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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. Ejecuta una consulta para combinar los resultados de una consulta $vectorSearch y una consulta $search usando 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

  • Ejecuta $vectorSearch para recuperar resultados semánticamente similares.

  • Ejecute $search para recuperar los resultados de texto completo mientras mejora los documentos que fueron devueltos por $vectorSearch.

  • Recupera resultados de $vectorSearch y $search.

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

  • Recupera resultados de $vectorSearch y $search.

  • 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 el procesamiento posterior de los resultados en etapas subsiguientes de agregación, como $group.

Admite el procesamiento posterior de los resultados en etapas subsiguientes de agregación, 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 cargaron en tu clúster 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 usando mongosh.

Para obtener más información, consulte Conéctate 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 query los datos de la colección sample_mflix.embedded_movies para charming animal en los campos plot_embedding_voyage_4_large y fullplot utilizando las etapas de pipeline $vectorSearch y $search, respectivamente. La query mejora la relevancia de las $search queries potenciando las puntuaciones de 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

Conéctate a tu clúster usando mongosh.

Abra mongosh en una ventana de terminal y conéctese a su clúster. Para obtener instrucciones detalladas sobre la conexión, consulta Conéctate a un clúster mediante mongosh.

3

Use the sample_mflix database.

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();  
[
  {
    _id: ObjectId('573a13a3f29313caabd0d0a8'),
    fullplot: "Dr. John Dolittle the beloved doctor is back, but this time around he plays cupid to bumbling circus bear Archie as he's so smitten by a Pacific Western bear female, Ava. Dr. Dolittle must help a group of forest creatures to save their forest. But with the aid of his mangy, madcap animal friends, Dr. Dolittle must teach Archie the ways of true romance in time to save his species and his home before their habit is gone. So John held a meeting for every animal in the forest to not give up without a fight no matter what kind of animal expression they have and everyone agrees to do it and save their home.",
    title: 'Dr. Dolittle 2',
    score: 3.2186570167541504
  },
  {
    _id: ObjectId('573a13c5f29313caabd71215'),
    title: 'The Bannen Way',
    fullplot: 'A slick, sexy, action-packed show about Neal Bannen, a charming con-man with a police chief for a father, a mob boss for an uncle and a weakness for beautiful women, who wants to turn his life around and leave the criminal lifestyle forever.',
    score: 3.066267967224121
  },
  {
    _id: ObjectId('573a1394f29313caabce074d'),
    title: 'Perri',
    fullplot: 'This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten, Perri finds time to fall in love with her prince-charming male squirrel.',
    score: 3.0323688983917236
  },
  {
    _id: ObjectId('573a139af29313caabcf0e1d'),
    title: 'Wild America',
    fullplot: 'Three brothers - Marshall, Marty and Mark dream of becoming naturalists and portraying animal life of America. One summer their dream comes true, they travel through America, filming alligators, bears and moose.',
    score: 2.9388742446899414
  },
  {
    _id: ObjectId('573a13e3f29313caabdc08f2'),
    title: 'Bang Bang',
    fullplot: "A chance encounter of the unassuming bank receptionist Harleen Sahni with the charming yet mysterious Rajveer Nanda, results in an on-rush of ditched planes, car chases, shoot-outs, bombing raids and general global mayhem. But as the transcontinental chase ensues with Rajveer convincing Harleen that he's the good guy, can she really trust him, and will trust matter when the bullets start flying?",
    score: 2.7579541206359863
  },
  {
    _id: ObjectId('573a1393f29313caabcdbe7c'),
    fullplot: "In this charming film based on the popular L. Frank Baum stories, Dorothy and her dog Toto are caught in a tornado's path and somehow end up in the land of Oz. Here she meets some memorable friends and foes in her journey to meet the Wizard of Oz who everyone says can help her return home and possibly grant her new friends their goals of a brain, heart and courage.",
    title: 'The Wizard of Oz',
    score: 2.703585147857666
  },
  {
    _id: ObjectId('573a1397f29313caabce80f6'),
    title: 'Gauche the Cellist',
    fullplot: 'A cellist in a small orchestra receives help from animals to help him practice his music.',
    score: 2.692498207092285
  },
  {
    _id: ObjectId('573a13bcf29313caabd55f51'),
    title: 'The Missing Lynx',
    fullplot: "A group of animals look for a way off of an eccentric billionaire's own personal Noah's Ark.",
    score: 2.6747734546661377
  },
  {
    _id: ObjectId('573a1397f29313caabce8aac'),
    title: 'The Grey Fox',
    fullplot: `Old West highwayman Bill Miner, known to Pinkertons as "The Gentleman Bandit," is released in 1901 after 33 years in prison, a genial and charming old man. He goes to Washington to live and work with his sister's family. But the world has changed much while he has been away, and he just can't adjust. So he goes to Canada and returns to the only thing familiar to him -- robbery (with stagecoaches changed to trains).`,
    score: 2.651318073272705
  },
  {
    _id: ObjectId('573a13c7f29313caabd75624'),
    fullplot: 'Extraordinary teen John Smith (Pettyfer) is a fugitive on the run from ruthless enemies sent to destroy him. Changing his identity, moving from town to town with his guardian Henri (Olyphant), John is always the new kid with no ties to his past. In the small Ohio town he now calls home, John encounters unexpected, life-changing events-his first love (Agron), powerful new abilities and a connection to the others who share his incredible destiny.',
    title: 'I Am Number Four',
    score: 2.6149134635925293
  },
  {
    _id: ObjectId('573a1392f29313caabcdb48b'),
    title: 'Snow White and the Seven Dwarfs',
    fullplot: 'The first, and by far most memorable full-length animated feature from the Disney Studios, "Snow White and the Seven Dwarfs" may have been superseded technically by many of the films that followed it. But its simple story of a charming little princess saved from the evil deeds of her wicked stepmother, the queen, by a group of seven adorable dwarfs made history when it was first released in December, 1937 and has since become an incomparable screen classic.',
    score: 2.6010334491729736
  },
  {
    _id: ObjectId('573a13b4f29313caabd4070d'),
    title: 'Paragraph 78',
    fullplot: 'This is a wonderful movie about a group of men frontier the Nashville area who like to make puppets out of real animals',
    score: 2.573139190673828
  },
  {
    _id: ObjectId('573a1393f29313caabcdc6e9'),
    title: 'Tom Dick and Harry',
    fullplot: "Working girl/telephone operator Janie is engaged to conservative, dull, but very reliable car salesman Tom, who offers her a safe, stable marriage. Then she meets unconventional slacker Harry, a philosophical car mechanic without much ambition although she does hear bells when they kiss. Through Harry, the fickle Janie meets Dick, a handsome and charming millionaire playboy, who embodies her greatest romantic fantasies, and after a champagne-filled overnight flight to Chicago, she thinks she's met her dream man, but has she?",
    score: 2.5526208877563477
  },
  {
    _id: ObjectId('573a13acf29313caabd28039'),
    title: 'The Science of Sleep',
    fullplot: 'Following the death of his father in Mexico, Stèphane Miroux, a shy insecure young man, agrees to come to Paris to draw closer to his widowed mother Christine. He lands a boring job at a calendar-making firm and falls in love with his charming neighbor Stèphanie. But conquering her is no bed of roses for the young man and the only solution he finds to put up with the difficulties he is going through is escape into a dream world...',
    score: 2.5526208877563477
  },
  {
    _id: ObjectId('573a13b3f29313caabd3b4e1'),
    fullplot: `Manny the woolly mammoth, Sid the sloth, Diego the saber-toothed tiger, and the hapless prehistoric squirrel/rat known as Scrat are still together and enjoying the perks of their now melting world. Manny may be ready to start a family, but nobody has seen another mammoth for a long time; Manny thinks he may be the last one. That is, until he miraculously finds Ellie, the only female mammoth left in the world. Their only problems: They can't stand each other--and Ellie somehow thinks she's a possum! Ellie comes with some excess baggage in the form of her two possum "brothers"-- Crash and Eddie, a couple of daredevil pranksters and cocky, loud-mouthed troublemakers. Manny, Sid and Diego quickly learn that the warming climate has one major drawback: A huge glacial dam holding off oceans of water is about to break, threatening the entire valley. The only chance of survival lies at the other end of the valley. So our three heroes, along with Ellie, Crash and Eddie, form the most unlikely family--in any "Age"-- as they embark on a mission across an ever-changing, increasingly dangerous landscape towards their salvation.`,
    title: 'Ice Age: The Meltdown',
    score: 2.518824338912964
  },
  {
    _id: ObjectId('573a1395f29313caabce1b5f'),
    title: 'Hatari!',
    fullplot: `John Wayne and his ensemble cast cavort over the African landscape filling orders from zoos for wild animals. Bruce Cabot plays "the Indian", a womanizing sharpshooter who is gored by a rhino in the opening scenes of the film. This becomes a running theme through the movie; their bad luck in catching rhinos, and provides the climactic ending chase. While Bruce is in the hospital, Elsa Martinelli shows up as a woman photographer from a Swiss zoo, and John wants to send her packing. She strongarms the Duke into letting her stay by promising that her zoo will buy most of their animals this season if she's allowed to go along on the hunts and take photos. Hardy Kruger, Gerard Blain, Michelle Girardon and Valentin de Vargas round out the group. They traipse over the African landscape capturing animals; Elsa also has a running gag where she collects baby elephants as the movie goes along. In the end she's acquired three of them.`,
    score: 2.4652297496795654
  },
  {
    _id: ObjectId('573a13c5f29313caabd6e7a4'),
    title: 'Merantau',
    fullplot: `In Minangkabau, West Sumatera, Yuda a skilled practitioner of Silat Harimau is in the final preparations to begin his "Merantau" a century's old rites-of-passage to be carried out by the community's young men that will see him leave the comforts of his idyllic farming village and make a name for himself in the bustling city of Jakarta. After a series of setbacks leave Yuda homeless and uncertain about his new future, a chance encounter results in him defending the orphaned Astri from becoming the latest victim of a European human trafficking ring led by the wildly psychotic, Ratger and his right-hand man Lars. With Ratger injured in the mèlèe and seeking both his "merchandise" and bloody retribution, Yuda's introduction to this bustling city is a baptism of fire as he is forced to go on the run with Astri and her younger brother Adit as all the pimps and gangsters that inhabit the night hound the streets chasing their every step. With escape seemingly beyond their grasp, Yuda has no choice but to face his attackers in an adrenaline charged, jaw-dropping finale.`,
    score: 2.4573898315429688
  },
  {
    _id: ObjectId('573a1398f29313caabceaddd'),
    title: 'Dead Heat',
    fullplot: 'Roger Mortis and Doug Bigelow are cops that are chasing crooks that are dead serious about crime. Or should I say they are chasing dead crooks perpetrating serious crimes? Seems some nutcase has learned how to bring back the dead and is sending them on crime sprees. Now these indestructable goons are in the way of officers Mortis and Bigelow. To even things up, when Mortis is killed (in the line of duty, of course) he gets a jump start from the Resurrection machine and takes the fight to the zombie bad guys.',
    score: 2.398913860321045
  },
  {
    _id: ObjectId('573a1393f29313caabcde3af'),
    title: 'Mighty Joe Young',
    fullplot: `In Africa, the girl Jill Young trades a baby gorilla with two natives and raises the animal. Twelve years later, the talkative and persuasive promoter Max O'Hara organizes a safari to Africa with the Oklahoma cowboy Gregg to bring attractions to his new night-club in Hollywood. They capture several lions and out of blue, they see a huge gorilla nearby their camping and they try to capture the animal. However, the teenager Jill Young stops the men that intended to kill her gorilla. Max seduces Jill with a fancy life in Hollywood and she signs a contract with him where the gorilla Joseph "Joe" Young would be the lead attraction. Soon she realizes that her dream is a nightmare to Joe and she asks Max to return to Africa. However he persuades her to stay a little longer in the show business. But when three alcoholic costumers give booze to Joe, the gorilla destroys the spot and is sentenced by the justice to be sacrificed. Will Jill, Gregg and Max succeed in saving Joe?`,
    score: 2.3978404998779297
  },
  {
    _id: ObjectId('573a13c7f29313caabd75592'),
    fullplot: 'FBI agent Malcolm Turner and his son, Trent, go undercover at an all-girls performing arts school after Trent witnesses a murder. Posing as Big Momma and Charmaine, they must find the murderer before he finds them.',
    title: 'Big Mommas: Like Father, Like Son',
    score: 2.3933444023132324
  }
]

Acerca del query

La query de muestra impulsa los resultados de $search en función de las puntuaciones de resultados de $vectorSearch para devolver los documentos más relevantes. Para el impulso semántico, realizarás los siguientes pasos:

  1. Ejecuta la consulta $vectorSearch.

    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.

Variables de query

Variables utilizadas en la query.

La query de ejemplo define las siguientes variables:

  • vectorWeight para aumentar el peso de la puntuación de búsqueda vectorial, donde un número más bajo proporciona un peso mayor.

  • vectorCutoff establecer la puntuación mínima de vector para que el resultado sea impulsado.

  • numResults para especificar el número deseado de resultados de búsqueda

  • numCandidates para calcular el número de candidatos a considerar durante la búsqueda inicial. Calcula el número de candidatos utilizando la siguiente fórmula:

    numCandidates = numResults x overRequestFactor
Etapas de query

Etapas del pipeline de agregación utilizadas en la query.

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

$vectorSearch

Busca en el campo plot_embedding_voyage_4_large la string animal encantador, especificada como incrustaciones vectoriales en el campo queryVector de la query. La query utiliza el modelo de incrustación voyage-4-large de Voyage AI, que es el mismo modelo utilizado para las incrustaciones en el campo plot_embedding_voyage_4_large.

$search

Busca películas que contengan el término animal encantador en el campo fullplot. Para los documentos de los resultados que coinciden con el _id de los resultados de la búsqueda vectorial, la query aumenta la puntuación mediante la puntuación de búsqueda vectorial ajustada para el documento.

Query de fusión de clasificación recíproca

Puede ejecutar una query que recupere los resultados de búsqueda ordenados de la búsqueda semántica y la búsqueda de texto completo, y asigne una puntuación de clasificación recíproca a los documentos que aparecen en los resultados en función de su posición en el arreglo de resultados. Para obtener más información sobre la técnica de fusión de clasificación recíproca y cómo ejecutar una query de fusión de clasificación recíproca, consulte Realizar búsqueda híbrida vectorial y de texto completo.

Query de fusión de puntuación relativa

Puede ejecutar una query que recupere los resultados de búsqueda calificados de las búsquedas semántica y de texto completo y aplique la expresión sigmoid para normalizar la puntuación antes de combinar los resultados de los pipelines en una única puntuación agregada para cada documento. Para obtener más información sobre la técnica de fusión de puntuaciones y cómo ejecutar una query de fusión de puntuaciones, consulte Realizar búsqueda híbrida de vectores y texto completo.

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