RAG & Knowledge
helix-db
An OLTP graph database with native vector and full-text search, built in Rust on object storage.
★ 6.1k starsApache-2.0
View guide →A blazing-fast vector index in Rust, built on TurboQuant - with Python bindings
turbovec is an open-source rag & knowledge project by RyanCodrai (RyanCodrai/turbovec on GitHub). It has 17,358 GitHub stars and 1,485 forks, is released under the MIT license, and was last updated on GitHub on 7 October 2026.
turbovec is an in-process index - ideal when your vectors can live with your application.
Install the Python package from PyPI, or clone the repo to use the Rust API or build from source.
Build an index, add your embeddings with IDs, and persist it to a file.
Query nearest neighbors, including filtered search over approved IDs.
Quantizes vectors to a low bit rate so large indexes fit in far less memory.
Search kernels use AVX512 on x86 and NEON on ARM for high throughput.
The Rust API mirrors the Python one, with documented file formats and staged search.
Restrict results to IDs another system approved - a SQL predicate, an ACL or a time window.
Drop-in stores for LangChain, LlamaIndex, Haystack and Agno via extras such as turbovec[langchain].
The repo publishes benchmark scripts and raw results against FAISS at matched bit rates, plus a 'when to use something else' section.
Get the source code from GitHub and enter the project directory.
git clone https://github.com/RyanCodrai/turbovec.git
cd turbovecInstall from PyPI; add a framework extra if you want the drop-in integration.
pip install turbovec
# or: pip install turbovec[langchain]The README's Python section shows creating an index, adding vectors and querying it.
# Follow the Python quick example in the README to add embeddings and searchIt is maintained by Ryan Codrai on GitHub.
Yes - turbovec is MIT licensed and free to use.
No. It is an embedded library: no server, account or API key involved.
Compared with FAISS, turbovec focuses on TurboQuant compression at low bit rates with a simpler embedded API and framework drop-ins; compared with server databases like Qdrant, it trades distribution and filtering features for zero operations.
Run pip install turbovec and follow the README's Python example to index and search your first vectors.
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