# RAG System Builder Advanced Reranking

> Sub-skill of rag-system-builder: Advanced: Reranking.

- Skill: `vamseeachanta/rag-system-builder-advanced-reranking` (Agent Skill)
- Install (CLI): `npx skillmds@latest add vamseeachanta/rag-system-builder-advanced-reranking`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vamseeachanta/rag-system-builder-advanced-reranking/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: vamseeachanta (https://skillmd.com/u/vamseeachanta)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/vamseeachanta/rag-system-builder-advanced-reranking

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# Advanced: Reranking

## Advanced: Reranking


Add a reranking step for improved precision:

```python
from sentence_transformers import CrossEncoder

class Reranker:
    def __init__(self, model_name='cross-encoder/ms-marco-MiniLM-L-6-v2'):
        self.model = CrossEncoder(model_name)

    def rerank(self, query, candidates, top_k=5):
        """Rerank candidates using cross-encoder."""
        pairs = [(query, c['text']) for c in candidates]
        scores = self.model.predict(pairs)

        for i, score in enumerate(scores):
            candidates[i]['rerank_score'] = float(score)

        reranked = sorted(candidates, key=lambda x: x['rerank_score'], reverse=True)
        return reranked[:top_k]

# Usage in RAG pipeline
def query_with_rerank(self, question, initial_k=20, final_k=5):
    # First pass: retrieve more candidates
    candidates = semantic_search(self.db_path, question, self.model, top_k=initial_k)

    # Second pass: rerank for precision
    reranked = self.reranker.rerank(question, candidates, top_k=final_k)

    return reranked
```