Coverage of Information A dense vector retriever (e.g., using embeddings from models like BERT) excels at capturing semantic relationships, retrieving documents that share meaning but lack exact keyword overlap. For example, a query like "pet care tips" might match a document discussing "how to groom dogs" even if the word "pet" is absent. However, it may miss precise keyword matches, especially for domain-specific terms (e.g., "TCP retransmission timeout" in networking). A hybrid retriever combines dense vectors with a lexical method (e.g., BM25), which relies on term frequency. This ensures coverage of both semantic matches and exact keyword matches, improving recall. For instance, a legal search system using a hybrid approach would find documents mentioning "breach of contract" (lexical) and those using synonyms like "violation of agreement" (dense).
System Complexity A dense-only system is simpler to implement and maintain. It requires a single embedding model and a vector database (e.g., FAISS), reducing infrastructure overhead. In contrast, a hybrid system adds complexity by integrating two distinct retrieval pipelines. For example, combining BM25 (lexical) and dense vector results requires merging strategies like score fusion or reranking. This introduces challenges like tuning weights for each method, maintaining separate indexes (inverted for BM25, vector for embeddings), and handling latency from dual queries. Tools like Elasticsearch simplify hybrid setups, but developers still need to manage synchronization between indexes and ensure scalability.
Trade-offs and Practical Considerations The hybrid approach improves coverage but demands more engineering effort. For domains requiring high precision on exact terms (e.g., technical documentation), the lexical component is critical. However, in applications where semantic understanding dominates (e.g., conversational AI), a dense retriever might suffice. A hybrid system also introduces computational costs: querying two indexes and merging results can increase latency. Developers must evaluate whether the improved recall justifies the added complexity. For example, a customer support chatbot might use hybrid retrieval to handle both slang ("can't log in") and formal terms ("authentication failure"), but a small-scale app with limited data might opt for dense-only simplicity.