The QdrantSink class provides functionality for storing and querying vector data within the Qdrant vector search engine, which is known for its performance in handling large-scale vector datasets.


Required properties:

  • url: The connection URL to the Qdrant service.
  • api_key: The API key for authenticating with the Qdrant API.
  • collection_name: The name of the collection within Qdrant where the data will be stored. You can define the collection name to any string you want.

Optional properties:

from neumai.SinkConnectors import QdrantSink

# Configure the QdrantSink with the necessary connection information
qdrant_sink = QdrantSink(
    url = "your-qdrant-url",
    api_key = "your-api-key",
    collection_name = "collection-name"