EncouRAGe: Evaluating RAG Local, Fast, and Reliable

Overview

EncouRAGe is a Python framework designed to evaluate Retrieval-Augmented Generation (RAG) systems using Large Language Models and Embedding Models. It focuses on scientific reproducibility and local deployment, offering a comprehensive evaluation across multiple datasets.

Key Insights

• RAG Underperformance: RAG systems underperform compared to Oracle Context.
  • Evidence: Evaluation across 25k QA pairs and 51k documents.
  • Verifiable: Yes
• Hybrid BM25 Performance: Hybrid BM25 consistently achieves the best results across all datasets.
  • Evidence: Consistent results across four datasets.
  • Verifiable: Yes
• Reranking Effects: Reranking offers marginal improvements but increases response latency.
  • Evidence: Observed during evaluations.
  • Verifiable: Yes

BFSI Relevance

• Why Relevant: Enhancing AI-driven customer service tools can improve efficiency and customer satisfaction.
• Primary Sector: Financial Services
• Subsectors: Customer Service, AI-driven Solutions
• Actionable Implications:
  • Evaluate current AI tools against Hybrid BM25.
  • Consider local deployment for data-sensitive applications.
  • Monitor latency impacts when implementing reranking.