Rank fusion is a helpful method for combining search results from multiple sources in Retrieval-Augmented Generation (RAG) systems. It provides a faster alternative to reranking, which can be slow, while still improving the relevance of the information retrieved. This technique helps the system capture more relevant content, offering a wider set of useful results for the language model to process, leading to more accurate and informative answers.

How it works:

1. Retrieve from Multiple Sources: The system gathers information from different sources, each potentially using its own retrieval methods or focusing on different parts of the data.
2. Identify Unique Documents: The results from all the sources are combined, and duplicates or redundant documents are identified.
3. Aggregate Ranks: For each unique document, the system calculates a combined or "fused" rank, which can be done by averaging ranks, taking the highest rank, or using a weighted combination of ranks from each source.
4. Order by Fused Rank: The unique documents are sorted based on their combined rank, with the most relevant ones at the top.
5. Select Top-N: The top documents are chosen as the final results.

Benefits of Rank Fusion:

- Faster Processing: It's quicker than reranking with a cross-encoder, especially when handling a large number of documents from multiple sources.
- Better Recall: By pulling results from multiple sources, it improves the chances of retrieving all relevant information.
- Flexibility: Rank fusion allows you to adjust how ranks are combined, enabling you to give more weight to certain sources or methods based on what you need.

This method helps streamline the process while maintaining strong retrieval performance.