ALucek/rag-reranking
An overview of popular reranking models and architectures for 2 stage RAG pipelines
When building AI assistants that answer questions from your documents, this project helps ensure the answers are as accurate and relevant as possible. It takes the initial list of document snippets your system finds and reorders them, putting the most helpful information at the top. This is for anyone creating intelligent systems that need to provide precise, context-aware answers from large text collections.
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Use this if your AI assistant sometimes provides answers that are 'close but not quite right' because the initial document search misses the absolute best matches.
Not ideal if you are looking for a first-stage document retrieval system, as this project focuses on refining results rather than the initial search itself.
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Jupyter Notebook
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MIT
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Last pushed
Jun 10, 2025
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