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Cómo realizar una búsqueda híbrida

Puede combinar consultas de MongoDB Vector Search y MongoDB Search en una búsqueda híbrida para obtener resultados unificados.

Este tutorial demuestra cómo ejecutar una búsqueda híbrida en el sample_mflix.embedded_movies Colección que contiene información sobre películas. En concreto, este tutorial te guiará por los siguientes pasos:

Crea un índice de MongoDB Vector Search en el campo plot_embedding_voyage_3_large . Este campo contiene incrustaciones vectoriales que representan el resumen de la trama de una película.
Crea un índice de Búsqueda de MongoDB en la colección sample_mflix.embedded_movies.
Ejecute una consulta para combinar los resultados de una $vectorSearch Consulta y consulta utilizando las siguientes $search técnicas:
- Impulso semántico
- Fusión de rangos recíprocos

¿Por qué búsqueda híbrida?

Una búsqueda híbrida consiste en la agregación de diferentes métodos de búsqueda, como la búsqueda de texto completo y la búsqueda semántica, para los mismos criterios de consulta. Si bien la búsqueda de texto completo es eficaz para encontrar coincidencias exactas de los términos de consulta, la búsqueda semántica ofrece la ventaja adicional de identificar documentos semánticamente similares, incluso si no contienen el término de consulta exacto. Esto garantiza que las coincidencias sinónimas y contextualmente similares también se incluyan en los resultados combinados de ambos métodos de búsqueda.

Por el contrario, si tienes tokens para nombres propios o palabras clave específicas en el conjunto de datos que no esperas que se tengan en cuenta en el entrenamiento de un modelo de incrustación en el mismo contexto que se usan en el conjunto de datos, la búsqueda vectorial podría beneficiarse de combinarse con una búsqueda en texto completo.

También puedes establecer pesos para cada método de búsqueda por query. En función de si los resultados de búsqueda semántica o de texto completo son los más relevantes y adecuados para un query, puedes aumentar la ponderación de ese método de búsqueda por query.

Puede aplicar las siguientes técnicas para lograr una búsqueda híbrida:

Técnica

Impulso semántico

Fusión recíproca de clasificación

Propósito

Mejora la exactitud de los resultados de la búsqueda de texto completo al potenciar los documentos que coinciden con la búsqueda vectorial. Útil al recuperar conjuntos de resultados muy grandes.

Combina resultados de diferentes métodos de búsqueda en un único conjunto de resultados clasificados calculando la clasificación recíproca de los documentos en los resultados.

Método de clasificación

$vectorSearch Ejecute para recuperar resultados semánticamente similares.
Ejecute para recuperar resultados de texto completo mientras potencia los documentos $search devueltos $vectorSearch por.

Recupera $vectorSearch y $search resultados.
Fusiona ambos conjuntos de resultados utilizando el rango de cada documento en los respectivos conjuntos de resultados.

Método de puntuación

Aumenta la puntuación de los resultados de texto completo de los documentos que coinciden con los resultados de la búsqueda vectorial, mediante la puntuación de búsqueda vectorial ponderada.

Calcula la puntuación utilizando la siguiente fórmula:

1.0/{document position in the results + constant value}

Luego, para cada método de búsqueda, se aplican diferentes ponderaciones (w) para otorgarle mayor importancia. Para cada documento, el rango recíproco ponderado se calcula multiplicando la ponderación por el rango recíproco del documento.

weighted_reciprocal_rank = w x reciprocal_rank

Funciones de búsqueda

Admite facetas para agrupar datos en categorías.
Admite la paginación de resultados utilizando searchAfter y.searchBefore

Admite un mayor procesamiento de los resultados en etapas de agregación posteriores, como $group.

Salida

Conjunto de resultados único que contiene documentos de $search

Conjunto de resultados único que contiene documentos de ambos métodos de búsqueda.

Requisitos previos

Antes de comenzar, debes tener lo siguiente:

Un clúster Atlas con MongoDB versión v8.0 o superior.
Nota
Asegúrese de que su clúster Atlas tenga suficiente memoria para almacenar los índices MongoDB Search y MongoDB Vector Search y ejecutar consultas de alto rendimiento.
Los datos sample_mflix cargados en su clúster Atlas.
mongosh Para ejecutar consultas en su clúster Atlas:
Project Data Access Admin acceso al proyecto para crear índices MongoDB Vector Search y MongoDB Search.

Cree los índices de búsqueda vectorial de MongoDB y los índices de búsqueda de MongoDB

Esta sección demuestra cómo crear los siguientes índices en los campos de la colección sample_mflix.embedded_movies:

un índice de MongoDB Vector Search en el campo plot_embedding_voyage_3_large para ejecutar queries de vectores en ese campo.
un índice de MongoDB Search en el campo title para realizar una búsqueda de texto completo en ese campo.

Procedimiento

Conéctese al clúster `mongosh` mediante.

Para obtener más información, consulte Conectarse a un clúster a través de mongosh.

Cambie a la base de datos que contiene la colección para la que desea crear el índice.

use sample_mflix

switched to db sample_mflix

Defina el índice de búsqueda vectorial de MongoDB.

Ejecute el siguiente comando. Esta definición de índice indexa el campo plot_embedding_voyage_3_large como el campo de búsqueda vectorial de MongoDB al consultar la colección.

1 db.embedded_movies.createSearchIndex(
2   "hybrid-vector-search", 
3   "vectorSearch", 
4   {
5     "fields": [
6       {
7         "type": "vector",
8         "path": "plot_embedding_voyage_3_large",
9         "numDimensions": 2048,
10         "similarity": "dotProduct"
11       }
12     ]
13   }
14 );

Define el índice de MongoDB Search.

La siguiente definición de índice indexa automáticamente todos los campos que se pueden indexar dinámicamente en la colección.

1 db.embedded_movies.createSearchIndex(
2   "hybrid-full-text-search", 
3   "search", 
4   {
5     "mappings": {
6       "dynamic": true
7     }
8   }
9 );

Impulso semántico

Esta sección muestra cómo consultar los datos de la colección sample_mflix.embedded_movies para Star Wars en los campos plot_embedding_voyage_3_large y plot mediante las etapas de canalización$vectorSearchy$search, respectivamente. La consulta mejora la relevancia de las consultas$searchal aumentar las puntuaciones de texto completo utilizando puntuaciones$vectorSearchpara los mismos documentos.

Procedimiento

Crea el archivo para las incrustaciones.

Crea un archivo llamado query-embeddings.js.
```
touch query-embeddings.js
```

Copie y pegue las siguientes incrustaciones en el archivo.

STAR_WARS_EMBEDDING = 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Guarde y cierre el archivo.

Conéctese al clúster usando `mongosh`.

Se debe abrir mongosh en una ventana de terminal y realizar la conexión al clúster. Para obtener instrucciones detalladas sobre cómo realizar la conexión, se debe consultar Conexión a un clúster a través de mongosh.

Utilice la `sample_mflix` base de datos.

Ejecuta el siguiente comando en el prompt mongosh:

use sample_mflix

switched to db sample_mflix

Carga las incrustaciones para usar en el query.

Cargue el archivo query-embeddings.js en mongosh.

load('<path-to-file>/query-embeddings.js');

Ejecute las siguientes consultas de MongoDB Search en la colección `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_3_large",
12             queryVector: STAR_WARS_EMBEDDING,
13             numCandidates: numCandidates,   
14             limit: numResults  
15         }  
16     },  
17     {  
18         $project: {  
19             _id: 1,  
20             title: 1,  
21             plot: 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: "star wars",  
48                             path: "plot"  
49                         }  
50                     },  
51                     ...Object.keys(vectorScoresMap).map(id => ({  
52                         equals: {  
53                             path: "_id",  
54                             value: id,  
55                             score: { boost: { value: vectorScoresMap[id] } }  
56                         }  
57                     }))  
58                 ]  
59             }  
60         }  
61     },  
62     {
63         $limit: 20
64     },
65     {  
66         $project: {  
67             _id: 1,  
68             title: 1,  
69             plot: 1,
70             score: { $meta: "searchScore" }  
71         }  
72     }  
73 ]).toArray();

[
  {
    _id: ObjectId('573a1397f29313caabce6f53'),
    plot: 'In this Star Wars take-off, the peaceful planet of Jillucia has been nearly wiped out by the Gavanas, whose leader takes orders from his mother (played a comic actor in drag) rather than ...',
    title: 'Message from Space',
    score: 4.386045455932617
  },
  {
    _id: ObjectId('573a139df29313caabcfa90b'),
    plot: 'In this Star Wars take-off, the peaceful planet of Jillucia has been nearly wiped out by the Gavanas, whose leader takes orders from his mother (played a comic actor in drag) rather than ...',
    title: 'Message from Space',
    score: 4.386045455932617
  },
  {
    _id: ObjectId('573a1398f29313caabce9851'),
    plot: 'Johnathan Cabot is a champion gymnast. In the tiny, yet savage, country of Parmistan, there is a perfect spot for a "star wars" site. For the US to get this site, they must compete in the ...',
    title: 'Gymkata',
    score: 4.269977569580078
  },
  {
    _id: ObjectId('573a13a4f29313caabd1137f'),
    plot: "A producer's film is endangered when his star walks off, so he decides to digitally create an actress to substitute for the star, becoming an overnight sensation that everyone thinks is a real person.",
    title: 'S1m0ne',
    score: 2.8472113609313965
  },
  {
    _id: ObjectId('573a1394f29313caabcdf65b'),
    plot: 'A fantastic tale of war, love, family and ambition set in the midst of the Japanese Civil Wars of the sixteenth century.',
    title: 'Ugetsu',
    score: 2.835911989212036
  },
  {
    _id: ObjectId('573a13a3f29313caabd0c992'),
    plot: 'The vampire Lestat becomes a rock star whose music wakes up the queen of all vampires.',
    title: 'Queen of the Damned',
    score: 2.664494037628174
  },
  {
    _id: ObjectId('573a139af29313caabcefe79'),
    plot: 'An all star baseball player becomes the unhealthy focus of a down on his luck salesman.',
    title: 'The Fan',
    score: 2.664494037628174
  },
  {
    _id: ObjectId('573a13a8f29313caabd1da86'),
    plot: 'During the Napoleonic Wars, a brash British captain pushes his ship and crew to their limits in pursuit of a formidable French war vessel around South America.',
    title: 'Master and Commander: The Far Side of the World',
    score: 2.6229333877563477
  },
  {
    _id: ObjectId('573a139af29313caabcf0e7d'),
    plot: 'A puppeteer discovers a portal that leads literally into the head of the movie star, John Malkovich.',
    title: 'Being John Malkovich',
    score: 2.6173996925354004
  },
  {
    _id: ObjectId('573a13b6f29313caabd4614f'),
    plot: 'A former pop star who now writes commercial jingles for a living experiences a mid-life crisis.',
    title: 'The Good Night',
    score: 2.6173996925354004
  },
  {
    _id: ObjectId('573a13cbf29313caabd7fbc9'),
    plot: 'Bertrand Tavernier is in top form with this gripping, superbly mounted drama set against the savage Catholic/Protestant wars that ripped France apart in the 16th century. Based on a novella...',
    title: 'The Princess of Montpensier',
    score: 2.4742777347564697
  },
  {
    _id: ObjectId('573a139bf29313caabcf2d8e'),
    plot: 'Updated version of the classic Rodgers and Hammerstein musical of the classic fairy-tale, with an all-star, multi-racial cast.',
    title: 'Cinderella',
    score: 2.4445695877075195
  },
  {
    _id: ObjectId('573a1399f29313caabcec853'),
    plot: 'An action movie star researching a role is allowed to tag along with a hardboiled New York policeman, who finds him superficial and irritating.',
    title: 'The Hard Way',
    score: 2.329218626022339
  },
  {
    _id: ObjectId('573a13b4f29313caabd3f550'),
    plot: 'A cop, a lawyer, and an assassin cross paths after the murder of a federal witness and the kidnapping of a famous pop star.',
    title: 'Divergence',
    score: 2.329218626022339
  },
  {
    _id: ObjectId('573a13aff29313caabd2fe20'),
    plot: 'An intimate story set during the 1860s in which a young Irish woman Sarah and her family find themselves on both sides of the turbulent wars between British and Maori during the British colonization of New Zealand.',
    title: 'River Queen',
    score: 2.2804131507873535
  },
  {
    _id: ObjectId('573a13b8f29313caabd4d8e1'),
    plot: "Thomas Michael, Paolo Mancini, Chris Klein and Joe Mantegna star in director Matthiew Klinck's holiday-themed comedy concerning two downsized Easter bunnies whose friendship starts to ...",
    title: 'Hank and Mike',
    score: 2.258181571960449
  },
  {
    _id: ObjectId('573a13aef29313caabd2df95'),
    plot: 'Story of two sisters that grew up in a small Serbian village in the beginning of the 1930s. The village is torn up by wars and years long blood oath. There are no men left in the village. ...',
    title: 'Tears for Sale',
    score: 2.2510178089141846
  },
  {
    _id: ObjectId('573a139af29313caabcf124d'),
    plot: 'As the Clone Wars near an end, the Sith Lord Darth Sidious steps out of the shadows, at which time Anakin succumbs to his emotions, becoming Darth Vader and putting his relationships with Obi-Wan and Padme at risk.',
    title: 'Star Wars: Episode III - Revenge of the Sith',
    score: 2.2223706245422363
  },
  {
    _id: ObjectId('573a13dbf29313caabdaf598'),
    plot: "Just as Clint Eastwood's star-making spaghetti Western A Fistful of Dollars was inspired by Akira Kurosawa's Yojimbo, Japanese-Korean filmmaker Sang-il Lee (Villain) has decided to ...",
    title: 'Unforgiven',
    score: 2.1913492679595947
  },
  {
    _id: ObjectId('573a13c1f29313caabd633ac'),
    plot: "Black Dynamite is the greatest African-American action star of the 1970s. When his only brother is killed by The Man it's up to him to find justice.",
    title: 'Black Dynamite',
    score: 2.1913492679595947
  }
]

Acerca del query

La consulta de ejemplo optimiza los $search resultados basándose en $vectorSearch las puntuaciones de para devolver los documentos más relevantes. Para optimizar la semántica, siga estos pasos:

Ejecute la $vectorSearch consulta.
Utilice un valor de solicitud de sobrepaso para especificar el número de candidatos que exceden el límite especificado y que se deben considerar. Esta solicitud le permite lograr lo siguiente:
- Incluya una gran cantidad y un conjunto amplio de documentos a considerar en la búsqueda inicial.
- Refinar el cálculo de la puntuación en etapas posteriores.
La consulta utiliza el siguiente cálculo para determinar la cantidad de candidatos a considerar durante la búsqueda de vectores:
```
numCandidates = limit * over request
```
Los resultados se filtran en función de un umbral de puntuación mínimo (vectorCutoff).
Escale la puntuación de búsqueda vectorial para ajustar la influencia de los resultados de la búsqueda vectorial en la puntuación y clasificación de los documentos.
Utiliza el parámetro vectorWeight para establecer el peso y multiplicar los puntajes de búsqueda vectorial por el vectorWeight. Esto controla la importancia de los resultados de búsqueda vectorial y garantiza que solo se consideren los documentos más relevantes semánticamente.
Ejecuta la consulta $search y mejora la puntuación de los documentos que coincidan con los resultados de la búsqueda vectorial.

Variables de consulta

Variables utilizadas en la consulta.

La consulta de muestra define las siguientes variables:

vectorWeight para agregar peso al puntaje de búsqueda vectorial, donde un número más bajo proporciona un mayor peso.
vectorCutoff para establecer la puntuación vectorial mínima para que se potencie el resultado.
numResults para especificar el número deseado de resultados de búsqueda
numCandidates Para calcular el número de candidatos a considerar durante la búsqueda inicial, se utiliza la siguiente fórmula:
```
numCandidates = numResults x overRequestFactor
```

Etapas de query

Etapas de la canalización de agregaciones utilizadas en la consulta.

La consulta de muestra utiliza las siguientes etapas de la canalización para realizar una búsqueda semántica en la colección y recuperar el rango recíproco de los documentos en los resultados:

`$vectorSearch`	Busca `plot_embedding_voyage_3_large` la cadena "star wars" en el campo, especificada como incrustaciones vectoriales en el `queryVector` campo de la consulta. La consulta utiliza el `voyage-3-large` modelo de incrustación de Voyage AI, el mismo que se utiliza para las incrustaciones del `plot_embedding_voyage_3_large` campo.
`$search`	Busca películas que contengan el término "Star Wars" en el campo de trama. Para los documentos que coinciden con `_id` el valor de la búsqueda vectorial, la consulta aumenta la puntuación según la puntuación ajustada de la búsqueda vectorial del documento.

Fusión recíproca de clasificación

Esta sección muestra cómo consultar los datos de la sample_mflix.embedded_movies colección para Star Wars en los plot_embedding_voyage_3_large title campos y mediante las etapas $vectorSearch de canalización y, y combinar las puntuaciones de cada documento de ambas etapas para reordenarlos en los resultados. Esto garantiza que los documentos que aparecen en ambas búsquedas aparezcan al principio de los resultados combinados.$search

Procedimiento

Configura las incrustaciones para usar en el query.

Crea un archivo llamado query-embeddings.js.

Copia y pega las siguientes incrustaciones en el archivo.

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Guarde y cierre el archivo.

Conéctese al clúster usando `mongosh`.

Utilice la `sample_mflix` base de datos.

Ejecuta el siguiente comando en el prompt mongosh:

use sample_mflix

switched to db sample_mflix

Carga las incrustaciones para usar en el query.

Ejecute el siguiente comando para cargar las incrustaciones después de reemplazar <path-to-file> con la ruta de acceso absoluta al archivo embeddings.js:

load('/<path-to-file>/embeddings.js');

Para verificar que las incrustaciones se cargaron correctamente, ejecuta el siguiente comando:

STAR_WARS_EMBEDDING.length

Ejecute las siguientes consultas de MongoDB Search en la colección `embedded_movies`.

1 db.embedded_movies.aggregate([
2   {
3     $rankFusion: {
4       input: {
5         pipelines: {
6           vectorPipeline: [
7             {
8               "$vectorSearch": {
9                 "index": "hybrid-vector-search",
10                 "path": "plot_embedding_voyage_3_large",
11                 "queryVector": STAR_WARS_EMBEDDING,
12                 "numCandidates": 100,
13                 "limit": 20
14               }
15             }
16           ],
17           fullTextPipeline: [
18             {
19               "$search": {
20                 "index": "hybrid-full-text-search",
21                 "phrase": {
22                   "query": "star wars",
23                   "path": "title"
24                 }
25               }
26             },
27             { "$limit": 20 }
28           ]
29         }
30       },
31       combination: {
32         weights: {
33           vectorPipeline: 0.5,
34           fullTextPipeline: 0.5
35         }
36       },
37       "scoreDetails": true
38     }
39   },
40   {
41     "$project": {
42       _id: 1,
43       title: 1,
44       plot: 1,
45       scoreDetails: {"$meta": "scoreDetails"}
46     }
47   },
48   {
49     "$limit": 10
50   }
51 ]);

[
  {
    _id: ObjectId('573a1397f29313caabce68f6'),
    plot: "Luke Skywalker joins forces with a Jedi Knight, a cocky pilot, a wookiee and two droids to save the universe from the Empire's world-destroying battle-station, while also attempting to rescue Princess Leia from the evil Darth Vader.",
    title: 'Star Wars: Episode IV - A New Hope',
    scoreDetails: {
      value: 0.01626123744050767,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'fullTextPipeline',
          rank: 2,
          weight: 0.5,
          value: 2.9629626274108887,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 1,
          weight: 0.5,
          value: 0.7987099885940552,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcf0f5f'),
    plot: 'Two Jedi Knights escape a hostile blockade to find allies and come across a young boy who may bring balance to the Force, but the long dormant Sith resurface to reclaim their old glory.',
    title: 'Star Wars: Episode I - The Phantom Menace',
    scoreDetails: {
      value: 0.015527202696196438,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'fullTextPipeline',
          rank: 2,
          weight: 0.5,
          value: 2.9629626274108887,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 7,
          weight: 0.5,
          value: 0.7771433591842651,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1397f29313caabce77d9'),
    plot: 'After the rebels have been brutally overpowered by the Empire on their newly established base, Luke Skywalker takes advanced Jedi training with Master Yoda, while his friends are pursued by Darth Vader as part of his plan to capture Luke.',
    title: 'Star Wars: Episode V - The Empire Strikes Back',
    scoreDetails: {
      value: 0.015504807692307693,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'fullTextPipeline',
          rank: 4,
          weight: 0.5,
          value: 2.7174296379089355,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 5,
          weight: 0.5,
          value: 0.7810181975364685,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1397f29313caabce8cdb'),
    plot: 'After rescuing Han Solo from the palace of Jabba the Hutt, the rebels attempt to destroy the second Death Star, while Luke struggles to make Vader return from the dark side of the Force.',
    title: 'Star Wars: Episode VI - Return of the Jedi',
    scoreDetails: {
      value: 0.015388257575757576,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'fullTextPipeline',
          rank: 4,
          weight: 0.5,
          value: 2.7174296379089355,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 6,
          weight: 0.5,
          value: 0.7782549858093262,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13c0f29313caabd62f62'),
    plot: 'Anakin Skywalker and Ahsoka Tano must rescue the kidnapped son of Jabba the Hutt, but political intrigue complicates their mission.',
    title: 'Star Wars: The Clone Wars',
    scoreDetails: {
      value: 0.015141165755919854,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'fullTextPipeline',
          rank: 1,
          weight: 0.5,
          value: 3.6164965629577637,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 12,
          weight: 0.5,
          value: 0.7561323642730713,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcf124d'),
    plot: 'As the Clone Wars near an end, the Sith Lord Darth Sidious steps out of the shadows, at which time Anakin succumbs to his emotions, becoming Darth Vader and putting his relationships with Obi-Wan and Padme at risk.',
    title: 'Star Wars: Episode III - Revenge of the Sith',
    scoreDetails: {
      value: 0.014854753521126762,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'fullTextPipeline',
          rank: 4,
          weight: 0.5,
          value: 2.7174296379089355,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 11,
          weight: 0.5,
          value: 0.756283700466156,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcf1258'),
    plot: 'Ten years after initially meeting, Anakin Skywalker shares a forbidden romance with Padmè, while Obi-Wan investigates an assassination attempt on the Senator and discovers a secret clone army crafted for the Jedi.',
    title: 'Star Wars: Episode II - Attack of the Clones',
    scoreDetails: {
      value: 0.014391447368421052,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'fullTextPipeline',
          rank: 4,
          weight: 0.5,
          value: 2.7174296379089355,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 16,
          weight: 0.5,
          value: 0.7400004267692566,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabce9091'),
    plot: "A Duke's son leads desert warriors against the galactic emperor and his father's evil nemesis when they assassinate his father and free their desert world from the emperor's rule.",
    title: 'Dune',
    scoreDetails: {
      value: 0.008064516129032258,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        { inputPipelineName: 'fullTextPipeline', rank: 'NA' },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 2,
          weight: 0.5,
          value: 0.7947750091552734,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1397f29313caabce6f53'),
    plot: 'In this Star Wars take-off, the peaceful planet of Jillucia has been nearly wiped out by the Gavanas, whose leader takes orders from his mother (played a comic actor in drag) rather than ...',
    title: 'Message from Space',
    scoreDetails: {
      value: 0.007936507936507936,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        { inputPipelineName: 'fullTextPipeline', rank: 'NA' },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 3,
          weight: 0.5,
          value: 0.7913960218429565,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139df29313caabcfa90b'),
    plot: 'In this Star Wars take-off, the peaceful planet of Jillucia has been nearly wiped out by the Gavanas, whose leader takes orders from his mother (played a comic actor in drag) rather than ...',
    title: 'Message from Space',
    scoreDetails: {
      value: 0.007936507936507936,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        { inputPipelineName: 'fullTextPipeline', rank: 'NA' },
        {
          inputPipelineName: 'vectorPipeline',
          rank: 3,
          weight: 0.5,
          value: 0.7913960218429565,
          details: []
        }
      ]
    }
  }
]

Acerca del query

El query de muestra recupera los resultados de búsqueda ordenados de la búsqueda semántica y la búsqueda de texto completo, y asigna una puntuación de calificación recíproca a los documentos que aparecen en los resultados en función de su posición en el arreglo de resultados. La puntuación de calificación recíproca se calcula usando la siguiente fórmula:

1.0/{document position in the results + constant value}

Luego, el query añade las puntuaciones de ambas búsquedas para cada documento, clasifica los documentos en función de la puntuación combinada y ordena los documentos para que devuelvan un solo resultado. El valor de la constante es siempre 60.

Pesos del query

Pesos utilizados en la consulta.

El query de muestra define los siguientes pesos para los pipelines para influir en la contribución de rango de cada pipeline a la puntuación final:

vectorPipeline = 0.5
fullTextPipeline = 0.5

Puedes ajustar los pesos para dar más importancia a un método de búsqueda. Ten en cuenta que mientras menor es el número mayor es el peso.

La puntuación recíproca ponderada se calcula utilizando la siguiente fórmula:

weight x reciprocal rank

El scoreDetails.details.value muestra la puntuación en bruto de ese pipeline antes de ponderarlo y combinarlo mediante una fusión de rangos recíprocos. El scoreDetails.value muestra la puntuación de rango recíproco ponderada.

Etapas de query

Etapas de la canalización de agregaciones utilizadas en la consulta.

El query de muestra utiliza la etapa $rankFusion para ejecutar los queries semánticos y de texto completo de manera independiente y luego desduplicar y combinar los resultados del query de entrada en un conjunto de resultados finales clasificados. Devuelve un conjunto de documentos clasificados según las clasificaciones que aparecen en sus pipelines de entrada y los pesos del pipeline. Específicamente, esta etapa toma las siguientes pipelines de entrada:

vectorPipeline

Este pipeline contiene el query $vectorSearch. Busca en el campo plot_embedding_voyage_3_large para el string star wars especificado como incrustaciones vectoriales en el campo queryVector del query. El query utiliza el modelo de incrustación voyage-3-large de Voyage IA, que es el mismo modelo utilizado para las incrustaciones en el campo plot_embedding_voyage_3_large. El query también especifica una búsqueda de hasta 100 vecinos más cercanos y limita los resultados a solo 20 documentos. Esta etapa devuelve los documentos ordenados de la búsqueda semántica en los resultados.

fullTextPipeline

Este pipeline contiene las siguientes etapas:

$search para buscar películas que contengan el término star wars en el campo title. Esta etapa devuelve los documentos ordenados de la búsqueda de texto completo en los resultados.
$limit para limitar la salida de la etapa $search a solo 20 resultados.

El query de muestra utiliza las siguientes etapas para combinar los resultados de la búsqueda semántica y de texto y devolver una única lista clasificada de documentos en los resultados:

`$project`	Incluye solo el siguiente campo en los resultados: `_id` `title` `plot` `scoreDetails`
`$limit`	Limita la salida a solo `10` resultados.

Aprende observando

Vea una demostración de una aplicación que presenta consultas de búsqueda híbridas que combinan la búsqueda de texto completo y vectorial de MongoDB Search para obtener un único conjunto de resultados fusionados. La aplicación implementa la Fusión de Puntuación Relativa (RSF) y la Fusión de Rango Recíproco (RRF) para obtener un conjunto fusionado creado mediante un algoritmo de fusión de rangos.

Duración: 2.43 minutos

Volver

Personalizar puntuación

Sinónimos

1	db.embedded_movies.createSearchIndex(
2	"hybrid-vector-search",
3	"vectorSearch",
4	{
5	"fields": [
6	{
7	"type": "vector",
8	"path": "plot_embedding_voyage_3_large",
9	"numDimensions": 2048,
10	"similarity": "dotProduct"
11	}
12	]
13	}
14	);

1	db.embedded_movies.createSearchIndex(
2	"hybrid-full-text-search",
3	"search",
4	{
5	"mappings": {
6	"dynamic": true
7	}
8	}
9	);

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_3_large",
12	queryVector: STAR_WARS_EMBEDDING,
13	numCandidates: numCandidates,
14	limit: numResults
15	}
16	},
17	{
18	$project: {
19	_id: 1,
20	title: 1,
21	plot: 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: "star wars",
48	path: "plot"
49	}
50	},
51	...Object.keys(vectorScoresMap).map(id => ({
52	equals: {
53	path: "_id",
54	value: id,
55	score: { boost: { value: vectorScoresMap[id] } }
56	}
57	}))
58	]
59	}
60	}
61	},
62	{
63	$limit: 20
64	},
65	{
66	$project: {
67	_id: 1,
68	title: 1,
69	plot: 1,
70	score: { $meta: "searchScore" }
71	}
72	}
73	]).toArray();

1	db.embedded_movies.aggregate([
2	{
3	$rankFusion: {
4	input: {
5	pipelines: {
6	vectorPipeline: [
7	{
8	"$vectorSearch": {
9	"index": "hybrid-vector-search",
10	"path": "plot_embedding_voyage_3_large",
11	"queryVector": STAR_WARS_EMBEDDING,
12	"numCandidates": 100,
13	"limit": 20
14	}
15	}
16	],
17	fullTextPipeline: [
18	{
19	"$search": {
20	"index": "hybrid-full-text-search",
21	"phrase": {
22	"query": "star wars",
23	"path": "title"
24	}
25	}
26	},
27	{ "$limit": 20 }
28	]
29	}
30	},
31	combination: {
32	weights: {
33	vectorPipeline: 0.5,
34	fullTextPipeline: 0.5
35	}
36	},
37	"scoreDetails": true
38	}
39	},
40	{
41	"$project": {
42	_id: 1,
43	title: 1,
44	plot: 1,
45	scoreDetails: {"$meta": "scoreDetails"}
46	}
47	},
48	{
49	"$limit": 10
50	}
51	]);

¿Por qué búsqueda híbrida?

Requisitos previos

Nota

Cree los índices de búsqueda vectorial de MongoDB y los índices de búsqueda de MongoDB

Procedimiento

Conéctese al clúster mongosh mediante.

Cambie a la base de datos que contiene la colección para la que desea crear el índice.

Defina el índice de búsqueda vectorial de MongoDB.

Define el índice de MongoDB Search.

Impulso semántico

Procedimiento

Crea el archivo para las incrustaciones.

Conéctese al clúster usando mongosh.

Utilice la sample_mflix base de datos.

Carga las incrustaciones para usar en el query.

Ejecute las siguientes consultas de MongoDB Search en la colección embedded_movies.

Acerca del query

Variables de consulta

Etapas de query

Fusión recíproca de clasificación

Procedimiento

Configura las incrustaciones para usar en el query.

Conéctese al clúster usando mongosh.

Utilice la sample_mflix base de datos.

Carga las incrustaciones para usar en el query.

Ejecute las siguientes consultas de MongoDB Search en la colección embedded_movies.

Acerca del query

Pesos del query

Etapas de query

Aprende observando

Conéctese al clúster `mongosh` mediante.

Conéctese al clúster usando `mongosh`.

Utilice la `sample_mflix` base de datos.

Ejecute las siguientes consultas de MongoDB Search en la colección `embedded_movies`.

Conéctese al clúster usando `mongosh`.

Utilice la `sample_mflix` base de datos.

Ejecute las siguientes consultas de MongoDB Search en la colección `embedded_movies`.