Query Transformations
This code implements three query transformation techniques to enhance the retrieval process in Retrieval-Augmented Generation (RAG) systems: 1. Query Rewriting 2. Step-back Prompting 3. Sub-query Decomposition Each technique aims to improve the relevance and comprehensiveness of retrieved information by modifying or expanding the original query.
RAG systems often face challenges in retrieving the most relevant information, especially when dealing with complex or ambiguous queries. These query transformation techniques address this issue by reformulating queries to better match relevant documents or to retrieve more comprehensive information.
What you'll learn
- 1Query Rewriting: Reformulates queries to be more specific and detailed.
- 2Step-back Prompting: Generates broader queries for better context retrieval.
- 3Sub-query Decomposition: Breaks down complex queries into simpler sub-queries.
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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