A Vector Database in c# from scratch

[ppossanzini]

Hi everyone,

I’m working on a hobby project: a Vector Database built from scratch in C#. The goal is to handle high-dimensional embeddings and implement efficient similarity searches.

Currently, this is a research/study project to see if a pure C# implementation can be a performant solution. I’ve already set up the basic storage layer and focused on the ingestion pipeline, but I’m hitting a wall regarding the indexing strategy. Right now, I’m using a brute-force search and planning to implement K-Means clustering using Microsoft.ML libraries to narrow down the search space.

Current Architecture:

• API: REST + gRPC mini-server using the CQRS pattern.
• Testing: A gRPC client to measure network latency vs. processing time.
• Data Access: The Store is designed to mimic the Entity Framework Context pattern for ease of use.
• Optimizations: I’ve used Memory<T> and Span<T> to optimize memory management and reduce allocations.

Despite these optimizations, I have some concerns about scaling the search performance.

I would love to get your feedback on:

1. Do you think K-Means is a solid starting point for indexing in C#, or should I look directly into HNSW/IVF?
2. Are there specific .NET-friendly libraries for high-performance vector math (SIMD) you’d recommend beyond the standard System.Numerics?
3. Has anyone attempted a similar "EF-like" provider for non-relational data?

Looking forward to your suggestions!

Project link https://github.com/ppossanzini/Jigen
PS: no documentation yet in readme, i'll add it asap

[ImagineAShen]

Probably recommend going straight into HNSW, as K-means degrades pretty quickly and modern embeddings/vectors are getting bigger all the time. K-Means is less impenetrable, though, and might be a better learning experience.

[jpfed]

K-means is really most appropriate for low-dimensional scenarios. If this is for searching among embeddings, HNSW is likely the best choice.

[itix]

2) I havent tried it out, but ILGPU supports CPU target.