Vector space allows ML Developers , Researchers to Run Ephermal Vector dbs . Helps in Also Exports of Indexes to cloud sources like s3 buckets. Locally it can spin up docker containers or ephermal pods on k8s

• Testing Data with different vector dbs and perform benchmarks
• Export Indexes as HNSW/IVF/PQ
• Faster Vector Db Validation and Usecase Implementation

This cli now supports k8s clusters locally and vectordbs like chromdb,qdrant

Before starting, make sure you have the following installed locally:

Minikube – Kubernetes local cluster

• Install from official docs: https://minikube.sigs.k8s.io/docs/start/

Kubectl – Kubernetes CLI tool

• Installation guide: https://kubernetes.io/docs/tasks/tools/

Docker – Required to build images

• https://docs.docker.com/get-docker/

1.Clone The repo

git clone https://github.com/NarmalaSk/Vectorspace.git

2. Install requirements.txt

pip install -r requirements.txt

3.Start the K8s crd

python cli.py start

4.Create a db

python cli.py create --name mydb --db qdrant --ttl 1h