> ## Documentation Index
> Fetch the complete documentation index at: https://helix-claude-document-return-objects-rxi6v.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Rerankers Overview

> Improve search result quality by reordering results after initial retrieval.

## 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

## 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

## Available Rerankers

HelixQL provides two powerful reranking strategies:

### 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.

```helixql theme={null}
::RerankRRF             // Uses default k=60
::RerankRRF(k: 30.0)    // Custom k parameter
```

### 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.

```helixql theme={null}
::RerankMMR(lambda: 0.7)                           
```

## Basic Usage Pattern

RRF Usage:

```helixql focus=3 theme={null}
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:

```helixql focus=3 theme={null}
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
```

## Chaining Rerankers

You can chain multiple rerankers together for complex result optimization:

```helixql focus={3-4} theme={null}
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
```

## 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

## Common Patterns

```helixql theme={null}
// 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)
```