bicardinal/brinicle

A resource-efficient C++ vector index engine built for low-RAM production workloads

37
/ 100
Emerging

This helps developers integrate efficient similarity search into their applications, especially when dealing with large datasets and limited memory. It takes in high-dimensional vectors (numerical representations of data like images or text) and quickly finds the most similar ones. This is for software engineers or data infrastructure engineers building systems where fast, approximate nearest neighbor search is critical.

Available on PyPI.

Use this if you are building an application that needs to perform fast similarity searches on large vector datasets, but your deployment environment has limited RAM and you need disk-first processing.

Not ideal if you need an out-of-the-box vector database solution rather than a library to integrate into your existing C++ or Python application.

similarity-search vector-search machine-learning-infrastructure resource-constrained-systems backend-development
No Dependents
Maintenance 10 / 25
Adoption 5 / 25
Maturity 22 / 25
Community 0 / 25

How are scores calculated?

Stars

10

Forks

Language

Python

License

Apache-2.0

Last pushed

Jan 29, 2026

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/bicardinal/brinicle"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.