Build Better Multimodal RAG Pipelines with FiftyOne, LlamaIndex, and Milvus

The talk by Jacob Marks at the Unstructured Data Meetup hosted by Zilliz focused on building robust multimodal Retrieval Augmented Generation (RAG) pipelines using FiftyOne, LlamaIndex, and Milvus. RAG enhances large language models' capabilities by augmenting their knowledge with relevant external data. The architecture of a text-based RAG system is simple, integrating LLMs with vector databases like Milvus or Zilliz Cloud to provide users with more accurate and contextually relevant responses. Multimodal RAG proves invaluable for systems that need multiple data types to make informed decisions. It combines information retrieval and generative modeling to enhance the capabilities of multimodal LLMs, integrating various data types such as text, images, audio, and video. The fiftyone-multimodal-rag-plugin can be used to implement a multimodal RAG pipeline using FiftyOne, LlamaIndex, and Milvus.