Cognee Qdrant Adapter

Installation

If published, the package can be simply installed via pip:

pip install cognee-community-vector-adapter-qdrant

In case it is not published yet, you can use poetry to locally build the adapter package:

pip install poetry
poetry install # run this command in the directory containing the pyproject.toml file

Connection Setup

For a quick local setup, you can run a docker container that qdrant provides (https://qdrant.tech/documentation/quickstart/). After this, you will be able to connect to the Qdrant DB through the appropriate ports. The command for running the docker container looks something like the following:

docker run -p 6333:6333 -p 6334:6334 \
    -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \
    qdrant/qdrant

Usage

Import and register the adapter in your code:

from cognee_community_vector_adapter_qdrant import register

Also, specify the dataset handler in the .env file:

VECTOR_DATASET_DATABASE_HANDLER="qdrant"

Quantization

The adapter supports Qdrant's quantization methods, including TurboQuant (introduced in Qdrant 1.18). Quantization compresses stored vectors and reduces memory cost; it is disabled by default.

Set the mode via env var:

QDRANT_QUANTIZATION=tq4
QDRANT_QUANTIZATION_ALWAYS_RAM=true
QDRANT_QUANTIZATION_RESCORE=true
QDRANT_QUANTIZATION_OVERSAMPLING=2.0

Requires qdrant-client>=1.18 and Qdrant server >=1.18 for any tq* mode.

Migrating existing collections

Setting QDRANT_QUANTIZATION only affects newly created collections. To enable quantization on an existing collection without re-ingesting data, call update_quantization:

from cognee.infrastructure.databases.vector import get_vector_engine

adapter = get_vector_engine()
await adapter.update_quantization("Entity_name")
await adapter.update_quantization("DocumentChunk_text")

Qdrant rebuilds the affected index in the background; queries during the rebuild transparently fall back to full vectors.

Example

See example in example.py file.