Teaching RAG to “Remember”: How MemoRAG Enhances Question-Answering Through Memory

Author(s): Florian June

Originally published on Towards AI.

Underlying Principles, Source Code, and Insights

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Existing RAG systems are limited in handling complex or ambiguous information needs that cannot be directly retrieved from external databases. For instance, traditional RAG systems excel in well-structured question-answering tasks but struggle when the task requires an implicit understanding of the underlying query or involves unstructured data retrieval.

A new study, “MemoRAG,” aims to address this issue by utilizing a long-term memory system that recalls relevant information based on context, significantly enhancing retrieval efficiency for complex tasks.

This article begins with an overview of MemoRAG, followed by an explanation of its principles through code analysis. Next, it details the training process and presents an evaluation along with a case study. Finally, I will share my thoughts and insights on this approach, including a comparison with GraphRAG.

As illustrated in Figure 1, Standard RAG, shown on the left, struggles to accurately locate the necessary evidence due to the implicit nature of the input query, resulting in a less precise answer. In contrast, MemoRAG, shown on the right, constructs a global memory across the entire database. When presented with the query, MemoRAG first recalls relevant clues, enabling the retrieval of useful information and leading… Read the full blog for free on Medium.

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Published via Towards AI