rag-reranking

# RAG Reranking Skill ## Capabilities - Implement cross-encoder reranking models - Configure Maximal Marginal Relevance (MMR) filtering - Set up Cohere Rerank integration - Design multi-stage retrieval pipelines - Implement diversity-aware reranking - Configure score normalization and thresholds ## Target Processes - advanced-rag-patterns - rag-pipeline-implementation ## Implementation Details ### Reranking Methods 1. **Cross-Encoder Reranking**: Sentence-transformer cross-encoders 2. **Cohere Rerank**: Cohere rerank-v3 API 3. **MMR Reranking**: Diversity-aware result filtering 4. **LLM Reranking**: Using LLM for relevance scoring 5. **Reciprocal Rank Fusion**: Combining multiple retrievers ### Configuration Options - Reranking model selection - Top-k after reranking - MMR lambda (relevance vs diversity) - Score threshold filtering - Batch size for reranking ### Best Practices - Use cross-encoders for quality - Balance relevance and diversity - Set appropriate thresholds - Monitor reranking latency ### Dependencies - sentence-transformers - cohere (optional)