MongoDB Search 和 MongoDB 向量搜索 本地开发快速入门

拉取最新的 mongodb/mongodb-atlas-local Docker 影像。

要拉取 mongodb/mongodb-atlas-local Docker映像，运行以下命令：

docker pull mongodb/mongodb-atlas-local:preview

要创建可重现的环境，请将图像固定到特定版本标签，而不是 preview 或 latest 等可变标签。将 <version> 替换为您需要的版本标签：

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

创建 docker-compose.yaml文件。

创建 docker-compose.yaml文件。有关完整详细的示例，请参阅 Docker Compose 示例。

1
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:

替换占位符值并保存文件。

将 <username> 和 <password> 占位符值替换为您的值，并保存文件。您还可以通过文件设置用户名和密码，以增加一层安全性。使用 MONGODB_INITDB_ROOT_USERNAME_FILE 和 MONGODB_INITDB_ROOT_PASSWORD_FILE 环境变量指定包含用户名和密码的文件的路径。

要使用自动嵌入，请通过环境变量 VOYAGE_API_KEY 提供 Voyage AI API 密钥（从 Atlas 用户界面创建）。默认情况下，嵌入提供商终结点为 https://ai.mongodb.com/v1/embeddings （用于从 Atlas 用户界面创建的密钥）。

要在初始化过程中自动加载 MongoDB 示例数据集，请将 MONGODB_LOAD_SAMPLE_DATA 环境变量设置为 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>
8
         - MONGODB_LOAD_SAMPLE_DATA=true
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         - VOYAGE_API_KEY=<voyage-api-key>

运行 Docker Compose。

运行以下命令以创建包含 MongoDB Search 和 MongoDB 向量搜索 的单节点副本集。

docker compose up -d

验证部署。

要验证部署，请运行以下命令：

docker ps

输出应如下所示：

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

连接到单节点副本集。

要连接到单节点副本集，请在用户名替换 <username> 为您在 docker-compose.yaml 文件中指定的用户名后运行以下命令：

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

(可选).如果您已加载示例数据集，请创建索引。

1. 要验证加载的是相同数据，请运行以下命令：

   show dbs

   隐藏输出

   __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. 要创建索引，请切换到 sample_mflix 数据库。

   use sample_mflix

3. 要在 sample_mflix.movies 集合上创建名为 movie-title-index 的 MongoDB Search 索引，请运行以下命令：

   以下索引定义在 movies 集合上创建一个名为 movie-index 的 MongoDB Search 索引。索引定义将 mappings 指定为 dynamic: true，以自动为集合文档中的所有动态可索引字段编制索引。

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

4. 要在 sample_mflix.embedded_movies 集合上创建名为 movie-vector-index 的 MongoDB 向量搜索索引，请运行以下命令：

   以下索引定义在 embedded_movies 集合的 plot_embedding_voyage_4_large 字段上创建一个命名为 movie-vector-index 的 MongoDB 向量搜索索引。该索引定义指定以下内容：

   • 使用 type 作为 vectorSearch 创建 MongoDB 向量搜索索引。

   • path 作为 plot_embedding_voyage_4_large 用于索引字段中的向量嵌入。

   • numDimensions 作为 2048 用于指定向量嵌入的维数。

   • similarity 作为 dotProduct 用于指定用于测量查询向量与索引向量之间相似度的相似度函数。

   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. 要验证索引是否已创建，请运行以下命令：

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

   查看输出

   [
     {
       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
     }
   ]

(可选).如果创建了索引，请在索引上运行查询。

1. 要在 movie-index 索引上运行 MongoDB Search 查询，请运行以下命令：

   此查询使用 movie-index 索引在 movies 集合的 plot 字段中搜索术语 courtroom lawyer。该查询使用 fuzzy 选项启用模糊匹配。该查询返回前 3 个结果，并仅投影 title 和 plot 字段。

   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
   ])

   查看输出

   [
     {
       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. 要在 movie-vector-index 索引上运行 MongoDB 向量搜索查询，请执行以下步骤：

   1. 创建名为 query-embeddings.js 的文件：

      touch query-embeddings.js

   2. 将词组 courtroom lawyer 的以下嵌入复制并粘贴到 query-embeddings.js 文件中：

      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. 将 query-embeddings.js 文件加载到 mongosh：

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

   4. 运行以下查询：

      以下查询使用 $vectorSearch 阶段执行以下操作：

      • 使用字符串 courtroom lawyer 的向量嵌入，在 plot_embedding_voyage_4_large 字段中搜索近似最近邻 (ANN)。

      • 最多考虑 100 个最近邻，但将输出限制为仅 3 个结果。

      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
      ])

      查看输出

      [
        {
          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
        }
      ]