Phantom Menace',
    vs_score: 0.0016393442622950822,
    fts_score: 0,
    score: 0.0016393442622950822
  },
  {
    _id: 'Star Wars: Episode V - The Empire Strikes Back',
    vs_score: 0.0016129032258064516,
    fts_score: 0,
    score: 0.0016129032258064516
  },
  {
    _id: 'Star Wars: Episode VI - Return of the Jedi',
    vs_score: 0.0015873015873015873,
    fts_score: 0,
    score: 0.0015873015873015873
  },
  {
    _id: 'Star Wars: The Clone Wars',
    vs_score: 0.0015625,
    fts_score: 0,
    score: 0.0015625
  },
  {
    _id: 'Message from Space',
    vs_score: 0.0015384615384615387,
    fts_score: 0,
    score: 0.0015384615384615387
  },
  {
    _id: 'Star Wars: Episode II - Attack of the Clones',
    vs_score: 0.0014925373134328358,
    fts_score: 0,
    score: 0.0014925373134328358
  },
  {
    _id: 'Guardians of the Galaxy',
    vs_score: 0.0014705882352941176,
    fts_score: 0,
    score: 0.0014705882352941176
  },
  {
    _id: 'Abiogenesis',
    vs_score: 0.0014285714285714286,
    fts_score: 0,
    score: 0.0014285714285714286
  },
  {
    _id: 'Dune',
    vs_score: 0.0014084507042253522,
    fts_score: 0,
    score: 0.0014084507042253522
  }
]

Se você classificar os resultados em ordem crescente substituindo o valor de score na linha 116 por 1, o Atlas Vector Search retornará os seguintes resultados:

[
  {
    _id: 'Cowboys & Aliens',
    vs_score: 0.0012658227848101266,
    fts_score: 0,
    score: 0.0012658227848101266
  },
  {
    _id: 'Planet of the Apes',
    vs_score: 0.001298701298701299,
    fts_score: 0,
    score: 0.001298701298701299
  },
  {
    _id: 'Starcrash',
    vs_score: 0.0013157894736842105,
    fts_score: 0,
    score: 0.0013157894736842105
  },
  {
    _id: 'Zathura: A Space Adventure',
    vs_score: 0.0013333333333333335,
    fts_score: 0,
    score: 0.0013333333333333335
  },
  {
    _id: 'Space Raiders',
    vs_score: 0.0013513513513513514,
    fts_score: 0,
    score: 0.0013513513513513514
  },
  {
    _id: 'Star Wars: Episode III - Revenge of the Sith',
    vs_score: 0.0013698630136986301,
    fts_score: 0,
    score: 0.0013698630136986301
  },
  {
    _id: 'The Ewok Adventure',
    vs_score: 0.001388888888888889,
    fts_score: 0,
    score: 0.001388888888888889
  },
  {
    _id: 'Dune',
    vs_score: 0.0014084507042253522,
    fts_score: 0,
    score: 0.0014084507042253522
  },
  {
    _id: 'Abiogenesis',
    vs_score: 0.0014285714285714286,
    fts_score: 0,
    score: 0.0014285714285714286
  },
  {
    _id: 'Guardians of the Galaxy',
    vs_score: 0.0014705882352941176,
    fts_score: 0,
    score: 0.0014705882352941176
  }
]
1

Defina o pipeline de query combinada.

Crie um arquivo chamado combined-query.js, e cole o seguinte código nele:

1import { MongoClient } from 'mongodb';
2
3async function run() {
4    // Connect to your Atlas cluster
5    const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING);
6
7    try {
8        await client.connect();
9        const database = client.db("sample_mflix");
10        const collection = database.collection("embedded_movies");
11
12        const vectorWeight = 0.1;
13        const fullTextWeight = 0.9;
14        const pipeline = [
15            {
16                "$vectorSearch": {
17                  "index": "rrf-vector-search",
18                  "path": "plot_embedding",
19                  "queryVector": 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77                    }, {
78                      "$addFields": {
79                        "fts_score": {
80                          "$multiply": [
81                            fullTextWeight, {
82                              "$divide": [
83                                1.0, {
84                                  "$add": ["$rank", 60]
85                                }
86                              ]
87                            }
88                          ]
89                        }
90                      } 
91                    },
92                    {
93                      "$project": {
94                        "fts_score": 1,
95                        "_id": "$docs._id",
96                        "title": "$docs.title"
97                      }
98                    }
99                  ]
100                }
101            }, {
102                "$group": {
103                  "_id": "$title",
104                  "vs_score": {"$max": "$vs_score"},
105                  "fts_score": {"$max": "$fts_score"}
106                }
107            }, {
108                "$project": {
109                  "_id": 1,
110                  "title": 1,
111                  "vs_score": {"$ifNull": ["$vs_score", 0]},
112                  "fts_score": {"$ifNull": ["$fts_score", 0]}
113                }
114            }, {
115                "$project": {
116                  "score": {"$add": ["$fts_score", "$vs_score"]},
117                  "_id": 1,
118                  "title": 1,
119                  "vs_score": 1,
120                  "fts_score": 1
121                }
122            },
123            {"$sort": {"score": -1}},
124            {"$limit": 10}
125        ];
126
127        // run pipeline
128        const result = collection.aggregate(pipeline);
129
130        for await (const doc of result) {
131            console.log(doc);
132        }
133    } catch (err) {
134        console.log(err.stack);
135    }
136    finally {
137        await client.close();
138    }
139}
140run().catch(console.dir);
2

Execute o código para executar as consultas na embedded_movies coleção.

node --env-file=.env combined-query.js
[
  {
    _id: 'Star Wars: Episode IV - A New Hope',
    vs_score: 0.0016666666666666668,
    fts_score: 0,
    score: 0.0016666666666666668
  },
  {
    _id: 'Star Wars: Episode I - The Phantom Menace',
    vs_score: 0.0016393442622950822,
    fts_score: 0,
    score: 0.0016393442622950822
  },
  {
    _id: 'Star Wars: Episode V - The Empire Strikes Back',
    vs_score: 0.0016129032258064516,
    fts_score: 0,
    score: 0.0016129032258064516
  },
  {
    _id: 'Star Wars: Episode VI - Return of the Jedi',
    vs_score: 0.0015873015873015873,
    fts_score: 0,
    score: 0.0015873015873015873
  },
  {
    _id: 'Star Wars: The Clone Wars',
    vs_score: 0.0015625,
    fts_score: 0,
    score: 0.0015625
  },
  {
    _id: 'Message from Space',
    vs_score: 0.0015384615384615387,
    fts_score: 0,
    score: 0.0015384615384615387
  },
  {
    _id: 'Star Wars: Episode II - Attack of the Clones',
    vs_score: 0.0014925373134328358,
    fts_score: 0,
    score: 0.0014925373134328358
  },
  {
    _id: 'Guardians of the Galaxy',
    vs_score: 0.0014705882352941176,
    fts_score: 0,
    score: 0.0014705882352941176
  },
  {
    _id: 'Abiogenesis',
    vs_score: 0.0014285714285714286,
    fts_score: 0,
    score: 0.0014285714285714286
  },
  {
    _id: 'Dune',
    vs_score: 0.0014084507042253522,
    fts_score: 0,
    score: 0.0014084507042253522
  }
]

Se você classificar os resultados em ordem crescente substituindo o valor de score na linha 123 por 1, o Atlas Vector Search retornará os seguintes resultados:

[
  {
    _id: 'Cowboys & Aliens',
    vs_score: 0.0012658227848101266,
    fts_score: 0,
    score: 0.0012658227848101266
  },
  {
    _id: 'Planet of the Apes',
    vs_score: 0.001298701298701299,
    fts_score: 0,
    score: 0.001298701298701299
  },
  {
    _id: 'Starcrash',
    vs_score: 0.0013157894736842105,
    fts_score: 0,
    score: 0.0013157894736842105
  },
  {
    _id: 'Zathura: A Space Adventure',
    vs_score: 0.0013333333333333335,
    fts_score: 0,
    score: 0.0013333333333333335
  },
  {
    _id: 'Space Raiders',
    vs_score: 0.0013513513513513514,
    fts_score: 0,
    score: 0.0013513513513513514
  },
  {
    _id: 'Star Wars: Episode III - Revenge of the Sith',
    vs_score: 0.0013698630136986301,
    fts_score: 0,
    score: 0.0013698630136986301
  },
  {
    _id: 'The Ewok Adventure',
    vs_score: 0.001388888888888889,
    fts_score: 0,
    score: 0.001388888888888889
  },
  {
    _id: 'Dune',
    vs_score: 0.0014084507042253522,
    fts_score: 0,
    score: 0.0014084507042253522
  },
  {
    _id: 'Abiogenesis',
    vs_score: 0.0014285714285714286,
    fts_score: 0,
    score: 0.0014285714285714286
  },
  {
    _id: 'Guardians of the Galaxy',
    vs_score: 0.0014705882352941176,
    fts_score: 0,
    score: 0.0014705882352941176
  }
]

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, o query adiciona as pontuações de ambos os pesquisar para cada documento, classifica os documento com base na pontuação combinada e classifica os documento para retornar um único resultado.

Variáveis da query

A query de amostra define as seguintes variáveis para adicionar peso à pontuação, com um número menor fornecendo maior peso:

  • vector_weight

  • full_text_weight

A pontuação de classificação recíproca ponderada é calculada usando a seguinte fórmula:

weight x reciprocal rank

Estágios de query

A query de exemplo usa os seguintes estágios do pipeline para realizar uma pesquisa semântica na coleção e recuperar a classificação recíproca dos documentos nos resultados:

Pesquisa no campo plot_embedding a string star wars especificada como incorporações vetoriais no campo queryVector da query. A query utiliza a incorporação ada-002-text, que é a mesma que a incorporação vetorial no campo plot_embedding. A query também especifica uma pesquisa para até 100 vizinhos mais próximos e limita os resultados somente a 20 documentos. Essa etapa retorna os documentos classificados da pesquisa semântica nos resultados.
Agrupa todos os documentos nos resultados da pesquisa semântica em um campo denominado docs.
Desconstrói a array de documentos no campo docs e armazena a posição do documento na array de resultados em um campo chamado rank.
Adiciona um novo campo chamado vs_score que contém a pontuação de classificação recíproca para cada documento nos resultados. Aqui, a pontuação de classificação recíproca é calculada dividindo 1.0 pela soma de rank e um valor constante de classificação de 60. Em seguida, a classificação recíproca ponderada é calculada multiplicando o peso vector_weight pela pontuação de classificação recíproca.

Inclui somente os seguintes campos nos resultados:

  • vs_score

  • _id

  • title

A query de amostra usa o estágio $unionWith para realizar uma pesquisa de texto na coleção e recuperar a classificação regressiva dos documentos nos resultados:

Pesquisa filmes que contenham o termo star wars no campo title. Esse estágio retorna os documentos classificados da pesquisa de texto completo nos resultados.
Limita a saída a apenas 20 resultados.
Agrupa todos os documentos da pesquisa de texto completo em um campo chamado docs.
Desconstrói a array de documentos no campo docs e armazena a posição do documento na array de resultados em um campo chamado rank.
Adiciona um novo campo chamado fts_score que contém a pontuação de classificação recíproca para cada documento nos resultados. Aqui, a pontuação de classificação recíproca é calculada dividindo 1.0 pela soma de rank e um valor constante de classificação de 60. Em seguida, a classificação recíproca ponderada é calculada multiplicando o peso full_text_weight pela pontuação de classificação recíproca.

Inclui somente os seguintes campos nos resultados:

  • fts_score

  • _id

  • title

A query de exemplo usa os seguintes estágios para combinar os resultados da pesquisa semântica e de texto e retornar uma única lista classificada de documentos nos resultados:

Agrupa os documentos nos resultados dos estágios anteriores por title, vs_score e fts_score.

Inclui somente os seguintes campos nos resultados:

  • vs_score

  • fts_score

  • _id

  • title

Adiciona um campo chamado score que contém a soma de vs_score e fts_score aos resultados.
Classifica os resultados por score em ordem decrescente.
Limita a saída a apenas 10 resultados.

Aprenda assistindo

Assista a uma demonstração de uma aplicação que apresenta query de pesquisa híbridas que combinam texto completo e pesquisa vetorial do Atlas Search para retornar um único conjunto de resultados mesclado. O aplicativo implementa a Relative Score Fusion (RSF) e a Reciprocal Class Fusion (RRF) para retornar um conjunto mesclado criado usando um algoritmo de fusão de classificação.

Duração: 2.43 Minutos

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