> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://api.qdrant.tech/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 { 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"] = '' 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() ```