Skip to main content

Dagster & Qdrant

Community integration

This is a community-maintained integration. To report bugs or leave feedback, open an issue in the Dagster community integrations repo.

The Qdrant library lets you integrate Qdrant's vector database with Dagster, making it easy to build AI-driven data pipelines. You can run vector searches and manage data directly within Dagster.

Installation​

uv add dagster-qdrant

Example​

from dagster_qdrant import QdrantConfig, QdrantResource

import dagster as dg


@dg.asset
def my_table(qdrant_resource: QdrantResource):
    with qdrant_resource.get_client() as qdrant:
        qdrant.add(
            collection_name="test_collection",
            documents=[
                "This is a document about oranges",
                "This is a document about pineapples",
                "This is a document about strawberries",
                "This is a document about cucumbers",
            ],
        )
        results = qdrant.query(
            collection_name="test_collection", query_text="hawaii", limit=3
        )


defs = dg.Definitions(
    assets=[my_table],
    resources={
        "qdrant_resource": QdrantResource(
            config=QdrantConfig(
                host="xyz-example.eu-central.aws.cloud.qdrant.io",
                api_key="<your-api-key>",
            )
        )
    },
)

About Qdrant​

Qdrant (read: quadrant) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage vectors with additional payload and extended filtering support. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications.

Learn more from the Qdrant documentation.