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.
➤ Use o menu suspenso Selecione seu idioma para selecionar a interface que deseja usar para criar seu índice.
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.
AVISO: Melhorias de navegação em andamento No momento, estamos implementando uma experiência de navegação nova e aprimorada. Se as etapas a seguir não corresponderem à sua visualização na IU do Atlas, consulte a documentação de pré-visualização.
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.
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:
|
Configuration Method | For a guided experience, select Visual Editor. To edit the raw index definition, select JSON Editor. |
Observação
O índice do Atlas Search é nomeado default por padrão. Se você mantiver esse nome, seu índice será o índice de pesquisa padrão para qualquer consulta do Atlas Search que não especifique uma opção index diferente nos operadores. Se você estiver criando vários índices, recomendamos que mantenha uma convenção de nomenclatura consistente e descritiva em todos os seus índices.
Especifique a definição do índice.
Exemplo
Essa definição de índice indexa o campo plot_embedding_voyage_3_large como o tipo vector com o binário automático quantization ativado e o campo genres como o tipo filter em um índice do Atlas Vector Search. O campo plot_embedding_voyage_3_large contém incorporações criadas utilizando o modelo de incorporação voyage-3-large da Voyage AI. A definição do índice especifica 2048 dimensões vetoriais e mede a distância usando a função de similaridade dotProduct.
O Atlas detecta automaticamente campos que contêm incorporações vetoriais, assim como suas dimensões correspondentes. Para a coleção sample_mflix.embedded_movies, selecione o campo plot_embedding_voyage_3_large.
Para configurar o índice, faça o seguinte:
Selecione Dot Product no menu suspenso Similarity Method.
Clique em Advanced, depois selecione a quantização Binary no menu suspenso.
Na seção Filter Field, especifique o campo
genrespara filtrar os dados.
Cole a seguinte definição de índice no editor JSON:
1 { 2 "fields": [ 3 { 4 "numDimensions": 2048, 5 "path": "plot_embedding_voyage_3_large", 6 "similarity": "dotProduct", 7 "type": "vector", 8 "quantization": "binary" 9 }, 10 { 11 "path": "genres", 12 "type": "filter" 13 } 14 ] 15 }
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.
Conecte ao sistema utilizando mongosh.
No seu terminal, conecte-se à implantação hospedada na nuvem do Atlas ou à implantação local a partir do mongosh. Para obter instruções detalhadas sobre como se conectar, consulte Conectar-se a um sistema.
Execute o método db.collection.createSearchIndex() para criar o índice.
db.embedded_movies.createSearchIndex( "vector_index", "vectorSearch", { "fields": [ { "numDimensions": 2048, "path": "plot_embedding_voyage_3_large", "similarity": "dotProduct", "type": "vector", "quantization": "binary" }, { "path": "genres", "type": "filter" } ] } } )
vector_index
Executar as queries
Você não pode executar queries do Atlas Vector Search na UI do Atlas Search Tester. Use mongosh ou um driver compatível para executar a query.
Esta seção demonstra como executar as queries ENN e ANN em relação à coleta indexada.
Prepare suas incorporações de query.
Salve as seguintes incorporações em um arquivo chamado query-embeddings.js:
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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-se a um cluster via mongosh.
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_voyage_3_large", 6 "filter": { 7 "$and": [ 8 { 9 "genres": { "$eq": "Action" } 10 }, 11 { 12 "genres": { "$ne": "Comedy" } 13 } 14 ] 15 }, 16 "queryVector": TIME_TRAVEL_EMBEDDING, 17 "exact": true, 18 "limit": 10 19 } 20 }, 21 { 22 "$project": { 23 "_id": 0, 24 "plot": 1, 25 "title": 1, 26 "genres": 1, 27 "score": { $meta: "vectorSearchScore" } 28 } 29 } 30 ])
[ { plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.', genres: [ 'Action', 'Crime', 'Drama' ], title: 'Retroactive', score: 0.760047972202301 }, { plot: 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...', genres: [ 'Action', 'Sci-Fi' ], title: 'A.P.E.X.', score: 0.7576861381530762 }, { plot: 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.', genres: [ 'Action', 'Crime', 'Sci-Fi' ], title: 'Timecop', score: 0.7576561570167542 }, { plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.', genres: [ 'Action', 'Sci-Fi', 'Thriller' ], title: 'Thrill Seekers', score: 0.7509932518005371 }, { plot: 'Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...', genres: [ 'Action', 'Adventure', 'Sci-Fi' ], title: 'Timerider: The Adventure of Lyle Swann', score: 0.7502642869949341 }, { plot: 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.', genres: [ 'Sci-Fi', 'Adventure', 'Action' ], title: 'The Time Machine', score: 0.7502503395080566 }, { plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.', genres: [ 'Action', 'Sci-Fi' ], title: 'The Final Countdown', score: 0.7469133734703064 }, { plot: "Ba'al travels back in time and prevents the Stargate program from being started. SG-1 must somehow restore history.", genres: [ 'Action', 'Adventure', 'Drama' ], title: 'Stargate: Continuum', score: 0.7468316555023193 }, { plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', genres: [ 'Action', 'Adventure', 'Drama' ], title: 'Love Story 2050', score: 0.7420939207077026 }, { plot: "Captain Picard and his crew pursue the Borg back in time to stop them from preventing Earth's first contact with an alien species. They also make sure that Zefram Cochrane makes his famous maiden flight at warp speed.", genres: [ 'Action', 'Adventure', 'Sci-Fi' ], title: 'Star Trek: First Contact', score: 0.7356286644935608 } ]
Essa query usa os estágios de pipeline a seguir
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Execute sua query de ANN.
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_voyage_3_large", 6 "filter": { 7 "$and": [ 8 { 9 "genres": { "$eq": "Action" } 10 }, 11 { 12 "genres": { "$ne": "Comedy" } 13 } 14 ] 15 }, 16 "queryVector": TIME_TRAVEL_EMBEDDING, 17 "numCandidates": 100, 18 "limit": 10 19 } 20 }, 21 { 22 "$project": { 23 "_id": 0, 24 "plot": 1, 25 "title": 1, 26 "genres": 1, 27 "score": { $meta: "vectorSearchScore" } 28 } 29 } 30 ])
[ { plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.', genres: [ 'Action', 'Crime', 'Drama' ], title: 'Retroactive', score: 0.760047972202301 }, { plot: 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...', genres: [ 'Action', 'Sci-Fi' ], title: 'A.P.E.X.', score: 0.7576861381530762 }, { plot: 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.', genres: [ 'Action', 'Crime', 'Sci-Fi' ], title: 'Timecop', score: 0.7576561570167542 }, { plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.', genres: [ 'Action', 'Sci-Fi', 'Thriller' ], title: 'Thrill Seekers', score: 0.7509932518005371 }, { plot: 'Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...', genres: [ 'Action', 'Adventure', 'Sci-Fi' ], title: 'Timerider: The Adventure of Lyle Swann', score: 0.7502642869949341 }, { plot: 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.', genres: [ 'Sci-Fi', 'Adventure', 'Action' ], title: 'The Time Machine', score: 0.7502503395080566 }, { plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.', genres: [ 'Action', 'Sci-Fi' ], title: 'The Final Countdown', score: 0.7469133734703064 }, { plot: "Ba'al travels back in time and prevents the Stargate program from being started. SG-1 must somehow restore history.", genres: [ 'Action', 'Adventure', 'Drama' ], title: 'Stargate: Continuum', score: 0.7468316555023193 }, { plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', genres: [ 'Action', 'Adventure', 'Drama' ], title: 'Love Story 2050', score: 0.7420939207077026 }, { plot: "Captain Picard and his crew pursue the Borg back in time to stop them from preventing Earth's first contact with an alien species. They also make sure that Zefram Cochrane makes his famous maiden flight at warp speed.", genres: [ 'Action', 'Adventure', 'Sci-Fi' ], title: 'Star Trek: First Contact', score: 0.7356286644935608 } ]
Essa query usa os estágios de pipeline a seguir
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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.
Recomendamos que você use listas de julgamento para uma lista estruturada de queries com resultados ideais para a consulta ANN ou valores de verdade real de ENN. Você pode usar os resultados da consulta ENN como lista de julgamento de referência e, em seguida, avaliar os resultados da consulta ANN em relação a essa lista de julgamento para medir o recall, a sobreposição e o desempenho. As listas de julgamento propiciam uma maneira de avaliar se queries ANN atendem aos limiares de precisão ou recall desejados em comparação com a linha de base ENN. Use LLMs para gerar queries de exemplo.