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Cómo combinar varias `$vectorSearch` query

A partir de la v8.1, la etapa de agregación $rankFusion MongoDB admite $vectorSearch dentro de la pipeline de entrada. Puedes usar $rankFusion para combinar varias queries $vectorSearch en la misma colección mediante el mismo pipeline. $rankFusion primero ejecuta todas las input pipelines de forma independiente y, luego, desduplica y combina los resultados de las input pipelines en un conjunto final de resultados clasificados. Para ejecutar varias consultas $vectorSearch en varias colecciones en un solo pipeline, usa $unionWith.

Casos de uso y beneficios

Puedes ejecutar los siguientes tipos de $vectorSearch queries mediante el pipeline $rankFusion:

Ejecuta múltiples consultas de $vectorSearch para términos similares en el mismo campo.

Esto te permite realizar una búsqueda exhaustiva en tu conjunto de datos de términos similares en la misma query.
Ejecutar varias consultas de $vectorSearch para el mismo término en varios campos.

Esto le permite buscar en múltiples campos de su conjunto de datos para determinar qué campos devuelven los mejores resultados para la query.
Ejecutar varias $vectorSearch query para el mismo término en las incrustaciones de diferentes modelos de incrustación.

Esto permite buscar embeddings de diferentes modelos para determinar las diferencias de interpretación semántica entre los diferentes modelos.

Acerca del Tutorial

Este tutorial muestra cómo ejecutar diferentes $vectorSearch queries contra la colección embedded_movies en la base de datos sample_mflix. Esta colección incluye los siguientes campos:

plot_embeddingque contiene embeddings creados utilizando el modelo de embeddings text-embedding-ada-002 de OpenAI.
plot_embedding_voyage_3_largeque contiene incrustaciones creadas mediante el modelo de incrustación voyage-3-large de Voyage IA. Este campo se utiliza para todas las queries de muestra en este tutorial.

Para la segunda query indicada en Casos de uso y beneficios, debes generar incrustaciones para otro campo. Para probar la query de muestra para este caso de uso, complete los pasos en Generar los Embeddings.

Este tutorial te guía a través de los siguientes pasos:

Configura un índice de MongoDB búsqueda vectorial en la colección sample_mflix.embedded_movies.
Ejecute una consulta $vectorSearch con $rankFusion para realizar las siguientes búsquedas:
- Busca en el campo plot_embedding_voyage_3_large los argumentos de películas que contienen películas con argumentos similares a light-hearted comedy with ghosts y slapstick humor with paranormal events.
- Busca en los campos plot_embedding_voyage_3_large y title_embedding películas con tramas similares a battle between good and evil usando "embeddings" de Voyage IA.
- Busca en los campos plot_embedding y plot_embedding_voyage_3_large películas con tramas similares a journey across lands utilizando representaciones vectoriales (embeddings) de OpenAI y Voyage IA, respectivamente.

Antes de comenzar, asegúrese de que su clúster cumpla con los requisitos descritos en los prerrequisitos.

Nota

Para ejecutar una $vectorSearch query con $rankFusion, tu clúster debe ejecutar MongoDB v8.1 o superior.

Genera los Embeddings

Para query el mismo término en varios campos, debes preparar el conjunto de datos para incluir incrustaciones para diferentes campos. Esta sección demuestra cómo generar embeddings para el campo title en la colección utilizando el modelo de embeddings voyage-3-large de Voyage IA.

Puedes omitir esta sección si solo quieres probar los otros casos de uso de muestra.

Requisitos previos

Un entorno para ejecutar cuadernos interactivos de Python como VS Code o Colab.
Clave API para los modelos de embedding de Voyage IA.

Procedimiento

En esta sección, se generan embeddings para el campo title en la colección embedded_movies utilizando el modelo de embeddings voyage-3-large de Voyage IA.

Configura el entorno.

Crear un cuaderno interactivo en Python guardando un archivo con la extensión .ipynb y luego ejecuta el siguiente comando en el cuaderno para instalar las dependencias:

pip install --quiet voyageai pymongo

Definir y utilizar una función para generar incrustaciones vectoriales.

Pegue y ejecute el siguiente código en su cuaderno después de reemplazar los siguientes valores de marcador de posición:

`<api-key>`	Tu clave API de Voyage IA (línea 6).
`<connection-string>`	La cadena de conexión de tu clúster de MongoDB (línea 18).

El siguiente código define una función que genera incrustaciones vectoriales mediante el uso de un modelo de incrustación patentado de Voyage IA. Específicamente, este código realiza lo siguiente:

Especifica el modelo de incrustación voyage-3-large.
Crea una función llamada get_embedding() que llama a la API del modelo para generar la incrustación de dimensión 2048 para una determinada entrada de texto.
Se conecta al clúster y obtiene 3500 documentos del namespace sample_mflix.embedded_movies.
Genera incrustaciones del campo title de cada documento utilizando la función get_embedding().
Actualiza cada documento con un nuevo campo llamado title_voyageai_embedding que contiene el valor de representación mediante el uso del MongoDB PyMongo Driver.

1 import os
2 import pymongo
3 import voyageai
4 
5 # Specify your Voyage API key and embedding model
6 os.environ["VOYAGE_API_KEY"] = "<api-key>"
7 
8 model = "voyage-3-large"
9 outputDimension = 2048
10 vo = voyageai.Client()
11 # Define a function to generate embeddings
12 def get_embedding(data, input_type = "document"):
13   embeddings = vo.embed(
14       data, model = model, output_dimension = outputDimension, input_type = input_type
15   ).embeddings
16   return embeddings[0]
17 
18 # Connect to your MongoDB Atlas cluster
19 mongo_client = pymongo.MongoClient("<connection-string>")
20 db = mongo_client["sample_mflix"]
21 collection = db["embedded_movies"]
22 
23 # Filter to exclude null or empty plot fields
24 filter = {"title": {"$nin": [None, ""]}}
25 
26 # Get a subset of documents in the collection
27 documents = collection.find(filter).limit(3500)
28 
29 # Update each document with a new embedding field
30 updated_doc_count = 0
31 for doc in documents:
32     embedding = get_embedding(doc["title"])
33     if embedding is not None:
34         collection.update_one({"_id": doc["_id"]}, {"$set": {"title_voyageai_embedding": embedding}})
35         updated_doc_count += 1
36 
37 print(f"Updated {updated_doc_count} documents.")

Updated 3500 documents.

Nota

La operación podría tardar hasta 15 minutos en completarse.

Cree el índice de búsqueda vectorial de MongoDB

En esta sección, creas un índice MongoDB Vector Search en el namespace sample_mflix.embedded_movies. La sección muestra una definición de índice que se puede utilizar para ejecutar varias consultas. Antes de crear el índice, debes completar el procedimiento para generar las representaciones.

Conéctese al clúster usando `mongosh`.

Abre mongosh en una ventana de terminal y conéctate a tu clúster de Atlas. Para obtener instrucciones detalladas sobre la conexión, consulta Conexión via mongosh.

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

Ejemplo

use sample_mflix

switched to db sample_mflix

Ejecute el método `db.collection.createSearchIndex()`.

1 db.embedded_movies.createSearchIndex(
2   "multiple-vector-search", 
3   "vectorSearch", 
4   {
5     "fields": [
6       {
7         "type": "vector",
8         "path": "plot_embedding",
9         "numDimensions": 1536,
10         "similarity": "dotProduct"
11       },
12       {
13         "type": "vector",
14         "path": "plot_embedding_voyage_3_large",
15         "numDimensions": 2048,
16         "similarity": "dotProduct"
17       },
18       {
19         "type": "vector",
20         "path": "title_voyageai_embedding",
21         "numDimensions": 2048,
22         "similarity": "dotProduct"
23       }
24     ]
25   }
26 );

Esta definición de índice indexa los siguientes campos:

plot_embedding campo como el vector tipo. Este campo contiene vector embeddings que representan el resumen de la trama de una película. La definición del índice:
- Especifica 1536 dimensiones vectoriales.
- Mide la similitud usando la similitud de dotProduct.
Esta asignación de campos es obligatoria para el tercer caso de uso.
plot_embedding_voyage_3_large campo como el vector tipo. Este campo contiene vector embeddings que representan el resumen de la trama de una película. La definición del índice:
- Especifica 2048 dimensiones vectoriales.
- Mide la similitud usando la similitud de dotProduct.
Este mapeo de campos es necesario para todas las consultas en este tutorial.
title_voyageai_embedding campo como el vector tipo. Este campo contiene vector embeddings que representan el título de la película. La definición del índice:
- Especifica 2048 dimensiones vectoriales.
- Mide la similitud usando la similitud de dotProduct.
Esta asignación de campos es obligatoria para el segundo caso de uso.

ejecutar las queries de MongoDB Vector Search

En esta sección, query la colección embedded_movies en la base de datos sample_mflix utilizando el índice llamado multiple-vector-search que creaste en la colección.

Crea un archivo para utilizar los embeddings en la query.

Crea un archivo llamado embeddings.js.
touch embeddings.js

Copia y pega los siguientes embeddings en el archivo embeddings.js.

Este archivo contiene las incrustaciones de los términos de query utilizados en las queries. La siguiente tabla muestra el query de término, la variable que contiene los embeddings para el query de término, el modelo de embeddings utilizado para generar los embeddings y el número de dimensiones.

Query Phrase	Variable	Modelo de incrustación	dimensión
comedia ligera con fantasmas	COMEDIA_INVOLUCRANDO_FANTASMAS	Voyage AI's `voyage-3-large`	2048
humor slapstick con eventos paranormales	HUMOR_INVOLVING_PARANORMAL	Voyage AI's `voyage-3-large`	2048
batalla entre el bien y el mal	BATTLE_GOOD_EVIL	Voyage AI's `voyage-3-large`	2048
viaje a través de tierras.	JOURNEY_ACROSS_LANDS_VOYAGEAI	Voyage AI's `voyage-3-large`	2048
viaje a través de tierras.	JOURNEY_ACROSS_LANDS_OPENAI	de OpenAI `text-embedding-ada-002`	1536

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Guarde y cierre el archivo.

Conéctese a su clúster `mongosh` en.

Abre mongosh en una ventana de terminal y conéctate a tu clúster. Para obtener instrucciones detalladas sobre cómo conectar, consulta Conéctate a través de mongosh.

Utilice la `sample_mflix` base de datos.

Ejecuta el siguiente comando en el aviso de mongosh:

use sample_mflix

Carga las incrustaciones para usar en el query.

Ejecuta el siguiente comando para cargar los embeddings en el archivo embeddings.js después de reemplazar <path-to-file> por la ruta absoluta a tu archivo embeddings.js.
load('/<path-to-file>/embeddings.js';
true
Verifique que las incrustaciones se cargaron correctamente.
Puedes verificarlo ejecutando un comando similar al siguiente:
COMEDY_INVOLVING_GHOSTS.length
2048

Ejecuta las consultas de MongoDB Vector Search en la colección `embedded_movies`.

Múltiples vectores de query contra un único vector de documento

Realice una búsqueda exhaustiva en el conjunto de datos para encontrar términos semánticamente similares, con el objetivo de determinar qué término de consulta ofrece los mejores resultados.

1 db.embedded_movies.aggregate([
2   {
3     $rankFusion: {
4       input: {
5         pipelines: {
6           vectorPipeline1: [
7             {
8               "$vectorSearch": {
9                 "index": "multiple-vector-search",
10                 "path": "plot_embedding_voyage_3_large",
11                 "queryVector": COMEDY_INVOLVING_GHOSTS,
12                 "numCandidates": 2000,
13                 "limit": 50
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "plot_embedding_voyage_3_large",
22                 "queryVector": HUMOR_INVOLVING_PARANORMAL,
23                 "numCandidates": 2000,
24                 "limit": 50
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 20
49   }
50 ]);

[
  {
    _id: ObjectId('573a139af29313caabcef0a4'),
    plot: 'A paranormal expert and his daughter bunk in an abandoned house populated by 3 mischievous ghosts and one friendly one.',
    title: 'Casper',
    scoreDetails: {
      value: 0.01639344262295082,
      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: 'vectorPipeline1',
          rank: 1,
          weight: 0.5,
          value: 0.8094863891601562,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 1,
          weight: 0.5,
          value: 0.7794423699378967,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabceaf10'),
    plot: "When Peter Plunkett's Irish castle turned hotel is about to be repossesed, he decides to spice up the attraction a bit for the 'Yanks' by having his staff pretend to haunt the castle. The ...",
    title: 'High Spirits',
    scoreDetails: {
      value: 0.01575682382133995,
      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: 'vectorPipeline1',
          rank: 2,
          weight: 0.5,
          value: 0.7949001789093018,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 5,
          weight: 0.5,
          value: 0.7545012831687927,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabce912c'),
    plot: 'Three unemployed parapsychology professors set up shop as a unique ghost removal service.',
    title: 'Ghostbusters',
    scoreDetails: {
      value: 0.015038444142921756,
      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: 'vectorPipeline1',
          rank: 6,
          weight: 0.5,
          value: 0.7781286239624023,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 7,
          weight: 0.5,
          value: 0.7519128918647766,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13a0f29313caabd056df'),
    plot: "A girl calls on her brother's imaginary friend to banish a mischievous boogeyman who has framed her for his pranks.",
    title: "Don't Look Under the Bed",
    scoreDetails: {
      value: 0.014913831197525408,
      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: 'vectorPipeline1',
          rank: 13,
          weight: 0.5,
          value: 0.7534659504890442,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 2,
          weight: 0.5,
          value: 0.7626002430915833,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1399f29313caabced85b'),
    plot: 'A comical Gothic horror-movie-type family tries to rescue their beloved uncle from his gold-digging new love.',
    title: 'Addams Family Values',
    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: 'vectorPipeline1',
          rank: 11,
          weight: 0.5,
          value: 0.7640036344528198,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 4,
          weight: 0.5,
          value: 0.7570045590400696,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13bdf29313caabd58456'),
    plot: 'Bertram Pincus is a man whose people skills leave much to be desired. When Pincus dies unexpectedly, but is miraculously revived after seven minutes, he wakes up to discover that he now has the annoying ability to see ghosts.',
    title: 'Ghost Town',
    scoreDetails: {
      value: 0.014709063378758382,
      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: 'vectorPipeline1',
          rank: 7,
          weight: 0.5,
          value: 0.7774010300636292,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 9,
          weight: 0.5,
          value: 0.7500401735305786,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabceace9'),
    plot: 'A couple of recently deceased ghosts contract the services of a "bio-exorcist" in order to remove the obnoxious new owners of their house.',
    title: 'Beetlejuice',
    scoreDetails: {
      value: 0.014186507936507936,
      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: 'vectorPipeline1',
          rank: 3,
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    scoreDetails: {
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    plot: 'Two cops are brought back to life to chase down supernatural criminals.',
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    title: 'The Peanut Butter Solution',
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    title: 'The Canterville Ghost',
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    title: 'Bhoothnath',
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Esta consulta de ejemplo utiliza el $rankFusion con las siguientes etapas de la canalización de entrada:

$vectorSearch

Busca en el campo plot_embedding_voyage_3_large la frase comedia ligera con fantasmas, especificada en el campo queryVector de la query como vectores de incrustación utilizando la variable COMEDY_INVOLVING_GHOSTS.
Especifica una búsqueda de hasta 2000 vecinos más cercanos.
Limita los resultados de esta etapa a 50 documentos.
Especifica una ponderación de 0.5 para influir en la contribución de rango de esa pipeline a la puntuación final.

$vectorSearch

Realiza una búsqueda vectorial secuencial en el campo plot_embedding_voyage_3_large para la frase humor slapstick con sucesos paranormales, especificada en el campo queryVector como incrustaciones vectoriales con la variable HUMOR_INVOLVING_PARANORMAL.
Especifica una búsqueda de hasta 2000 vecinos más cercanos.
Limita los resultados de esta etapa a 50 documentos.
Especifica una ponderación de 0.5 para influir en la contribución de rango de esa pipeline a la puntuación final.

La query de muestra también especifica las siguientes etapas de la pipeline.

`$project`	Solo incluye los campos `plot` y `title` en los resultados. Agrega un campo llamado `scoreDetails` en los resultados.
`$limit`	Limita los resultados devueltos a 20 documentos.

MongoDB Vector Search combina los resultados de ambas consultas en un único conjunto de resultados. En los resultados:

El scoreDetails.value muestra la puntuación en bruto de ese pipeline antes de ponderarlo y combinarlo mediante una fusión de rangos recíprocos.
El score.details.rank muestra el rango del documento en los resultados del pipeline.
El scoreDetails.details.value contiene la puntuación de rango recíproco ponderada.

Puedes hacer lo siguiente:

Ajusta los pesos asignados a cada pipeline en la query para refinar más los resultados.
Aumenta el número de documentos en los resultados si encuentras resultados dispares.

Misma query por término vectorial frente a múltiples vectores de documentos

Busca en múltiples campos del conjunto de datos para determinar qué campos devuelven los mejores resultados para la misma query.

1 db.embedded_movies.aggregate([
2   {
3     $rankFusion: {
4       input: {
5         pipelines: {
6           vectorPipeline1: [
7             {
8               "$vectorSearch": {
9                 "index": "multiple-vector-search",
10                 "path": "plot_embedding_voyage_3_large",
11                 "queryVector": BATTLE_GOOD_EVIL,
12                 "numCandidates": 2000,
13                 "limit": 200
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "title_voyageai_embedding",
22                 "queryVector": BATTLE_GOOD_EVIL,
23                 "numCandidates": 2000,
24                 "limit": 200
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 20
49   }
50 ]);

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    plot: 'Wind and Cloud find themselves up against a ruthless Japanese warlord intent on invading China.',
    title: 'Storm Warriors',
    scoreDetails: {
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    title: 'Warriors of Heaven and Earth',
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    plot: 'A short tale about the demons that slumber deep in the human soul and have the power to push people into the abyss of blind hate, fury and rage.',
    title: 'Paths of Hate',
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    plot: 'The Son of Satan attempts to bring his father to Earth with the aid of some mystic stones.',
    title: 'Warlock: The Armageddon',
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    plot: 'Eight classic monsters fight to the death in an explosive wrestling tournament set inside an abandoned and cursed graveyard.',
    title: 'Monster Brawl',
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    plot: 'A young man must stop the Lord of Darkness from both destroying daylight and marrying the woman he loves.',
    title: 'Legend',
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    _id: ObjectId('573a13a5f29313caabd15bbd'),
    plot: "A desert warrior rises up against the evil army that is destroying his homeland. He captures the enemy's key sorcerer, takes her deep into the desert and prepares for a final showdown.",
    title: 'The Scorpion King',
    scoreDetails: {
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Esta consulta de ejemplo utiliza el $rankFusion con las siguientes etapas de la canalización de entrada:

$vectorSearch

Busca en el campo plot_embedding_voyage_3_large la frase batalla entre el bien y el mal, especificada en el campo queryVector de la query como vector de embeddings utilizando la variable BATTLE_GOOD_EVIL.
Especifica una búsqueda de hasta 2000 vecinos más cercanos.
Limita los resultados a 200 documentos solamente.
Especifica una ponderación de 0.5 para influir en la contribución de rango de esa pipeline a la puntuación final.

$vectorSearch

Realiza una búsqueda secuencial de vectores en el campo title_voyageai_embedding para la string"battle between good and evil", especificada en el campo queryVector como incrustaciones vectoriales utilizando la variable BATTLE_GOOD_EVIL.
Especifica una búsqueda de hasta 2000 vecinos más cercanos.
Limita los resultados a 200 documentos solamente.
Especifica una ponderación de 0.5 para influir en la contribución de rango de esa pipeline a la puntuación final.

La query de muestra también especifica las siguientes etapas de la pipeline.

`$project`	Solo incluye los campos `plot` y `title` en los resultados. Agrega un campo llamado `scoreDetails` en los resultados.
`$limit`	Limita los resultados devueltos a 20 documentos.

MongoDB Vector Search combina los resultados de ambas consultas en un único conjunto de resultados. En los resultados:

El scoreDetails.value muestra la puntuación en bruto de ese pipeline antes de ponderarlo y combinarlo mediante una fusión de rangos recíprocos.
El score.details.rank muestra el rango del documento en los resultados del pipeline.
El scoreDetails.details.value contiene la puntuación ponderada del rango recíproco, que muestra cuáles campos devuelven el resultado más relevante para el término de consulta.

Por ejemplo, el primer y el cuarto documentos en los resultados sugieren una coincidencia significativa para el término en el campo plot, mientras que el segundo y el quinto documentos sugieren una coincidencia significativa en el campo title. Puedes hacer lo siguiente:

Ajusta los pesos asignados a cada pipeline en la query para refinar más los resultados.
Aumenta el número de documentos en los resultados si encuentras resultados dispares.

Query del mismo término utilizando múltiples modelos

Busque incrustaciones de diferentes modelos de incrustación para determinar las diferencias en la interpretación semántica entre los distintos modelos.

1 db.embedded_movies.aggregate([
2   {
3     $rankFusion: {
4       input: {
5         pipelines: {
6           vectorPipeline1: [
7             {
8               "$vectorSearch": {
9                 "index": "multiple-vector-search",
10                 "path": "plot_embedding",
11                 "queryVector": JOURNEY_ACROSS_LANDS_OPENAI,
12                 "numCandidates": 2000,
13                 "limit": 100
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "plot_embedding_voyage_3_large",
22                 "queryVector": JOURNEY_ACROSS_LANDS_VOYAGEAI,
23                 "numCandidates": 2000,
24                 "limit": 100
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 20
49   }
50 ]);

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    scoreDetails: {
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          weight: 0.5,
          value: 0.9153545498847961,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 61,
          weight: 0.5,
          value: 0.7023824453353882,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13aaf29313caabd218b4'),
    plot: 'An adventurous girl finds another world that is a strangely idealized version of her frustrating home, but it has sinister secrets.',
    title: 'Coraline',
    scoreDetails: {
      value: 0.010934744268077601,
      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: 'vectorPipeline1',
          rank: 21,
          weight: 0.5,
          value: 0.9102445840835571,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 45,
          weight: 0.5,
          value: 0.7056041955947876,
          details: []
        }
      ]
    }
  }
]

Esta consulta de ejemplo utiliza el $rankFusion con las siguientes etapas de la canalización de entrada:

$vectorSearch

Busca en el campo plot_embedding la string viaje por tierras especificada en el campo queryVector de la query como incrustaciones vectoriales utilizando la variable JOURNEY_ACROSS_LANDS_OPENAI.
Especifica una búsqueda de hasta 2000 vecinos más cercanos.
Limita los resultados a 100 documentos.
Especifica una ponderación de 0.5 para influir en la contribución de rango de esa pipeline a la puntuación final.

$vectorSearch

Realiza una búsqueda vectorial secuencial en el campo plot_embedding_voyage_3_large para la string journey across lands, especificada en el campo queryVector como incrustaciones vectoriales utilizando la variable JOURNEY_ACROSS_LANDS_VOYAGEAI.
Especifica una búsqueda de hasta 2000 vecinos más cercanos.
Limita los resultados a 100 documentos.
Especifica una ponderación de 0.5 para influir en la contribución de rango de esa pipeline a la puntuación final.

La query de muestra también especifica las siguientes etapas de la pipeline.

`$project`	Solo incluye los campos `plot` y `title` en los resultados. Agrega un campo llamado `scoreDetails` en los resultados.
`$limit`	Limita los resultados devueltos a 20 documentos.

MongoDB Vector Search combina los resultados de ambas consultas en un único conjunto de resultados. En los resultados:

El scoreDetails.value muestra la puntuación en bruto de ese pipeline antes de ponderarlo y combinarlo mediante una fusión de rangos recíprocos.
El score.details.rank muestra el rango del documento en los resultados del pipeline.
El scoreDetails.details.value contiene la puntuación de rango recíproco ponderada, que muestra los puntos fuertes y las diferencias en la interpretación semántica del query término por parte de los diferentes modelos de incrustación.

Por ejemplo, el primero y el quinto documento en los resultados sugieren una representación semántica más similar utilizando el modelo en el vectorPipeline2, mientras que el segundo y el cuarto documento en los resultados sugieren una interpretación semántica más cercana utilizando el modelo en el vectorPipeline1.

Volver

Búsqueda vectorial y búsqueda de texto completo combinadas

Revisa las opciones de implementación

1	import os
2	import pymongo
3	import voyageai
4
5	# Specify your Voyage API key and embedding model
6	os.environ["VOYAGE_API_KEY"] = "<api-key>"
7
8	model = "voyage-3-large"
9	outputDimension = 2048
10	vo = voyageai.Client()
11	# Define a function to generate embeddings
12	def get_embedding(data, input_type = "document"):
13	embeddings = vo.embed(
14	data, model = model, output_dimension = outputDimension, input_type = input_type
15	).embeddings
16	return embeddings[0]
17
18	# Connect to your MongoDB Atlas cluster
19	mongo_client = pymongo.MongoClient("<connection-string>")
20	db = mongo_client["sample_mflix"]
21	collection = db["embedded_movies"]
22
23	# Filter to exclude null or empty plot fields
24	filter = {"title": {"$nin": [None, ""]}}
25
26	# Get a subset of documents in the collection
27	documents = collection.find(filter).limit(3500)
28
29	# Update each document with a new embedding field
30	updated_doc_count = 0
31	for doc in documents:
32	embedding = get_embedding(doc["title"])
33	if embedding is not None:
34	collection.update_one({"_id": doc["_id"]}, {"$set": {"title_voyageai_embedding": embedding}})
35	updated_doc_count += 1
36
37	print(f"Updated {updated_doc_count} documents.")

1	db.embedded_movies.createSearchIndex(
2	"multiple-vector-search",
3	"vectorSearch",
4	{
5	"fields": [
6	{
7	"type": "vector",
8	"path": "plot_embedding",
9	"numDimensions": 1536,
10	"similarity": "dotProduct"
11	},
12	{
13	"type": "vector",
14	"path": "plot_embedding_voyage_3_large",
15	"numDimensions": 2048,
16	"similarity": "dotProduct"
17	},
18	{
19	"type": "vector",
20	"path": "title_voyageai_embedding",
21	"numDimensions": 2048,
22	"similarity": "dotProduct"
23	}
24	]
25	}
26	);

1	db.embedded_movies.aggregate([
2	{
3	$rankFusion: {
4	input: {
5	pipelines: {
6	vectorPipeline1: [
7	{
8	"$vectorSearch": {
9	"index": "multiple-vector-search",
10	"path": "plot_embedding_voyage_3_large",
11	"queryVector": COMEDY_INVOLVING_GHOSTS,
12	"numCandidates": 2000,
13	"limit": 50
14	}
15	}
16	],
17	vectorPipeline2: [
18	{
19	"$vectorSearch": {
20	"index": "multiple-vector-search",
21	"path": "plot_embedding_voyage_3_large",
22	"queryVector": HUMOR_INVOLVING_PARANORMAL,
23	"numCandidates": 2000,
24	"limit": 50
25	}
26	}
27	]
28	}
29	},
30	combination: {
31	weights: {
32	vectorPipeline1: 0.5,
33	vectorPipeline2: 0.5
34	}
35	},
36	"scoreDetails": true
37	}
38	},
39	{
40	"$project": {
41	_id: 1,
42	title: 1,
43	plot: 1,
44	scoreDetails: {"$meta": "scoreDetails"}
45	}
46	},
47	{
48	"$limit": 20
49	}
50	]);

Cómo combinar varias $vectorSearch query

Casos de uso y beneficios

Acerca del Tutorial

Nota

Genera los Embeddings

Requisitos previos

Procedimiento

Configura el entorno.

Definir y utilizar una función para generar incrustaciones vectoriales.

Nota

Cree el índice de búsqueda vectorial de MongoDB

Conéctese al clúster usando mongosh.

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

Ejemplo

Ejecute el método db.collection.createSearchIndex().

ejecutar las queries de MongoDB Vector Search

Crea un archivo para utilizar los embeddings en la query.

Conéctese a su clúster mongosh en.

Utilice la sample_mflix base de datos.

Carga las incrustaciones para usar en el query.

Ejecuta las consultas de MongoDB Vector Search en la colección embedded_movies.

Múltiples vectores de query contra un único vector de documento

Misma query por término vectorial frente a múltiples vectores de documentos

Query del mismo término utilizando múltiples modelos

Cómo combinar varias `$vectorSearch` query

Conéctese al clúster usando `mongosh`.

Ejecute el método `db.collection.createSearchIndex()`.

Conéctese a su clúster `mongosh` en.

Utilice la `sample_mflix` base de datos.

Ejecuta las consultas de MongoDB Vector Search en la colección `embedded_movies`.