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Core Idea:

HyDE (Hypothetical Document Embedding) is an information retrieval strategy that generates a hypothetical answer to a query first, then uses this generated content's embedding to find relevant documents, significantly improving semantic search accuracy.

Key Principles:

1. Hypothetical Document Generation:
   • Uses a language model to create a hypothetical ideal answer to the query
   • This synthetic document represents what a perfect match might contain
2. Embedding Translation:
   • Converts the query's intent into a document-like representation
   • Bridges the gap between question space and answer space
3. Vector Similarity Search:
   • Embeds the hypothetical document into vector space
   • Searches for actual documents with similar embeddings

Why It Matters:

• Query-Document Mismatch Reduction:
  • Solves the vocabulary mismatch problem by converting questions to answer-like text
  • Dramatically improves retrieval performance for complex queries
• Intent Clarification:
  • Expands abbreviated or ambiguous queries into fully-formed expressions of intent
  • Captures nuance that direct query embedding might miss
• Improved Relevance:
  • Retrieves documents based on semantic relevance rather than keyword matching
  • Particularly effective for finding conceptually similar information

How to Implement:

1. Generate Hypothetical Document:
   • Use a language model to create an ideal answer to the user's query
   • The model synthesizes what a perfect matching document might contain
2. Create and Compare Embeddings:
   • Generate vector embedding of the hypothetical document
   • Compare against your knowledge base's document embeddings
3. Retrieve and Rank Results:
   • Select top N documents with highest similarity scores
   • Optionally re-rank results using additional criteria

Example:

• Scenario:

  • User query: "How does climate change affect agriculture?"

• Application:

    // 1. Generate hypothetical document
    hypothetical = LLM("Climate change significantly impacts agriculture through several mechanisms. Rising temperatures alter growing seasons and crop yields. Changing precipitation patterns lead to droughts in some regions and flooding in others. Extreme weather events damage crops and infrastructure...")
    
    // 2. Create embedding and search
    embedding = embed_model(hypothetical)
    results = vector_db.similarity_search(embedding, top_k=20)

• Result:

  • Retrieved documents include relevant information about agricultural impacts even if they use different terminology
  • More accurate results than direct query embedding

Connections:

• Related Concepts:
  • Semantic Search: The broader information retrieval approach HyDE enhances
  • Note Embeddings: The vector representations used in the similarity search phase
• Broader Concepts:
  • RAG (Retrieval Augmented Generation): Framework combining retrieval with generation
  • AI-Enhanced Note Taking: Category of tools using AI to improve knowledge work

References:

1. Primary Source:
   • "HyDE: Using a Hypothetical Document Embedding for Query Expansion" (Yang et al., 2022)
2. Additional Resources:
   • Smart Connections Plugin documentation (implementation in PKM context)
   • LangChain documentation on HyDE retrievers

Tags:

#retrieval #search #embeddings #LLM #RAG #information-retrieval

Connections:

Sources:

• From: Obsidian Plugin Smart Connections