Como medir a precisão dos resultados da sua query

Nesta página

Casos de uso

Pré-requisitos
Procedimentos
Criar o índice Atlas Vector Search Index
Executar as queries
Compare os resultados

Você pode medir a precisão de sua query do Atlas Vector Search avaliando a proximidade dos resultados de uma pesquisa de ANN aos resultados de uma pesquisa de ENN em relação a vetores quantizados usando os mesmos critérios de query. Ou seja, você pode comparar os resultados da pesquisa ANN com os resultados da pesquisa ENN e medir com que frequência os resultados da pesquisa ANN incluem os vizinhos mais próximos nos resultados da pesquisa ENN.

Casos de uso

Talvez você queira medir a precisão dos resultados se tiver alguma das seguintes opções:

vetores quantizados
Um grande número de vetores
Vetor de baixa dimensionamento

Pré-requisitos

Para experimentar os exemplos desta página, você precisa do seguinte:

Um Atlas cluster com, opcionalmente, o conjunto de dados de amostra.
mongosh

Procedimentos

Para avaliar a precisão dos $vectorSearch resultados da query, você deve fazer o seguinte:

Crie um índice do Atlas Vector Search no campo de vetor e em quaisquer outros campos nos quais você deseja pré-filtrar os dados.
Recomendamos o uso de vetores quantizados para melhorar o armazenamento de seus vetores e a velocidade de suas queries. Se você não tiver vetores quantizados, poderá ativar a quantização automática ao indexar seu campo do tipo vector .
Construa e execute a query ENN seguida pela query ANN.
Compare os resultados da query ANN com os resultados da query ENN para avaliar as similaridades e diferenças nos resultados.

Esta seção demonstra como executar as etapas anteriores do 3 em relação aos dados na coleção sample_mflix.embedded_movies. Se não quiser usar o conjunto de dados de amostra, você poderá executar os procedimentos em seus próprios dados.

Criar o índice Atlas Vector Search Index

Esta seção demonstra como criar um índice do Atlas Vector Search para executar queries ANN e ENN do Atlas Vector Search .

No Atlas, VáGo para a Clusters página do seu projeto.

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

Acesse a página do Atlas Search do seu cluster.

Você pode acessar a página do Atlas Search pela barra lateral, pelo Data Explorer ou pela página de detalhes do cluster.

Na barra lateral, clique em Atlas Search sob o título Services.
Observação
Se você não tiver clusters, clique em Create cluster para criar um. Para saber mais, consulte Criar um cluster.
No menu suspenso Select data source, selecione seu cluster e clique em Go to Atlas Search.
A página Atlas Search é exibida.

Clique no botão Browse Collections para o seu cluster.
Expanda o banco de dados e selecione a coleção.
Clique na guia Search Indexes da coleção.
A página Atlas Search é exibida.

Clique no nome do seu cluster.
Clique na aba Atlas Search.
A página Atlas Search é exibida.

Clique em Create Search Index.

Inicie a configuração do seu índice.

Faça as seguintes seleções na página e clique em Next.

Search Type	Selecione o tipo de índice Vector Search.
Index Name and Data Source	Especifique as seguintes informações: Index Name: `vector_index` Database and Collection: `sample_mflix` database `embedded_movies` collection
Configuration Method	For a guided experience, select Visual Editor. To edit the raw index definition, select JSON Editor.

Especifique a definição do índice.

Exemplo

Esta definição de índice indexa o campo plot_embedding como o tipo vector com quantization binário automático habilitado e o campo genres como o tipo filter em um índice do Atlas Vector Search . O campo plot_embedding contém incorporações criadas utilizando o modelo de incorporações text-embedding-ada-002 do OpenAI. A definição de índice especifica 1536 dimensões vetoriais e mede a distância usando euclidean função de similaridade.

O Atlas detecta automaticamente campos que contêm incorporações vetoriais, assim como suas dimensões correspondentes. Para a coleção sample_mflix.embedded_movies, o campo plot_embedding é exibido.

Para configurar o índice, faça o seguinte:

Selecione Euclidean no menu suspenso Similarity Method.
Clique em Advanced, depois selecione a quantização Binary no menu suspenso.
Na seção Filter Field, especifique o campo genres para filtrar os dados.

Cole a seguinte definição de índice no editor JSON:

1 {
2   "fields": [
3     {
4       "numDimensions": 1536,
5       "path": "plot_embedding",
6       "similarity": "euclidean",
7       "type": "vector",
8       "quantization": "binary"
9     },
10     {
11       "path": "genres",
12       "type": "filter"
13     }
14   ]
15 }

Clique em Next para revisar o índice.

Clique em Create Vector Search Index.

O Atlas exibe uma janela modal para que você saiba que seu índice está crescendo.

Feche a janela modal You're All Set! clicando no botão Close.

Verifique o status.

O índice recém-criado é exibido na aba Atlas Search. Enquanto o índice está construindo, o campo Status lê Build in Progress. Quando o índice terminar de construir, o campo Status lê Active.

Observação

Collections maiores demoram mais tempo para indexar. Você receberá uma notificação por e-mail quando seu índice terminar a criação.

Executar as queries

Esta seção demonstra como executar as queries ENN e ANN em relação à coleta indexada.

Conecte-se ao seu Atlas cluster usando `mongosh`.

Abra uma janela de terminal e conecte-se ao seu cluster usando mongosh. Para saber mais,consulte Conectar mongosh via.

Mude para seu banco de dados.

Exemplo

Use o banco de dados sample_mflix. Para alternar para o sample_mflix banco de dados, execute o seguinte comando no mongosh prompt:

use sample_mflix

switched to db sample_mflix

Execute sua query ENN.

Exemplo

Copie e cole a seguinte query de exemplo no seu terminal e execute-a usando mongosh. mongosh pode ficar um pouco lento quando você cola na query devido ao número de caracteres na incorporação vetorial.

1 db.embedded_movies.aggregate([
2   {
3     "$vectorSearch": {
4       "index": "vector_index",
5       "path": "plot_embedding",
6       "filter": {
7         "$and": [
8           {
9             "genres": { "$eq": "Action" }
10           }, 
11           {
12             "genres": { "$ne": "Western" }
13           }
14         ]
15       },
16       "queryVector": [-0.0241895392537117, -0.01683979481458664, -0.0017179687274619937, -0.03551029786467552, -0.032710395753383636, 0.026625992730259895, -0.018266668543219566, -0.010210219770669937, -0.01335337944328785, -0.022843431681394577, 0.010095800273120403, 0.008514124900102615, -0.009308327920734882, 0.0010996009223163128, 0.015332155860960484, -0.011024614796042442, 0.030825844034552574, -0.008668927475810051, 0.0013419002061709762, -0.03448725491762161, -0.0006524411146529019, 0.011031344532966614, -0.0005321328062564135, -0.004745026119053364, -0.0017970525659620762, -0.010573668405413628, 0.006505060940980911, -0.014847557991743088, 0.011482290923595428, -0.010277525521814823, -0.0018811841728165746, 0.003577278694137931, -0.010990961454808712, -0.02285689301788807, -0.012559176422655582, -0.009092951193451881, -0.006249300669878721, -0.010842889547348022, -0.013400493189692497, 0.015332155860960484, 0.007827610708773136, 0.012942816130816936, -0.008655466139316559, 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17       "exact": true,
18       "limit": 10
19     }
20   },
21   {
22     "$project": {    
23       "_id": 0,
24       "plot": 1,
25       "title": 1,
26       "genres": 1,
27       "score": { $meta: "vectorSearchScore" }    
28     }
29   }
30 ])

[
  {
    plot: "A thief with a unique code of professional ethics is double-crossed by his crew and left for dead. Assuming a new disguise and forming an unlikely alliance with a woman on the inside, he looks to hijack the score of the crew's latest heist.",
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'Parker',
    score: 0.7952193021774292
  },
  {
    plot: 'An adrenaline junkie walks away from a whirlwind romance and embraces a new life as a thief, though he soon finds himself pursued by veteran police officer and engaged in a turf war with a local gangster.',
    genres: [ 'Action', 'Comedy', 'Romance' ],
    title: 'Kick',
    score: 0.7876788377761841
  },
  {
    plot: 'The dying words of a thief spark a madcap cross-country rush to find some treasure.',
    genres: [ 'Action', 'Adventure', 'Comedy' ],
    title: "It's a Mad, Mad, Mad, Mad World",
    score: 0.7861514687538147
  },
  {
    plot: 'A group of professional bank robbers start to feel the heat from police when they unknowingly leave a clue at their latest heist.',
    genres: [ 'Action', 'Crime', 'Drama' ],
    title: 'Heat',
    score: 0.7858744859695435
  },
  {
    plot: 'A romantic and action packed story of three best friends, a group of high end art thieves, who come into trouble when a love-triangle forms between them.',
    genres: [ 'Action', 'Comedy', 'Crime' ],
    title: 'Once a Thief',
    score: 0.7815249562263489
  },
  {
    plot: 'A recently released ex-con and his loyal wife go on the run after a heist goes awry.',
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'The Getaway',
    score: 0.7795301079750061
  },
  {
    plot: "The story of what happens after a master thief achieves his last big score, when the FBI agent who promised he'd capture him is about to do just that.",
    genres: [ 'Action', 'Comedy', 'Crime' ],
    title: 'After the Sunset',
    score: 0.779007613658905
  },
  {
    plot: 'A group of bank robbers find their multi-million dollar plan interrupted by a hard-boiled detective.',
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'Takers',
    score: 0.7787627577781677
  },
  {
    plot: "After he's shot during a heist in East L.A., an armored truck driver wrestles with rehabilitation and tracking down the man who committed the crime.",
    genres: [ 'Action', 'Crime', 'Drama' ],
    title: 'The Take',
    score: 0.7776619791984558
  },
  {
    plot: 'While doing a friend a favor and searching for a runaway teenager, a police detective stumbles upon a bizarre band of criminals about to pull off a bank robbery.',
    genres: [ 'Action', 'Crime', 'Drama' ],
    title: 'No Good Deed',
    score: 0.7773410677909851
  }
]

Essa query usa os estágios de pipeline a seguir

`$vectorSearch`	Pré-filtra os documentos para procurar filmes no gênero `Action` e não no gênero `Western`. Pesquisa no campo `plot_embedding` os vizinhos exatos mais próximos usando incorporações vetoriais para a string `thrilling heist with twists`. Limita a saída a apenas 10 resultados.
`$project`	Exclui todos os campos exceto `plot`, `title` e `genres` dos documentos nos resultados. Adiciona um campo chamado `score` que mostra a pontuação dos documentos dos resultados.

Execute sua query de ANN.

Exemplo

1 db.embedded_movies.aggregate([
2   {
3     "$vectorSearch": {
4       "index": "vector_index",
5       "path": "plot_embedding",
6       "filter": {
7         "$and": [
8           {
9             "genres": { "$eq": "Action" }
10           }, 
11           {
12             "genres": { "$ne": "Western" }
13           }
14         ]
15       },
16       "queryVector": [-0.0241895392537117, -0.01683979481458664, -0.0017179687274619937, -0.03551029786467552, -0.032710395753383636, 0.026625992730259895, -0.018266668543219566, -0.010210219770669937, -0.01335337944328785, -0.022843431681394577, 0.010095800273120403, 0.008514124900102615, -0.009308327920734882, 0.0010996009223163128, 0.015332155860960484, -0.011024614796042442, 0.030825844034552574, -0.008668927475810051, 0.0013419002061709762, -0.03448725491762161, -0.0006524411146529019, 0.011031344532966614, -0.0005321328062564135, -0.004745026119053364, -0.0017970525659620762, -0.010573668405413628, 0.006505060940980911, -0.014847557991743088, 0.011482290923595428, -0.010277525521814823, -0.0018811841728165746, 0.003577278694137931, -0.010990961454808712, -0.02285689301788807, -0.012559176422655582, -0.009092951193451881, -0.006249300669878721, -0.010842889547348022, -0.013400493189692497, 0.015332155860960484, 0.007827610708773136, 0.012942816130816936, -0.008655466139316559, 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17       "numCandidates": 100,
18       "limit": 10
19     }
20   },
21   {
22     "$project": {    
23       "_id": 0,
24       "plot": 1,
25       "title": 1,
26       "genres": 1,
27       "score": { $meta: "vectorSearchScore" }    
28     }
29   }
30 ])

[
  {
    plot: "A thief with a unique code of professional ethics is double-crossed by his crew and left for dead. Assuming a new disguise and forming an unlikely alliance with a woman on the inside, he looks to hijack the score of the crew's latest heist.",
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'Parker',
    score: 0.7952193021774292
  },
  {
    plot: 'An adrenaline junkie walks away from a whirlwind romance and embraces a new life as a thief, though he soon finds himself pursued by veteran police officer and engaged in a turf war with a local gangster.',
    genres: [ 'Action', 'Comedy', 'Romance' ],
    title: 'Kick',
    score: 0.7876788377761841
  },
  {
    plot: 'The dying words of a thief spark a madcap cross-country rush to find some treasure.',
    genres: [ 'Action', 'Adventure', 'Comedy' ],
    title: "It's a Mad, Mad, Mad, Mad World",
    score: 0.7861514687538147
  },
  {
    plot: 'A group of professional bank robbers start to feel the heat from police when they unknowingly leave a clue at their latest heist.',
    genres: [ 'Action', 'Crime', 'Drama' ],
    title: 'Heat',
    score: 0.7858744859695435
  },
  {
    plot: 'A romantic and action packed story of three best friends, a group of high end art thieves, who come into trouble when a love-triangle forms between them.',
    genres: [ 'Action', 'Comedy', 'Crime' ],
    title: 'Once a Thief',
    score: 0.7815249562263489
  },
  {
    plot: 'A recently released ex-con and his loyal wife go on the run after a heist goes awry.',
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'The Getaway',
    score: 0.7795301079750061
  },
  {
    plot: "The story of what happens after a master thief achieves his last big score, when the FBI agent who promised he'd capture him is about to do just that.",
    genres: [ 'Action', 'Comedy', 'Crime' ],
    title: 'After the Sunset',
    score: 0.779007613658905
  },
  {
    plot: 'A group of bank robbers find their multi-million dollar plan interrupted by a hard-boiled detective.',
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'Takers',
    score: 0.7787627577781677
  },
  {
    plot: "After he's shot during a heist in East L.A., an armored truck driver wrestles with rehabilitation and tracking down the man who committed the crime.",
    genres: [ 'Action', 'Crime', 'Drama' ],
    title: 'The Take',
    score: 0.7776619791984558
  },
  {
    plot: 'A high-tech police surveillance team attempts to take down a gang of ruthless bank robbers.',
    genres: [ 'Action', 'Crime' ],
    title: 'Cold Eyes',
    score: 0.775409996509552
  }
]

Essa query usa os estágios de pipeline a seguir

`$vectorSearch`	Pré-filtra os documentos para pesquisar por filmes no gênero `Action` e não no gênero `Western`. Pesquisa o campo `plot_embedding` para os vizinhos mais próximos aproximados usando incorporações vetoriais para a string `thrilling heist with twists`. Considera até `100` vizinhos mais próximos, mas limita a saída a apenas 10 resultados.
`$project`	Exclui todos os campos exceto `plot`, `title` e `genres` dos documentos nos resultados. Adiciona um campo chamado `score` que mostra a pontuação dos documentos dos resultados.

Compare os resultados

Os nove principais documentos no exemplo de resultados de query ENN e ANN são os mesmos e têm a mesma pontuação. Isso mostra um alto nível de similaridade nos principais resultados da query. No entanto, o décimo documento nos resultados das queries ENN e ANN é diferente, o que reflete uma leve variação na pesquisa exata e aproximada do vizinho mais próximo.

A pesquisaENN examina todos os possíveis candidatos e retorna a correspondência mais próxima da query com base na pontuação de similaridade. A pesquisaANN utiliza aproximações para acelerar a pesquisa, o que pode alterar a pontuação dos documentos. Se você aumentar o numCandidates valor de na query ANN, os resultados serão uma correspondência mais próxima dos resultados da query ENN. No entanto, isso consumiria recursos computacionais adicionais e pode reduzir a velocidade da query. O décimo documento nos resultados reflete a compensação entre precisão e velocidade.

Depois de avaliar numericamente os resultados em relação à verdade fundamental da ENN, recomendamos testar um conjunto de 100 queries da mesma maneira e calcular a "semelhança de jáccard" entre os conjuntos de resultados. A similaridade de Jaccard pode ser calculada dividindo a interseção entre dois conjuntos, ou seja, os itens sobrepostos, pelo tamanho total do conjunto. Isso dá uma noção do desempenho de recuperação das queries de ANN, incluindo aquelas realizadas em vetores quantizados.

Se você notar grandes discrepâncias entre os resultados da query ENN e ANN, recomendamos ajustar o numCandidates valor de para atingir um equilíbrio ideal entre precisão e velocidade para sua aplicação.

Voltar

Recursos da API

Melhore o desempenho

1	{
2	"fields": [
3	{
4	"numDimensions": 1536,
5	"path": "plot_embedding",
6	"similarity": "euclidean",
7	"type": "vector",
8	"quantization": "binary"
9	},
10	{
11	"path": "genres",
12	"type": "filter"
13	}
14	]
15	}

Casos de uso

Pré-requisitos

Procedimentos

Criar o índice Atlas Vector Search Index

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

Acesse a página do Atlas Search do seu cluster.

Observação

Clique em Create Search Index.

Inicie a configuração do seu índice.

Especifique a definição do índice.

Exemplo

Clique em Next para revisar o índice.

Clique em Create Vector Search Index.

Feche a janela modal You're All Set! clicando no botão Close.

Verifique o status.

Observação

Executar as queries

Conecte-se ao seu Atlas cluster usando mongosh.

Mude para seu banco de dados.

Exemplo

Execute sua query ENN.

Exemplo

Execute sua query de ANN.

Exemplo

Compare os resultados

No Atlas, VáGo para a Clusters página do seu projeto.

Conecte-se ao seu Atlas cluster usando `mongosh`.