Reranking
Reranking is a crucial step in Retrieval-Augmented Generation (RAG) systems that aims to improve the relevance and quality of retrieved documents. It involves reassessing and reordering initially retrieved documents to ensure that the most pertinent information is prioritized for subsequent processing or presentation.
The primary motivation for reranking in RAG systems is to overcome limitations of initial retrieval methods, which often rely on simpler similarity metrics. Reranking allows for more sophisticated relevance assessment, taking into account nuanced relationships between queries and documents that might be missed by traditional retrieval techniques. This process aims to enhance the overall performance of RAG systems by ensuring that the most relevant information is used in the generation phase.
What you'll learn
- 1Initial Retriever: Often a vector store using embedding-based similarity search.
- 2Reranking Model: This can be either:
- 3A Large Language Model (LLM) for scoring relevance
- 4A Cross-Encoder model specifically trained for relevance assessment
- 5Scoring Mechanism: A method to assign relevance scores to documents
- 6Sorting and Selection Logic: To reorder documents based on new scores
About this tutorial
This hands-on Jupyter notebook is part of RAG Techniques, a free open-source repository by Nir Diamant covering rag techniques with runnable code examples and detailed explanations.
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