ragas-evaluation

The model: assemble a dataset (question + answer + retrieval contexts + ground truth), import the metrics relevant to the evaluation goal, run evaluate(), and inspect per-metric per-row scores (per rg-gh).

When to use

• The repo uses LangChain / LlamaIndex / Haystack / direct retriever→LLM RAG pipelines.
• You need RAG-specific retrieval + generation quality scoring, or agents-style eval (tool-call and goal accuracy), and want the widest metric variety in OSS LLM-eval.