> ## 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

@tags: reranker, rerank, ranking, relevance, rrf, mmr, hybrid, diversify

### What are rerankers?

* 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?

* 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

### Best practices for reranking

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

### Reciprocal Rank Fusion using `RerankRRF`

* Combine multiple ranked lists without requiring score calibration.
* Good for merging results from different search methods.

#### Syntax

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

#### Example: Hybrid search fusion

* Schema:

```schema.hx theme={null}
V::Document {
    content: String,
    created_at: Date
}
```

* Query:

```query.hx 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
```

### Maximal Marginal Relevance using `RerankMMR`

* Balances relevance with diversity to reduce redundancy.
* Good for showing varied results instead of similar or duplicate content.

#### Syntax

```query.hx theme={null}
::RerankMMR(lambda: 0.7)
```

#### Example: Simple diversification

* Schema:

```schema.hx theme={null}
V::Document {
    content: String,
    created_at: Date
}
```

* Query:

```query.hx 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

#### Example: Fusion and diversification

* Schema:

```schema.hx theme={null}
V::Document {
    content: String,
    created_at: Date
}
```

* Query:

```query.hx 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
```