Efficiently processing and analyzing video content is crucial across sectors like media, security, and education due to the widespread consumption of videos on platforms such as YouTube and Instagram. A proposed solution involves using the LlamaIndex Python API in conjunction with OpenAI's GPT4V and LanceDB to streamline video processing. This approach utilizes retrieval-augmented generation (RAG), which combines information retrieval with generative AI to produce contextually relevant responses by accessing large data repositories. The RAG architecture employs a dense vector search engine for document retrieval and a transformer model for response generation, enabling multimodal integration of text, images, audio, and video to enhance information sourcing. The process includes downloading video content, extracting multimodal data, building a multi-modal index and vector store, retrieving relevant content, and using GPT4V for reasoning and response generation. This method allows for a comprehensive analysis that can be applied to various applications, such as content creation and educational purposes, highlighting the growing potential of AI-driven solutions in video analysis.