Qdrant

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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.