RAG-Fusion (Fusion Retrieval RAG)

Original Paper: https://arxiv.org/pdf/2402.03367

Code Sample: https://github.com/NirDiamant/RAG_Techniques/blob/main/all_rag_techniques/fusion_retrieval.ipynb

RAG-Fusion or Fusion-retrieval RAG, is an advanced technique that enhances the traditional Retrieval Augmented Generation (RAG) approach used in AI and natural language processing. This method combines the strengths of RAG with Reciprocal Rank Fusion (RRF) to produce more accurate, comprehensive, and contextually rich responses.

Key Components of RAG-Fusion

Multi-Query Generation RAG-Fusion begins by generating multiple queries based on the original user input. This process expands the search criteria and allows the system to explore various perspectives of the given context.

Vector Search For each generated query, the system performs a vector search to retrieve relevant documents from a knowledge base.

Reciprocal Rank Fusion (RRF) RRF is a crucial component of RAG-Fusion. It involves:

1. Assigning scores to retrieved documents
2. Reranking documents based on these scores
3. Fusing the results to prioritize the most relevant information

Advantages of RAG-Fusion

1. Improved Accuracy: By considering multiple query perspectives, RAG-Fusion can provide more precise and relevant answers.
2. Enhanced Context Understanding: The multi-query approach allows for a broader exploration of the topic, leading to more comprehensive responses.
3. Reduced Limitations: RAG-Fusion addresses some of the shortcomings of traditional RAG models, such as low recall and precision.
4. Versatility: This method has shown effectiveness in various use cases, including technical product information, sales strategies, and customer-oriented explanations.

Considerations

While RAG-Fusion offers significant improvements, it's important to note that:

• It may have a longer runtime due to the complexity of multiple API calls and document reranking.
• The quality of external knowledge sources and overall system complexity remain areas for ongoing research and development.

RAG-Fusion represents a significant advancement in retrieval-augmented generation techniques, offering a more sophisticated approach to generating AI responses that are both accurate and contextually rich.

This is an AI generated summary by Athina AI

Relevant Links:

1. https://towardsdatascience.com/forget-rag-the-future-is-rag-fusion-1147298d8ad1
2. https://becomingahacker.org/comparing-rag-rag-fusion-with-raptor-different-ai-retrieval-augmented-implementations-1aa76fce6a5c?gi=8c4c1297866e
3. https://docsbot.ai/article/advanced-rag-techniques-multiquery-and-rank-fusion
4. https://arxiv.org/html/2402.03367v2
5. https://cloud.google.com/use-cases/retrieval-augmented-generation
6. https://blogs.oracle.com/fusioninsider/post/learn-why-rag-is-genais-hottest-topic
7. https://www.linkedin.com/pulse/revolutionize-your-searches-ai-discover-rag-fusion-lab-palanisamy-mncje