DeeperML

Advancements are rapidly being made in the field of Multimodal Retrieval-Augmented Generation (RAG) systems, which are changing the way AI handles complex information by merging retrieval and generation capabilities across text, images, and video. Researchers and developers are focusing on creating tools that enhance information processing and retrieval by combining document processing, web search and AI agents. This includes the development of Multimodal LangChain, and the creation of the open-source research assistant, the King RAGent, designed to streamline research.

One example of advancement in the field is VITA-1.5, a multimodal large language model, created by researchers from NJU, Tencent Youtu Lab, XMU, and CASIA. This model integrates vision, language, and speech through a three-stage training methodology. Unlike previous models, VITA-1.5 uses an end-to-end framework that reduces latency and enables near real-time interactions. Further innovation has been released by EPFL researchers, with the introduction of 4M, an open-source framework, that can unify diverse data representations across 21 modalities, using a Transformer-based architecture to streamline the training process.

ImgSrc: miro.medium.com

References :

• pub.towardsai.net: Building Multimodal RAG Application #7: Multimodal RAG with Multimodal LangChain

Classification:

• HashTags: #MultimodalAI #RAG #LangChain
• Target: AI Research
• Product: RAG
• Feature: Multimodal RAG
• Type: AI
• Severity: Medium