Qdrant

One can use Qdrant as the document store for DocumentArray. It is useful when one wants to have faster Document retrieval on embeddings, i.e. .match(), .find().

Tip

This feature requires qdrant-client. You can install it via pip install "docarray[qdrant]".

Usage

Start Qdrant service

To use Qdrant as the storage backend, you need a running Qdrant server. You can use the Qdrant Docker image to run a server. Create docker-compose.yml as follows:

---
version: '3.4'
services:
  qdrant:
    image: qdrant/qdrant:v0.7.0
    ports:
      - "6333:6333"
    ulimits: # Only required for tests, as there are a lot of collections created
      nofile:
        soft: 65535
        hard: 65535
...

Then

docker-compose up

Create DocumentArray with Qdrant backend

Assuming service is started using the default configuration (i.e. server address is http://localhost:6333), one can instantiate a DocumentArray with Qdrant storage like so:

from docarray import DocumentArray

da = DocumentArray(storage='qdrant', config={'n_dim': 10})

The usage would be the same as the ordinary DocumentArray.

To access a DocumentArray formerly persisted, one can specify the collection_name, the host and the port.

from docarray import DocumentArray

da = DocumentArray(
    storage='qdrant',
    config={
        'collection_name': 'persisted',
        'host': 'localhost',
        'port': '6333',
        'n_dim': 10,
    },
)

da.summary()

Note that specifying the n_dim is mandatory before using Qdrant as a backend for DocumentArray.

Other functions behave the same as in-memory DocumentArray.

Config

The following configs can be set:

Name	Description	Default
n_dim	Number of dimensions of embeddings to be stored and retrieved	This is always required
collection_name	Qdrant collection name client	Random collection name generated
host	Hostname of the Qdrant server	'localhost'
port	port of the Qdrant server	6333
distance	Distance metric to be used during search. Can be 'cosine', 'dot' or 'euclidean'	'cosine'
scroll_batch_size	batch size used when scrolling over the storage	64
ef_construct	Number of neighbours to consider during the index building. Larger the value - more accurate the search, more time required to build index.	None, defaults to the default value in Qdrant*
full_scan_threshold	Minimal amount of points for additional payload-based indexing.	None, defaults to the default value in Qdrant*
m	Number of edges per node in the index graph. Larger the value - more accurate the search, more space required.	None, defaults to the default value in Qdrant*

*You can read more about the HNSW parameters and their default values here

Minimum example

Create docker-compose.yml:

---
version: '3.4'
services:
  qdrant:
    image: qdrant/qdrant:v0.7.0
    ports:
      - "6333:6333"
    ulimits: # Only required for tests, as there are a lot of collections created
      nofile:
        soft: 65535
        hard: 65535
...

pip install -U docarray[qdrant]
docker compose up

import numpy as np

from docarray import DocumentArray

N, D = 100, 128

da = DocumentArray.empty(N, storage='qdrant', config={'n_dim': D})  # init

da.embeddings = np.random.random([N, D])

print(da.find(np.random.random(D), limit=10))

<DocumentArray (length=10) at 4917906896>