Advanced RAG: RAG-Fusion Using LangChain

RAG-Fusion Pipeline (image created by the author)

Various innovative approaches have been developed to improve the results obtained from simple Retrieval-Augmented Generation (RAG) methods. Multi Query and RAG-Fusion are two approaches that share similarities, with RAG-Fusion representing a more advanced iteration of the Multi Query method. The Multi Query approach was explored in detail in our previous article.

RAG-Fusion works by expanding a single user query into multiple related queries, each of which is then used to perform a vector search, retrieving a variety of documents. These documents are subsequently re-ranked using the reciprocal rank fusion algorithm to ensure the most relevant information is prioritized (this part serves as an extension to the Multi Query approach). The method focuses on bridging the gap between the user’s explicit query and their actual intent, thereby enhancing the quality and relevance of the generated responses. This approach is particularly useful for addressing broader or less specific inquiries, making it a powerful tool for improving search and response generation systems. Addtional reference can be found here: ref1, ref2.

This article will explore various steps and provide the source code for applying the RAG-Fusion Method through the LangChain framework. We can access the original LangChain example of this method here (also here). We can also monitor and…