How can using multiple embedding models improve RAG retrieval (for instance, combining dense and sparse embeddings), and what complexity does this add to the system?

Using multiple embedding models in RAG (Retrieval-Augmented Generation) systems can improve retrieval accuracy by leveraging the complementary strengths of different embedding types. For example, dense embeddings (like those from models such as BERT) excel at capturing semantic relationships between words, allowing them to retrieve documents that share meaning but use different terminology. Sparse embeddings (like BM25 or TF-IDF), on the other hand, prioritize exact keyword matches, making them effective for queries where specific terms are critical. Combining both ensures the system can handle both broad semantic intent and precise keyword relevance. For instance, a query like “How do solar panels generate electricity?” might retrieve documents mentioning “photovoltaic cells” (via dense embeddings) and those explicitly containing “solar panels” (via sparse embeddings), leading to more comprehensive results.

However, this hybrid approach adds complexity. First, the system must manage two separate retrieval pipelines: one for dense embeddings (using vector databases like FAISS) and another for sparse embeddings (using inverted indexes like Elasticsearch). This requires additional infrastructure and computational resources. Second, merging results from both pipelines introduces challenges in ranking. For example, a document ranked highly by both methods should likely be prioritized, but weighting their scores requires tuning. Techniques like reciprocal rank fusion (RRF) or weighted sum scoring can help, but they add overhead in testing and optimization. Third, latency increases because two retrieval steps run in parallel or sequence. Developers must balance speed and accuracy, potentially caching frequent queries or precomputing embeddings to mitigate delays.

A practical example of this hybrid approach is in legal document retrieval. A query like “rights during police stops” might use dense embeddings to find documents discussing “Fourth Amendment protections” (semantically related) and sparse embeddings to ensure results include the exact phrase “police stops.” The complexity here lies in maintaining consistency between the two pipelines—for instance, ensuring both embedding types are updated when new documents are added. Debugging also becomes harder; if a relevant document is missed, developers must check both retrieval systems to identify the failure point. Despite these challenges, the improved recall and precision often justify the added effort, especially in domains where queries demand both broad understanding and specificity.