Milvus DB: AI-Ready Vector Database Environment

Important: For step by step guide on how to setup this vm , please refer to our Getting Started guide

This virtual machine bundles Milvus, the industry-leading open-source vector database, in a fully integrated environment designed for building and testing AI Agents with semantic search, and Retrieval-Augmented Generation (RAG) capabilities.

Ideal for developers, data scientists, and AI researchers, this VM offers a secure, private workspace with all the tools needed to work with vector embeddings, local language models, and interactive data exploration.

Milvus is an open-source, high-performance vector database built to accelerate applications involving unstructured data such as text, images, audio, and video. It's designed with both speed and scalability in mind, making it a preferred choice for modern AI, search, and recommendation systems.

Key Features of Milvus:

High-performance vector similarity search (supports billion-scale data)

Multiple distance metrics (L2, Cosine, Inner Product)

Hybrid search support (combine vector and structured fields)

Scalable indexing options (IVF, HNSW, etc.)

gRPC and RESTful APIs

Common Use Cases:

Semantic search engines

Retrieval-Augmented Generation (RAG) pipelines

Recommendation systems

Visual similarity search (images, video, audio)

Anomaly detection using embeddings

Included Tools & Add-ons

In addition to Milvus, this VM includes a curated set of tools to make development and experimentation seamless:

JupyterHub (with Python Virtual Environment)

JupyterHub provides a multi-user, browser-based interface for running Jupyter notebooks. It enables interactive coding, data visualization, and experimentation in a shared Python environment.

Accessible through the browser

Pre-configured with:

pymilvus: Milvus Python SDK

milvus-lite: lightweight in-memory version for testing

ollama Python client

Provides a ready-to-run RAG demo notebook, including:

Document loading and embedding

Vector insertion and search in Milvus

Local LLM-based question answering

Milvus CLI

Lightweight command-line tool for managing collections, indexes, and inspecting schemas

Can be used as an alternative to WebUI for users who prefer terminal access

Milvus Web UI

GUI for managing collections, viewing schema, and monitoring the database

Restricted to RDP for security, as WebUI currently but can be made accessible through brows-er with ready to run script

Ollama LLM Runtime

Ollama is a lightweight, local runtime for deploying and running large language models (LLMs) on your machine. It allows you to generate text, create embeddings, and build AI workflows without relying on external APIs.

Supports embedding and generation models for local inference

Integrates with the RAG pipeline in the demo notebook

What's Included

Milvus (Docker): Vector DB running in standalone mode

JupyterHub: Python IDE preloaded with SDKs & demo

Milvus CLI: Optional command-line tool for DB operations

Ollama (host): Local LLM runtime for embedding + generation

Demo Notebook: End-to-end RAG example

Ideal For

AI/ML engineers building GenAI apps or semantic search systems

Researchers evaluating vector DBs and RAG architectures

Teams building domain-specific search or retrieval tools

Educational demos or internal POCs

Secure & Private

All tools run locally inside the VM

Milvus Web UI is restricted to RDP for controlled access with the option to make it accessible in browser

Suitable for air-gapped or sensitive environments

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