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

Short summary

RAG systems often achieve high recall@10 but place the correct answer at rank 4-7, making it invisible to the LLM. A reranker—a second model that re-scores top-K candidates—improves precision@3 by 40-60% with <200ms latency. This tutorial implements a production reranker: local BGE cross-encoder for cost/speed, Cohere API fallback, reciprocal rank fusion, graceful degradation, and evaluation harness. Complete runnable code.

• •RAG precision improves 40-60% with reranking; first-stage retriever should return 50-100 candidates, not 10
• •Production implementation requires local model (BGE) + API fallback (Cohere) + cost/latency budgets + graceful degradation
• •Author provides complete, runnable Python code including evaluation harness for real deployment

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