Production Reranker Layer for RAG in Python: Cross-Encoder, Cohere Fallback, and Reciprocal Rank Fusion (Runnable Code)

I shipped my fifth RAG pipeline to production in February. Top-10 recall@10 was 0.94. The team ran a demo, executive nodded, we declared victory. Two weeks later customer complaints started landing. The model was citing stale 2023 policy docs, ignoring the 2026 rewrite that ranked 4th. Somewhere between rank 4 and rank 1, the answer everyone needed was getting buried. That is the thing nobody warns you about with RAG. Your retriever can be statistically excellent at top-10 and still hand the LLM