Author(s): Florian June

Originally published on Towards AI.

The Dual RAG Engine of Thinking and Memory

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This article is the 16th in this promising series. Today, we will explore the advancement of RAG.

The video contains a mind map:

Training code and data: https://github.com/smallporridge/AssistRAG.

ASSISTRAG works as a helpful assistant that helps organize your work materials and keeps track of what’s important. When you have a difficult problem to solve, ASSISTRAG helps by breaking it into smaller, easier parts and finding the information you need.

Now, let’s dive into the detailed introduction.

Early RAG methods like the “Retrieve-Read” framework were inadequate for complex reasoning tasks. While subsequent prompt-based RAG strategies and Supervised Fine-Tuning (SFT) methods improved performance, they required frequent retraining and risked altering foundational LLM capabilities.

Figure 1: Comparisons of Naive, Prompt-based, SFT-based and our Assistant-based RAG frameworks. Source: ASSISTRAG.

By introducing an intelligent information assistant that integrates memory and knowledge management, ASSISTRAG compensates for LLMs’ shortcomings in information accuracy and reasoning depth. As shown in Figure 1, this approach consists of a trainable assistant that manages information and a static main LLM that handles task execution.

Figure 2: Overview of ASSISTRAG. ASSISTRAG enhances LLMs by providing an intelligent information assistant. Endowed with the ability of tool usage, action execution, memory building… Read the full blog for free on Medium.

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Published via Towards AI