如何为向量搜索建立索引字段

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{
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    "database": "<name-of-database>",
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    "collectionName": "<name-of-collection>",
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    "type": "vectorSearch",
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    "name": "<index-name>",
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    "fields":[ 
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      {
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        "type": "vector",
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        "path": "<field-to-index>",
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        "numDimensions": <number-of-dimensions>,
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        "similarity": "euclidean | cosine | dotProduct"
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      },
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      {
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        "type": "filter",
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        "path": "<field-to-index>"
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      },
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      ...
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    ]
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}

atlas clusters search indexes create --clusterName [cluster_name] --file [vector_index].json

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{
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  "fields":[
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    {
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      "type": "vector",
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      "path": <field-to-index>,
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      "numDimensions": <number-of-dimensions>,
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      "similarity": "euclidean | cosine | dotProduct"
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    },
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    {
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      "type": "filter",
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      "path": "<field-to-index>"
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    },
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    ...
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  ]
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}

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db.<collectionName>.createSearchIndex(
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  "<index-name>",
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  "vectorSearch", //index type
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  {
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    fields: [
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      {
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        "type": "vector",
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        "numDimensions": <number-of-dimensions>,
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        "path": "<field-to-index>",
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        "similarity": "euclidean | cosine | dotProduct"
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      },
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      {
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        "type": "filter",
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        "path": "<field-to-index>"
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      },
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      ...
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    ]
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  }
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);

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const { MongoClient } = require("mongodb");
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// connect to your Atlas deployment
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const uri =  "<connectionString>";
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const client = new MongoClient(uri);
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async function run() {
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   try {
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     const database = client.db("<databaseName>");
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     const collection = database.collection("<collectionName>");
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     // define your Atlas Vector Search index
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     const index = {
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         name: "<indexName>",
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         type: "vectorSearch",
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         definition: {
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           "fields": [
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             {
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               "type": "vector",
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               "numDimensions": <numberOfDimensions>,
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               "path": "<fieldToIndex>",
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               "similarity": "euclidean | cosine | dotProduct"
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             },
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             {
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               "type": "filter",
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               "path": "<fieldToIndex>"
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             },
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             ...
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           ]
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         }
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     }
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     // run the helper method
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     const result = await collection.createSearchIndex(index);
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     console.log(result);
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   } finally {
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     await client.close();
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   }
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}
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run().catch(console.dir);

node <file-name>.js

python <file-name>.py

atlas clusters search indexes list --clusterName [cluster_name] --db <db-name> --collection <collection-name>

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db.<collectionName>.getSearchIndexes( "<index-name>" );

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const { MongoClient } = require("mongodb");
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// connect to your Atlas deployment
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const uri =  "<connectionString>";
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const client = new MongoClient(uri);
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async function run() {
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  try {
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    const database = client.db("<databaseName>");
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    const collection = database.collection("<collectionName>");
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    // run the helper method
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    const result = await collection.listSearchIndexes("<indexName>").toArray();
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    console.log(result);
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  } finally {
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    await client.close();
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  }
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}
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run().catch(console.dir);

node <file-name>.js

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from pymongo.mongo_client import MongoClient
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# Connect to your Atlas deployment
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uri = "<connectionString>"
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client = MongoClient(uri)
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# Access your database and collection
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database = client["<databaseName>"]
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collection = database["<collectionName>"]
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# Get a list of the collection's search indexes and print them
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cursor = collection.list_search_indexes()
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for index in cursor:
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    print(index)

python <file-name>.py

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{
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    "database": "<name-of-database>",
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    "collectionName": "<name-of-collection>",
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    "type": "vectorSearch",
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    "name": "<index-name>",
6
    "fields":[ 
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      {
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        "type": "vector",
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        "path": "<field-to-index>",
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        "numDimensions": <number-of-dimensions>,
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        "similarity": "euclidean | cosine | dotProduct"
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      },
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      {
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        "type": "filter",
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        "path": "<field-to-index>"
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      },
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      ...
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    ]
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}

atlas clusters search indexes update <indexId> --clusterName [cluster_name] --file [vector-_index].json

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db.<collectionName>.updateSearchIndex(
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  "<index-name>",
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  {
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    fields: [
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      {
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        "type": "vector",
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        "numDimensions": <number-of-dimensions>,
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        "path": "<field-to-index>",
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        "similarity": "euclidean | cosine | dotProduct"
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      },
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      {
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        "type": "filter",
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        "path": "<field-to-index>"
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      },
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      ...
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    ]
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  }
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);

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const { MongoClient } = require("mongodb");
2
3
// connect to your Atlas deployment
4
const uri =  "<connection-string>";
5
6
const client = new MongoClient(uri);
7
8
async function run() {
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  try {
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    const database = client.db("<databaseName>");
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    const collection = database.collection("<collectionName>");
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    // define your Atlas Search index
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    const index = {
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        name: "<indexName>",
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        type: "vectorSearch",
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        //updated search index definition
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        definition: {
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          "fields": [
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            {
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              "type": "vector",
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              "numDimensions": <numberOfDimensions>,
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              "path": "<field-to-index>",
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              "similarity": "euclidean | cosine | dotProduct"
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            },
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            {
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              "type": "filter",
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              "path": "<fieldToIndex>"
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            },
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            ...
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          ]
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        }
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    }
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    // run the helper method
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    await collection.updateSearchIndex("<index-name>", index);
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  } finally {
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    await client.close();
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  }
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}
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run().catch(console.dir);

node <file-name>.js

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from pymongo.mongo_client import MongoClient
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# Connect to your Atlas deployment
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uri = "<connectionString>"
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client = MongoClient(uri)
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# Access your database and collection
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database = client["<databaseName>"]
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collection = database["<collectionName>"]
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definition = {
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  "fields": [
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    {
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      "type": "vector",
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      "numDimensions": <numberofDimensions>,
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      "path": "<fieldToIndex>",
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      "similarity": "euclidean | cosine | dotProduct"
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    },
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    {
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      "type": "filter",
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      "path": "<fieldToIndex>"
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    },
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    ...
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  ]
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}
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# Update your search index
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collection.update_search_index("<indexName>", definition)

python <file-name>.py

atlas clusters search indexes delete <indexId> [options]

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db.<collectionName>.dropSearchIndex( "<index-name>" );

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const { MongoClient } = require("mongodb");
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3
// connect to your Atlas deployment
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const uri = "<connectionString>";
5
6
const client = new MongoClient(uri);
7
8
async function run() {
9
  try {
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    const database = client.db("<databaseName>");
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    const collection = database.collection("<collectionName>");
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    // run the helper method
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    await collection.dropSearchIndex("<indexName>");
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  } finally {
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    await client.close();
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  }
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}
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run().catch(console.dir);

node <file-name>.js

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from pymongo.mongo_client import MongoClient
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# Connect to your Atlas deployment
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uri = "<connectionString>"
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client = MongoClient(uri)
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# Access your database and collection
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database = client["<databaseName>"]
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collection = database["<collectionName>"]
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# Delete your search index
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collection.drop_search_index("<indexName>")

python <file-name>.py