TurboRAG
Fast mode when you need it, exact when you must
TurboRAG launched on April 17, 2026.
What it does
Production-grade compressed vector retrieval with 8x memory compression and perfect recall. Built on Google Research's TurboQuant, QJL, and PolarQuant.
Production-grade compressed vector retrieval with 8x memory compression and perfect recall. Built on Google Research
Does a similar job
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TurboQuant for vector search – 2-4 bit compressionMar 2026 · github.com · ▲89A vector index built on TurboQuant, written in Rust with Python bindings - RyanCodrai/turbovec
TurboQuant-WASM – Google's vector quantization in the browserApr 2026 · github.com · ▲165TurboQuant WASM SIMD vector compression — 3 bits/dim with fast dot product. Requires relaxed SIMD (Chrome 114+, Firefox 128+, Safari 18+, Node 20+) - teamchong/turboquant-wasm
FastGraphRAG – Better RAG using good old PageRank2024 · github.com · ▲457Hey there HN! We’re Antonio, Luca, and Yuhang, and we’re excited to introduce Fast GraphRAG, an open-source RAG approach that leverages knowledge graphs and the 25 years old PageRank for better information retrieval and reasoning. Building a good RAG pipeline these days takes a lot of manual optimizations. Most engineers intuitively start from naive RAG: throw everything in a vector database and hope that semantic search is powerful enough. This can work for use cases where accuracy isn’t too important and hallucinations are tolerable, but it doesn’t work for more difficult queries that…
Turbolite – a SQLite VFS serving sub-250ms cold JOIN queries from S3Mar 2026 · github.com · ▲185I built a SQLite VFS in Rust that serves cold queries directly from S3 with sub-second performance, and often much faster. It’s called turbolite. It is experimental, buggy, and may corrupt data. I would not trust it with anything important yet. I wanted to explore whether object storage has gotten fast enough to support embedded databases over cloud storage. Filesystems reward tiny random reads and in-place mutation. S3 rewards fewer requests, bigger transfers, immutable objects, and aggressively parallel operations where bandwidth is often the real constraint. This was explicitly inspired…
GibRAM an in-memory ephemeral GraphRAG runtime for retrievalJan 2026 · github.com · ▲60Hi HN, I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses. After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, which…
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