Overview

The blog post introduces a new paradigm called multi-modal Retrieval-Augmented Generation (RAG), highlighting advancements in LlamaIndex that support multi-modal large language models (LLMs) and embeddings. This includes integration with vector databases for multi-modal indexing and retrieval, allowing the processing of both text and images. A significant development is the release of the GPT-4V API by OpenAI, which can handle both text and image inputs and outputs, extending the capabilities of LLMs. The text emphasizes the transformative impact of RAG in accelerating insights from unstructured text data and explores how these concepts can be applied to a hybrid image/text domain. It discusses the development of new abstractions, like the MultiModalEmbedding class and MultiModalVectorIndex, which enable the storage and retrieval of text and image data in vector databases. The post also provides examples of how these technologies can be used, such as in multi-modal querying and retrieval-augmented captioning, demonstrating the ability to synthesize responses across different data types.