How do you implement reranking in multimodal RAG systems?

Implementing reranking in multimodal RAG (Retrieval-Augmented Generation) systems involves refining initial search results by combining relevance signals from multiple data types (text, images, etc.) to improve final output quality. Reranking addresses limitations in first-stage retrieval, which might prioritize speed over accuracy or struggle to balance multimodal context. For example, a system searching for “red sports cars in cityscapes” might retrieve images tagged with “car” and text about “urban environments,” but fail to surface results where both modalities align. Reranking evaluates these candidates more deeply, using cross-modal relationships to prioritize the most cohesive matches.

A common approach uses a dedicated reranking model that scores each retrieved item based on its alignment with the query and other modalities. For text-heavy systems, this might involve a transformer-based model that computes semantic similarity between the query and retrieved text, while also analyzing associated images via a vision encoder. For instance, CLIP (Contrastive Language-Image Pretraining) can generate joint embeddings for text and images, allowing direct comparison. Developers might compute a combined score using weighted averages of text-text, image-text, and image-image similarity metrics. If the initial retrieval returns 100 candidates, the reranker processes this subset, reorders them, and passes the top 10 to the generator. This balances efficiency (avoiding costly full-dataset processing) with improved relevance.

Implementation typically requires three steps:

1. Retrieve candidates using fast, approximate methods (e.g., vector search with FAISS or Elasticsearch).
2. Extract features from all modalities (e.g., ResNet for images, BERT for text) and compute pairwise similarity scores.
3. Fuse scores using rules (e.g., weighted sum) or train a small neural network to predict optimal rankings. For example, a travel app might rerank hotel descriptions and photos by ensuring image captions match amenities mentioned in reviews. Tools like Sentence Transformers or PyTorch Lightning simplify building custom rerankers. Key considerations include computational overhead (prioritize lightweight models for reranking) and ensuring alignment between the reranker’s training data and the application domain. Testing with A/B frameworks helps validate whether the added latency of reranking justifies the accuracy gains.