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Cómo realizar una búsqueda híbrida

Puedes combinar las 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 la colección sample_mflix.embedded_movies, que contiene detalles sobre películas. Específicamente, este tutorial lo lleva a través de los siguientes pasos:

  1. Cree un índice de búsqueda vectorial de MongoDB en el campo plot_embedding_voyage_4_large. Este campo contiene incrustaciones vectoriales (de 2048 dimensiones creadas con el modelo de incrustación voyage-4-large de Voyage AI) que representan el resumen de la trama de una película.

  2. Crea un índice de Búsqueda de MongoDB en la colección sample_mflix.embedded_movies.

  3. Ejecute una query para combinar los resultados de una $vectorSearch query y una $search query utilizando las siguientes técnicas:

    • Boosting semántico

    • Fusión recíproca de rangos

    • Fusión de puntuación

¿Por qué búsqueda híbrida?

Una búsqueda híbrida es una agregación de diferentes métodos de búsqueda, como una búsqueda de texto completo y búsqueda semántica, para los mismos criterios de query. Si bien el texto completo es eficaz para encontrar coincidencias exactas de términos de consulta, la búsqueda semántica ofrece el beneficio adicional de identificar documentos semánticamente similares, incluso si los documentos no contienen el término exacto de la consulta. 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

Aceleración semántica

Fusión recíproca de clasificación

Fusión de puntuación relativa

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 el rango recíproco de los documentos en los resultados.

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

Método de ordenación

  • Ejecutar $vectorSearch para recuperar resultados semánticamente similares.

  • ejecutar $search para recuperar resultados de texto completo mientras se potencian los documentos que fueron devueltos por $vectorSearch.

  • recuperar $vectorSearch y $search resultados.

  • Fusiona ambos conjuntos de resultados utilizando el orden de cada documento en los respectivos conjuntos de resultados.

  • recuperar $vectorSearch y $search resultados.

  • Fusiona ambos conjuntos de resultados utilizando la puntuación de cada documento en los respectivos conjuntos de resultados.

Método de puntuación

Mejora la puntuación de resultados de texto completo para los documentos que coinciden con los resultados de la búsqueda vectorial, según la puntuación ponderada de búsqueda vectorial.

Calcula la puntuación usando la siguiente fórmula:

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

Luego, para cada método de búsqueda, aplica diferentes ponderaciones (w) para dar más importancia a ese método de búsqueda. Para cada documento, el rango recíproco ponderado se calcula multiplicando el peso por el rango recíproco del documento.

weighted_reciprocal_rank = w x reciprocal_rank

Calcula la puntuación usando la siguiente fórmula:

score = w1 x score1 + w2 x score2

Funcionalidades de búsqueda

  • Ofrece soporte para facetas para agrupar datos en categorías.

  • Admite la paginación de resultados utilizando searchAfter y searchBefore.

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

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

Salida

Conjunto de resultados único que contiene documentos de $search

Un solo conjunto de resultados que contiene documentos de ambos métodos de búsqueda.

Un solo conjunto de resultados que contiene documentos de ambos métodos de búsqueda.

Requisitos previos

Antes de comenzar, debes tener lo siguiente:

  • Un clúster de Atlas con MongoDB versión v8.0 o superior.

    Nota

    Asegúrate de que tu clúster de Atlas tenga suficiente memoria para almacenar tanto los índices de MongoDB Search como los de MongoDB Vector Search y ejecutar queries con buen rendimiento.

  • Los datos sample_mflix se han cargado en su clúster de Atlas.

  • mongosh para ejecutar consultas en tu clúster de Atlas:

  • Project Data Access Admin acceso al proyecto para crear MongoDB Vector Search e índices de 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_4_large para ejecutar queries de vectores en ese campo.

  • Un índice de MongoDB Search que indexa dinámicamente todos los campos indexables dinámicamente para ejecutar una búsqueda de texto completo en esos campos.

1

Conéctese al clúster utilizando mongosh.

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

2

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
3

Defina el índice de búsqueda vectorial de MongoDB.

Ejecute el siguiente comando. Esta definición de índice indexa el campo plot_embedding_voyage_4_large como el campo de búsqueda vectorial de MongoDB al consultar la colección.

1db.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);
4

Define el índice de MongoDB Search.

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

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

Ejecutar las consultas de búsqueda híbrida

Esta sección demuestra cómo hacer query sobre los datos en la colección de sample_mflix.embedded_movies para charming animal en los campos plot_embedding_voyage_4_large y fullplot utilizando las etapas $vectorSearch y $search del pipeline, respectivamente. La query mejora la relevancia de las query de $search potenciando las puntuaciones del texto completo utilizando las puntuaciones de $vectorSearch de los mismos documentos.

Query de aceleración semántica

1

Crea el archivo para las incrustaciones.

  1. Crea un archivo llamado query-embeddings.js.

    touch query-embeddings.js

  2. Copie y pegue las siguientes incrustaciones en el archivo.

    CHARMING_ANIMAL_EMBEDDING=[-0.02300029993057251, -0.040718235075473785, 0.026521876454353333, -0.007813499309122562, 0.01958877220749855, -0.032134391367435455, 0.015186801552772522, 0.023990744724869728, -0.0029025499243289232, -0.0019671309273689985, 0.01254561822861433, -0.016287293285131454, -0.029053010046482086, 0.01408630795776844, -0.00891399197280407, 0.01958877220749855, -0.0508427694439888, -0.015737047418951988, 0.012215470895171165, -0.0036316262558102608, 0.0033152345567941666, 0.0033840155228972435, -0.00748335151001811, 0.002737476024776697, 0.03257458657026291, -0.01892847754061222, -0.012050396762788296, 0.02432089112699032, 0.028392715379595757, 0.033454980701208115, 0.005144803784787655, 0.0072632525116205215, 0.0212395116686821, -0.01683754101395607, -0.002751232124865055, 0.030813800171017647, -0.025861581787467003, 0.00748335151001811, -0.0032326977234333754, -0.030813800171017647, 0.006768031045794487, 0.024651039391756058, -0.03565596789121628, -0.02575153298676014, -0.005970173515379429, 0.02575153298676014, -0.08011587709188461, 0.0022284979932010174, -0.03257458657026291, 0.00522734085097909, -0.0028062567580491304, 0.011059952899813652, 0.0061627598479390144, 0.021349560469388962, -0.0061627598479390144, 0.02200985699892044, 0.006988129578530788, -0.006850568111985922, 0.010784829966723919, -0.0035490894224494696, -0.007538375910371542, 0.0023110350593924522, -0.031033897772431374, -0.04291922226548195, -0.03389517962932587, -0.004181872587651014, -0.02024906873703003, 0.003769187955185771, -0.01650739274919033, -0.0031226484570652246, -0.0026686950586736202, 0.03389517962932587, 0.020689265802502632, -0.025531433522701263, 0.02024906873703003, 0.03125399723649025, 0.006602956913411617, -0.01441645622253418, -0.04974227771162987, 0.005887636449187994, -0.013150889426469803, 0.007593400776386261, 0.00352157698944211, -0.014196357689797878, -0.009849410504102707, -0.019698821008205414, -0.01441645622253418, 0.0018708378775045276, -0.007703450042754412, 0.0020771801937371492, -0.03565596789121628, 0.027402272447943687, 0.007593400776386261, 0.005695050582289696, 0.03389517962932587, 0.014196357689797878, -0.002682451391592622, 0.030373603105545044, -0.01061975583434105, -0.017387786880135536, -0.00522734085097909, -0.013205913826823235, 0.007813499309122562, 0.02509123645722866, 0.00304011139087379, -0.04225892573595047, -0.011500149965286255, 0.005887636449187994, 0.0016163488617166877, -0.03477557376027107, 0.04423981159925461, -0.005144803784787655, -0.004401971120387316, -0.028062567114830017, 0.0087489178404212, 0.0029850867576897144, -0.0062177847139537334, -0.012215470895171165, -0.001430640695616603, 0.017277738079428673, -0.007813499309122562, -0.03191429376602173, 0.033454980701208115, 0.018818426877260208, 0.0035766016226261854, -0.030373603105545044, -0.056125134229660034, -0.01815813221037388, 0.0031501606572419405, 0.013756160624325275, -0.03785695508122444, 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  3. Guarde y cierre el archivo.

2

Conéctese al clúster mediante mongosh.

Abrir mongosh en una ventana de terminal y conéctese a su clúster. Para obtener instrucciones detalladas sobre cómo realizar la conexión, consulte Conectarse a un clúster a través de mongosh.

3

Usa la base de datos sample_mflix.

Ejecuta el siguiente comando en el prompt mongosh:

use sample_mflix
switched to db sample_mflix
4

Carga las incrustaciones para usar en el query.

Carga el archivo query-embeddings.js en mongosh.

load('<path-to-file>/query-embeddings.js');
5

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

1vectorWeight = 1;  
2vectorCutoff = 0.7; 
3numResults = 20;
4numCandidates = numResults * 10;  
5
6// Execute the vector search  
7vectorResults = 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  
33vectorScoresMap = {};  
34vectorResults.forEach(result => {  
35    vectorScoresMap[result._id] = result.vectorScore * vectorWeight;  
36});  
37
38// Execute the text search with boosting based just on ID  
39textResults = 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();

Acerca del query

La query de muestra mejora los $search resultados en función de $vectorSearch puntuaciones de resultados para devolver los documentos más relevantes. Para el aumento semántico, realice los siguientes pasos:

  1. Ejecutar la $vectorSearch query.

    Utiliza un valor de solicitud excesiva para especificar el número de candidatos que exceden el límite establecido a considerar. La solicitud excesiva te permite realizar lo siguiente:

    • Incluye una gran cantidad y una amplia variedad de documentos para considerar en la búsqueda inicial.

    • Refina el cálculo de puntuación en etapas posteriores.

    La query utiliza el siguiente cálculo para determinar el número de opciones a considerar durante la búsqueda de vectores:

    numCandidates = limit * over request

    Los resultados se filtran en función de un umbral de puntuación mínima (vectorCutoff).

  2. Ajusta la puntuación de búsqueda vectorial para modificar la influencia de los resultados de búsqueda vectorial en la clasificación y puntuación 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.

  3. Ejecuta la consulta $search y mejora la puntuación de los documentos que coincidan con los resultados de la búsqueda vectorial.

Query de fusión de clasificación recíproca

Puede ejecutar una query que 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. Para aprender más sobre la técnica de fusión de rango recíproco y cómo ejecutar una query de fusión de rango recíproco, consulte Realizar búsqueda híbrida de vectores y texto completo.

Query de fusión de puntuación relativa

Puede ejecutar una query que recupera los resultados de búsqueda calificados de las búsquedas semántica y de texto completo y aplica la expresión sigmoid para normalizar la calificación antes de combinar los resultados de los pipelines en una única calificación agregada para cada documento. Para aprender más sobre la técnica de fusión de puntuaciones y cómo ejecutar una query de fusión de puntuaciones, consulte Realizar búsquedas híbridas de vectores y de texto completo.

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