Fusion Retrieval
This code implements a Fusion Retrieval system that combines vector-based similarity search with keyword-based BM25 retrieval. The approach aims to leverage the strengths of both methods to improve the overall quality and relevance of document retrieval.
Traditional retrieval methods often rely on either semantic understanding (vector-based) or keyword matching (BM25). Each approach has its strengths and weaknesses. Fusion retrieval aims to combine these methods to create a more robust and accurate retrieval system that can handle a wider range of queries effectively.
What you'll learn
- 1PDF processing and text chunking
- 2Vector store creation using FAISS and OpenAI embeddings
- 3BM25 index creation for keyword-based retrieval
- 4Custom fusion retrieval function that combines both methods
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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