RAG • Chapter 8

Advanced RAG: HyDE & Parent Document

RAG engineering module on Advanced RAG.

6 note blocks4 exam topics

🎯 Exam Focus Areas

Evaluate chunking and embedding strategies.Understand Vector DB indexing architectures like HNSW.Analyze RAG prompts for injection vulnerabilities.Calculate and utilize RAGAS evaluation metrics.

Basic RAG (Naive RAG) often fails when user queries are short or vaguely worded. Advanced RAG techniques modify the retrieval process to improve semantic matching.

Advanced System Mechanics

HyDE (Hypothetical Document Embeddings) uses an LLM to generate a fake, hypothetical answer to the user's query, and then embeds that fake answer to search the vector database. Because the fake answer looks more like a target document than a short query does, retrieval accuracy skyrockets. Parent Document Retrieval involves chunking documents into small pieces for highly accurate search, but returning the larger 'parent' chunk to the LLM to provide full surrounding context.

1Understand the vector space implications of this concept.
2Identify potential hallucination risks.
3Optimize for low latency and high relevance.
4Ensure robust system prompts.

Implementation Blueprint

# Conceptual HyDE Implementation
def hyde_retrieval(query, llm, vector_db):
    # 1. Generate hypothetical document
    hypothetical_doc = llm.generate(f"Write a paragraph answering: {query}")
    
    # 2. Search vector DB using the hypothetical document's embedding
    # instead of the user's raw query
    results = vector_db.similarity_search(hypothetical_doc)
    return results

📝 Quick Revision Points

1Review the differences between similarity metrics.
2Practice the LangChain/LlamaIndex code snippets.
3Understand the HyDE architecture deeply.
4Memorize the security guardrail implementations.

← PreviousPrompt Engineering for RAG Next →Evaluation Metrics & RAGAS

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# Conceptual HyDE Implementation def hyde_retrieval(query, llm, vector_db): # 1. Generate hypothetical document hypothetical_doc = llm.generate(f"Write a paragraph answering: {query}") # 2. Search vector DB using the hypothetical document's embedding # instead of the user's raw query results = vector_db.similarity_search(hypothetical_doc) return results