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.

Filter these filesAll formatsJSONNPYPARQUETAll purposesConversion testingData engineeringEmbeddingsJSON parsingML training data

3 of 3 files

json

13.5 KB

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 data/ Embeddings+2· Conversion set

parquet

9.3 KB

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 data/ Embeddings+2· Conversion set

npy

1.6 KB

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 data/ Embeddings+1· Conversion set