Dynamic Retrieval Augmented Generation: Real-time Information Update for Enhanced Conversational Continuity
1. Introduction
In the realm of conversational AI, the ability to generate coherent, relevant, and dynamic responses is paramount. Traditionally, models have either relied on a fixed knowledge base or external retrieval mechanisms that are static in nature. The Dynamic Retrieval Augmented Generation (Dynamic RAG) approach offers a groundbreaking method, enabling models to search and update information in real-time as the conversation progresses. This not only enhances conversational continuity but also allows for a deeper and richer interaction experience.
2. Conceptual Framework of Dynamic RAG
2.1 Traditional RAG:
Retrieval Augmented Generation combines the benefits of retrieval-based and generation-based models. In a typical RAG, the model retrieves relevant documents or passages and then generates a response based on the retrieved information.
2.2 Dynamic RAG:
Building upon the foundations of traditional RAG, Dynamic RAG introduces real-time information retrieval. Rather than relying solely on a static set of retrieved data, the model can update its knowledge on-the-fly, either before a user’s prompt or during its response generation.
