Cross-Encoder Reranker — Precision Retrieval for RAG & Search

I will implement a cross-encoder reranker for your retrieval pipeline — adding a reranking pass that scores each retrieved document against the query using joint encoding (attending to both query and document simultaneously), producing more precise ranking than the bi-encoder similarity that initial retrieval uses. Cross-encoder reranking consistently improves precision over bi-encoder retrieval because bi-encoders must compress query and document into independent embeddings, losing fine-grained interaction signals; cross-encoders process query and document jointly, attending to every word of both simultaneously and producing significantly more accurate relevance scores at the cost of higher computational expense.

The implementation covers cross-encoder model selection (BGE-reranker, Cohere Rerank, or FlashRank), integration after your initial retrieval step, top-k retrieval and reranking pipeline, latency measurement, and precision comparison against retrieval without reranking.