nano-vectordb 0.0.4.1

A simple, easy-to-hack Vector Database implementation

nano-VectorDB

A simple, easy-to-hack Vector Database

🌬️ A vector database implementation with single-dependency (numpy).

🎁 It can handle a query from 100,000 vectors and return in 100 milliseconds.

🏃 It's okay for your prototypes, maybe even more.

Install

Install from PyPi

pip install nano-vectordb

Install from source

# clone this repo first
cd nano-vectordb
pip install -e .

Quick Start

Faking your data:

from nano_vectordb import NanoVectorDB
import numpy as np

data_len = 100_000
fake_dim = 1024
fake_embeds = np.random.rand(data_len, fake_dim)    

fakes_data = [{"__vector__": fake_embeds[i], **ANYFIELDS} for i in range(data_len)]

You can add any fields to a data. But there are two keywords:

• __id__: If passed, NanoVectorDB will use your id, otherwise a generated id will be used.
• __vector__: must pass, your embedding np.ndarray.

Init a DB:

vdb = NanoVectorDB(fake_dim, storage_file="fool.json")

Next time you init vdb from fool.json, NanoVectorDB will load the index automatically.

Upsert:

r = vdb.upsert(fakes_data)
print(r["update"], r["insert"])

Query:

print(vdb.query(np.random.rand(fake_dim)))

Save:

# will create/overwrite 'fool.json'
vdb.save()

Get, Delete:

# get and delete the inserted data
print(vdb.get(r["insert"]))
vdb.delete(r["insert"])

Benchmark

Embedding Dim: 1024. Device: MacBook M3 Pro

• Save a index with 100,000 vectors will generate a roughly 520M json file.
• Insert 100,000 vectors will cost roughly 2s
• Query from 100,000 vectors will cost roughly 0.1s