Beyond Vector Databases: Efficient Text Embedding Processing with Parquet and Polars

This article presents a method for efficient text embedding processing without relying on vector databases. The author uses Parquet files to store tabular data containing Magic: The Gathering card embeddings and their metadata, and leverages the Polars library for fast similarity search and data filtering. Polars' zero-copy feature and excellent support for nested data make this approach faster and more efficient than traditional CSV or Pickle methods, maintaining high performance even when filtering the dataset. The author compares other storage methods such as CSV, Pickle, and NumPy, concluding that Parquet combined with Polars is the optimal choice for handling medium-sized text embeddings, with vector databases only becoming necessary for extremely large datasets.