Explainable Retrieval
This code implements an Explainable Retriever, a system that not only retrieves relevant documents based on a query but also provides explanations for why each retrieved document is relevant. It combines vector-based similarity search with natural language explanations, enhancing the transparency and interpretability of the retrieval process.
Traditional document retrieval systems often work as black boxes, providing results without explaining why they were chosen. This lack of transparency can be problematic in scenarios where understanding the reasoning behind the results is crucial. The Explainable Retriever addresses this by offering insights into the relevance of each retrieved document.
What you'll learn
- 1Vector store creation from input texts
- 2Base retriever using FAISS for efficient similarity search
- 3Language model (LLM) for generating explanations
- 4Custom ExplainableRetriever class that combines retrieval and explanation generation
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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