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    Query EnhancementRAG Techniques

    HyPE (Hypothetical Prompt Embedding)

    This code implements a Retrieval-Augmented Generation (RAG) system enhanced by Hypothetical Prompt Embeddings (HyPE). Unlike traditional RAG pipelines that struggle with query-document style mismatch, HyPE precomputes hypothetical questions during the indexing phase. This transforms retrieval into a question-question matching problem, eliminating the need for expensive runtime query expansion techniques.

    What you'll learn

    • 1
      PDF processing and text extraction
    • 2
      Text chunking to maintain coherent information units
    • 3
      Hypothetical Prompt Embedding Generation using an LLM to create multiple proxy questions per chunk
    • 4
      Vector store creation using FAISS and OpenAI embeddings
    • 5
      Retriever setup for querying the processed documents
    • 6
      Evaluation of the RAG system

    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.

    Free and open-sourceRunnable Jupyter notebookActive community support
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