For guidance, examples, and limits, see [Search](/guides/index-data-search-search-overview).

:::code-group
```python Python
# pip install "pinecone[grpc]"
from pinecone.grpc import PineconeGRPC as Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")
index = pc.Index("docs-example")

index.query(
    namespace="example-namespace",
    vector=[0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
    filter={
        "genre": {"$eq": "documentary"}
    },
    top_k=3,
    include_values=True
)
```

```javascript JavaScript
// npm install @pinecone-database/pinecone
import { Pinecone } from '@pinecone-database/pinecone'

const pc = new Pinecone({ apiKey: 'YOUR_API_KEY' })
const index = pc.index("docs-example")

const queryResponse = await index.namespace('example-namespace').query({
    vector: [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
    filter: {
      'genre': {'$eq': 'documentary'}
    },
    topK: 3,
    includeValues: true
});
```

```java Java
import com.google.protobuf.Struct;
import com.google.protobuf.Value;
import io.pinecone.clients.Index;
import io.pinecone.clients.Pinecone;
import io.pinecone.unsigned_indices_model.QueryResponseWithUnsignedIndices;

import java.util.Arrays;
import java.util.List;

public class QueryByMetadataExample {
    public static void main(String[] args) {
        Pinecone pc = new Pinecone.Builder("YOUR_API_KEY").build();
        Index index = pc.getIndexConnection("docs-example");
        List<Float> query = Arrays.asList(0.3f, 0.3f, 0.3f, 0.3f, 0.3f, 0.3f, 0.3f, 0.3f);
        Struct filter = Struct.newBuilder()
                .putFields("genre", Value.newBuilder()
                        .setStructValue(Struct.newBuilder()
                                .putFields("$eq", Value.newBuilder()
                                        .setStringValue("documentary")
                                        .build()))
                        .build())
                .build();

        QueryResponseWithUnsignedIndices queryResponse = index.query(3, query, null, null, null, "example-namespace", filter, false, true);

        System.out.println(queryResponse);
    }
}
```

```go Go
package main

import (
    "context"
    "encoding/json"
    "fmt"
    "log"

    "github.com/pinecone-io/go-pinecone/v2/pinecone"
    "google.golang.org/protobuf/types/known/structpb"
)

func prettifyStruct(obj interface{}) string {
	bytes, _ := json.MarshalIndent(obj, "", "  ")
	return string(bytes)
}

func main() {
    ctx := context.Background()

    pc, err := pinecone.NewClient(pinecone.NewClientParams{
        ApiKey: "YOUR_API_KEY",
    })
    if err != nil {
        log.Fatalf("Failed to create Client: %v", err)
    }

    idx, err := pc.DescribeIndex(ctx, "docs-example")
    if err != nil {
        log.Fatalf("Failed to describe index \"%v\": %v", idx.Name, err)
    }

    idxConnection, err := pc.Index(pinecone.NewIndexConnParams{Host: idx.Host, Namespace: "example-namespace"})
    if err != nil {
        log.Fatalf("Failed to create IndexConnection for Host %v: %v", idx.Host, err)
	}

    queryVector := []float32{0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3}

    metadataMap := map[string]interface{}{
        "genre": map[string]interface{}{
            "$eq": "documentary",
        },
    }

    metadataFilter, err := structpb.NewStruct(metadataMap)
    if err != nil {
        log.Fatalf("Failed to create metadata map: %v", err)
    }

    res, err := idxConnection.QueryByVectorValues(ctx, &pinecone.QueryByVectorValuesRequest{
        Vector:         queryVector,
        TopK:           3,
        MetadataFilter: metadataFilter,
        IncludeValues:  true,
    })
    if err != nil {
        log.Fatalf("Error encountered when querying by vector: %v", err)
    } else {
        fmt.Printf(prettifyStruct(res))
    }
}
```

```csharp C#
using Pinecone;

var pinecone = new PineconeClient("YOUR_API_KEY");

var index = pinecone.Index("docs-example");

var queryResponse = await index.QueryAsync(new QueryRequest {
    Vector = new[] { 0.3f, 0.3f, 0.3f, 0.3f, 0.3f, 0.3f, 0.3f, 0.3f },
    Namespace = "example-namespace",
    TopK = 3,
    Filter = new Metadata
    {
        ["genre"] =
            new Metadata
            {
                ["$eq"] = "documentary",
            }
    }
});

Console.WriteLine(queryResponse);
```

```shell curl
PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="INDEX_HOST"

curl "https://$INDEX_HOST/query" \
  -H "Api-Key: $PINECONE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2024-07" \
  -d '{
    "vector": [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
    "filter": {"genre": {"$eq": "documentary"}},
    "topK": 3,
    "includeValues": true
  }'
```
:::

:::code-group
```json curl
{
  "matches":[
    {
      "id": "vec3",
      "score": 0,
      "values": [0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3]
    },
    {
      "id": "vec2",
      "score": 0.0800000429,
      "values": [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]
    },
    {
      "id": "vec4",
      "score": 0.0799999237,
      "values": [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4]
    }
  ],
  "namespace": "example-namespace",
  "usage": {"read_units": 6}
}
```
:::

`POST /query`

#### Authorizations

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `Api-Key` | `string` | - |  |

An API Key is required to call Pinecone APIs. Get yours from the [console](https://app.pinecone.io/).

#### Body

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `namespace?` | `string` | - | The namespace to query. Example: example-namespace |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `topK` | `integer` | - | The number of results to return for each query. Required range: 1 <= x <= 10000. Example: 10 |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `filter?` | `object` | - |  |

The filter to apply. You can use vector metadata to limit your search. See [Understanding metadata](/guides/index-data-indexing-overview#metadata).

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `includeValues?` | `boolean` | `false` | Indicates whether vector values are included in the response. For on-demand indexes, setting this to true may increase latency, especially with higher topK values, because vector values are retrieved from object storage. Unless you need vector values, set this to false for better performance. Example: true |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `includeMetadata?` | `boolean` | `false` | Indicates whether metadata is included in the response as well as the ids. Example: true |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| ~~`queries?`~~ | `object[]` | - | DEPRECATED. Use vector or id instead. Required string length: 1 - 10 |

::::accordion{title="Show child attributes"}
| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `values` | `number[]` | - | The query vector values. This should be the same length as the dimension of the index being queried. Required string length: 1 - 20000 |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `sparseValues?` | `object` | - | Vector sparse data. Represented as a list of indices and a list of corresponded values, which must be with the same length. |

:::accordion{title="Show child attributes"}
| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `indices` | `integer[]` | - | The indices of the sparse data. Required string length: 1 - 1000 |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `values` | `number[]` | - | The corresponding values of the sparse data, which must be with the same length as the indices. Required string length: 1 - 1000 |
:::

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `topK?` | `integer` | - | An override for the number of results to return for this query vector. Required range: 1 <= x <= 10000. Example: 10 |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `namespace?` | `string` | - | An override the namespace to search. Example: example-namespace |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `filter?` | `object` | - | An override for the metadata filter to apply. This replaces the request-level filter. |
::::

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `vector?` | `number[]` | - | The query vector. This should be the same length as the dimension of the index being queried. Each request can contain either the id or vector parameter. Required string length: 1 - 20000 |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `sparseVector?` | `object` | - | Vector sparse data. Represented as a list of indices and a list of corresponded values, which must be with the same length. |

:::accordion{title="Show child attributes"}
| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `indices` | `integer[]` | - | The indices of the sparse data. Required string length: 1 - 1000 |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `values` | `number[]` | - | The corresponding values of the sparse data, which must be with the same length as the indices. Required string length: 1 - 1000 |
:::

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `id?` | `string` | - | The unique ID of the vector to be used as a query vector. Each request can contain either the vector or id parameter. Required string length: 0 - 512. Example: example-vector-1 |

#### Response

`200` — A successful response.

The response for the `query` operation. These are the matches found for a particular query vector. The matches are ordered from most similar to least similar.

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| ~~`results?`~~ | `object[]` | - | DEPRECATED. The results of each query. The order is the same as QueryRequest.queries. |

:::::accordion{title="Show child attributes"}
| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `matches?` | `object[]` | - | The matches for the vectors. |

::::accordion{title="Show child attributes"}
| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `id` | `string` | - | This is the vector's unique id. Required string length: 1 - 512. Example: example-vector-1 |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `score?` | `number` | - | This is a measure of similarity between this vector and the query vector. The higher the score, the more they are similar. Example: 0.08 |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `values?` | `number[]` | - | This is the vector data, if it is requested. |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `sparseValues?` | `object` | - | Vector sparse data. Represented as a list of indices and a list of corresponded values, which must be with the same length. |

:::accordion{title="Show child attributes"}
| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `indices` | `integer[]` | - | The indices of the sparse data. Required string length: 1 - 1000 |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `values` | `number[]` | - | The corresponding values of the sparse data, which must be with the same length as the indices. Required string length: 1 - 1000 |
:::

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `metadata?` | `object` | - | This is the metadata, if it is requested. |
::::

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `namespace?` | `string` | - | The namespace for the vectors. Example: example-namespace |
:::::

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `matches?` | `object[]` | - | The matches for the vectors. |

::::accordion{title="Show child attributes"}
| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `id` | `string` | - | This is the vector's unique id. Required string length: 1 - 512. Example: example-vector-1 |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `score?` | `number` | - | This is a measure of similarity between this vector and the query vector. The higher the score, the more they are similar. Example: 0.08 |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `values?` | `number[]` | - | This is the vector data, if it is requested. |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `sparseValues?` | `object` | - | Vector sparse data. Represented as a list of indices and a list of corresponded values, which must be with the same length. |

:::accordion{title="Show child attributes"}
| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `indices` | `integer[]` | - | The indices of the sparse data. Required string length: 1 - 1000 |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `values` | `number[]` | - | The corresponding values of the sparse data, which must be with the same length as the indices. Required string length: 1 - 1000 |
:::

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `metadata?` | `object` | - | This is the metadata, if it is requested. |
::::

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `namespace?` | `string` | - | The namespace for the vectors. |

| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `usage?` | `object` | - |  |

:::accordion{title="Show child attributes"}
| Prop | Type | Default | Description |
| --- | --- | --- | --- |
| `readUnits?` | `integer` | - | The number of read units consumed by this operation. Example: 5 |
:::

## Related pages

- [Account management](./account-management-index.md)
- [Admin](./admin-2-index.md)
- [Admin](./admin-index.md)
- [APIs](./apis-index.md)
- [Architecture](./architecture-index.md)
- [Bring Your Own Cloud](./bring-your-own-cloud-index.md)
- [Build an assistant](./build-an-assistant-index.md)
- [Build an integration](./build-an-integration-index.md)
- [Changelog](./changelog-index.md)
- [Changelog](../changelog.md)

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