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:
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 embeddingvoyage-4-largeda Voyage AI) que representam o resumo do enredo de um filme.Crie um índice de pesquisa do MongoDB na coleção
sample_mflix.embedded_movies.Execute uma query para combinar os resultados de uma query
$vectorSearche de uma query$searchusando as seguintes técnicas: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 |
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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: Em seguida, para cada método de pesquisa, aplique diferentes pesos ( | Calcula a pontuação usando a seguinte fórmula: | |||
Recursos de pesquisa |
| Suporta o processamento adicional de resultados em estágios de agregação subsequentes, como | Suporta o processamento adicional de resultados em estágios de agregação subsequentes, como | |||
Saída | Conjunto de resultados único que contém documentos de | 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.
Os dados sample_mflix carregados em seu Atlas cluster.
mongoshpara executar queries em seu Atlas cluster:Project Data Access Adminacesso 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_largepara 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.
Para saber mais, consulte Conectar-se a um cluster via mongosh.
2
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.
1 db.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 );
Executar as query de pesquisa híbrida
Esta seção demonstra como consultar os dados na coleção sample_mflix.embedded_movies de charming animal nos campos plot_embedding_voyage_4_large e fullplot usando os estágios de pipeline $vectorSearch e $search, respectivamente. A query aprimora a relevância das queries $search aumentando as pontuações de texto completo usando pontuações $vectorSearch para os mesmos documentos.
Query de impulsionamento semântica
1
Crie o arquivo para as incorporações.
Crie um arquivo denominado
query-embeddings.js.touch query-embeddings.js Copie e cole as seguintes incorporações no arquivo.
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2
Connect to your cluster using 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
5
Execute as seguintes queries do MongoDB Search na coleção embedded_movies.
1 vectorWeight = 1; 2 vectorCutoff = 0.7; 3 numResults = 20; 4 numCandidates = numResults * 10; 5 6 // Execute the vector search 7 vectorResults = 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 33 vectorScoresMap = {}; 34 vectorResults.forEach(result => { 35 vectorScoresMap[result._id] = result.vectorScore * vectorWeight; 36 }); 37 38 // Execute the text search with boosting based just on ID 39 textResults = 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
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:
Execute a consulta
$vectorSearch.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).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
vectorWeightpara definir o peso e multiplicar as pontuações da pesquisa vetorial pelovectorWeight. Isso controla a importância dos resultados da pesquisa vetorial e garante que somente os documentos mais semanticamente relevantes sejam considerados.Execute a
$searchquery e aumente a pontuação dos documentos que correspondem aos resultados da pesquisa vetorial.
A query de amostra define as seguintes variáveis:
vectorWeightpara adicionar peso à pontuação do vetor de pesquisa, com um número menor fornecendo maior peso.vectorCutoffpara definir a pontuação mínima do vetor para que o resultado seja impulsionado.numResultspara especificar o número desejado de resultados de pesquisanumCandidatespara calcular o número de candidatos a considerar durante a pesquisa inicial. Ele calcula o número de candidatos usando a seguinte fórmula:numCandidates = numResults x overRequestFactor
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 | |
Pesquisa filmes que contenham o termo charming animal no campo fullplot. Para os documentos nos resultados que correspondem ao |
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
Você pode executar uma query que recupera os resultados da pesquisa pontuada das pesquisas semântica e de texto completo e aplica a expressão sigmoid para normalizar a pontuação antes de combinar os resultados dos pipelines em uma única pontuação agregada para cada documento. Para saber mais sobre a técnica de fusão de pontuação e como executar uma query de fusão de pontuação, consulte Executar pesquisa híbrida vetorial e de texto completo.