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Cómo combinar varios `$vectorSearch` Queries

Las etapas $rankFusion y $scoreFusion están disponibles como funciones de vista previa. Para obtener más información, consulte Características de vista previa.

A partir de la versión8.1, la etapa de agregación de MongoDB $rankFusion admite $vectorSearch dentro de la canalización de entrada. Puede usar $rankFusion para combinar varias consultas en la misma colección dentro $vectorSearch de la misma canalización. $rankFusion primero ejecuta todas las canalizaciones de entrada de forma independiente y, a continuación, desduplica y combina los resultados de la canalización de entrada en un conjunto final de resultados clasificados. Para ejecutar varias consultas en varias colecciones dentro de una misma $vectorSearch canalización,$unionWith use.

Casos de uso y beneficios

Puede ejecutar los siguientes tipos de consultas mediante $vectorSearch la $rankFusion canalización:

Ejecute varias $vectorSearch consultas para términos similares en el mismo campo.
Esto le permite realizar una búsqueda exhaustiva de su conjunto de datos para términos similares en la misma consulta.
Ejecute múltiples consultas $vectorSearch para el mismo término en múltiples campos.
Esto le permite buscar múltiples campos en su conjunto de datos para determinar qué campos devuelven los mejores resultados para la consulta.
Ejecute varias consultas $vectorSearch para el mismo término contra incrustaciones de diferentes modelos de incrustación.
Esto le permite buscar incrustaciones de diferentes modelos de incrustación para determinar las diferencias de interpretación semántica entre los diferentes modelos.

Acerca del Tutorial

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

plot_embedding, que contiene incrustaciones creadas mediante el modelo de incrustación text-embedding-ada-002 de OpenAI.
plot_embedding_voyage_3_large, que contiene incrustaciones creadas mediante el modelo de incrustación voyage-3-large de Voyage AI. Este campo se utiliza para todas las consultas de ejemplo de este tutorial.

Para la segunda consulta de la sección "Casos de uso y beneficios", debe generar incrustaciones para otro campo. Para probar la consulta de ejemplo para este caso de uso, siga los pasos de "Generar incrustaciones".

Este tutorial lo guiará a través de los siguientes pasos:

Configura un índice de MongoDB búsqueda vectorial en la colección sample_mflix.embedded_movies.
Ejecute la consulta $vectorSearch con $rankFusion para realizar las siguientes búsquedas:
- Busque en el campo plot_embedding_voyage_3_large tramas de películas que contengan películas con tramas similares a light-hearted comedy with ghosts y slapstick humor with paranormal events.
- Busque en los campos plot_embedding_voyage_3_large y title_embedding películas con tramas similares a battle between good and evil utilizando incrustaciones de Voyage AI.
- Busque en los campos plot_embedding y plot_embedding_voyage_3_large películas con tramas similares a journey across lands utilizando incrustaciones de OpenAI y Voyage AI respectivamente.

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

Nota

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

Generar las incrustaciones

Para consultar el mismo término en varios campos, debe preparar el conjunto de datos para incluir incrustaciones en diferentes campos. Esta sección muestra cómo generar incrustaciones para el campo title de la colección mediante el modelo de incrustación voyage-3-large de Voyage AI.

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 Codeo Colab.
Clave API para modelos de integración de Voyage AI.

Procedimiento

En esta sección, generará incrustaciones para el campo title en la colección embedded_movies utilizando el modelo de incrustación voyage-3-large de Voyage AI.

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 IA de viaje Clave API 6(línea).
`<connection-string>`	Su cadena de conexión del clúster MongoDB (línea 18).

El siguiente código define una función que genera incrustaciones vectoriales mediante un modelo de incrustación propietario de Voyage AI. En concreto, este código hace lo siguiente:

Especifica el modelo de incrustación voyage-3-large.
Crea una función denominada get_embedding() que llama a la API del modelo para generar una 2048 incrustación de dimensión para una entrada de texto determinada.
Se conecta al clúster y obtiene 3500 documentos del namespace sample_mflix.embedded_movies.
Genera incrustaciones desde el 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 incrustación mediante el controlador PyMongo de MongoDB.

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, creará un índice de búsqueda vectorial de MongoDB en el espacio de nombres sample_mflix.embedded_movies. Esta sección muestra una definición de índice que puede usarse para ejecutar diversas consultas. Antes de crear el índice, complete el procedimiento para generar las incrustaciones.

Conéctese al clúster `mongosh` mediante.

Abrir mongosh en una ventana de terminal para conectarse al clúster de Atlas. Para obtener instrucciones detalladas sobre cómo realizar la conexión, se debe consultar Conectar a través de 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 `db.collection.createSearchIndex()` método.

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 vector de tipo. Este campo contiene incrustaciones vectoriales 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.
Este mapeo de campos es necesario para el tercer caso de uso.
plot_embedding_voyage_3_large Campo vector de tipo. Este campo contiene incrustaciones vectoriales 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.
Esta asignación de campo es necesaria para todas las consultas de este tutorial.
title_voyageai_embedding Campo vector de tipo. Este campo contiene incrustaciones vectoriales que representan el título de la película. Definición del índice:
- Especifica 2048 dimensiones vectoriales.
- Mide la similitud usando la similitud de dotProduct.
Esta asignación de campos es necesaria para el segundo caso de uso.

Ejecutar las consultas de búsqueda de vectores de MongoDB

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

Cree un archivo para las incrustaciones que se utilizarán en la consulta.

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

Copie y pegue las siguientes incrustaciones en el archivo embeddings.js.

Este archivo contiene incrustaciones para los términos de consulta utilizados en las consultas. La siguiente tabla muestra el término de consulta, la variable que contiene las incrustaciones para el término de consulta, el modelo de incrustación utilizado para generarlas y el número de dimensiones.

Frase de consulta	Variable	Modelo de incrustación	Dimensiones
comedia alegre con fantasmas	COMEDIA CON 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	VIAJE A TRAVÉS DE TIERRAS VOYAGEAI	Voyage AI's `voyage-3-large`	2048
viaje a través de tierras	VIAJE A TRAVÉS DE TIERRAS ABIERTAS	OpenAI `text-embedding-ada-002`	1536

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Guarde y cierre el archivo.

Conéctese a su clúster `mongosh` en.

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.

Ejecute el siguiente comando en el mongosh indicador:

use sample_mflix

Carga las incrustaciones para usar en el query.

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

Ejecute las consultas de búsqueda vectorial de MongoDB contra la `embedded_movies` colección.

Múltiples vectores de consulta contra un único vector de documento

Realice una búsqueda exhaustiva del conjunto de datos en busca de términos semánticamente similares para determinar qué término de consulta devuelve 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: [
        {
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    scoreDetails: {
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    title: 'The Peanut Butter Solution',
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    title: 'Bhoothnath',
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Esta consulta de muestra utiliza $rankFusion con las siguientes etapas de canalización de entrada:

$vectorSearch

Busca en el plot_embedding_voyage_3_large campo la frase comedia alegre con fantasmas, especificada en el queryVector campo de la consulta como incrustaciones vectoriales mediante la COMEDY_INVOLVING_GHOSTS variable.
Especifica una búsqueda de hasta 2000 vecinos más cercanos.
Limita los resultados de esta etapa a 50 documentos.
Especifica un peso de 0.5 para influir en la contribución de la clasificación de esa canalización a la puntuación final.

$vectorSearch

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

La consulta de muestra también especifica las siguientes etapas del pipeline.

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

La búsqueda vectorial de MongoDB fusiona 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:

Ajuste los pesos asignados a cada canalización en la consulta para refinar aún más los resultados.
Aumente la cantidad de documentos en los resultados si ve resultados disjuntos.

Misma query por término vectorial frente a múltiples vectores de documentos

Busque varios campos en el conjunto de datos para determinar qué campos devuelven los mejores resultados para la misma consulta.

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',
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    title: 'Warriors of Heaven and Earth',
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    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.",
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Esta consulta de muestra utiliza $rankFusion con las siguientes etapas de canalización de entrada:

$vectorSearch

Busca en el plot_embedding_voyage_3_large campo la frase batalla entre el bien y el mal, especificada en el queryVector campo de la consulta como incrustaciones vectoriales mediante la BATTLE_GOOD_EVIL variable.
Especifica una búsqueda de hasta 2000 vecinos más cercanos.
Limita los resultados a 200 documentos únicamente.
Especifica un peso de 0.5 para influir en la contribución de la clasificación de esa canalización a la puntuación final.

$vectorSearch

Realiza una búsqueda vectorial secuencial en el title_voyageai_embedding campo para la cadena batalla entre el bien y el mal, especificada en el queryVector campo como incrustaciones vectoriales mediante la BATTLE_GOOD_EVIL variable.
Especifica una búsqueda de hasta 2000 vecinos más cercanos.
Limita los resultados a 200 documentos únicamente.
Especifica un peso de 0.5 para influir en la contribución de la clasificación de esa canalización a la puntuación final.

La consulta de muestra también especifica las siguientes etapas del pipeline.

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

La búsqueda vectorial de MongoDB fusiona 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 clasificación recíproca ponderada, que muestra qué campos devuelven el resultado más relevante para el término de consulta.

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

Ajuste los pesos asignados a cada canalización en la consulta para refinar aún más los resultados.
Aumente la cantidad de documentos en los resultados si ve resultados disjuntos.

Consulta del mismo término utilizando múltiples modelos

Busque incrustaciones de diferentes modelos de incrustación para determinar las diferencias de interpretación semántica entre los diferentes 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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        {
          inputPipelineName: 'vectorPipeline1',
          rank: 11,
          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 muestra utiliza $rankFusion con las siguientes etapas de canalización de entrada:

$vectorSearch

Busca plot_embedding en el campo la cadena "viaje" queryVector JOURNEY_ACROSS_LANDS_OPENAI a través de las tierras especificadas en el campo de la consulta como incrustaciones vectoriales mediante la variable.
Especifica una búsqueda de hasta 2000 vecinos más cercanos.
Limita los resultados a 100 documentos.
Especifica un peso de 0.5 para influir en la contribución de la clasificación de esa canalización a la puntuación final.

$vectorSearch

Realiza una búsqueda vectorial secuencial en el plot_embedding_voyage_3_large campo para la cadena journey across lands, especificada en el queryVector campo como incrustaciones vectoriales mediante el uso de la JOURNEY_ACROSS_LANDS_VOYAGEAI variable.
Especifica una búsqueda de hasta 2000 vecinos más cercanos.
Limita los resultados a 100 documentos.
Especifica un peso de 0.5 para influir en la contribución de la clasificación de esa canalización a la puntuación final.

La consulta de muestra también especifica las siguientes etapas del pipeline.

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

La búsqueda vectorial de MongoDB fusiona 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 ponderado, que muestra las fortalezas y las diferencias en la interpretación semántica del término de consulta por parte de los diferentes modelos de inserción.

Por ejemplo, el primer y quinto documento en los resultados sugieren una representación semántica más similar por parte del modelo utilizado en vectorPipeline2, mientras que el segundo y cuarto documento en los resultados sugieren una interpretación semántica más cercana por parte del modelo utilizado en 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	]);

Casos de uso y beneficios

Acerca del Tutorial

Nota

Generar las incrustaciones

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 mongosh mediante.

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

Ejemplo

Ejecute el db.collection.createSearchIndex() método.

Ejecutar las consultas de búsqueda de vectores de MongoDB

Cree un archivo para las incrustaciones que se utilizarán en la consulta.

Conéctese a su clúster mongosh en.

Utilice la sample_mflix base de datos.

Carga las incrustaciones para usar en el query.

Ejecute las consultas de búsqueda vectorial de MongoDB contra la embedded_movies colección.

Múltiples vectores de consulta contra un único vector de documento

Misma query por término vectorial frente a múltiples vectores de documentos

Consulta del mismo término utilizando múltiples modelos

Conéctese al clúster `mongosh` mediante.

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

Conéctese a su clúster `mongosh` en.

Utilice la `sample_mflix` base de datos.

Ejecute las consultas de búsqueda vectorial de MongoDB contra la `embedded_movies` colección.