https://github.com/infinilabs/diskann

DiskANN in Pure Rust — Built for Large-Scale Approximate Nearest Neighbor Search.
https://github.com/infinilabs/diskann

diskann pizza rust vector-search

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DiskANN in Pure Rust — Built for Large-Scale Approximate Nearest Neighbor Search.

• Host: GitHub
• URL: https://github.com/infinilabs/diskann
• Owner: infinilabs
• License: mit
• Created: 2025-06-25T05:37:52.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2025-07-28T06:20:39.000Z (2 months ago)
• Last Synced: 2025-07-28T08:25:01.682Z (2 months ago)
• Topics: diskann, pizza, rust, vector-search
• Language: Rust
• Homepage:
• Size: 9.02 MB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
[![DiskANN Paper](https://img.shields.io/badge/Paper-NeurIPS%3A_DiskANN-blue)](https://papers.nips.cc/paper/9527-rand-nsg-fast-accurate-billion-point-nearest-neighbor-search-on-a-single-node.pdf)

[![DiskANN Paper](https://img.shields.io/badge/Paper-Arxiv%3A_Fresh--DiskANN-blue)](https://arxiv.org/abs/2105.09613)

[![DiskANN Paper](https://img.shields.io/badge/Paper-Filtered--DiskANN-blue)](https://harsha-simhadri.org/pubs/Filtered-DiskANN23.pdf)

# DiskANN in Rust

DiskANN is a suite of scalable, accurate, cost-effective approximate nearest neighbor (ANN) search algorithms for large-scale vector search. 

It uses a graph-based index (Vamana) to manage very large datasets with high recall and low latency. 

For example, DiskANN can index, store, and query a billion-point dataset on a single machine with 64GB RAM and an SSD, achieving high recall and low query latency.  

The INFINI Labs DiskANN project provides a pure Rust implementation of these ideas. This crate was originally forked from Microsoft’s partial Rust port (the DiskANN/rust folder) and extended to be complete. 

In particular, it implements the previously-missing disk-based query functionality, enabling full end-to-end DiskANN indexing and search in Rust.

# Features

Key features:

- Pure Rust implementation – no C/C++ dependencies; leverages Rust’s memory safety and concurrency.

- Disk-based indexing and querying – supports very large datasets that do not fit in memory.

- High recall and low latency – optimized for fast approximate nearest neighbor search.

- Parallelism – leverages Rust’s concurrency features for efficient indexing and querying.

# Installation

Add the diskann crate to your project by including it in Cargo.toml. For example:

```

[dependencies]

diskann = "0.1"

```

# Usage

```rust

```

# Contributing

Contributions and bug reports are welcome! Please open issues or pull requests on the GitHub repository. We follow the standard Rust community conventions.

# Notice

The code is based on ideas from the [DiskANN](https://papers.nips.cc/paper/9527-rand-nsg-fast-accurate-billion-point-nearest-neighbor-search-on-a-single-node.pdf), [Fresh-DiskANN](https://arxiv.org/abs/2105.09613) and the [Filtered-DiskANN](https://harsha-simhadri.org/pubs/Filtered-DiskANN23.pdf) papers with further improvements.

This project forked off from [Microsoft/DiskANN](https://github.com/microsoft/DiskANN/tree/main/rust) and [code for NSG](https://github.com/ZJULearning/nsg) .

# License

This project is released under the MIT License. See the [LICENSE](LICENSE) file for details.