landing_page/qdrant-landing/content/documentation/frameworks/dagster.md

title, short_description, description

title	short_description	description
Dagster	Orchestrate AI data pipelines in Dagster with the Qdrant resource to ingest, embed, and query vector collections from declarative assets.	Use the Qdrant Dagster integration to build observable AI data pipelines that ingest documents, manage collections, and run vector search from Dagster assets.

Dagster

Dagster is a Python framework for data orchestration built for data engineers, with integrated lineage, observability, a declarative programming model, and best-in-class testability.

The dagster-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

pip install dagster 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>",
            )
        )
    },
)

Next steps

• Dagster documentation

• Dagster examples