Quick Start
This example covers the most basic use-case - collection creation and basic vector search.
For additional information please refer to the API documentation.
Docker 🐳
Use latest pre-built image from DockerHub
docker pull qdrant/qdrant
Run it with default configuration:
docker run -p 6333:6333 qdrant/qdrant
[!CAUTION]
Starts an insecure deployment without authentication open to all network interfaces. Please refer to secure your instance.
Build your own from source
docker build . --tag=qdrant/qdrant
And once you need a fine-grained setup, you can also define a storage path and custom configuration:
docker run -p 6333:6333 \
-v $(pwd)/path/to/data:/qdrant/storage \
-v $(pwd)/path/to/snapshots:/qdrant/snapshots \
-v $(pwd)/path/to/custom_config.yaml:/qdrant/config/production.yaml \
qdrant/qdrant
/qdrant/storage - is the place where Qdrant persists all your data.
Make sure to mount it as a volume, otherwise docker will drop it with the container./qdrant/snapshots - is the place where Qdrant stores snapshots/qdrant/config/production.yaml - is the file with engine configuration. You can override any value from the reference config. In a real production environment, you should enable authentication by setting service.apiKey.- For production environments, consider also setting
--read-only and --user=1000:2000 to further secure your Qdrant instance. Or use our Helm chart or Qdrant Cloud which sets these by default.
Now Qdrant should be accessible at localhost:6333.
Create collection
First - let's create a collection with dot-production metric.
curl -X PUT 'http://localhost:6333/collections/test_collection' \
-H 'Content-Type: application/json' \
--data-raw '{
"vectors": {
"size": 4,
"distance": "Dot"
}
}'
Expected response:
{
"result": true,
"status": "ok",
"time": 0.031095451
}
We can ensure that collection was created:
curl 'http://localhost:6333/collections/test_collection'
Expected response:
{
"result": {
"status": "green",
"vectors_count": 0,
"segments_count": 5,
"disk_data_size": 0,
"ram_data_size": 0,
"config": {
"params": {
"vectors": {
"size": 4,
"distance": "Dot"
}
},
"hnsw_config": {
"m": 16,
"ef_construct": 100,
"full_scan_threshold": 10000
},
"optimizer_config": {
"deleted_threshold": 0.2,
"vacuum_min_vector_number": 1000,
"default_segment_number": 2,
"max_segment_size": null,
"memmap_threshold": null,
"indexing_threshold": 20000,
"flush_interval_sec": 5,
"max_optimization_threads": null
},
"wal_config": {
"wal_capacity_mb": 32,
"wal_segments_ahead": 0
}
}
},
"status": "ok",
"time": 2.1199e-5
}
Add points
Let's now add vectors with some payload:
curl -L -X PUT 'http://localhost:6333/collections/test_collection/points?wait=true' \
-H 'Content-Type: application/json' \
--data-raw '{
"points": [
{"id": 1, "vector": [0.05, 0.61, 0.76, 0.74], "payload": {"city": "Berlin"}},
{"id": 2, "vector": [0.19, 0.81, 0.75, 0.11], "payload": {"city": ["Berlin", "London"] }},
{"id": 3, "vector": [0.36, 0.55, 0.47, 0.94], "payload": {"city": ["Berlin", "Moscow"] }},
{"id": 4, "vector": [0.18, 0.01, 0.85, 0.80], "payload": {"city": ["London", "Moscow"] }},
{"id": 5, "vector": [0.24, 0.18, 0.22, 0.44], "payload": {"count": [0] }},
{"id": 6, "vector": [0.35, 0.08, 0.11, 0.44]}
]
}'
Expected response:
{
"result": {
"operation_id": 0,
"status": "completed"
},
"status": "ok",
"time": 0.000206061
}
Search with filtering
Let's start with a basic request:
curl -L -X POST 'http://localhost:6333/collections/test_collection/points/search' \
-H 'Content-Type: application/json' \
--data-raw '{
"vector": [0.2,0.1,0.9,0.7],
"top": 3
}'
Expected response:
{
"result": [
{ "id": 4, "score": 1.362, "payload": null, "version": 0 },
{ "id": 1, "score": 1.273, "payload": null, "version": 0 },
{ "id": 3, "score": 1.208, "payload": null, "version": 0 }
],
"status": "ok",
"time": 0.000055785
}
But result is different if we add a filter:
curl -L -X POST 'http://localhost:6333/collections/test_collection/points/search' \
-H 'Content-Type: application/json' \
--data-raw '{
"filter": {
"should": [
{
"key": "city",
"match": {
"value": "London"
}
}
]
},
"vector": [0.2, 0.1, 0.9, 0.7],
"top": 3
}'
Expected response:
{
"result": [
{ "id": 4, "score": 1.362 },
{ "id": 2, "score": 0.871 }
],
"status": "ok",
"time": 0.000093972
}