MongoDB Search and MongoDB Vector Search Local Development Quickstart

Pull the latest mongodb/mongodb-atlas-local Docker image.

mongodb/mongodb-atlas-local Dockerイメージをプルするには、次のコマンドを実行します。

docker pull mongodb/mongodb-atlas-local:preview

docker-compose.yaml ファイルを作成します。

Create the docker-compose.yaml file. For a complete and detailed example, see Docker Compose Example.

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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:

Replace the placeholder values and save the file.

Replace the <username> and <password> placeholder values with your own values and save the file. You can also set the username and password through a file for an extra layer of security. Use the MONGODB_INITDB_ROOT_USERNAME_FILE and MONGODB_INITDB_ROOT_PASSWORD_FILE environment variables to specify the path to the files that contain the username and password.

To use Automated Embedding, provide your Voyage AI API key (created from the Atlas UI) through the environment variable VOYAGE_API_KEY. By default, the embedding provider endpoint is https://ai.mongodb.com/v1/embeddings (for keys created from the Atlas UI).

To automatically load the MongoDB sample dataset during initialization, set the MONGODB_LOAD_SAMPLE_DATA environment variable to true.

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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_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>

Docker Composer を実行します。

Run the following command to create a single node replica set with MongoDB Search and MongoDB Vector Search.

docker compose up -d

Verify the deployment.

To verify the deployment, run the following command:

docker ps

出力は、次のようになります。

CONTAINER ID   IMAGE                                COMMAND                  CREATED       STATUS                 PORTS                                             NAMES
f68ee6df9907   mongodb/mongodb-atlas-local:latest   "/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

Connect to the single node replica set.

To connect to the single node replica set, run the following command after replacing <username> with the username you specified in the docker-compose.yaml file:

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

(Optional). If you loaded the sample dataset, create an index.

1. To verify that the same data is loaded, run the following command:

   show dbs

   出力を非表示

   __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. To create indexes, switch to the sample_mflix database.

   use sample_mflix

3. To create a MongoDB Search index named movie-title-index on the sample_mflix.movies collection, run the following command:

   The following index definition creates a MongoDB Search index named movie-index on the movies collection. The index definition specifies the mappings as dynamic: true to automatically index all dynamically indexable fields in the documents of the collection.

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

4. To create a MongoDB Vector Search index named movie-vector-index on the sample_mflix.embedded_movies collection, run the following command:

   The following index definition creates a MongoDB Vector Search index named movie-vector-index on the plot_embedding_voyage_4_large field of the embedded_movies collection. The index definition specifies the following:

   • The type as vectorSearch to create a MongoDB Vector Search index.

   • The path as plot_embedding_voyage_4_large to index the vector embeddings in the field.

   • The numDimensions as 2048 to specify the number of dimensions in the vector embeddings.

   • The similarity as dotProduct to specify the similarity function to use to measure the similarity between the query vector and the indexed vectors.

   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. To verify that the indexes were created, run the following command:

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

   出力を表示

   [
     {
       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
     }
   ]

(Optional). If you created the index, run a query on the index.

1. To run a MongoDB Search query on the movie-index index, run the following command:

   This query searches for the term courtroom lawyer in the plot field of the movies collection using the movie-index index. The query uses the fuzzy option to enable fuzzy matching. The query returns the top 3 results and projects only the title and plot fields.

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   db.movies.aggregate([
   2
     {
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       "$search": {
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         "index": "movie-index",
   5
         "text": {
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           "query": "courtroom lawyer",
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           "path": "plot",
   8
           "fuzzy": {}
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         }
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       }
   11
     },
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     {
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           "$limit": 3
   14
     },
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     {
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       "$project": {
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         "_id": 0,
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         "title": 1,
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         "plot": 1
   20
       }
   21
     }
   22
   ])

   出力を表示

   [
     {
       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. To run a MongoDB Vector Search query on the movie-vector-index index, perform the following steps:

   1. Create a file named query-embeddings.js:

      touch query-embeddings.js

   2. Copy and paste the following embeddings for the phrase courtroom lawyer into the query-embeddings.js file:

      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, 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   3. Load the query-embeddings.js file into mongosh:

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

   4. 次のクエリを実行します。

      The following query uses the $vectorSearch stage to:

      • Search the plot_embedding_voyage_4_large field for approximate nearest neighbors by using vector embeddings for the string courtroom lawyer.

      • Consider up to 100 nearest neighbors, but limit the output to only 3 results.

      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
      ])

      出力を表示

      [
        {
          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
        }
      ]