> 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. # Search batch points POST http://localhost:6333/collections/{collection_name}/points/search/batch Content-Type: application/json Retrieves the closest points in batches based on vector similarity and given filtering conditions. Reference: https://api.qdrant.tech/v-1-18-x/api-reference/search/batch-points ## 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 search in ### Query parameters - `consistency` (ReadConsistency, optional) — Define read consistency guarantees for the operation - `timeout` (integer, optional) — If set, overrides global timeout for this request. Unit is seconds. ### Body (application/json) This endpoint expects a SearchRequestBatch. - `searches` (list of SearchRequest, required) ## Response ### 200 successful operation - `usage` (CollectionsCollectionNamePointsSearchBatchPostResponsesContentApplicationJsonSchemaUsage, optional) - `time` (double, optional) — Time spent to process this request - `status` (string, optional) - `result` (list of list of ScoredPoint, optional) ## Types ### SearchRequest Search request. Holds all conditions and parameters for the search of most similar points by vector similarity given the filtering restrictions. - `vector` (NamedVectorStruct, required) — Vector data separator for named and unnamed modes Unnamed mode: \{ "vector": \[1.0, 2.0, 3.0] } or named mode: \{ "vector": \{ "vector": \[1.0, 2.0, 3.0], "name": "image-embeddings" } } - `limit` (integer, required) — Max number of result to return - `shard_key` (SearchRequestShardKey, optional) — Specify in which shards to look for the points, if not specified - look in all shards - `filter` (SearchRequestFilter, optional) — Look only for points which satisfies this conditions - `params` (SearchRequestParams, optional) — Additional search params - `offset` (integer, optional, nullable) — Offset of the first result to return. May be used to paginate results. Note: large offset values may cause performance issues. - `with_payload` (SearchRequestWithPayload, optional) — Select which payload to return with the response. Default is false. - `with_vector` (SearchRequestWithVector, optional) — Options for specifying which vectors to include into response. Default is false. - `score_threshold` (double, optional, nullable) — Define a minimal score threshold for the result. If defined, less similar results will not be returned. Score of the returned result might be higher or smaller than the threshold depending on the Distance function used. E.g. for cosine similarity only higher scores will be returned. ### ReadConsistency Read consistency parameter Defines how many replicas should be queried to get the result * `N` - send N random request and return points, which present on all of them * `majority` - send N/2+1 random request and return points, which present on all of them * `quorum` - send requests to all nodes and return points which present on majority of them * `all` - send requests to all nodes and return points which present on all of them Default value is `Factor(1)` ### CollectionsCollectionNamePointsSearchBatchPostResponsesContentApplicationJsonSchemaUsage ### ScoredPoint Search result - `id` (ExtendedPointId, required) — Type, used for specifying point ID in user interface - `version` (uint64, required) — Point version - `score` (double, required) — Points vector distance to the query vector - `payload` (ScoredPointPayload, optional) — Payload - values assigned to the point - `vector` (ScoredPointVector, optional) — Vector of the point - `shard_key` (ScoredPointShardKey, optional) — Shard Key - `order_value` (ScoredPointOrderValue, optional) — Order-by value ### NamedVectorStruct Vector data separator for named and unnamed modes Unnamed mode: \{ "vector": \[1.0, 2.0, 3.0] } or named mode: \{ "vector": \{ "vector": \[1.0, 2.0, 3.0], "name": "image-embeddings" } } ### SearchRequestShardKey Specify in which shards to look for the points, if not specified - look in all shards ### SearchRequestFilter Look only for points which satisfies this conditions ### SearchRequestParams Additional search params ### SearchRequestWithPayload Select which payload to return with the response. Default is false. ### SearchRequestWithVector Options for specifying which vectors to include into response. Default is false. ### Usage Usage of the hardware resources, spent to process the request - `hardware` (UsageHardware, optional) - `inference` (UsageInference, optional) ### ExtendedPointId Type, used for specifying point ID in user interface ### ScoredPointPayload Payload - values assigned to the point ### ScoredPointVector Vector of the point ### ScoredPointShardKey Shard Key ### ScoredPointOrderValue Order-by value ### NamedVector Dense vector data with name - `name` (string, required) — Name of vector data - `vector` (list of double, required) — Vector data ### NamedSparseVector Sparse vector data with name - `name` (string, required) — Name of vector data - `vector` (SparseVector, required) — Sparse vector structure ### 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 ### SearchParams Additional parameters of the search - `hnsw_ef` (integer, optional, nullable) — Params relevant to HNSW index Size of the beam in a beam-search. Larger the value - more accurate the result, more time required for search. - `exact` (boolean, optional, default: false) — Search without approximation. If set to true, search may run long but with exact results. - `quantization` (SearchParamsQuantization, optional) — Quantization params - `indexed_only` (boolean, optional, default: false) — If enabled, the engine will only perform search among indexed or small segments. Using this option prevents slow searches in case of delayed index, but does not guarantee that all uploaded vectors will be included in search results - `acorn` (SearchParamsAcorn, optional) — ACORN search params ### UsageHardware ### UsageInference ### SparseVector Sparse vector structure - `indices` (list of uint, required) — Indices must be unique - `values` (list of double, required) — Values and indices must be the same length ### 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 ### SearchParamsQuantization Quantization params ### SearchParamsAcorn ACORN search params ### 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) ### MinShould - `conditions` (list of Condition, required) - `min_count` (integer, required) ### QuantizationSearchParams Additional parameters of the search - `ignore` (boolean, optional, default: false) — If true, quantized vectors are ignored. Default is false. - `rescore` (boolean, optional, nullable) — If true, use original vectors to re-score top-k results. Might require more time in case if original vectors are stored on disk. If not set, qdrant decides automatically apply rescoring or not. - `oversampling` (double, optional, nullable) — Oversampling factor for quantization. Default is 1.0. Defines how many extra vectors should be pre-selected using quantized index, and then re-scored using original vectors. For example, if `oversampling` is 2.4 and `limit` is 100, then 240 vectors will be pre-selected using quantized index, and then top-100 will be returned after re-scoring. ### AcornSearchParams ACORN-related search parameters - `enable` (boolean, optional, default: false) — If true, then ACORN may be used for the HNSW search based on filters selectivity. Improves search recall for searches with multiple low-selectivity payload filters, at cost of performance. - `max_selectivity` (double, optional, nullable) — Maximum selectivity of filters to enable ACORN. If estimated filters selectivity is higher than this value, ACORN will not be used. Selectivity is estimated as: `estimated number of points satisfying the filters / total number of points`. 0.0 for never, 1.0 for always. Default is 0.4. ### ModelUsage - `tokens` (uint64, required) ### Condition ### 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 { "searches": [ { "vector": [ 1.1 ], "limit": 1 } ] } ``` **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": [ [ { "id": 42, "version": 3, "score": 0.75, "payload": {}, "vector": {}, "shard_key": "region_1", "order_value": 42 } ] ] } ``` **SDK Code** ```python from qdrant_client import QdrantClient, models client = QdrantClient(url="http://localhost:6333") filter_ = models.Filter( must=[ models.FieldCondition( key="city", match=models.MatchValue( value="London", ), ) ] ) search_queries = [ models.SearchRequest(vector=[0.2, 0.1, 0.9, 0.7], filter=filter_, limit=3), models.SearchRequest(vector=[0.5, 0.3, 0.2, 0.3], filter=filter_, limit=3), ] client.search_batch(collection_name="{collection_name}", requests=search_queries) ``` ```java import java.util.List; import static io.qdrant.client.ConditionFactory.matchKeyword; import io.qdrant.client.QdrantClient; import io.qdrant.client.QdrantGrpcClient; import io.qdrant.client.grpc.Common.Filter; import io.qdrant.client.grpc.Points.SearchPoints; QdrantClient client = new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build()); Filter filter = Filter.newBuilder().addMust(matchKeyword("city", "London")).build(); List searches = List.of( SearchPoints.newBuilder() .addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f)) .setFilter(filter) .setLimit(3) .build(), SearchPoints.newBuilder() .addAllVector(List.of(0.5f, 0.3f, 0.2f, 0.3f)) .setFilter(filter) .setLimit(3) .build()); client.searchBatchAsync("{collection_name}", searches, null).get(); ``` ```go package client import ( "context" "fmt" "github.com/qdrant/go-client/qdrant" ) func searchBatch() { client, err := qdrant.NewClient(&qdrant.Config{ Host: "localhost", Port: 6334, }) if err != nil { panic(err) } filter := qdrant.Filter{ Must: []*qdrant.Condition{ qdrant.NewMatch("city", "London"), }, } limit := uint64(3) results, err := client.QueryBatch(context.Background(), &qdrant.QueryBatchPoints{ CollectionName: "{collection_name}", QueryPoints: []*qdrant.QueryPoints{ { CollectionName: "{collection_name}", Query: qdrant.NewQuery(0.2, 0.1, 0.9, 0.7), Limit: &limit, Filter: &filter, }, { CollectionName: "{collection_name}", Query: qdrant.NewQuery(0.5, 0.3, 0.2, 0.3), Limit: &limit, Filter: &filter, }, }, }) if err != nil { panic(err) } fmt.Println("Query results: ", results) } ``` ```typescript import { QdrantClient } from "@qdrant/js-client-rest"; const client = new QdrantClient({ host: "localhost", port: 6333 }); const filter = { must: [ { key: "city", match: { value: "London", }, }, ], }; const searches = [ { vector: [0.2, 0.1, 0.9, 0.7], filter, limit: 3, }, { vector: [0.5, 0.3, 0.2, 0.3], filter, limit: 3, }, ]; client.searchBatch("{collection_name}", { searches, }); ``` ```rust use qdrant_client::qdrant::{ Condition, Filter, SearchBatchPointsBuilder, SearchPointsBuilder, }; use qdrant_client::Qdrant; let client = Qdrant::from_url("http://localhost:6334").build()?; let filter = Filter::must([Condition::matches("city", "London".to_string())]); let searches = vec![ SearchPointsBuilder::new("{collection_name}", vec![0.2, 0.1, 0.9, 0.7], 3) .filter(filter.clone()) .build(), SearchPointsBuilder::new("{collection_name}", vec![0.5, 0.3, 0.2, 0.3], 3) .filter(filter.clone()) .build(), ]; client .search_batch_points(SearchBatchPointsBuilder::new("{collection_name}", searches)) .await?; ``` ```csharp using Qdrant.Client; using Qdrant.Client.Grpc; using static Qdrant.Client.Grpc.Conditions; var client = new QdrantClient("localhost", 6334); var filter = MatchKeyword("city", "London"); var searches = new List { new() { Vector = { new float[] { 0.2f, 0.1f, 0.9f, 0.7f } }, Filter = filter, Limit = 3 }, new() { Vector = { new float[] { 0.5f, 0.3f, 0.2f, 0.3f } }, Filter = filter, Limit = 3 } }; await client.SearchBatchAsync(collectionName: "{collection_name}", searches: searches); ``` ```ruby require 'uri' require 'net/http' url = URI("http://localhost:6333/collections/collection_name/points/search/batch") http = Net::HTTP.new(url.host, url.port) request = Net::HTTP::Post.new(url) request["api-key"] = '' request["Content-Type"] = 'application/json' request.body = "{\n \"searches\": [\n {\n \"vector\": [\n 1.1\n ],\n \"limit\": 1\n }\n ]\n}" response = http.request(request) puts response.read_body ``` ```php request('POST', 'http://localhost:6333/collections/collection_name/points/search/batch', [ 'body' => '{ "searches": [ { "vector": [ 1.1 ], "limit": 1 } ] }', 'headers' => [ 'Content-Type' => 'application/json', 'api-key' => '', ], ]); echo $response->getBody(); ``` ```swift import Foundation let headers = [ "api-key": "", "Content-Type": "application/json" ] let parameters = ["searches": [ [ "vector": [1.1], "limit": 1 ] ]] as [String : Any] let postData = JSONSerialization.data(withJSONObject: parameters, options: []) let request = NSMutableURLRequest(url: NSURL(string: "http://localhost:6333/collections/collection_name/points/search/batch")! as URL, cachePolicy: .useProtocolCachePolicy, timeoutInterval: 10.0) request.httpMethod = "POST" 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() ```