Rust Vector Index with Python Bindings for 10M Vectors in 4 GB RAM

Описание и применение

Rust‑библиотека turbovec позволяет хранить 10 млн векторов в 4 ГБ RAM и быстро выполнять поиск ближайших соседей из Python.

Задача

Python developers need to store and query millions of high‑dimensional vectors for similarity search but are limited by memory constraints.

Результат

The turbovec library enables Python users to index and query 10 million vectors within 4 GB of RAM, dramatically reducing memory usage and improving query speed.

Рабочий процесс

• 1. Clone the turbovec repository from GitHub.
• 2. Build the Rust library and generate Python bindings using Cargo and PyO3.
• 3. Load vectors into the Rust index via the Python API.
• 4. Perform similarity queries directly from Python, benefiting from Rust’s performance.
• 5. Monitor memory usage to confirm the 4 GB footprint.

Доказательства

Builds a Rust vector index with Python bindings that fits 10 million document vectors into 4 GB of RAM.

Источник