Guía de inicio rápido de desarrollo local de MongoDB Search y MongoDB búsqueda vectorial

Extraiga la última imagen de Docker mongodb/mongodb-atlas-local.

Para obtener la imagen de Docker mongodb/mongodb-atlas-local, ejecuta el siguiente comando:

docker pull mongodb/mongodb-atlas-local:preview

Para crear un entorno reproducible, fije la imagen a una etiqueta de versión específica en lugar de una etiqueta móvil como preview o latest. Reemplace <version> con la etiqueta de versión que desee:

docker pull mongodb/mongodb-atlas-local:<version>

Crea un archivo docker-compose.yaml.

Cree el archivo docker-compose.yaml. Para obtener un ejemplo completo y detallado, consulte Ejemplo de Docker Compose.

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services:
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   mongodb:
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      image: mongodb/mongodb-atlas-local:preview
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      hostname: mongodb
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      environment:
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         - MONGODB_INITDB_ROOT_USERNAME=<username>
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         - MONGODB_INITDB_ROOT_PASSWORD=<password>
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      ports:
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         - 27017:27017
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      volumes:
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         - ./init:/docker-entrypoint-initdb.d
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         - db:/data/db
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         - configdb:/data/configdb
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         - mongot:/data/mongot
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volumes:
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   db:
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   configdb:
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   mongot:

Reemplaza los valores de marcador de posición y guarda el archivo.

Reemplace los valores de marcador de posición <username> y <password> con sus propios valores y guarde el archivo. También puede establecer el nombre de usuario y la contraseña a través de un archivo para una capa extra de seguridad. Utilice las variables de entorno MONGODB_INITDB_ROOT_USERNAME_FILE y MONGODB_INITDB_ROOT_PASSWORD_FILE para especificar la ruta a los archivos que contienen el nombre de usuario y la contraseña.

Para utilizar la incrustación automatizada, proporcione su clave de API de Voyage AI (creada desde la interfaz de usuario de Atlas) a través de la variable de entorno VOYAGE_API_KEY. Por defecto, el punto final del proveedor de incrustación es https://ai.mongodb.com/v1/embeddings (para claves creadas desde la interfaz de usuario de Atlas).

Para cargar automáticamente el conjunto de datos de muestra de MongoDB durante la inicialización, establezca la variable de entorno MONGODB_LOAD_SAMPLE_DATA en true.

1
services:
2
   mongodb:
3
      image: mongodb/mongodb-atlas-local:preview
4
      hostname: mongodb
5
      environment:
6
         - MONGODB_INITDB_ROOT_USERNAME_FILE=<path-to-username-file>
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         - MONGODB_INITDB_ROOT_PASSWORD_FILE=<path-to-password-file>
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         - MONGODB_LOAD_SAMPLE_DATA=true
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         - VOYAGE_API_KEY=<voyage-api-key>

Ejecutar Docker Compose.

Ejecute el siguiente comando para crear un set de réplicas de un solo nodo con MongoDB Search y MongoDB búsqueda vectorial.

docker compose up -d

Verifique la implementación.

Para verificar la implementación, ejecute el siguiente comando:

docker ps

La salida debería asemejarse a lo siguiente:

CONTAINER ID   IMAGE                                 COMMAND                  CREATED       STATUS                 PORTS                                             NAMES
f68ee6df9907   mongodb/mongodb-atlas-local:preview   "/usr/local/bin/runn…"   4 hours ago   Up 4 hours (healthy)   0.0.0.0:27017->27017/tcp, [::]:27017->27017/tcp   search-in-community-mongodb-1

Conéctese al set de réplicas de un solo nodo.

Para conectarse al set de réplicas de un solo nodo, ejecute el siguiente comando después de reemplazar <username> por el nombre de usuario que especificó en el archivo docker-compose.yaml:

mongosh "mongodb://localhost:27017" -u <username>

(opcional). Si cargó el conjunto de datos de muestra, cree un índice.

1. Para verificar que se cargan los mismos datos, ejecute el siguiente comando:

   show dbs

   OCULTAR SALIDA

   __mdb_internal_search   56.00 KiB
   admin                  252.00 KiB
   config                 172.00 KiB
   local                  193.25 MiB
   sample_airbnb           52.52 MiB
   sample_analytics         9.48 MiB
   sample_geospatial        1.15 MiB
   sample_guides           40.00 KiB
   sample_mflix            96.16 MiB
   sample_restaurants       6.43 MiB
   sample_supplies          1.04 MiB
   sample_training         46.91 MiB
   sample_weatherdata       2.58 MiB

2. Para crear índices, cambie a la base de datos sample_mflix.

   use sample_mflix

3. Para crear un índice de MongoDB Search llamado movie-title-index en la colección sample_mflix.movies, ejecute el siguiente comando:

   La siguiente definición de índice crea un índice de MongoDB Search denominado movie-index en la colección movies. La definición de índice especifica el mappings como dynamic: true para indexar automáticamente todos los campos que se pueden indexar dinámicamente en los documentos de la colección.

   1
   db.movies.createSearchIndex(
   2
     "movie-index",
   3
     { mappings: { dynamic: true } }
   4
   )

4. Para crear un índice de MongoDB búsqueda vectorial llamado movie-vector-index en la colección sample_mflix.embedded_movies, ejecutar el siguiente comando:

   La siguiente definición de índice crea un índice de búsqueda vectorial de MongoDB denominado movie-vector-index en el campo plot_embedding_voyage_4_large de la colección embedded_movies. La definición del índice especifica lo siguiente:

   • El type como vectorSearch para crear un índice de MongoDB búsqueda vectorial.

   • El path como plot_embedding_voyage_4_large para indexar las incrustación de vectores en el campo.

   • El numDimensions como 2048 para especificar el número de dimensiones en las incrustaciones vectoriales.

   • El similarity como dotProduct para especificar la función de similitud que se utilizará para medir la similitud entre el vector de query y los vectores indexados.

   1
   db.embedded_movies.createSearchIndex(
   2
     "movie-vector-index",
   3
     "vectorSearch",
   4
     {
   5
       fields: [
   6
         { type: "vector", path: "plot_embedding_voyage_4_large", numDimensions: 2048, similarity: "dotProduct" }
   7
       ]
   8
     }
   9
   )

5. Para verificar que se crearon los índices, ejecute el siguiente comando:

   db.movies.getSearchIndexes().forEach(printjson)

   VER SALIDA

   [
     {
       id: '6a22ddd3709bac3ce7a37252',
       name: 'movie-index',
       status: 'READY',
       queryable: true,
       latestDefinitionVersion: {
         version: 0,
         createdAt: ISODate('2026-06-05T14:31:47.000Z')
       },
       latestDefinition: {
         mappings: {
           dynamic: true
         }
       },
       statusDetail: [ {
         hostname: '6a22dbdcb2393a40012a14b2',
         status: 'READY',
         queryable: true,
         mainIndex: {
           status: 'READY',
           queryable: true,
           definitionVersion: {
             version: 0,
             createdAt: ISODate('2026-06-05T14:31:47.000Z')
           },
           definition: {
             mappings: {
               dynamic: true,
              fields: {}
             }
           }
         }
       } ],
       numDocs: 21349
     },
     {
       id: '6a22dde9709bac3ce7a37254',
       name: 'movie-vector-index',
       status: 'READY',
       queryable: true,
       latestDefinitionVersion: {
         version: 0,
         createdAt: ISODate('2026-06-05T14:32:09.000Z')
       },
       latestDefinition: {
       fields: [
         {
           type: 'vector',
           path: 'plot_embedding_voyage_4_large',
           numDimensions: 2048,
           similarity: 'dotProduct'
         }
       ]
     },
     statusDetail: [
       {
         hostname: '6a22dbdcb2393a40012a14b2',
         status: 'READY',
         queryable: true,
         mainIndex: {
           status: 'READY',
           queryable: true,
           definitionVersion: {
             version: 0,
             createdAt: ISODate('2026-06-05T14:32:09.000Z')
           },
           definition: {
             fields: [
               {
                 type: 'vector',
                 path: 'plot_embedding_voyage_4_large',
                 numDimensions: 2048,
                   similarity: 'dotProduct'
                 }
               ]
             }
           }
         }
       ],
       numDocs: 21349
     }
   ]

(opcional). Si creó el índice, ejecute una query en el índice.

1. Para ejecutar una query de MongoDB Search en el índice movie-index, ejecute el siguiente comando:

   Esta query busca el término courtroom lawyer en el campo plot de la colección movies utilizando el índice movie-index. La query utiliza la opción fuzzy para habilitar la coincidencia aproximada. La query devuelve los 3 resultados principales y proyecta solo los campos title y plot.

   1
   db.movies.aggregate([
   2
     {
   3
       "$search": {
   4
         "index": "movie-index",
   5
         "text": {
   6
           "query": "courtroom lawyer",
   7
           "path": "plot",
   8
           "fuzzy": {}
   9
         }
   10
       }
   11
     },
   12
     {
   13
           "$limit": 3
   14
     },
   15
     {
   16
       "$project": {
   17
         "_id": 0,
   18
         "title": 1,
   19
         "plot": 1
   20
       }
   21
     }
   22
   ])

   VER SALIDA

   [
     {
       plot: 'Academy Award-winning documentary filmmaker, Jean-Xavier de Lestrade, presents a gripping courtroom thriller, offering a rare and revealing inside look at a high-profile murder trial. In ...',
       title: 'Death on the Staircase'
     },
     {
       plot: 'William Kunstler was one of the most famous lawyers of the 20th century. The New York Times called him "the most hated and most loved lawyer in America." His clients included Martin Luther ...',
       title: 'William Kunstler: Disturbing the Universe'
     },
     {
       plot: "A dying millionnaire has her soul transferred into a younger, willing woman. But something goes wrong, and she finds herself in her lawyer's body - together with the lawyer.",
       title: 'All of Me'
     }
   ]

2. Para ejecutar una query de búsqueda vectorial de MongoDB en el índice movie-vector-index, realice los siguientes pasos:

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

      touch query-embeddings.js

   2. Copie y pegue las siguientes incrustaciones para la frase courtroom lawyer en el archivo query-embeddings.js:

      QUERY_EMBEDDING=[0.021390099078416824, 0.0055892206728458405, -0.02259857952594757, -0.005287100560963154, -0.04519715905189514, -0.026828259229660034, 0.004954768810421228, 0.01788550615310669, -0.016193633899092674, 0.010574201121926308, 0.0007590766181237996, -0.03963815048336983, 0.00014633940008934587, 0.02054416388273239, -0.025015540421009064, -0.0024169604294002056, 0.033112358301877975, -0.007250881288200617, -0.01492473017424345, 0.009969961829483509, 0.0055892206728458405, -0.006858124863356352, 0.008217665366828442, -0.011359713971614838, 0.005256888922303915, -0.04906429350376129, -0.016435330733656883, 0.03142048418521881, -0.009486569091677666, 0.03915475681424141, -0.014078794047236443, -0.027311652898788452, 0.014078794047236443, -0.01208480168133974, 0.0006080165621824563, 0.028157588094472885, 0.047855816781520844, 0.016797874122858047, -0.03552931547164917, -0.00486413249745965, -0.005679856985807419, 0.02332366816699505, -0.018006354570388794, 0.015106001868844032, 0.012386921793222427, -0.0066164289601147175, 0.0023716422729194164, -0.030937092378735542, -0.01492473017424345, -0.012145225889980793, 0.0016918722540140152, -0.015951938927173615, -0.010876321233808994, 0.03577101230621338, 0.010272081941366196, -0.019577378407120705, 0.03238726779818535, -0.010332505218684673, -0.0009063601610250771, -0.02078585885465145, 0.0002218694135081023, 0.004773496650159359, -0.030574548989534378, 0.01740211434662342, -0.0181272029876709, -0.018006354570388794, 0.009426145814359188, 0.007039397023618221, -0.0029909885488450527, -0.0018127203220501542, -0.016556179150938988, 0.000262466783169657, 0.027069956064224243, -0.005226676817983389, 0.025378083810210228, -0.0028248224407434464, -0.00036254405858926475, -0.023202819749712944, -0.03601270914077759, 0.023444514721632004, -0.013776673935353756, 0.015408121980726719, 0.0038218186236917973, -0.0049849809147417545, 0.012689041905105114, 0.0030514125246554613, -0.02259857952594757, -0.010695049539208412, 0.001503047184087336, -0.011420137248933315, 0.011661834083497524, 0.011057593859732151, -0.0007817356381565332, -0.02694910764694214, 0.04108832776546478, -0.02332366816699505, 0.030453700572252274, -0.03214557096362114, 0.02151094749569893, 0.021994339302182198, -0.014864305965602398, 0.02852013148367405, 0.0033082144800573587, 0.025619780644774437, -0.0006495580892078578, -0.03142048418521881, -0.02332366816699505, 0.017281265929341316, 0.007311305031180382, -0.004320316482335329, -0.015589394606649876, 0.005951764993369579, -0.014622610062360764, -0.025861475616693497, -0.010815897956490517, -0.04519715905189514, -0.0059819770976901054, 0.013655826449394226, -0.020060770213603973, 0.033837445080280304, -0.0015710241859778762, 0.003670758567750454, 0.036979492753744125, -0.020302467048168182, 0.005377736873924732, 0.023927908390760422, 0.006767489016056061, -0.009849113412201405, -0.02126925066113472, 0.02779504470527172, 0.007341517135500908, 0.03456253185868263, 0.003232684452086687, -0.007250881288200617, 0.008882328867912292, -0.022115187719464302, 0.03746288642287254, -0.002779504517093301, -0.0025982323568314314, -0.009546993300318718, -0.018248051404953003, -0.005015192553400993, 0.028882676735520363, -0.02876182831823826, -0.04205511137843132, 0.03577101230621338, -0.019939923658967018, -0.005921552889049053, -0.0020090981852263212, 0.0004305210604798049, 0.009909537620842457, 0.0027341863606125116, 0.022356882691383362, -0.01607278548181057, -0.027432499453425407, -0.005105828866362572, -0.018489746376872063, -0.0035801224876195192, 0.009969961829483509, 0.011299289762973785, -0.006102825049310923, 0.0012084802147001028, 0.006525793112814426, -0.022115187719464302, -0.0077040609903633595, 0.003021200420334935, 0.015347698703408241, -0.00870105717331171, -0.015951938927173615, 0.004954768810421228, -0.029486916959285736, -0.03033285215497017, 0.010030385106801987, 0.009909537620842457, 0.02078585885465145, -0.030937092378735542, 0.012809890322387218, -0.0194565299898386, 0.002688868436962366, -0.047614119946956635, 0.007190457079559565, 0.0007288646302185953, 0.020665010437369347, 0.026586564257740974, 0.005619432777166367, 0.006102825049310923, -0.028157588094472885, -0.027553347870707512, 0.004350528586655855, -0.0011480561224743724, 0.00029456705669872463, -0.021148402243852615, -0.02489469200372696, -0.023444514721632004, -0.02054416388273239, -0.025015540421009064, -0.010695049539208412, 0.029728611931204796, -0.010090809315443039, 0.00906360149383545, -0.04688902944326401, 0.04568054899573326, 0.028882676735520363, -0.0013142222305759788, 0.027069956064224243, -0.019939923658967018, 0.024048754945397377, -0.0298494603484869, 0.0033535324037075043, -0.01244734600186348, -0.06960845738649368, -0.006284096743911505, -0.01583109050989151, -0.021148402243852615, 0.013172433711588383, -0.019577378407120705, -0.019819075241684914, -0.001888250233605504, -0.013716249726712704, 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   3. Cargue el archivo query-embeddings.js en mongosh:

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

   4. Ejecute la siguiente query:

      La siguiente query utiliza la etapa $vectorSearch para:

      • Busque en el campo plot_embedding_voyage_4_large los ANN utilizando incrustaciones vectoriales para el string courtroom lawyer.

      • Considere hasta 100 vecinos más cercanos, pero limite la salida a solo 3 resultados.

      1
      db.embedded_movies.aggregate([
      2
        {
      3
          "$vectorSearch": {
      4
            "index": "movie-vector-index",
      5
            "path": "plot_embedding_voyage_4_large",
      6
            "queryVector": QUERY_EMBEDDING,
      7
            "numCandidates": 100,
      8
            "limit": 3
      9
          }
      10
        },
      11
        {
      12
          "$project": {
      13
            "_id": 0,
      14
            "title": 1,
      15
            "plot": 1,
      16
            "score": { $meta: "vectorSearchScore" }
      17
          }
      18
        }
      19
      ])

      VER SALIDA

      [
        {
          plot: 'An attorney defends an officer on trial for ordering his troops to fire on civilians after they stormed a U.S. embassy in a third world country.',
          title: 'Rules of Engagement',
          score: 0.695757269859314
        },
        {
          plot: 'An American attorney on business in China, ends up wrongfully on trial for murder and his only key to innocence is a female defense lawyer from the country.',
          title: 'Red Corner',
          score: 0.6952665448188782
        },
        {
          plot: 'A lawyer becomes a target by a corrupt politician and his NSA goons when he accidentally receives key evidence to a serious politically motivated crime.',
          title: 'Enemy of the State',
          score: 0.6868826150894165
        }
      ]