Production Reranker Layer for RAG in Python: Cross-Encoder, Cohere Fallback, and Reciprocal Rank Fusion (Runnable Code)

1. 1Implement a Cross-Encoder reranker using the Hugging Face Transformers library
2. 2Configure a Cohere fallback model for handling out-of-vocabulary queries
3. 3Apply Reciprocal Rank Fusion to combine the scores of multiple rerankers
4. 4Test the Reranker layer using a sample dataset to evaluate its performance
5. 5Deploy the Reranker layer to a production environment using a Python framework such as Flask or Django

This benefits data scientists and machine learning engineers working on RAG pipelines, as it improves the accuracy of their models

💡 Using a combination of Cross-Encoder, Cohere fallback, and Reciprocal Rank Fusion can significantly improve the accuracy of a RAG pipeline

🚀 Improve your RAG pipeline's recall@10 rate with a production-ready Reranker layer in Python! 🤖