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A collection is a named set of points (vectors with a payload) that you can search. Creating a collection is the first step before inserting data into Qdrant.

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API Endpoint

PUT /collections/{collection_name}

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Create Collection with Dense Vectors

1

Define collection parameters

Create a collection with a single dense vector configuration. This is the most common use case.
2

Send the request

Use the REST API or Python client to create the collection.
curl -X PUT http://localhost:6333/collections/my_collection \
  -H 'Content-Type: application/json' \
  -d '{
    "vectors": {
      "size": 384,
      "distance": "Cosine"
    }
  }'

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Vector Configuration Parameters

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vectors.size
integer
required
Size of the vector (number of dimensions).
​
vectors.distance
string
required
Distance metric for vector comparison. Available options:
  • Cosine - Cosine similarity
  • Euclid - Euclidean distance
  • Dot - Dot product
  • Manhattan - Manhattan distance
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vectors.hnsw_config
object
HNSW index configuration for fast approximate nearest neighbor search.
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vectors.quantization_config
object
Quantization configuration to reduce memory usage.
​
vectors.on_disk
boolean
default:"false"
Store vectors on disk instead of RAM to save memory.

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Create Collection with Named Vectors

Use named vectors when you need multiple vector representations per point (e.g., text and image embeddings). 
curl -X PUT http://localhost:6333/collections/multi_vector_collection \
  -H 'Content-Type: application/json' \
  -d '{
    "vectors": {
      "text": {
        "size": 768,
        "distance": "Cosine"
      },
      "image": {
        "size": 512,
        "distance": "Euclid"
      }
    }
  }'

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Create Collection with Sparse Vectors

Sparse vectors are useful for keyword-based search and can be combined with dense vectors for hybrid search. 
curl -X PUT http://localhost:6333/collections/sparse_collection \
  -H 'Content-Type: application/json' \
  -d '{
    "vectors": {
      "dense": {
        "size": 384,
        "distance": "Cosine"
      }
    },
    "sparse_vectors": {
      "text": {}
    }
  }'

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Additional Configuration Options

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Optimizers Config

Control how Qdrant optimizes storage and indexing. 
curl -X PUT http://localhost:6333/collections/optimized_collection \
  -H 'Content-Type: application/json' \
  -d '{
    "vectors": {
      "size": 384,
      "distance": "Cosine"
    },
    "optimizers_config": {
      "indexing_threshold": 20000,
      "memmap_threshold": 50000
    }
  }'

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Replication Factor

For distributed deployments, specify how many replicas to create. 
curl -X PUT http://localhost:6333/collections/replicated_collection \
  -H 'Content-Type: application/json' \
  -d '{
    "vectors": {
      "size": 384,
      "distance": "Cosine"
    },
    "replication_factor": 2,
    "write_consistency_factor": 1
  }'
Replication factor must be less than or equal to the number of nodes in your cluster.

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Response Format

Successful collection creation returns:
​
result
boolean
Indicates whether the operation was successful.
​
status
string
Operation status, typically “ok” on success.
​
time
number
Time taken to execute the operation in seconds.
Response Example
{
  "result": true,
  "status": "ok",
  "time": 0.031095
}

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Query Parameters

​
timeout
integer
Wait timeout in seconds for the operation to complete. If timeout is reached, the service will return an error.
Collection names must be unique. Creating a collection with an existing name will return an error.

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Check if Collection Exists

Before creating a collection, you can check if it already exists: 
GET /collections/{collection_name}/exists

curl http://localhost:6333/collections/my_collection/exists