The rapid evolution of AI and large language models (LLMs) has significantly transformed productivity tools, with current LLMs capable of handling multiple modalities, including text and images. This advancement is exemplified by the integration of multimodal capabilities into retrieval-augmented generation (RAG) applications, combining text and image data to enhance the accuracy of generated responses. Using tools like LlamaIndex and Neo4j, developers can implement multimodal RAG pipelines by indexing text and images as vector representations, utilizing models like CLIP and ada-002 for embedding. The process involves querying these indexed vectors to generate comprehensive answers, demonstrating an innovative approach to mixed media information retrieval. As LLMs continue to develop, there is potential for their comprehension to extend to videos, further enriching the interaction and information processing capabilities of AI systems.