Combining Multiple Retrieval Models for Robust Results: RAPTOR
Last Updated on November 3, 2024 by Editorial Team
Author(s): Surya Maddula
Originally published on Towards AI.
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Letβs 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 π
In my previous article titled βNot RAG, but RAG Fusion? Understanding Next-Gen Info Retrievalβ, 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:
βHow does combining different retrieval models improve the reliability of search results in practical applications?β
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 weβll discuss in… Read the full blog for free on Medium.
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