Vector embedding fixtures

The same texts as a normalised vector set in JSON, Parquet, and NumPy .npy, for testing vector stores, similarity search, and embedding loaders.

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Text Embeddings: 16-dim (JSON)

A set of 24 L2-normalised 16-dimensional text embeddings as JSON: each record pairs an id and its source text with a float vector. A fixture for testing vector stores, similarity search, and embedding loaders. Parquet and .npy twins included.

File: JSON · Embeddings · 24 records
Use case: ML training dataEmbeddings+2· Conversion set

Text Embeddings: 16-dim (Parquet)

The same 16-dimensional embeddings as Apache Parquet: id and text columns plus one column per dimension. The columnar twin, for testing analytics engines and Parquet-based vector pipelines.

File: PARQUET · Embeddings · 24 rows
Use case: ML training dataEmbeddings+2· Conversion set

Text Embeddings: 24×16 matrix (NumPy .npy)

The embeddings as a raw NumPy array: a 24×16 float32 matrix in .npy format, loadable with numpy.load. The binary twin of the JSON and Parquet files, for testing tensor and matrix loaders.

File: NPY · Embeddings
Use case: ML training dataEmbeddings+1· Conversion set