ray-project/llm-applications resource

Health v2 · maintenance only

Adoption not part of the score

1855 stars · 257 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A comprehensive tutorial repository (Jupyter notebooks plus guide) for building production-grade RAG-based LLM applications using Ray. It covers developing, scaling, evaluating, and serving RAG pipelines end to end.

Use cases

• learn how to build a rag application from scratch
• scale embedding and indexing pipelines for llm apps
• evaluate rag configurations for retrieval quality
• serve a rag llm application in production
• fine-tune and route between open and closed llms

When to choose

• you want a hands-on, end-to-end RAG tutorial with runnable notebooks
• you plan to use Ray or Anyscale to scale LLM workloads
• you need guidance on evaluating and optimizing RAG pipelines

When to avoid

• you need a ready-made production RAG framework rather than a guide
• you don't want dependencies on OpenAI or Anyscale services
• you need a maintained software library with a stable API

Facets

learning-resource · maturity active

rag llm-inference machine-learning benchmarking documentation large-language-models machine-learning tutorials python cloud jupyter-notebook ray llamaindex production-guide fine-tuning vector-search retrieval-augmented-generation gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ray-project/llm-applications")