Qdrant (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points (vectors with additional payload).

It is tailored on extended support of filtering, which makes it useful for all sorts of neural-network or semantic based matching, faceted search, and other applications.

Qdrant is written in Rust 🦀, which makes it reliable even under high load.

Online OpenAPI 3.0 documentation is available here. OpenAPI makes it easy to generate client for virtually any framework or programing language.

You can also download raw OpenAPI definitions.

Qdrant supports any combinations of 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.

Once service confirmed an update - it won't lose data even in case of power shut down. All operations are stored in the update journal and the latest database state could be easily reconstructed at any moment.

Qdrant does not rely on any external database or orchestration controller, which makes it very easy to configure.

Build your own from source

docker build . --tag=qdrant/qdrant

Or use latest pre-built image from DockerHub

docker pull ... todo

To run container use command:

docker run -p 6333:6333 \
    -v $(pwd)/path/to/data:/qdrant/storage \
    -v $(pwd)/path/to/custom_config.yaml:/qdrant/config/production.yaml \
    qdrant/qdrant

• /qdrant/storage - is a place where Qdrant persists all your data. Make sure to mount it as a volume, otherwise docker will drop it with the container.
• /qdrant/config/production.yaml - is the file with engine configuration. You can override any value from the reference config

Now Qdrant should be accessible at localhost:6333

This example covers the most basic use-case - collection creation and basic vector search. For additional information please refer to the documentation.

First - let's create a collection with dot-production metric.

curl -X POST 'http://localhost:6333/collections' \
-H 'Content-Type: application/json' \
--data-raw '{
    "create_collection": {
        "name": "test_collection",
        "vector_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": {
    "vectors_count": 0,
    "segments_count": 5,
    "disk_data_size": 0,
    "ram_data_size": 0,
    "config": {
      "vector_size": 4,
      "index": {
        "type": "plain",
        "options": {}
      },
      "distance": "Dot",
      "storage_type": {
        "type": "in_memory"
      }
    }
  },
  "status": "ok",
  "time": 2.1199e-05
}

Let's now add vectors with some payload:

curl -L -X POST 'http://localhost:6333/collections/test_collection?wait=true' \
-H 'Content-Type: application/json' \
--data-raw '{
  "upsert_points": {
    "points": [
      {"id": 1, "vector": [0.05, 0.61, 0.76, 0.74], "payload": {"city": {"type": "keyword", "value": "Berlin"}}},
      {"id": 2, "vector": [0.19, 0.81, 0.75, 0.11], "payload": {"city": {"type": "keyword", "value": ["Berlin", "London"] }}},
      {"id": 3, "vector": [0.36, 0.55, 0.47, 0.94], "payload": {"city": {"type": "keyword", "value": ["Berlin", "Moscow"] }}},
      {"id": 4, "vector": [0.18, 0.01, 0.85, 0.80], "payload": {"city": {"type": "keyword", "value": ["London", "Moscow"]}}},
      {"id": 5, "vector": [0.24, 0.18, 0.22, 0.44], "payload": {"count": {"type": "integer", "value": [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
}

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 },
        { "id": 1, "score": 1.273 },
        { "id": 3, "score": 1.208 }
    ],
    "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": [
            {
                "match": {
                    "key": "city",
                    "keyword": "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
}