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Valori – A Python-native Vector Database I built from scratch

9 points by varshith17 ↗ HN
I’ve been working on a project called Valori, a Python-native vector database I built from the ground up — not by reinventing every algorithm, but by wiring together efficient, well-known indexing and search techniques into a cohesive, hackable framework.

The idea came from my frustration with existing vector DBs that were either too heavy for experimentation or too opaque to modify. I wanted something simple, modular, and extensible — so I built it.

What it does:

Lets you store, index, and search high-dimensional vectors

Supports multiple indices (Flat, HNSW, IVF, LSH, Annoy)

Has memory, disk, and hybrid storage backends

Includes a full document processing pipeline (parsing, cleaning, chunking, embedding)

Offers quantization, persistence, and plugin-based extensibility

All written in Python, integrated with NumPy, and production-tested with logging and monitoring built in.

Install:

pip install valori

GitHub: https://github.com/varshith-Git/valori

PyPI: https://pypi.org/project/valori

I’d love to hear your thoughts —

What’s missing for you in current vector DBs?

If you’ve built LLM or RAG systems, what do you wish a lightweight, pure Python DB like this handled better?

Would you prefer tighter integrations (LangChain, Haystack, etc.) or a more “build-it-yourself” style?

Feedback, criticism, or collaboration ideas are all welcome. — Varshith (varshith.gudur17@gmail.com )

8 comments

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bendtb
What’s the advantage if this being in python?
varshith17
The point isn’t raw speed it’s hackability. You can plug in new models or indexing layers in minutes without dropping to C++.
redskyluan
dude you already missed the window.

nothing is better than sqlite as a library and don't use high perforamnce as your value for a python product

varshith17
SQLite’s perfect if you’ve got rows and tables. Valori’s for when you’ve got embeddings and chaos.
mattfrommars
how much was this vibe coded? looks cool but its too much for me to digest.

where did you get the original mental model to begin building it?

varshith17
It’s definitely dense, but not as wild as it looks. The mental model was: take the core building blocks from FAISS and Milvus, make them composable in Python, and expose everything clearly.

The “vibe” part came from trying to make it feel like a system that could run in production, not just a toy. So yeah, it’s a little heavy, but it earned the vibe honestly.

luke-stanley
I am very picky, hard to place, but from a quick look at the README, I'd say the API interface on display seemed like the right level of abstraction for having to deal with the messy reality.

Since you're asking for feedback:

- perhaps some of the document type specific dependencies by optional?

- could there be LESS config surface?

- I noticed GitHub CI action has a cross.

It's good to add how to use with Astral "uv" these days, especially anything that might pull in PyTorch dependency hell, which they have mostly solved if used correctly!

Nice work!