"description": "API description for Qdrant vector search engine.\n\nThis document describes CRUD and search operations on collections of points (vectors with payload).\n\nQdrant supports any combinations of `should`, `min_should`, `must` and `must_not` conditions, which makes it possible to use in applications when object could not be described solely by vector. It could be location features, availability flags, and other custom properties businesses should take into account.\n## Examples\nThis examples cover the most basic use-cases - collection creation and basic vector search.\n### Create collection\nFirst - let's create a collection with dot-production metric.\n```\ncurl -X PUT 'http://localhost:6333/collections/test_collection' \\\n -H 'Content-Type: application/json' \\\n --data-raw '{\n \"vectors\": {\n \"size\": 4,\n \"distance\": \"Dot\"\n }\n }'\n\n```\nExpected response:\n```\n{\n \"result\": true,\n \"status\": \"ok\",\n \"time\": 0.031095451\n}\n```\nWe can ensure that collection was created:\n```\ncurl 'http://localhost:6333/collections/test_collection'\n```\nExpected response:\n```\n{\n \"result\": {\n \"status\": \"green\",\n \"vectors_count\": 0,\n \"segments_count\": 5,\n \"disk_data_size\": 0,\n \"ram_data_size\": 0,\n \"config\": {\n \"params\": {\n \"vectors\": {\n \"size\": 4,\n \"distance\": \"Dot\"\n }\n },\n \"hnsw_config\": {\n \"m\": 16,\n \"ef_construct\": 100,\n \"full_scan_threshold\": 10000\n },\n \"optimizer_config\": {\n \"deleted_threshold\": 0.2,\n \"vacuum_min_vector_number\": 1000,\n \"default_segment_number\": 2,\n \"max_segment_size\": null,\n \"memmap_threshold\": null,\n \"indexing_threshold\": 20000,\n \"flush_interval_sec\": 5,\n \"max_optimization_threads\": null\n },\n \"wal_config\": {\n \"wal_capacity_mb\": 32,\n \"wal_segments_ahead\": 0\n }\n }\n },\n \"status\": \"ok\",\n \"time\": 2.1199e-05\n}\n```\n\n### Add points\nLet's now add vectors with some payload:\n```\ncurl -L -X PUT 'http://localhost:6333/collections/test_collection/points?wait=true' \\ -H 'Content-Type: application/json' \\ --data-raw '{\n \"points\": [\n {\"id\": 1, \"vector\": [0.05, 0.61, 0.76, 0.74], \"payload\": {\"city\": \"Berlin\"}},\n {\"id\": 2, \"vector\": [0.19, 0.81, 0.75, 0.11], \"payload\": {\"city\": [\"Berlin\", \"London\"] }},\n {\"id\": 3, \"vector\": [0.36, 0.55, 0.47, 0.94], \"payload\": {\"city\": [\"Berlin\", \"Moscow\"] }},\n {\"id\": 4, \"vector\": [0.18, 0.01, 0.85, 0.80], \"payload\": {\"city\": [\"London\", \"Moscow\"] }},\n {\"id\": 5, \"vector\": [0.24, 0.18, 0.22, 0.44], \"payload\": {\"count\": [0]}},\n {\"id\": 6, \"vector\": [0.35, 0.08, 0.11, 0.44]}\n ]\n}'\n```\nExpected response:\n```\n{\n \"result\": {\n \"operation_id\": 0,\n \"status\": \"completed\"\n },\n \"status\": \"ok\",\n \"time\": 0.000206061\n}\n```\n### Search with filtering\nLet's start with a basic request:\n```\ncurl -L -X POST 'http://localhost:6333/collections/test_collection/points/search' \\ -H 'Content-Type: application/json' \\ --data-raw '{\n \"vector\": [0.2,0.1,0.9,0.7],\n \"top\": 3\n}'\n```\nExpected response:\n```\n{\n \"result\": [\n { \"id\": 4, \"score\": 1.362, \"payload\": null, \"version\": 0 },\n { \"id\": 1, \"score\": 1.273, \"payload\": null, \"version\": 0 },\n { \"id\": 3, \"score\": 1.208, \"payload\": null, \"version\": 0 }\n ],\n \"status\": \"ok\",\n \"time\": 0.000055785\n}\n```\nBut result is different if we add a filter:\n```\ncurl -L -X POST 'http://localhost:6333/collections/test_collection/points/search' \\ -H 'Content-Type: application/json' \\ --data-raw '{\n \"filter\": {\n \"should\": [\n {\n \"key\": \"city\",\n \"match\": {\n \"value\": \"London\"\n }\n }\n ]\n },\n \"vector\": [0.2, 0.1, 0.9, 0.7],\n \"top\": 3\n}'\n```\nExpected response:\n```\n{\n \"result\": [\n { \"id\": 4, \"score\": 1.362, \"payload\": null, \"version\": 0 },\n { \"id\": 2, \"score\": 0.871, \"payload\": null, \"version\": 0 }\n ],\n \"status\": \"ok\",\n \"time\": 0.000093972\n}\n```\n",

"vectors_count": {

"description": "DEPRECATED: Approximate number of vectors in collection. All vectors in collection are available for querying. Calculated as `points_count x vectors_per_point`. Where `vectors_per_point` is a number of named vectors in schema.",

"type": "integer",

"format": "uint",

"minimum": 0,

"nullable": true

},

optional uint64 vectors_count = 3; // Approximate number of vectors in the collection

/// Approximate number of vectors in the collection

#[prost(uint64, optional, tag = "3")]

pub vectors_count: ::core::option::Option<u64>,

info.vectors_count = info

.vectors_count

.zip(response.vectors_count)

.map(|(a, b)| a + b);

// Do not display vectors count, as it is an approximate number

// and many users are confused by its behavior

info.vectors_count = None;

vectors_count,

vectors_count: vectors_count.map(|count| count as u64),

vectors_count,

vectors_count: vectors_count.map(|count| count as usize),

/// DEPRECATED:

/// Approximate number of vectors in collection.

/// All vectors in collection are available for querying.

/// Calculated as `points_count x vectors_per_point`.

/// Where `vectors_per_point` is a number of named vectors in schema.

#[serde(skip_serializing_if = "Option::is_none")]

pub vectors_count: Option<usize>,

vectors_count: Some(0),

vectors_count,

vectors_count: Some(vectors_count),

/// Approximate number of vectors in shard.

/// All vectors in shard are available for querying.

/// Calculated as `points_count x vectors_per_point`.

/// Where `vectors_per_point` is a number of named vectors in schema.

pub vectors_count: usize,

let mut vectors_count = 0;

vectors_count += segment_info.num_vectors;

vectors_count,