xsfa/pointstorm

Real-time embeddings for data on the move

42
/ 100
Emerging

Continuously transforms incoming text data, like customer feedback or social media posts, into numerical representations as it arrives. It takes a stream of text from Kafka and outputs real-time embeddings that can power live analytics or recommendation engines. This is for data engineers or data scientists who need to process dynamic text streams for immediate use.

No commits in the last 6 months. Available on PyPI.

Use this if you need to convert a constant flow of text data into machine-readable numerical vectors (embeddings) in real-time, especially when using Kafka.

Not ideal if you're processing static, batch-oriented text files or if your data sources are not stream-based like Kafka.

data-streaming real-time-analytics text-processing data-engineering machine-learning-operations
Stale 6m
Maintenance 0 / 25
Adoption 6 / 25
Maturity 25 / 25
Community 11 / 25

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Stars

21

Forks

3

Language

Python

License

Apache-2.0

Last pushed

Nov 16, 2023

Commits (30d)

0

Dependencies

10

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