Chroma: Vector DB for AI Development

This virtual machine offers a pre-configured environment combining ChromaDB, an open-source embedding database designed for AI and LLM applications, with JupyterHub for collaborative notebook-based development.

It provides an easy way to explore retrieval-augmented generation (RAG), vector search, and semantic indexing workflows.

Whether you're experimenting with embeddings, evaluating model retrieval quality, or building intelligent applications that combine search and generation, this setup gives you everything you need out of the box.

Chroma is a modern open-source vector database built for machine learning and LLM-based workflows.

ChromaDB’s in-memory and persistent modes make it ideal for research, prototyping, or embedding evaluation without heavy infrastructure.

This environment comes with JupyterHub, a collaborative, web-based notebook server ideal for research, development, and teaching.

Users can create and manage notebooks directly in the browser, write Python code, visualize data, and run experiments—all in an isolated environment tied to the virtual machine.

To demonstrate real-world use cases, this VM includes a Generative AI Benchmarking App.

The app showcases how ChromaDB can power retrieval-enhanced generation and embedding similarity workflows. It benchmarks retrieval precision, response quality, and semantic matching between query and corpus embeddings.

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