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
}

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
}