> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://api.qdrant.tech/v-1-17-x/api-reference/points/update-vectors/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://api.qdrant.tech/_mcp/server.

# Update vectors

PUT http://localhost:6333/collections/{collection_name}/points/vectors
Content-Type: application/json

Updates specified vectors on points. All other unspecified vectors will stay intact.

Reference: https://api.qdrant.tech/api-reference/points/update-vectors

## Authentication

- `api-key` header (required) — API Key authentication via header

## Servers

- `http://localhost:6333` (http, default)
- `https://localhost:6333` (https)

## Request

### Path parameters

- `collection_name` (string, required) — Name of the collection to update from

### Query parameters

- `wait` (boolean, optional) — If true, wait for changes to actually happen
- `ordering` (enum, optional) — define ordering guarantees for the operation
  - Allowed values: `weak`, `medium`, `strong`
- `timeout` (integer, optional) — Timeout for the operation

### Body (application/json)

This endpoint expects an UpdateVectors.

- `points` (list of PointVectors, required) — Points with named vectors
- `shard_key` (UpdateVectorsShardKey, optional)
- `update_filter` (UpdateVectorsUpdateFilter, optional)

## Response

### 200

successful operation

- `usage` (CollectionsCollectionNamePointsVectorsPutResponsesContentApplicationJsonSchemaUsage, optional)
- `time` (double, optional) — Time spent to process this request
- `status` (string, optional)
- `result` (UpdateResult, optional)

## Types

### PointVectors

- `id` (ExtendedPointId, required) — Type, used for specifying point ID in user interface
- `vector` (VectorStruct, required) — Full vector data per point separator with single and multiple vector modes

### UpdateVectorsShardKey

### UpdateVectorsUpdateFilter

### CollectionsCollectionNamePointsVectorsPutResponsesContentApplicationJsonSchemaUsage

### UpdateResult

- `status` (enum, required) — `Acknowledged` - Request is saved to WAL and will be process in a queue. `Completed` - Request is completed, changes are actual. `WaitTimeout` - Request is waiting for timeout.
  - Allowed values: `acknowledged`, `completed`, `wait_timeout`
- `operation_id` (uint64, optional, nullable) — Sequential number of the operation

### ExtendedPointId

Type, used for specifying point ID in user interface

### VectorStruct

Full vector data per point separator with single and multiple vector modes

### Filter

- `should` (FilterShould, optional) — At least one of those conditions should match
- `min_should` (FilterMinShould, optional) — At least minimum amount of given conditions should match
- `must` (FilterMust, optional) — All conditions must match
- `must_not` (FilterMustNot, optional) — All conditions must NOT match

### Usage

Usage of the hardware resources, spent to process the request

- `hardware` (UsageHardware, optional)
- `inference` (UsageInference, optional)

### Document

WARN: Work-in-progress, unimplemented Text document for embedding. Requires inference infrastructure, unimplemented.

- `text` (string, required) — Text of the document. This field will be used as input for the embedding model.
- `model` (string, required) — Name of the model used to generate the vector. List of available models depends on a provider.
- `options` (DocumentOptions, optional) — Additional options for the model, will be passed to the inference service as-is. See model cards for available options.

### Image

WARN: Work-in-progress, unimplemented Image object for embedding. Requires inference infrastructure, unimplemented.

- `image` (any, required) — Image data: base64 encoded image or an URL
- `model` (string, required) — Name of the model used to generate the vector. List of available models depends on a provider.
- `options` (map from string to any, optional, nullable) — Parameters for the model Values of the parameters are model-specific

### InferenceObject

WARN: Work-in-progress, unimplemented Custom object for embedding. Requires inference infrastructure, unimplemented.

- `object` (any, required) — Arbitrary data, used as input for the embedding model. Used if the model requires more than one input or a custom input.
- `model` (string, required) — Name of the model used to generate the vector. List of available models depends on a provider.
- `options` (map from string to any, optional, nullable) — Parameters for the model Values of the parameters are model-specific

### FilterShould

At least one of those conditions should match

### FilterMinShould

At least minimum amount of given conditions should match

### FilterMust

All conditions must match

### FilterMustNot

All conditions must NOT match

### UsageHardware

### UsageInference

### DocumentOptions

Option variants for text documents. Ether general-purpose options or BM25-specific options. BM25-specific will only take effect if the `qdrant/bm25` is specified as a model.

### MinShould

- `conditions` (list of Condition, required)
- `min_count` (integer, required)

### HardwareUsage

Usage of the hardware resources, spent to process the request

- `cpu` (integer, required)
- `payload_io_read` (integer, required)
- `payload_io_write` (integer, required)
- `payload_index_io_read` (integer, required)
- `payload_index_io_write` (integer, required)
- `vector_io_read` (integer, required)
- `vector_io_write` (integer, required)

### InferenceUsage

- `models` (map from string to ModelUsage, required)

### Bm25Config

Configuration of the local bm25 models.

- `k` (double, optional, default: 1.2) — Controls term frequency saturation. Higher values mean term frequency has more impact. Default is 1.2
- `b` (double, optional, default: 0.75) — Controls document length normalization. Ranges from 0 (no normalization) to 1 (full normalization). Higher values mean longer documents have less impact. Default is 0.75.
- `avg_len` (double, optional, default: 256) — Expected average document length in the collection. Default is 256.
- `tokenizer` (enum, optional)
  - Allowed values: `prefix`, `whitespace`, `word`, `multilingual`
- `language` (string, optional, nullable) — Defines which language to use for text preprocessing. This parameter is used to construct default stopwords filter and stemmer. To disable language-specific processing, set this to `"language": "none"`. If not specified, English is assumed.
- `lowercase` (boolean, optional, nullable) — Lowercase the text before tokenization. Default is `true`.
- `ascii_folding` (boolean, optional, nullable) — If true, normalize tokens by folding accented characters to ASCII (e.g., "ação" -> "acao"). Default is `false`.
- `stopwords` (Bm25ConfigStopwords, optional) — Configuration of the stopwords filter. Supports list of pre-defined languages and custom stopwords. Default: initialized for specified `language` or English if not specified.
- `stemmer` (Bm25ConfigStemmer, optional) — Configuration of the stemmer. Processes tokens to their root form. Default: initialized Snowball stemmer for specified `language` or English if not specified.
- `min_token_len` (integer, optional, nullable) — Minimum token length to keep. If token is shorter than this, it will be discarded. Default is `None`, which means no minimum length.
- `max_token_len` (integer, optional, nullable) — Maximum token length to keep. If token is longer than this, it will be discarded. Default is `None`, which means no maximum length.

### Condition

### ModelUsage

- `tokens` (uint64, required)

### Bm25ConfigStopwords

Configuration of the stopwords filter. Supports list of pre-defined languages and custom stopwords. Default: initialized for specified `language` or English if not specified.

### Bm25ConfigStemmer

Configuration of the stemmer. Processes tokens to their root form. Default: initialized Snowball stemmer for specified `language` or English if not specified.

### FieldCondition

All possible payload filtering conditions

- `key` (string, required) — Payload key
- `match` (FieldConditionMatch, optional) — Check if point has field with a given value
- `range` (FieldConditionRange, optional) — Check if points value lies in a given range
- `geo_bounding_box` (FieldConditionGeoBoundingBox, optional) — Check if points geolocation lies in a given area
- `geo_radius` (FieldConditionGeoRadius, optional) — Check if geo point is within a given radius
- `geo_polygon` (FieldConditionGeoPolygon, optional) — Check if geo point is within a given polygon
- `values_count` (FieldConditionValuesCount, optional) — Check number of values of the field
- `is_empty` (boolean, optional, nullable) — Check that the field is empty, alternative syntax for `is_empty: "field_name"`
- `is_null` (boolean, optional, nullable) — Check that the field is null, alternative syntax for `is_null: "field_name"`

### IsEmptyCondition

Select points with empty payload for a specified field

- `is_empty` (PayloadField, required) — Payload field

### IsNullCondition

Select points with null payload for a specified field

- `is_null` (PayloadField, required) — Payload field

### HasIdCondition

ID-based filtering condition

- `has_id` (list of ExtendedPointId, required)

### HasVectorCondition

Filter points which have specific vector assigned

- `has_vector` (string, required)

### NestedCondition

- `nested` (Nested, required) — Select points with payload for a specified nested field

### FieldConditionMatch

Check if point has field with a given value

### FieldConditionRange

Check if points value lies in a given range

### FieldConditionGeoBoundingBox

Check if points geolocation lies in a given area

### FieldConditionGeoRadius

Check if geo point is within a given radius

### FieldConditionGeoPolygon

Check if geo point is within a given polygon

### FieldConditionValuesCount

Check number of values of the field

### PayloadField

Payload field

- `key` (string, required) — Payload field name

### Nested

Select points with payload for a specified nested field

- `key` (string, required)
- `filter` (Filter, required)

### GeoBoundingBox

Geo filter request Matches coordinates inside the rectangle, described by coordinates of lop-left and bottom-right edges

- `top_left` (GeoPoint, required) — Geo point payload schema
- `bottom_right` (GeoPoint, required) — Geo point payload schema

### GeoRadius

Geo filter request Matches coordinates inside the circle of `radius` and center with coordinates `center`

- `center` (GeoPoint, required) — Geo point payload schema
- `radius` (double, required) — Radius of the area in meters

### GeoPolygon

Geo filter request Matches coordinates inside the polygon, defined by `exterior` and `interiors`

- `exterior` (GeoLineString, required) — Ordered sequence of GeoPoints representing the line
- `interiors` (list of GeoLineString, optional, nullable) — Interior lines (if present) bound holes within the surface each GeoLineString must consist of a minimum of 4 points, and the first and last points must be the same.

### ValuesCount

Values count filter request

- `lt` (integer, optional, nullable) — point.key.length() \< values\_count.lt
- `gt` (integer, optional, nullable) — point.key.length() > values_count.gt
- `gte` (integer, optional, nullable) — point.key.length() >= values_count.gte
- `lte` (integer, optional, nullable) — point.key.length() \<= values\_count.lte

### GeoPoint

Geo point payload schema

- `lon` (double, required)
- `lat` (double, required)

### GeoLineString

Ordered sequence of GeoPoints representing the line

- `points` (list of GeoPoint, required)

## Examples

**Request**

```json
{
  "points": [
    {
      "id": 42,
      "vector": {}
    }
  ]
}
```

**Response**

```json
{
  "usage": {
    "hardware": {
      "cpu": 1,
      "payload_io_read": 1,
      "payload_io_write": 1,
      "payload_index_io_read": 1,
      "payload_index_io_write": 1,
      "vector_io_read": 1,
      "vector_io_write": 1
    },
    "inference": {
      "models": {}
    }
  },
  "time": 0.002,
  "status": "ok",
  "result": {
    "status": "acknowledged",
    "operation_id": 1
  }
}
```

**SDK Code**

```python
from qdrant_client import QdrantClient, models

client = QdrantClient(url="http://localhost:6333")

client.update_vectors(
    collection_name="{collection_name}",
    points=[
        models.PointVectors(
            id=1,
            vector={
                "image": [0.1, 0.2, 0.3, 0.4],
            },
        ),
        models.PointVectors(
            id=2,
            vector={
                "text": [0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2],
            },
        ),
    ],
)

```

```java
import static io.qdrant.client.PointIdFactory.id;
import static io.qdrant.client.VectorFactory.vector;
import static io.qdrant.client.VectorsFactory.namedVectors;

import java.util.List;
import java.util.Map;

import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;

QdrantClient client = new QdrantClient(
                QdrantGrpcClient.newBuilder("localhost", 6334, false).build());

client
    .updateVectorsAsync(
        "{collection_name}",
        List.of(
            PointVectors.newBuilder()
                .setId(id(1))
                .setVectors(namedVectors(Map.of("image", vector(List.of(0.1f, 0.2f, 0.3f, 0.4f)))))
                .build(),
            PointVectors.newBuilder()
                .setId(id(2))
                .setVectors(
                    namedVectors(
                        Map.of(
                            "text", vector(List.of(0.9f, 0.8f, 0.7f, 0.6f, 0.5f, 0.4f, 0.3f, 0.2f)))))
                .build()))
    .get();

```

```go
package client

import (
	"context"

	"github.com/qdrant/go-client/qdrant"
)

func updateVectors() {
	client, err := qdrant.NewClient(&qdrant.Config{
		Host: "localhost",
		Port: 6334,
	})
	if err != nil {
		panic(err)
	}

	_, err = client.UpdateVectors(context.Background(), &qdrant.UpdatePointVectors{
		CollectionName: "{collection_name}",
		Points: []*qdrant.PointVectors{
			{
				Id: qdrant.NewIDNum(1),
				Vectors: qdrant.NewVectorsMap(map[string]*qdrant.Vector{
					"image": qdrant.NewVector(0.1, 0.2, 0.3, 0.4),
				}),
			},
			{
				Id: qdrant.NewIDNum(2),
				Vectors: qdrant.NewVectorsMap(map[string]*qdrant.Vector{
					"text": qdrant.NewVector(0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2),
				}),
			},
		},
	})
	if err != nil {
		panic(err)
	}
}

```

```typescript
import { QdrantClient } from "@qdrant/js-client-rest";

const client = new QdrantClient({ host: "localhost", port: 6333 });

client.updateVectors("{collection_name}", {
    points: [
        {
            id: 1,
            vector: {
                image: [0.1, 0.2, 0.3, 0.4],
            },
        },
        {
            id: 2,
            vector: {
                text: [0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2],
            },
        },
    ],
});

```

```rust
use qdrant_client::qdrant::{PointVectors, UpdatePointVectorsBuilder};
use qdrant_client::Qdrant;
use std::collections::HashMap;

let client = Qdrant::from_url("http://localhost:6334").build()?;

client
    .update_vectors(
        UpdatePointVectorsBuilder::new(
            "{collection_name}",
            vec![
                PointVectors {
                    id: Some(1.into()),
                    vectors: Some(
                        HashMap::from([("image".to_string(), vec![0.1, 0.2, 0.3, 0.4])])
                            .into(),
                    ),
                },
                PointVectors {
                    id: Some(2.into()),
                    vectors: Some(
                        HashMap::from([(
                            "text".to_string(),
                            vec![0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2],
                        )])
                        .into(),
                    ),
                },
            ],
        )
        .wait(true),
    )
    .await?;

```

```csharp
using Qdrant.Client;
using Qdrant.Client.Grpc;

var client = new QdrantClient("localhost", 6334);

await client.UpdateVectorsAsync(
  collectionName: "{collection_name}",
  points: new List<PointVectors>
  {
    new() { Id = 1, Vectors = ("image", new float[] { 0.1f, 0.2f, 0.3f, 0.4f }) },
    new()
    {
      Id = 2,
      Vectors = ("text", new float[] { 0.9f, 0.8f, 0.7f, 0.6f, 0.5f, 0.4f, 0.3f, 0.2f })
    }
  }
);

```

```ruby
require 'uri'
require 'net/http'

url = URI("http://localhost:6333/collections/collection_name/points/vectors")

http = Net::HTTP.new(url.host, url.port)

request = Net::HTTP::Put.new(url)
request["api-key"] = '<apiKey>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"points\": [\n    {\n      \"id\": 42,\n      \"vector\": {}\n    }\n  ]\n}"

response = http.request(request)
puts response.read_body
```

```php
<?php
require_once('vendor/autoload.php');

$client = new \GuzzleHttp\Client();

$response = $client->request('PUT', 'http://localhost:6333/collections/collection_name/points/vectors', [
  'body' => '{
  "points": [
    {
      "id": 42,
      "vector": {}
    }
  ]
}',
  'headers' => [
    'Content-Type' => 'application/json',
    'api-key' => '<apiKey>',
  ],
]);

echo $response->getBody();
```

```swift
import Foundation

let headers = [
  "api-key": "<apiKey>",
  "Content-Type": "application/json"
]
let parameters = ["points": [
    [
      "id": 42,
      "vector": []
    ]
  ]] as [String : Any]

let postData = JSONSerialization.data(withJSONObject: parameters, options: [])

let request = NSMutableURLRequest(url: NSURL(string: "http://localhost:6333/collections/collection_name/points/vectors")! as URL,
                                        cachePolicy: .useProtocolCachePolicy,
                                    timeoutInterval: 10.0)
request.httpMethod = "PUT"
request.allHTTPHeaderFields = headers
request.httpBody = postData as Data

let session = URLSession.shared
let dataTask = session.dataTask(with: request as URLRequest, completionHandler: { (data, response, error) -> Void in
  if (error != nil) {
    print(error as Any)
  } else {
    let httpResponse = response as? HTTPURLResponse
    print(httpResponse)
  }
})

dataTask.resume()
```