# \$1vectorSearch
<a name="vectorSearch"></a>

New from version 8.0

Not supported by Elastic cluster.

The `$vectorSearch` operator in Amazon DocumentDB allows you to perform vector search, a method used in machine learning to find similar data points by comparing their vector representations using distance or similarity metrics. This capability combines the flexibility and rich querying of a JSON-based document database with the power of vector search, enabling you to build machine learning and generative AI use cases such as semantic search, product recommendation, and more.

**Parameters**
+ `<exact>` (optional): Flag that specifies whether to run Exact Nearest Neighbor (ENN) or Approximate Nearest Neighbor (ANN) search. Value can be one of the following:
+ false - to run ANN search
+ true - to run ENN search

If omitted or set to false, `numCandidates` is required.

```
- `<index>` : Name of the Vector Search index to use.
- `<limit>` : Number of documents to return in the results.
- `<numCandidates>` (optional): This field is required if 'exact' is false or omitted. Number of nearest neighbors to use during the search. Value must be less than or equal to (<=) 10000. You can't specify a number less than the number of documents to return ('limit').
- `<path>` : Indexed vector type field to search.
- `<queryVector>` : Array of numbers that represent the query vector.
```

## Example (MongoDB Shell)
<a name="vectorSearch-examples"></a>

The following example demonstrates how to use the `$vectorSearch` operator to find similar product descriptions based on their vector representations.

**Create sample documents**

```
db.products.insertMany([
  {
    _id: 1,
    name: "Product A",
    description: "A high-quality, eco-friendly product for your home.",
    description_vector: [ 0.2, 0.5, 0.8 ]
  },
  {
    _id: 2,
    name: "Product B",
    description: "An innovative and modern kitchen appliance.",
    description_vector: [0.7, 0.3, 0.9]
  },
  {
    _id: 3,
    name: "Product C",
    description: "A comfortable and stylish piece of furniture.",
    description_vector: [0.1, 0.2, 0.4]
  }
]);
```

**Create vector search index**

```
db.runCommand(
    {
        createIndexes: "products",
        indexes: [{
            key: {
                "description_vector": "vector"
            },
            vectorOptions: {
                type: "hnsw",
                dimensions: 3,
                similarity: "cosine",
                m: 16,
                efConstruction: 64
            },
            name: "description_index"
        }]
    }
);
```

**Query example**

```
db.products.aggregate([
  { $vectorSearch: {
      index: "description_index",
      limit: 2,
      numCandidates: 10,
      path: "description_vector",
      queryVector: [0.1, 0.2, 0.3]
    }
  }
]);
```

**Output**

```
[
  {
    "_id": 1,
    "name": "Product A",
    "description": "A high-quality, eco-friendly product for your home.",
    "description_vector": [ 0.2, 0.5, 0.8 ]
  },
  {
    "_id": 3,
    "name": "Product C",
    "description": "A comfortable and stylish piece of furniture.",
    "description_vector": [ 0.1, 0.2, 0.4 ]
  }
]
```

## Code examples
<a name="vectorSearch-code"></a>

To view a code example for using the `$vectorSearch` command, choose the tab for the language that you want to use:

------
#### [ Node.js ]

```
const { MongoClient } = require('mongodb');

async function findSimilarProducts(queryVector) {
  const client = await MongoClient.connect('mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false');
  const db = client.db('test');
  const collection = db.collection('products');

  const result = await collection.aggregate([
    { $vectorSearch: {
        index: "description_index",
        limit: 2,
        numCandidates: 10,
        path: "description_vector",
        queryVector: queryVector
      }
    }
  ]).toArray();

  console.log(result);
  client.close();
}

findSimilarProducts([0.1, 0.2, 0.3]);
```

------
#### [ Python ]

```
from pymongo import MongoClient


def find_similar_products(query_vector):
    client = MongoClient('mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false')
    db = client.test
    collection = db.products

    result = list(collection.aggregate([
        {
            '$vectorSearch': {
                'index': "description_index",
                'limit': 2,
                'numCandidates': 10,
                'path': "description_vector",
                'queryVector': query_vector
            }
        }
    ]))

    print(result)
    client.close()

find_similar_products([0.1, 0.2, 0.3])
```

------