Open the file named io_config.yaml at the root of your Mage project and enter qdrant required fields:

version: 0.1.1
  QDRANT_COLLECTION: collection_name
  QDRANT_PATH: path of the qdrant persisitant storage


The dependency libraries are not installed in the docker image by default. You’ll need to add the libraries to project requirements.txt file manually and install them.


Using Python block

  1. Create a new pipeline or open an existing pipeline.
  2. Add a data loader or data exporter using the Qdrant template under the “Databases” category. Both the data loader and exporter use SentenceTransformer ‘all-MiniLM-L6-v2’ as the default embedding function.
  3. Add your customized code into the loader, exporter or add extra transformer blocks.
  4. Run the block.

Available functions

  • Qdrant data loader arguments:

    • limit_results (int): Number of results to return.
    • query_vector (List): vector lit used for query.
    • collection_name (str): name of the collection. Default to use the name defined in io_config.yaml.
  • Qdrant data exporter arguments:

    • df (DataFrame): Data to export.
    • document_column (str): Column name containinng documents to export.
    • id_column (str): Column name of the id. Default will use index in df.
    • vector_column (str): Column name of the vector. Will use default encoder to auto generate query vector.
    • collection_name (str): name of the collection. Deafult to use the name defined in io_config.yaml.
    • vector_size (int): dimension size of vector.
    • distance (models.Distance): distance metric to use.

At the same time there is create_collection function can be used in your block to create new collection.