How does Simple Fusion Retriever in LlamaIndex work?

 The Simple Fusion Retriever in LlamaIndex combines the results of multiple retrievers to improve the overall retrieval performance. Its core idea is that different retrieval methods might capture different aspects of relevance, and by fusing their results, you can get a more comprehensive and accurate set of retrieved documents or nodes.   

Here's a breakdown of how it works and its core idea:

Core Idea: The Simple Fusion Retriever leverages the strengths of different retrieval methods by combining their outputs. It assumes that each retriever might find a subset of relevant documents, and by merging these subsets (and potentially re-ranking them), you can increase the chances of retrieving all the truly relevant information.   

How it Works:

Multiple Retrievers: You provide the SimpleFusionRetriever with a list of other retrievers. These can be any type of retriever available in LlamaIndex, such as BM25Retriever, VectorStoreRetriever, KeywordRetriever, etc.

Independent Retrieval: When you issue a query, each of the underlying retrievers independently retrieves the top-k documents or nodes according to its own criteria.

Fusion (Merging and Ranking): The SimpleFusionRetriever then combines the results from all the individual retrievers.  There are a couple of ways this fusion can happen:

Simple Union: The simplest approach is just to take the union of all the retrieved documents. This means all unique documents returned by at least one retriever become part of the combined set.

Ranked Fusion (More Common): A more sophisticated approach is to combine the results and then re-rank them based on some criteria. This might involve:

Score Aggregation: Each document gets a new score based on the scores it received from the individual retrievers. This can be a simple sum, a weighted sum, or a more complex function.

Re-ranking: The combined set of documents is then re-ranked based on these aggregated scores. This allows documents that were considered relevant by multiple retrievers to be ranked higher.   

Return Results: The SimpleFusionRetriever returns the top-k documents from the re-ranked set as the final retrieved context.

Example (Conceptual):

Let's say you have a query about "artificial intelligence in healthcare."

BM25 Retriever: Might find documents that contain the keywords "artificial intelligence," "healthcare," and related terms.

Vector Store Retriever: Might find documents that are semantically similar to the query, even if they don't contain the exact keywords.   

The Simple Fusion Retriever would combine the results from both retrievers. Documents that were highly ranked by both retrievers would likely be ranked even higher in the fused results, because they are relevant from both a keyword and semantic perspective.

Benefits:

Improved Recall: By combining results from multiple retrievers, you can increase the chances of retrieving all the relevant documents.

Better Relevance: Re-ranking based on aggregated scores can improve the overall relevance of the retrieved results.   

Flexibility: You can easily combine different types of retrievers to leverage their complementary strengths.

When to use it:

When you have multiple retrieval methods available: If you're using different types of retrievers (BM25, vector search, etc.), the Simple Fusion Retriever can be a good way to combine their results.

When you want to improve recall: If you're concerned about missing relevant documents, fusing results can help.

When you want to balance different aspects of relevance: Different retrieval methods might capture different aspects of relevance (e.g., keyword match vs. semantic similarity). Fusion allows you to combine these different perspectives.

In LlamaIndex, you would use the SimpleFusionRetriever class and pass it a list of your other retrievers. You can then use the SimpleFusionRetriever like any other retriever to fetch context for your LLM queries.