nyas : A simple Vector Database

Nyas is a lightweight and efficient vector database engine designed for similarity search of embeddings. It supports multiple distance metrics (L2, Cosine, Dot), hybrid CPU/GPU execution, and flexible storage backends.

---

References

• “DiskANN: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node” (NeurIPS 2019) https://proceedings.neurips.cc/paper_files/paper/2019/file/09853c7fb1d3f8ee67a61b6bf4a7f8e6-Paper.pdf

• “FreshDiskANN: A Fast and Accurate Graph-Based ANN Index for Streaming Similarity Search” (arXiv 2021) https://arxiv.org/abs/2105.09613

These papers influenced Nyas’s design, particularly in indexing structures, graph search, and efficient query traversal.

---

Quick Start

Prerequisites

• Rust (latest stable)
• Protobuf Compiler (for gRPC)
• Docker (optional, for containerized run)

Running the gRPC Service (vecd)

Using Cargo

# Start the VectorDB service
cargo run -p vecd

Using Docker

# Build the image
docker build -t vecd .

# Run the container
docker run -p 50051:50051 vecd

Running Examples

SIFT 10k gRPC Example

This example demonstrates connecting to the running vecd service, inserting the SIFT 10k dataset, and calculating recall.

1. Ensure vecd is running (see above).
2. Download the SIFT 10k dataset into examples/data/siftsmall:(ftp://ftp.irisa.fr/local/texmex/corpus/siftsmall.tar.gz) (Note: The example expects siftsmall_base.fvecs, siftsmall_query.fvecs, and siftsmall_groundtruth.ivecs)
3. Run the example:

   cargo run --bin sift10k_index_grpc --release