GPT-4V with Context: Using Retrieval Augmented Generation with Multimodal Models

The recent integration of image understanding capabilities into large language models (LLMs) like ChatGPT has opened up new avenues for multimodal text and image models. By incorporating retrieval augmented generation (RAG), these models can be steered towards producing more accurate and relevant results by providing them with the most recent and accurate context from data, including images. This approach is particularly useful in mitigating hallucinations often generated by powerful LLMs and LMMs. The multimodal vector store created using CLIP and Astra DB can be queried to provide contextual understanding for multimodal models like MiniGPT-4, improving their accuracy and relevance. As multimodal models become more accessible, the potential applications of these technologies continue to expand, offering exciting possibilities for the future of AI.

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