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title: DREAM: Distributed RAG Experimentation Framework | daily.dev
description: DREAM is a blueprint for distributed RAG experimentation using Ray, LlamaIndex, Ragas, MLFlow, and MinIO on Kubernetes. It helps in comparing and contrasting...
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# DREAM: Distributed RAG Experimentation Framework

**[MLOps Community](https://daily.dev/sources/mlopscommunity)** · 7 min read · 1 upvotes · 1 comments

## Summary

DREAM is a blueprint for distributed RAG experimentation using Ray, LlamaIndex, Ragas, MLFlow, and MinIO on Kubernetes. It helps in comparing and contrasting different combinations of RAG parameters to find the best one. The architecture includes technologies like Ray, LlamaIndex, ragas, MinIO, MLflow, Jupyter notebooks, Kubernetes, and ArgoCD.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://mlops.community/dream-distributed-rag-experimentation-framework/>

## Community discussion

Top comments from developers on daily.dev.

**@ahmetozel** · 0 upvotes

> The thing that usually breaks these sweeps is combinatorics: chunk size x overlap x top-k x reranker is already hundreds of runs, and in my experience most of the variance comes from the chunking stage alone. Pinning the retriever and sweeping chunk boundaries first (semantic vs fixed, overlap ratio) narrows the grid a lot before you start burning GPU on generation configs. Also worth logging retrieval-only metrics separately from end-to-end answer quality - otherwise a good retriever hidden behind a weak prompt looks like a bad retriever.

---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#llama](https://daily.dev/tags/llama)

[View this post on daily.dev](https://daily.dev/posts/dream-distributed-rag-experimentation-framework-drrdetetv)

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