Combining Multiple Retrieval Models for Robust Results: RAPTOR

Author(s): Surya Maddula

Originally published on Towards AI.

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Let’s discuss how different techniques can be applied to retrieval systems, using various algorithms to improve accuracy and resilience against errors.

Also includes details about RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval.

Read along to find more 🙂

In my previous article titled “Not RAG, but RAG Fusion? Understanding Next-Gen Info Retrieval”, we discussed RAG Fusion for its potential to improve information retrieval. I wrote that RAG Fusion integrates generative models and retrieval techniques to produce results with higher accuracy and contextual relevance.

But building on that basis, it is quite natural to ask:

“How does combining different retrieval models improve the reliability of search results in practical applications?”

To answer this question, we must move beyond the limitations of single-retrieval approaches and consider the advantages of integrating multiple models. This way, we can analyze how diverse algorithms can fortify systems against errors and enhance result quality, especially in real-world scenarios.

This article discusses the techniques and strategies used in retrieval systems, especially on ensemble methods, their applications, and the benefits they bring.

But for this we need a deeper analysis of techniques and strategies that are used in retrieval systems, which is what we’ll discuss in… Read the full blog for free on Medium.

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Published via Towards AI