rag-reranking
Cross-encoder reranking and MMR diversity filtering for improved retrieval quality
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技能内容
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
- Cross-Encoder Reranking: Sentence-transformer cross-encoders
- Cohere Rerank: Cohere rerank-v3 API
- MMR Reranking: Diversity-aware result filtering
- LLM Reranking: Using LLM for relevance scoring
- 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)
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原文件路径
library/specializations/ai-agents-conversational/skills/rag-reranking/SKILL.md