> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://api.qdrant.tech/v-1-10-x/api-reference/collections/create-collection/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://api.qdrant.tech/_mcp/server.

# Create a collection

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

Creates a new collection with the given parameters.

Reference: https://api.qdrant.tech/api-reference/collections/create-collection

## 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 new collection

### Query parameters

- `timeout` (integer, optional) — Wait for operation commit timeout in seconds. If timeout is reached - request will return with service error.

### Body (application/json)

This endpoint expects a CreateCollection.

- `vectors` (VectorsConfig, optional) — Vector params separator for single and multiple vector modes Single mode: \{ "size": 128, "distance": "Cosine" } or multiple mode: \{ "default": \{ "size": 128, "distance": "Cosine" } }
- `shard_number` (uint, optional, nullable) — For auto sharding: Number of shards in collection. - Default is 1 for standalone, otherwise equal to the number of nodes - Minimum is 1 For custom sharding: Number of shards in collection per shard group. - Default is 1, meaning that each shard key will be mapped to a single shard - Minimum is 1
- `sharding_method` (CreateCollectionShardingMethod, optional) — Sharding method Default is Auto - points are distributed across all available shards Custom - points are distributed across shards according to shard key
- `replication_factor` (uint, optional, nullable) — Number of shards replicas. Default is 1 Minimum is 1
- `write_consistency_factor` (uint, optional, nullable) — Defines how many replicas should apply the operation for us to consider it successful. Increasing this number will make the collection more resilient to inconsistencies, but will also make it fail if not enough replicas are available. Does not have any performance impact.
- `on_disk_payload` (boolean, optional, nullable) — If true - point's payload will not be stored in memory. It will be read from the disk every time it is requested. This setting saves RAM by (slightly) increasing the response time. Note: those payload values that are involved in filtering and are indexed - remain in RAM.
- `hnsw_config` (CreateCollectionHnswConfig, optional) — Custom params for HNSW index. If none - values from service configuration file are used.
- `wal_config` (CreateCollectionWalConfig, optional) — Custom params for WAL. If none - values from service configuration file are used.
- `optimizers_config` (CreateCollectionOptimizersConfig, optional) — Custom params for Optimizers. If none - values from service configuration file are used.
- `init_from` (CreateCollectionInitFrom, optional) — Specify other collection to copy data from.
- `quantization_config` (CreateCollectionQuantizationConfig, optional) — Quantization parameters. If none - quantization is disabled.
- `sparse_vectors` (map from string to SparseVectorParams, optional, nullable) — Sparse vector data config.

## Response

### 200

successful operation

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

## Types

### VectorsConfig

Vector params separator for single and multiple vector modes Single mode: \{ "size": 128, "distance": "Cosine" } or multiple mode: \{ "default": \{ "size": 128, "distance": "Cosine" } }

### CreateCollectionShardingMethod

Sharding method Default is Auto - points are distributed across all available shards Custom - points are distributed across shards according to shard key

### CreateCollectionHnswConfig

Custom params for HNSW index. If none - values from service configuration file are used.

### CreateCollectionWalConfig

Custom params for WAL. If none - values from service configuration file are used.

### CreateCollectionOptimizersConfig

Custom params for Optimizers. If none - values from service configuration file are used.

### CreateCollectionInitFrom

Specify other collection to copy data from.

### CreateCollectionQuantizationConfig

Quantization parameters. If none - quantization is disabled.

### SparseVectorParams

Params of single sparse vector data storage

- `index` (SparseVectorParamsIndex, optional) — Custom params for index. If none - values from collection configuration are used.
- `modifier` (SparseVectorParamsModifier, optional) — Configures addition value modifications for sparse vectors. Default: none

### VectorParams

Params of single vector data storage

- `size` (uint64, required) — Size of a vectors used
- `distance` (enum, required) — Type of internal tags, build from payload Distance function types used to compare vectors
  - Allowed values: `Cosine`, `Euclid`, `Dot`, `Manhattan`
- `hnsw_config` (VectorParamsHnswConfig, optional) — Custom params for HNSW index. If none - values from collection configuration are used.
- `quantization_config` (VectorParamsQuantizationConfig, optional) — Custom params for quantization. If none - values from collection configuration are used.
- `on_disk` (boolean, optional, nullable) — If true, vectors are served from disk, improving RAM usage at the cost of latency Default: false
- `datatype` (VectorParamsDatatype, optional) — Defines which datatype should be used to represent vectors in the storage. Choosing different datatypes allows to optimize memory usage and performance vs accuracy. - For `float32` datatype - vectors are stored as single-precision floating point numbers, 4 bytes. - For `float16` datatype - vectors are stored as half-precision floating point numbers, 2 bytes. - For `uint8` datatype - vectors are stored as unsigned 8-bit integers, 1 byte. It expects vector elements to be in range `[0, 255]`.
- `multivector_config` (VectorParamsMultivectorConfig, optional)

### HnswConfigDiff

- `m` (integer, optional, nullable) — Number of edges per node in the index graph. Larger the value - more accurate the search, more space required.
- `ef_construct` (integer, optional, nullable) — Number of neighbours to consider during the index building. Larger the value - more accurate the search, more time required to build the index.
- `full_scan_threshold` (integer, optional, nullable) — Minimal size (in kilobytes) of vectors for additional payload-based indexing. If payload chunk is smaller than `full_scan_threshold_kb` additional indexing won't be used - in this case full-scan search should be preferred by query planner and additional indexing is not required. Note: 1Kb = 1 vector of size 256
- `max_indexing_threads` (integer, optional, nullable) — Number of parallel threads used for background index building. If 0 - automatically select from 8 to 16. Best to keep between 8 and 16 to prevent likelihood of building broken/inefficient HNSW graphs. On small CPUs, less threads are used.
- `on_disk` (boolean, optional, nullable) — Store HNSW index on disk. If set to false, the index will be stored in RAM. Default: false
- `payload_m` (integer, optional, nullable) — Custom M param for additional payload-aware HNSW links. If not set, default M will be used.

### WalConfigDiff

- `wal_capacity_mb` (integer, optional, nullable) — Size of a single WAL segment in MB
- `wal_segments_ahead` (integer, optional, nullable) — Number of WAL segments to create ahead of actually used ones

### OptimizersConfigDiff

- `deleted_threshold` (double, optional, nullable) — The minimal fraction of deleted vectors in a segment, required to perform segment optimization
- `vacuum_min_vector_number` (integer, optional, nullable) — The minimal number of vectors in a segment, required to perform segment optimization
- `default_segment_number` (integer, optional, nullable) — Target amount of segments optimizer will try to keep. Real amount of segments may vary depending on multiple parameters: - Amount of stored points - Current write RPS It is recommended to select default number of segments as a factor of the number of search threads, so that each segment would be handled evenly by one of the threads If `default_segment_number = 0`, will be automatically selected by the number of available CPUs
- `max_segment_size` (integer, optional, nullable) — Do not create segments larger this size (in kilobytes). Large segments might require disproportionately long indexation times, therefore it makes sense to limit the size of segments. If indexation speed have more priority for your - make this parameter lower. If search speed is more important - make this parameter higher. Note: 1Kb = 1 vector of size 256
- `memmap_threshold` (integer, optional, nullable) — Maximum size (in kilobytes) of vectors to store in-memory per segment. Segments larger than this threshold will be stored as read-only memmaped file. Memmap storage is disabled by default, to enable it, set this threshold to a reasonable value. To disable memmap storage, set this to `0`. Note: 1Kb = 1 vector of size 256
- `indexing_threshold` (integer, optional, nullable) — Maximum size (in kilobytes) of vectors allowed for plain index, exceeding this threshold will enable vector indexing Default value is 20,000, based on \<[https://github.com/google-research/google-research/blob/master/scann/docs/algorithms.md>](https://github.com/google-research/google-research/blob/master/scann/docs/algorithms.md>). To disable vector indexing, set to `0`. Note: 1kB = 1 vector of size 256.
- `flush_interval_sec` (uint64, optional, nullable) — Minimum interval between forced flushes.
- `max_optimization_threads` (integer, optional, nullable) — Max number of threads (jobs) for running optimizations per shard. Note: each optimization job will also use `max_indexing_threads` threads by itself for index building. If null - have no limit and choose dynamically to saturate CPU. If 0 - no optimization threads, optimizations will be disabled.

### InitFrom

Operation for creating new collection and (optionally) specify index params

- `collection` (string, required)

### SparseVectorParamsIndex

Custom params for index. If none - values from collection configuration are used.

### SparseVectorParamsModifier

Configures addition value modifications for sparse vectors. Default: none

### VectorParamsHnswConfig

Custom params for HNSW index. If none - values from collection configuration are used.

### VectorParamsQuantizationConfig

Custom params for quantization. If none - values from collection configuration are used.

### VectorParamsDatatype

Defines which datatype should be used to represent vectors in the storage. Choosing different datatypes allows to optimize memory usage and performance vs accuracy. - For `float32` datatype - vectors are stored as single-precision floating point numbers, 4 bytes. - For `float16` datatype - vectors are stored as half-precision floating point numbers, 2 bytes. - For `uint8` datatype - vectors are stored as unsigned 8-bit integers, 1 byte. It expects vector elements to be in range `[0, 255]`.

### VectorParamsMultivectorConfig

### SparseIndexParams

Configuration for sparse inverted index.

- `full_scan_threshold` (integer, optional, nullable) — We prefer a full scan search upto (excluding) this number of vectors. Note: this is number of vectors, not KiloBytes.
- `on_disk` (boolean, optional, nullable) — Store index on disk. If set to false, the index will be stored in RAM. Default: false
- `datatype` (SparseIndexParamsDatatype, optional) — Defines which datatype should be used for the index. Choosing different datatypes allows to optimize memory usage and performance vs accuracy. - For `float32` datatype - vectors are stored as single-precision floating point numbers, 4 bytes. - For `float16` datatype - vectors are stored as half-precision floating point numbers, 2 bytes. - For `uint8` datatype - vectors are quantized to unsigned 8-bit integers, 1 byte. Quantization to fit byte range `[0, 255]` happens during indexing automatically, so the actual vector data does not need to conform to this range.

### MultiVectorConfig

- `comparator` (enum, required)
  - Allowed values: `max_sim`

### SparseIndexParamsDatatype

Defines which datatype should be used for the index. Choosing different datatypes allows to optimize memory usage and performance vs accuracy. - For `float32` datatype - vectors are stored as single-precision floating point numbers, 4 bytes. - For `float16` datatype - vectors are stored as half-precision floating point numbers, 2 bytes. - For `uint8` datatype - vectors are quantized to unsigned 8-bit integers, 1 byte. Quantization to fit byte range `[0, 255]` happens during indexing automatically, so the actual vector data does not need to conform to this range.

## Examples

**Request**

```json
{}
```

**Response**

```json
{
  "time": 1.1,
  "status": "string",
  "result": true
}
```

**SDK Code**

```rust
use qdrant_client::qdrant::{CreateCollectionBuilder, Distance, VectorParamsBuilder};
use qdrant_client::Qdrant;

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

client
    .create_collection(
        CreateCollectionBuilder::new("{collection_name}")
            .vectors_config(VectorParamsBuilder::new(100, Distance::Cosine)),
    )
    .await?;

```

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

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

await client.CreateCollectionAsync(
	collectionName: "{collection_name}",
	vectorsConfig: new VectorParams { Size = 100, Distance = Distance.Cosine }
);

// Or with sparse vectors

await client.CreateCollectionAsync(
	collectionName: "{collection_name}",
	sparseVectorsConfig: ("splade-model-name", new SparseVectorParams{
        Index = new SparseIndexConfig {
            OnDisk = false,
        }
    })
);
```

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

import io.qdrant.client.grpc.Collections.Distance;
import io.qdrant.client.grpc.Collections.VectorParams;

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

client.createCollectionAsync("{collection_name}",
        VectorParams.newBuilder().setDistance(Distance.Cosine).setSize(100).build()).get();

// Or with sparse vectors

client.createCollectionAsync(
    CreateCollection.newBuilder()
        .setCollectionName("{collection_name}")
        .setSparseVectorsConfig(
            Collections.SparseVectorConfig.newBuilder().putMap(
                "splade-model-name",
                Collections.SparseVectorParams.newBuilder()
                    .setIndex(
                        Collections.SparseIndexConfig
                            .newBuilder()
                            .setOnDisk(false)
                            .build()
                    ).build()
            ).build()
        ).build()
).get();
```

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

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

client.createCollection("{collection_name}", {
  vectors: { size: 100, distance: "Cosine" },
});

// or with sparse vectors

client.createCollection("{collection_name}", {
  vectors: { size: 100, distance: "Cosine" },
  sparse_vectors: {
    "splade-model-name": {
      index: {
        on_disk: false
      }
    }
  }
});
```

```python
from qdrant_client import QdrantClient, models

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

client.create_collection(
    collection_name="{collection_name}",
    vectors_config=models.VectorParams(size=100, distance=models.Distance.COSINE),
)

```

```go
package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "http://localhost:6333/collections/collection_name"

	payload := strings.NewReader("{}")

	req, _ := http.NewRequest("PUT", url, payload)

	req.Header.Add("api-key", "<apiKey>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(res)
	fmt.Println(string(body))

}
```

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

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

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 = "{}"

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', [
  'body' => '{}',
  '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 = [] as [String : Any]

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

let request = NSMutableURLRequest(url: NSURL(string: "http://localhost:6333/collections/collection_name")! 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()
```