Basic similarity search retrieves the most similar documents—but they may be redundant. Advanced retrievers address this: MMR balances similarity and diversity, self-query filters by metadata, parent-document retrieval chunks contextually. This post covers each strategy and when to use them.
Maximum Marginal Relevance (MMR)
MMR returns diverse results: similar to the query but different from each other.
# MMR retrieval
results = vector_store.max_marginal_relevance_search( "What is semantic search?", k=5, fetch_k=20, # Retrieve more candidates lambda_mult=0.25 # Balance: 0=diversity, 1=similarity )
Self-Query Retrieval
Translate user queries into metadata filters + similarity search.
from langchain.retrievers.self_query.base import SelfQueryRetriever
retriever = SelfQueryRetriever.from_llm_and_db( llm=llm, db=vector_store, document_content_description="Academic papers" )
# Automatically filters by year=2024, then searches results = retriever.get_relevant_documents("Papers from 2024 about transformers")
Conclusion
Advanced retrieval improves quality: MMR adds diversity, self-query adds filtering, parent-document retrieval preserves context. Choosing the right retriever depends on your data and queries. Next: evaluating RAG systems with LangSmith to measure faithfulness and relevance.