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Credentials

Open the file named io_config.yaml at the root of your Mage project and enter qdrant required fields: 
version: 0.1.1
default:
  QDRANT_COLLECTION: collection_name
  QDRANT_PATH: path of the qdrant persisitant storage

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Dependencies

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. 
qdrant-client==1.6.9
sentence-transformers==2.2.2

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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.

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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.