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What are Rerankers?

Rerankers are powerful post-processing operations that improve the quality and diversity of search results by reordering them after the initial retrieval phase. They enable you to:
  • Combine results from multiple search strategies (hybrid search)
  • Reduce redundancy by diversifying results
  • Optimize the relevance-diversity trade-off
  • Improve the overall user experience of your search application

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When to Use Rerankers

Apply rerankers in your query pipeline when you need to:
  • Merge multiple search methods: Combine vector search with BM25 keyword search, or merge results from multiple vector searches
  • Diversify results: Eliminate near-duplicate content and show varied perspectives
  • Optimize ranking: Fine-tune the balance between relevance and variety based on your use case
  • Improve search quality: Leverage sophisticated ranking algorithms without changing your underlying search infrastructure

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Available Rerankers

HelixQL provides two powerful reranking strategies:

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RerankRRF (Reciprocal Rank Fusion)

A technique for combining multiple ranked lists without requiring score calibration. Perfect for hybrid search scenarios where you want to merge results from different search methods. 
::RerankRRF             // Uses default k=60
::RerankRRF(k: 30.0)    // Custom k parameter

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RerankMMR (Maximal Marginal Relevance)

A diversification technique that balances relevance with diversity to reduce redundancy. Ideal when you want to show varied results instead of similar or duplicate content. 
::RerankMMR(lambda: 0.7)

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Basic Usage Pattern

RRF Usage: 
QUERY SearchDocuments(query_vec: [F64]) =>
    results <- SearchV<Document>(query_vec, 100)
        ::RerankRRF             // Apply reranking
        ::RANGE(0, 10)          // Get top 10 results
    RETURN results
MMR Usage: 
QUERY SearchDocuments(query_vec: [F64]) =>
    results <- SearchV<Document>(query_vec, 100)
        ::RerankMMR(lambda: 0.7)  // Apply reranking
        ::RANGE(0, 10)            // Get top 10 results
    RETURN results

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Chaining Rerankers

You can chain multiple rerankers together for complex result optimization: 
QUERY AdvancedSearch(query_vec: [F64]) =>
    results <- SearchV<Document>(query_vec, 150)
        ::RerankRRF(k: 60)       // First: combine multiple rankings
        ::RerankMMR(lambda: 0.6) // Then: diversify results
        ::RANGE(0, 10)
    RETURN results

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Best Practices

  1. Retrieve more results initially: Fetch 100-200 candidates to give rerankers sufficient options to work with
  2. Apply rerankers before RANGE: Rerank first, then limit the number of results returned
  3. Choose the right reranker: Use RRF for combining searches, MMR for diversification
  4. Test with your data: Experiment with different parameters to find what works best for your use case

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Common Patterns

// Pattern 1: Simple diversification
SearchV<Document>(vec, 100)::RerankMMR(lambda: 0.7)::RANGE(0, 10)

// Pattern 2: Hybrid search fusion
SearchV<Document>(vec, 100)::RerankRRF::RANGE(0, 10)

// Pattern 3: Fusion + diversification
SearchV<Document>(vec, 150)::RerankRRF::RerankMMR(lambda: 0.6)::RANGE(0, 10)
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