Compare responses from different LLMs to the same query. Analyze latency, token usage, confidence scores, and hallucination risk across Llama 3.2, Mistral 7B, and Qwen 2.5.
Explain how hybrid search combines dense and sparse retrieval in RAG systems
Complex technical explanation about RAG architecture
Hybrid search in RAG systems combines two complementary retrieval methods: dense retrieval uses neural embeddings to capture semantic meaning, while sparse retrieval (like BM25) matches exact keywords. The system typically runs both in parallel, then fuses results using Reciprocal Rank Fusion (RRF) or learned weights. This approach captures both semantic similarity and lexical precision, significantly improving recall on queries where either method alone would struggle.
Hybrid search merges vector similarity (dense) with keyword matching (sparse/BM25). Results are combined via score fusion. This catches both semantic matches and exact term hits that pure embedding search might miss.
Hybrid search architectures in RAG leverage the strengths of both paradigms. Dense retrieval converts queries and documents into high-dimensional vectors using models like E5 or BGE, enabling semantic matching. Sparse retrieval uses inverted indices with TF-IDF or BM25 scoring for precise lexical matching. The fusion layer combines ranked results—commonly using RRF where scores are summed as 1/(k+rank). Advanced implementations may use learned fusion weights or cross-encoder reranking on the merged candidate set for optimal precision.
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