Milvus DB: AI-Ready Vector Database Environment

This virtual machine bundles Milvus, the industry-leading open-source vector database, in a fully inte-grated 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
• Support for both CPU and GPU acceleration

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 Milvus 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 browser 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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