Hybrid Search for RAG: BM25, SPLADE, and Vector Search Combined

Hybrid search combines sparse and dense retrieval methods to improve search results by running both a sparse retriever, such as BM25 or SPLADE, and a dense vector retriever simultaneously. The results from these methods are merged using a fusion algorithm, such as Reciprocal Rank Fusion (RRF) or convex combination, before being passed to a language model for further processing. This approach addresses the limitations of each method—dense retrieval often misses exact keyword matches, while sparse retrieval lacks semantic understanding. Hybrid search is particularly beneficial for domains with specialized terminology or where vocabulary mismatches occur between queries and documents. SPLADE, a learned sparse model, offers advantages by expanding query and document representations with related terms. However, hybrid search requires careful tuning of fusion parameters to ensure performance gains, and it may not always be beneficial, especially in datasets with strong lexical overlap. Adding a cross-encoder reranker after the fusion stage can enhance precision by accurately ranking the most relevant documents. Various vector databases, including Qdrant, Weaviate, Elasticsearch, and Pinecone, support hybrid search with differing features and configurations.

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