> ## Documentation Index
> Fetch the complete documentation index at: https://docs.neum.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# QdrantSink

> QdrantSink integrates with Qdrant vector search engine to manage vector data, enabling powerful search capabilities and efficient data storage.

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.

## Properties

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:

<CodeGroup>
  ```python Local Development
  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"
  )
  ```

  ```json Cloud
  {
      "sink": {
          "sink_name":"QdrantSink",
          "sink_information":{
              "url": "your-qdrant-url",
              "api_key": "your-api-key",
              "collection_name": "collection-name"
          }
      }
  }
  ```
</CodeGroup>