Vector Search Quality Metrics

Retrieval Augmented Generation Assessment framework for reference-free evaluation of RAG pipelines. RAGAS provides automated metrics for retrieval quality, context relevance, and generation faithfulness.

Performance measurement framework for vector search and RAG systems including recall, precision, nDCG, MRR, and context relevance metrics to evaluate retrieval quality and relevance.

Comprehensive LLM evaluation framework offering 50+ ready-to-use metrics for RAG, agents, and chatbots, featuring G-Eval for custom criteria and multi-turn conversation evaluation with human-like accuracy.

Industry-standard metrics for evaluating Retrieval-Augmented Generation systems, including Answer Relevancy, Faithfulness, Context Relevance, Context Recall, and Context Precision to ensure quality and reliability.

RAG evaluation metric assessing retriever's ability to rank relevant chunks higher than irrelevant ones, measuring context relevance and ranking quality for optimal retrieval.

Massive Text Embedding Benchmark leaderboard covering 58 datasets across 112 languages and 8 embedding tasks. Industry-standard benchmark for comparing text embedding models.