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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 de pesquisa vetorial MongoDB no campo plot_embedding_voyage_4_large. Este campo contém embeddings de vetor (de 2048 dimensões criados usando o modelo de embedding voyage-4-large da Voyage AI) que representam o resumo do enredo de um filme.

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

  3. Run a query to combine the results from a $vectorSearch query and a $search query by using the following techniques:

    • Impulsionamento semântica

    • Fusão de classificação recíproca

    • Score Fusion

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

Fusão de pontuação relativa

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.

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

Método de classificação

  • Run $vectorSearch to retrieves semantically similar results.

  • Run $search to retrieve full-text results while boosting the documents that were returned by $vectorSearch.

  • Retrieves $vectorSearch and $search results.

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

  • Retrieves $vectorSearch and $search results.

  • Mescla os dois conjuntos de resultados usando a pontuação 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

Calcula a pontuação usando a seguinte fórmula:

score = w1 x score1 + w2 x score2

Recursos de pesquisa

  • Suporta facet para agrupar dados em categorias.

  • Suporta a paginação de resultados utilizando searchAfter e searchBefore.

Supports further processing of results in subsequent aggregation stages such as $group.

Supports further processing of results in subsequent aggregation stages such as $group.

Saída

Conjunto de resultados único que contém documentos de $search

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

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.

  • The sample_mflix data loaded into your 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 pesquisa vetorial no campo plot_embedding_voyage_4_large para executar query vetoriais nesse campo.

  • Um índice do MongoDB Search que indexa dinamicamente todos os campos indexáveis dinamicamente para executar a pesquisa de texto completo nesses campos.

1

Connect to the cluster using mongosh.

To learn more, see Connect to a 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_4_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_4_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);

Executar as query de pesquisa híbrida

This section demonstrates how to query the data in the sample_mflix.embedded_movies collection for charming animal in the plot_embedding_voyage_4_large and fullplot fields by using the $vectorSearch and $search pipeline stages, respectively. The query enhances relevancy of $search queries by boosting the full-text scores using $vectorSearch scores for the same documents.

Query de impulsionamento semântica

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.

    CHARMING_ANIMAL_EMBEDDING=[-0.02300029993057251, -0.040718235075473785, 0.026521876454353333, -0.007813499309122562, 0.01958877220749855, -0.032134391367435455, 0.015186801552772522, 0.023990744724869728, -0.0029025499243289232, -0.0019671309273689985, 0.01254561822861433, -0.016287293285131454, -0.029053010046482086, 0.01408630795776844, -0.00891399197280407, 0.01958877220749855, -0.0508427694439888, -0.015737047418951988, 0.012215470895171165, -0.0036316262558102608, 0.0033152345567941666, 0.0033840155228972435, -0.00748335151001811, 0.002737476024776697, 0.03257458657026291, -0.01892847754061222, -0.012050396762788296, 0.02432089112699032, 0.028392715379595757, 0.033454980701208115, 0.005144803784787655, 0.0072632525116205215, 0.0212395116686821, -0.01683754101395607, -0.002751232124865055, 0.030813800171017647, -0.025861581787467003, 0.00748335151001811, -0.0032326977234333754, -0.030813800171017647, 0.006768031045794487, 0.024651039391756058, -0.03565596789121628, 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  3. Salve e feche o arquivo.

2

Connect to your cluster using mongosh.

Open mongosh in a terminal window and connect to your cluster. For detailed instructions on connecting, see Connect to a Cluster via mongosh.

3

Use o banco de dados sample_mflix.

Run the following command at mongosh prompt:

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 coleção embedded_movies.

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_4_large",
12            "queryVector": CHARMING_ANIMAL_EMBEDDING,
13            "numCandidates": numCandidates,   
14            "limit": numResults  
15        }  
16    },  
17    {  
18        $project: {  
19            "_id": 1,  
20            "title": 1,  
21            "fullplot": 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": "charming animal",  
48                            "path": "fullplot",
49                            "fuzzy": {}
50                        }  
51                    },  
52                    ...Object.keys(vectorScoresMap).map(id => ({  
53                        "equals": {  
54                            "path": "_id",  
55                            "value": id,  
56                            "score": { boost: { value: vectorScoresMap[id] } }  
57                        }  
58                    }))  
59                ]  
60            }  
61        }  
62    },  
63    {
64        $limit: 20
65    },
66    {  
67        $project: {  
68            "_id": 1,  
69            "title": 1,  
70            "fullplot": 1,
71            "score": { $meta: "searchScore" }  
72        }  
73    }  
74]).toArray();

Sobre a query

The sample query boosts the $search results based on $vectorSearch result scores to return the most relevant documents. For semantic boosting, you perform the following steps:

  1. Run the $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.

Query de fusão de classificação recíproca

You can run a query that retrieves the sorted search results from the semantic search and the full-text search, and assigns a reciprocal rank score to the documents in the results based on their position in the results array. To learn more about the reciprocal rank fusion technique and how to run a reciprocal rank fusion query, see Perform Hybrid Vector and Full-Text Search.

Query de fusão de pontuação relativa

You can run a query that retrieves the scored search results from the semantic and full-text searches and applies the sigmoid expression to normalize the score before combining the results from the pipelines into a single aggregated score for each document. To learn more about the score fusion technique and how to run a score fusion query, see Perform Hybrid Vector and Full-Text Search.

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