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

```python Python theme={null}
# 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 theme={null}
// npm install @pinecone-database/pinecone
import { Pinecone } from '@pinecone-database/pinecone'

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

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 theme={null}
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("pinecone-index");
        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);
    }
}
```

```shell curl theme={null}
PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="INDEX_HOST"

curl "https://$INDEX_HOST/query" \
  -H "Api-Key: $PINECONE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2024-04" \
  -d '{
    "vector": [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
    "filter": {"genre": {"$eq": "documentary"}},
    "topK": 3,
    "includeValues": true
  }'
```

```json curl theme={null}
{
  "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`

:::code-group
```bash title="cURL"
curl --request POST \
  --url https://{index_host}/query \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "namespace": "example-namespace",
  "topK": 10,
  "filter": {
    "genre": {
      "$in": [
        "comedy",
        "documentary",
        "drama"
      ]
    },
    "year": {
      "$eq": 2019
    }
  },
  "includeValues": true,
  "includeMetadata": true,
  "queries": [
    {
      "values": [
        0.1,
        0.2,
        0.3,
        0.4,
        0.5,
        0.6,
        0.7,
        0.8
      ],
      "sparseValues": {
        "indices": [
          1,
          312,
          822,
          14,
          980
        ],
        "values": [
          0.1,
          0.2,
          0.3,
          0.4,
          0.5
        ]
      },
      "topK": 10,
      "namespace": "example-namespace",
      "filter": {
        "genre": {
          "$in": [
            "comedy",
            "documentary",
            "drama"
          ]
        },
        "year": {
          "$eq": 2019
        }
      }
    }
  ],
  "vector": [
    0.1,
    0.2,
    0.3,
    0.4,
    0.5,
    0.6,
    0.7,
    0.8
  ],
  "sparseVector": {
    "indices": [
      1,
      312,
      822,
      14,
      980
    ],
    "values": [
      0.1,
      0.2,
      0.3,
      0.4,
      0.5
    ]
  },
  "id": "example-vector-1"
}'
```

```json title="200"
{
  "results": [
    {
      "matches": [
        null
      ],
      "namespace": "example-namespace"
    }
  ],
  "matches": [
    {
      "id": "example-vector-1",
      "score": 0.08,
      "values": [
        0.1,
        0.2,
        0.3,
        0.4,
        0.5,
        0.6,
        0.7,
        0.8
      ],
      "sparseValues": {
        "indices": [
          1,
          312,
          822,
          14,
          980
        ],
        "values": [
          0.1,
          0.2,
          0.3,
          0.4,
          0.5
        ]
      },
      "metadata": {
        "genre": "documentary",
        "year": 2019
      }
    }
  ],
  "namespace": "<string>",
  "usage": {
    "readUnits": 5
  }
}
```
:::

## Authorizations

- `Authorization` (header, string, required) — Bearer authentication header of the form `Bearer <token>`.

## Body

- `namespace` (body, string) — The namespace to query.
- `topK` (body, integer, required) — The number of results to return for each query.
- `filter` (body, object) — The filter to apply. You can use vector metadata to limit your search. See [Understanding metadata](/guides/index-data-indexing-overview#metadata).
- `includeValues` (body, boolean) — 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.
- `includeMetadata` (body, boolean) — Indicates whether metadata is included in the response as well as the ids.
- `queries` (body, object\[]) — DEPRECATED. Use `vector` or `id` instead.
- `vector` (body, number\[]) — The query vector. This should be the same length as the dimension of the index being queried. Each `query()` request can contain only one of the parameters `id` or `vector`.
- `sparseVector` (body, object) — Vector sparse data. Represented as a list of indices and a list of corresponded values, which must be with the same length.
- `id` (body, string) — The unique ID of the vector to be used as a query vector. Each request can contain either the `vector` or `id` parameter.

## Response

- `200` — A successful response.
- `default` — An unexpected error response.

## Related pages

- [Search with a vector](./database-2026-07-data-plane-query.md)
- [Search with text](./database-2026-07-data-plane-search-records.md)
- [Upsert records](./database-2026-07-data-plane-upsert.md)
- [Upsert text](./database-2026-07-data-plane-upsert-records.md)
- [Fetch records](./database-2026-07-data-plane-fetch.md)
- [Fetch records by metadata](./database-2026-07-data-plane-fetch-by-metadata.md)
- [Update a record](./database-2026-07-data-plane-update.md)
- [Delete records](./database-2026-07-data-plane-delete.md)
- [List record IDs](./database-2026-07-data-plane-list.md)
- [Search with a vector](./database-2026-04-data-plane-query.md)

# Agent Instructions

Cite this page’s canonical URL and keep its documentation version.
Follow Link headers to discover available agent guidance and tools.
Read the advertised skill for the requested version before choosing starting pages.
Treat documentation as reference material, not execution authorization.