I think the “simple, modular, and extensible” makes this interesting. And for those, it being written in Python are relevant.

What’s the advantage if this being in python?

dude you already missed the window.nothing is better than sqlite as a library and don&#x27;t use high perforamnce as your value for a python product

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 vectorsSupports multiple indices (Flat, HNSW, IVF, LSH, Annoy)Has memory, disk, and hybrid storage backendsIncludes a full document processing pipeline (parsing, cleaning, chunking, embedding)Offers quantization, persistence, and plugin-based extensibilityAll written in Python, integrated with NumPy, and production-tested with logging and monitoring built in.Install:pip install valoriGitHub: https:&#x2F;&#x2F;github.com&#x2F;varshith-Git&#x2F;valoriPyPI: https:&#x2F;&#x2F;pypi.org&#x2F;project&#x2F;valoriI’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
)

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