> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://api.qdrant.tech/v-1-13-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` ### Body (application/json) This endpoint expects an UpdateVectors. - `points` (list of PointVectors, required) — Points with named vectors - `shard_key` (UpdateVectorsShardKey, 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 ### 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. - Allowed values: `acknowledged`, `completed` - `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 ### HardwareUsage Usage of the hardware resources, spent to process the request - `cpu` (integer, required) - `io_read` (integer, required) - `io_write` (integer, required) ### 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` (map from string to any, optional, nullable) — Parameters for the model Values of the parameters are model-specific ### 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 ## Examples **Request** ```json { "points": [ { "id": 42, "vector": {} } ] } ``` **Response** ```json { "usage": { "cpu": 1, "io_read": 1, "io_write": 1 }, "time": 0.002, "status": "ok", "result": { "status": "acknowledged", "operation_id": 1 } } ``` **SDK Code** ```csharp using Qdrant.Client; using Qdrant.Client.Grpc; var client = new QdrantClient("localhost", 6334); await client.UpdateVectorsAsync( collectionName: "{collection_name}", points: new List { 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 }) } } ); ``` ```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?; ``` ```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], }, }, ], }); ``` ```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"] = '' 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 request('PUT', 'http://localhost:6333/collections/collection_name/points/vectors', [ 'body' => '{ "points": [ { "id": 42, "vector": {} } ] }', 'headers' => [ 'Content-Type' => 'application/json', 'api-key' => '', ], ]); echo $response->getBody(); ``` ```swift import Foundation let headers = [ "api-key": "", "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() ```