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.

🏃 Support naive multi-tenancy.

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

# query with embedding 
vdb.query(np.random.rand(fake_dim))

# arguments:
vdb.query(np.random.rand(fake_dim), top_k=5, better_than_threshold=0.01)

Conditional filter

vdb.query(np.random.rand(fake_dim), filter_lambda=lambda x: x["any_field"] == "any_value")

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"])

Additional Data

vdb.store_additional_data(a=1, b=2, c=3)
print(vdb.get_additional_data())

Multi-Tenancy

If you have multiple vectorDB to use, you can use MultiTenantNanoVDB to manage:

from nano_vectordb import NanoVectorDB, MultiTenantNanoVDB

multi_tenant = MultiTenantNanoVDB(1024)
tenant_id = multi_tenant.create_tenant()

# tenant is a NanoVectorDB, you can upsert, query, get... on this.
tenant: NanoVectorDB = multi_tenant.get_tenant(tenant_id)

# some chores:
multi_tenant.delete_tenant(tenant_id)
multi_tenant.contain_tenant(tenant_id)

# save it
multi_tenant.save()

MultiTenantNanoVDB use a queue to manage the total vector dbs in memory, you can adjust the parameter:

# There will be only `max_capacity` NanoVectorDB in the memory.
multi_tenant = MultiTenantNanoVDB(1024, max_capacity=1)

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