Article ID: 2025DAP0003
This paper presents a novel approach to enhance Retrieval- Augmented Generation (RAG) systems through the integration of three advanced techniques: Parent-Child Architecture, Hybrid Retrieval, and Contextual Compression with cross-encoder re-ranking. We implement this framework using Langchain and FAISS vector search, with Anthropic's Claude as the foundation model. Our comprehensive evaluation across 14 diverse Wikipedia-based knowledge domains employs the RAGAS framework to measure multiple performance dimensions. Results demonstrate that our advanced framework yields significant improvements in key metrics: Context Precision 10.11% , Context Recall 2.25%,and BLEU scores 1.14% compared to Naive RAG implementations. Domain analysis reveals particularly strong performance in Medicine 8.0% BLEU, Science 4.9%, and specialized Technology domains 4.9%. While, some technical domains such as Cybersecurity (-2.4%) and Biology (-6.6%) show performance degradation. Our framework achieves these improvements with minimal computational overhead by 1.89% ,offering a practical approach to implementing domain-adaptive RAG systems that optimize context quality for improved generation performance.
