Como realizar a pesquisa híbrida

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Pré-requisitos

Crie o Atlas Vector Search e o índice de pesquisa do Atlas Search
Acesso necessário
Procedimento
Executar uma pesquisar combinada de query de pesquisa semântica e pesquisar de texto completo
Sobre a query
Procedimento
Aprenda assistindo

Este tutorial demonstra como combinar os resultados da pesquisa semântica de uma query $vectorSearch com os resultados da pesquisa de texto completo de uma query $search usando a fusão de classificação recíproca. A fusão recíproca de classificação é uma maneira de combinar resultados de diferentes tipos de pesquisas em um único resultado. Este tutorial orienta você pelas seguintes etapas:

Crie um Atlas Vector Search índice de pesquisa no campo plot_embeddings na collection sample_mflix.embedded_movies .
Crie um Atlas Search índice de pesquisa no campo title na collection sample_mflix.embedded_movies .
Combine e execute uma query { $vectorSearch plot_embeddings na collection sample_mflix.embedded_movies com uma query { $search } no campo title usando a fusão de classificação recíproca.

Pré-requisitos

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

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.

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_embeddings 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.

Acesso necessário

Para criar Atlas Vector Search e Atlas Search índice de pesquisa, você deve ter pelo menos Project Data Access Admin acesso ao projeto.

Procedimento

No Atlas, acesse a página Clusters do seu projeto.

Se ainda não estiver exibido, selecione a organização que contém o projeto desejado no menu Organizations na barra de navegação.
Se ainda não estiver exibido, selecione o projeto desejado no menu Projects na barra de navegação.
Se a página Clusters ainda não estiver exibida, clique em Database na barra lateral.

Acesse a página Atlas Search do seu cluster.

Clique no nome do seu cluster.
Clique na aba Atlas Search.

Clique em Create Index.

Clique em Atlas Search em Services na barra de navegação.
Selecione seu cluster no menu suspenso Select data source e clique em Go to Atlas Search.

Defina o índice do Atlas Vector Search.

Clique em Create Search Index.
Em Atlas Vector Search, selecione JSON Editor e clique em Next.
Na seção Database and Collection, localize o banco de dados sample_mflix e selecione a coleção embedded_movies.
No campo Index Name, digite rrf-vector-search.
Substitua a definição padrão pela seguinte definição de índice e clique em Next.

Defina o índice do Atlas Vector Search.

Substitua a definição padrão pela seguinte definição de índice.

Esta definição de índice indexa o campo plot_embedding como o tipo de vetor . O campo plot_embedding contém incorporações criadas utilizando o modelo de incorporação text-embedding-ada-002 do OpenAI. A definição de índice especifica 1536 dimensões vetoriais e mede a similaridade usando euclidean.

1 {
2   "fields": [
3     {
4       "type": "vector",
5       "path": "plot_embedding",
6       "numDimensions": 1536,
7       "similarity": "euclidean"
8     }
9   ]
10 }

Revise a definição do índice e clique em Create Search Index.

Uma janela modal é exibida para que você saiba que seu índice está construindo.

Clique em Close para fechar a janela modal You're All Set! e aguarde até que o índice termine de construir.

O índice deve levar cerca de um minuto para ser criado. Enquanto constrói, a coluna Status lê Initial Sync. Quando termina de se formar, a coluna Status mostra Active.

Clique em Create Index para definir o índice do Atlas Search.

Selecione um Atlas Search Configuration Method e clique Next em .

Para uma experiência abada, selecione a Pesquisa Atlas Visual Editor.
Para editar a definição de índice bruto, selecione a Pesquisa Atlas JSON Editor.

Insira o Nome do Índice e defina o reconhecimento de data center e a collection.

No campo Index Name, digite rrf-full-text-search.
Observação
Se você nomear seu índice como default, não precisará especificar um parâmetro index ao usar o estágio do pipeline $search. Caso contrário, você deve especificar o nome do índice utilizando o parâmetro index.
Na seção Database and Collection, localize o banco de dados sample_mflix e selecione a coleção embedded_movies.

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.

Você pode usar o Visual Editor do Atlas Search ou o JSON Editor do Atlas Search na interface de usuário do Atlas para criar o índice.

Clique em Create Search Index.

Uma janela modal é exibida para que você saiba que seu índice está construindo.

Feche a janela modal You're All Set! clicando em Close e aguarde o índice terminar de construir.

O índice deve levar cerca de um minuto para ser criado. Enquanto está se formando, a coluna Status mostra Initial Sync. Quando terminar de se formar, a coluna Status mostrará Active.

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

Esta seção demonstra como fazer query dos dados na collection sample_mflix.embedded_movies 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.

Sobre a query

A query a seguir 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 em sua posição na array de resultados. A pontuação de classificação recíproca é calculada usando a seguinte fórmula:

1.0/{document position in the results + vector or full-text penalty + 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_penalty
full_text_penalty

Estágios de query

A query de amostra utiliza os seguintes estágios de pipeline:

Estágio $vectorSearch para pesquisar no campo plot_embeddings a string nova iorque especificada como incorporações vetoriais no campo queryVector da query. A query utiliza ada-002-text incorporação, que é o mesmo que a incorporação do vetor no campo plot_embedding . A query também especifica uma pesquisar para até 100 vizinhos mais próximos e limita os resultados somente para 20 documento. Esse estágio retorna os documentos classificados da pesquisa semântica nos resultados.
Etapa $group para agrupar todos os documentos nos resultados da pesquisa semântica em um campo chamado docs.
$unwind estágio para desenrolar a matriz de documentos no campo docs e armazenar a posição do documento na matriz de resultados em um campo chamado rank.
Etapa $addFields para adicionar um novo campo chamado vs_score que contém a pontuação de classificação recíproca de cada documento nos resultados. Aqui, a pontuação de classificação recíproca é calculada dividindo 1.0 pela soma de rank, o peso vector_penalty e um valor constante de 1.
$project para incluir apenas os seguintes campos nos resultados:
- vs_score
- _id
- title
$unionWith estágio para combinar os resultados dos estágios anteriores com os resultados dos seguintes estágios no subpipeline:
- $search estágio para pesquisar filmes que contenham o termo new york no campo title . Esse estágio retorna os documentos classificados da pesquisa de texto completo nos resultados.
- Estágio $limit para limitar a saída somente aos resultados 20 .
- Etapa $group para agrupar todos os documentos da pesquisa de texto completo em um campo chamado docs.
- $unwind estágio para desenrolar a matriz de documentos no campo docs e armazenar a posição do documento na matriz de resultados em um campo chamado rank.
- Etapa $addFields para adicionar um novo campo chamado fts_score que contém a pontuação de classificação recíproca de cada documento nos resultados. Aqui, a pontuação de classificação recíproca é calculada dividindo 1.0 pela soma do valor de rank, o peso full_text penalty e um valor constante de 1.
- $project para incluir apenas os seguintes campos nos resultados:
  - fts_score
  - _id
  - title
Etapa $group para agrupar os documentos nos resultados das etapas anteriores por title, vs_score e fts_score.
$project para incluir apenas os seguintes campos nos resultados:
- vs_score
- fts_score
- _id
- title
Etapa $project para adicionar um campo chamado score que contém a soma de vs_score e fts_score aos resultados.
Estágio $sort para classificar os resultados por score em ordem decrescente.
Estágio $limit para limitar a saída somente aos resultados 10 .

Procedimento

Conecte-se ao seu cluster usando `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 do `sample_mflix` .

Execute o seguinte comando no prompt mongosh :

use sample_mflix

switched to db sample_mflix

Execute as seguintes queries do Atlas Search na collection `embedded_movies` .

1 var vector_penalty = 1;
2 var full_text_penalty = 10;
3 db.embedded_movies.aggregate([
4   {
5     "$vectorSearch": {
6       "index": "rrf-vector-search",
7       "path": "plot_embedding",
8       "queryVector": 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62               "$divide": [
63                 1.0,
64                 {"$add": ["$rank", full_text_penalty, 1]}
65               ]
66             }
67           }
68         },
69         {
70           "$project": {
71             "fts_score": 1,
72             "_id": "$docs._id",
73             "title": "$docs.title"
74           }
75         }
76       ]
77     }
78   },
79   {
80     "$group": {
81       "_id": "$title",
82       "vs_score": {"$max": "$vs_score"},
83       "fts_score": {"$max": "$fts_score"}
84     }
85   },
86   {
87     "$project": {
88       "_id": 1,
89       "title": 1,
90       "vs_score": {"$ifNull": ["$vs_score", 0]},
91       "fts_score": {"$ifNull": ["$fts_score", 0]}
92     }
93   },
94   {
95     "$project": {
96       "score": {"$add": ["$fts_score", "$vs_score"]},
97       "_id": 1,
98       "title": 1,
99       "vs_score": 1,
100       "fts_score": 1
101     }
102   },
103   {"$sort": {"score": -1}},
104   {"$limit": 10}
105 ])

[
  { _id: 'Pixels', vs_score: 0.5, fts_score: 0, score: 0.5 },
  {
    _id: 'Fighting',
    vs_score: 0.3333333333333333,
    fts_score: 0,
    score: 0.3333333333333333
  },
  {
    _id: 'A Most Violent Year',
    vs_score: 0.25,
    fts_score: 0,
    score: 0.25
  },
  {
    _id: 'Mistletoe Over Manhattan',
    vs_score: 0.2,
    fts_score: 0,
    score: 0.2
  },
  {
    _id: 'End of Days',
    vs_score: 0.16666666666666666,
    fts_score: 0,
    score: 0.16666666666666666
  },
  { _id: 'Sam', vs_score: 0.125, fts_score: 0, score: 0.125 },
  {
    _id: 'Rumble in the Bronx',
    vs_score: 0.1111111111111111,
    fts_score: 0,
    score: 0.1111111111111111
  },
  {
    _id: 'Escape from New York',
    vs_score: 0.047619047619047616,
    fts_score: 0.05555555555555555,
    score: 0.10317460317460317
  },
  { _id: 'The Other Guys', vs_score: 0.1, fts_score: 0, score: 0.1 },
  {
    _id: 'Sky Captain and the World of Tomorrow',
    vs_score: 0.09090909090909091,
    fts_score: 0,
    score: 0.09090909090909091
  }
]

Se você classificar os resultados em ordem crescente substituindo o valor de score na linha 103 por 1, o Atlas Vector Search retornará os seguintes resultados:

[
  {
    _id: '2 Days in New York',
    vs_score: 0,
    fts_score: 0.03333333333333333,
    score: 0.03333333333333333
  },
  {
    _id: 'An Englishman in New York',
    vs_score: 0,
    fts_score: 0.034482758620689655,
    score: 0.034482758620689655
  },
  {
    _id: 'New York, I Love You',
    vs_score: 0,
    fts_score: 0.03571428571428571,
    score: 0.03571428571428571
  },
  {
    _id: 'New York: A Documentary Film',
    vs_score: 0,
    fts_score: 0.037037037037037035,
    score: 0.037037037037037035
  },
  {
    _id: 'A Couch in New York',
    vs_score: 0,
    fts_score: 0.038461538461538464,
    score: 0.038461538461538464
  },
  {
    _id: 'Sherlock Holmes in New York',
    vs_score: 0,
    fts_score: 0.04,
    score: 0.04
  },
  {
    _id: 'A King in New York',
    vs_score: 0,
    fts_score: 0.041666666666666664,
    score: 0.041666666666666664
  },
  {
    _id: 'Live from New York!',
    vs_score: 0,
    fts_score: 0.043478260869565216,
    score: 0.043478260869565216
  },
  {
    _id: 'Sleepless in New York',
    vs_score: 0,
    fts_score: 0.045454545454545456,
    score: 0.045454545454545456
  },
  {
    _id: 'Gangs of New York',
    vs_score: 0,
    fts_score: 0.047619047619047616,
    score: 0.047619047619047616
  }
]

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

← Como realizar semantic search nos dados do cluster do Atlas

Integre a pesquisa vetorial com tecnologias de IA →

1	{
2	"fields": [
3	{
4	"type": "vector",
5	"path": "plot_embedding",
6	"numDimensions": 1536,
7	"similarity": "euclidean"
8	}
9	]
10	}

Pré-requisitos

Observação

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

Acesso necessário

Procedimento

No Atlas, acesse a página .css-h15tq0{font-style:normal;font-weight:700;}Clusters do seu projeto.

Acesse a página Atlas Search do seu cluster.

Clique em Create Index.

Defina o índice do Atlas Vector Search.

Defina o índice do Atlas Vector Search.

Revise a definição do índice e clique em Create Search Index.

Clique em Close para fechar a janela modal You're All Set! e aguarde até que o índice termine de construir.

Clique em Create Index para definir o índice do Atlas Search.

Selecione um Atlas Search Configuration Method e clique Next em .

Insira o Nome do Índice e defina o reconhecimento de data center e a collection.

Observação

Defina o Atlas Search índice de pesquisa.

Clique em Create Search Index.

Feche a janela modal You're All Set! clicando em Close e aguarde o índice terminar de construir.

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

Sobre a query

Variáveis da query

Estágios de query

Procedimento

Conecte-se ao seu cluster usando mongosh.

Use o banco de dados do sample_mflix .

Execute as seguintes queries do Atlas Search na collection embedded_movies .

Aprenda assistindo

No Atlas, acesse a página Clusters do seu projeto.

Conecte-se ao seu cluster usando `mongosh`.

Use o banco de dados do `sample_mflix` .

Execute as seguintes queries do Atlas Search na collection `embedded_movies` .