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Babysitter rag-reranking

Cross-encoder reranking and MMR diversity filtering for improved retrieval quality

install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/ai-agents-conversational/skills/rag-reranking" ~/.claude/skills/a5c-ai-babysitter-rag-reranking && rm -rf "$T"
manifest: library/specializations/ai-agents-conversational/skills/rag-reranking/SKILL.md
tags
#reranking#cross-encoder#mmr-filtering#retrieval#rag-enhancement
source content

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)