HyDE (Hypothetical Document Embedding)
This code implements a Hypothetical Document Embedding (HyDE) system for document retrieval. HyDE is an innovative approach that transforms query questions into hypothetical documents containing the answer, aiming to bridge the gap between query and document distributions in vector space.
Traditional retrieval methods often struggle with the semantic gap between short queries and longer, more detailed documents. HyDE addresses this by expanding the query into a full hypothetical document, potentially improving retrieval relevance by making the query representation more similar to the document representations in the vector space.
What you'll learn
- 1PDF processing and text chunking
- 2Vector store creation using FAISS and OpenAI embeddings
- 3Language model for generating hypothetical documents
- 4Custom HyDERetriever class implementing the HyDE technique
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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