HyDE: Hypothetical Document Embeddings

What is HyDE? HyDE (Hypothetical Document Embeddings) is a retrieval technique that generates a hypothetical answer to the query, then uses that to find similar real documents.

The Problem HyDE Solves User queries and documents often have vocabulary mismatch:

• Query: "How to fix slow database?"
• Document: "PostgreSQL query optimization using indexing..."

Direct embedding similarity may not connect these well.

How HyDE Works

User Query
    |
    v
[LLM generates hypothetical answer]
    |
    v
Hypothetical Document
    |
    v
[Embed hypothetical document]
    |
    v
[Search for similar real documents]
    |
    v
Retrieved Documents

Implementation

def hyde_search(query: str, vector_store, llm) -> list:
    # Generate hypothetical answer
    hypothetical = llm.generate(f"""
Write a detailed answer to this question:
{query}

Write as if you are writing a document that would answer this.
    """)

    # Embed the hypothetical document
    hypo_embedding = embed(hypothetical)

    # Search with hypothetical embedding
    results = vector_store.search(hypo_embedding, top_k=10)

    return results

Why It Works

The hypothetical document is in the same semantic space as real documents, improving similarity matching.

When to Use HyDE

Limitations

• Adds LLM call latency
• Hypothetical may be wrong (can mislead retrieval)
• Works best with capable LLMs
• Not necessary if query matches document vocabulary well

Variants

• Multi-HyDE: Generate multiple hypothetical docs, combine results
• Query + HyDE: Use both original query and hypothetical embedding
• Domain-specific prompts: Tailor hypothetical generation to domain