Making RAG Accurate in Python
📰 Dev.to · Puneet Gupta
Improve RAG accuracy in Python using hybrid dense+BM25 search and evaluation metrics like recall@k and precision@k
Action Steps
- Implement hybrid dense+BM25 search using Python libraries like Faiss or Hnswlib
- Apply metadata filtering to narrow down search results
- Configure chunking strategy to optimize search performance
- Measure recall@k, precision@k, MRR, and nDCG to evaluate RAG accuracy
- Use Reciprocal Rank Fusion to combine multiple ranking models
Who Needs to Know This
NLP engineers and data scientists can benefit from this article to improve the accuracy of their RAG models, leading to better information retrieval systems
Key Insight
💡 Hybrid dense+BM25 search with metadata filtering and chunking strategy can significantly improve RAG accuracy
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Boost RAG accuracy with hybrid search & evaluation metrics! 🚀
Key Takeaways
Improve RAG accuracy in Python using hybrid dense+BM25 search and evaluation metrics like recall@k and precision@k
Full Article
Improving retrieval quality beyond cosine top-k: hybrid dense+BM25 search with Reciprocal Rank Fusion, metadata filtering, chunking strategy, and measuring recall@k, precision@k, MRR, and nDCG.
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