Introduction

Building an efficient Retrieval-Augmented Generation (RAG) application involves fine-tuning a hybrid search system that combines keyword-based methods (BM25) and vector (embedding) search techniques. The balance is controlled by the Alpha parameter, which requires careful adjustment to optimize retrieval performance for different user query types, such as web searches, concept-seeking, and fact-seeking queries. The blog post explores tuning Alpha within the Weaviate vector database using LlamaIndex's Retrieval Evaluation module, measuring success with Hit Rate and Mean Reciprocal Rank (MRR) metrics. Experiments were conducted with varying Alpha values and query types, both with and without rerankers, revealing that hybrid search typically outperforms individual search methods but requires careful evaluation based on the specific query context. The results suggest that rerankers boost retrieval metrics like Hit Rate and MRR, and highlight the need to adjust Alpha as more documents are indexed to maintain optimal performance across various query scenarios.

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