Elasticsearch Reciprocal Rank Fusion (RRF) Retriever

What it does

The Reciprocal Rank Fusion (RRF) Retriever takes multiple ranked lists of search results and combines them into a single, optimized list. It uses the reciprocal rank fusion algorithm to calculate a score for each document based on its position in each input list, effectively boosting items that appear high in multiple result sets. It's particularly useful when you need to merge results from different search strategies or data sources while maintaining relevance.

Syntax

The RRF Retriever is typically used within the rrf retriever type via the Elasticsearch Retriever API. Here's a basic syntax:

{
  "retriever": {
    "rrf": {
      "retrievers": [
        // ... other retrievers to run (use standard to execute a simple query)
      ],
      "rank_window_size": 50,
      "rank_constant": 20
    }
  }
}

For detailed information, refer to the official Elasticsearch documentation.

Example Usage

Here's an example of using the RRF Retriever to combine results from a match query and a more_like_this query:

GET /my_index/_search
{
  "retriever": {
    "rrf": {
      "retrievers": [
        {
          "standard": {
            "query": {
              "match": {
                "title": "elasticsearch guide"
              }
            }
          }
        },
        {
          "knn": {
            "field": "vector_field",
            "query_vector": [13, 6, 234, 65],
            "k": 10,
            "num_candidates": 10
          }
        }
      ],
      "rank_window_size": 100,
      "rank_constant": 20
    }
  }
}

Common Issues

1. Performance impact: RRF queries can be more resource-intensive than single queries, especially with large rank_window_size values.
2. Tuning challenges: Finding the right balance of rank_window_size and rank_constant may require experimentation.
3. Query complexity: Combining too many queries can lead to slower response times.

Best Practices

• Start with a moderate rank_window_size (e.g., 50-100) and adjust based on performance and result quality.
• Use RRF when you have multiple, equally important search strategies that you want to combine.
• Monitor query performance and adjust parameters if response times become too long.
• Consider using RRF in conjunction with other relevance tuning techniques for optimal results.

Frequently Asked Questions

Q: What is the purpose of the rank_window_size parameter in RRF?
A: The rank_window_size parameter determines how many top results from each input query are considered for fusion. A larger window size can improve result quality but may impact performance.

Q: How does the rank_constant affect RRF scoring?
A: The rank_constant influences how quickly the score decreases as the rank increases. A higher value gives more weight to lower-ranked documents, while a lower value emphasizes top-ranked results more strongly.

Q: Can RRF be used with different types of queries?
A: Yes, RRF can combine results from various query types, including full-text queries, more_like_this queries, and even nested or function score queries.

Q: Is RRF suitable for all search scenarios?
A: While RRF is powerful, it's most beneficial when combining results from multiple, diverse search strategies. For simple searches, a single well-tuned query might be more efficient.

Q: How can I evaluate the effectiveness of an RRF query?
A: You can compare the relevance of RRF results against individual queries using techniques like A/B testing, relevance judgments, or analyzing user interaction metrics like click-through rates.