Chroma: Vector DB for AI 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.

It allows developers to:

• Store, index, and query text or multimodal embeddings
• Build retrieval-augmented generation (RAG) systems
• Run semantic similarity search across documents or datasets
• Integrate seamlessly with frameworks like LangChain, LlamaIndex, and OpenAI APIs
• Persist data locally or in client-server mode, with lightweight dependencies

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

JupyterHub Integration

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.

Generative Benchmarking Sample App

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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