For most of AI history, "search" meant looking through text. But your company’s most valuable data often lives in slide decks, screenshots, and product videos. **Multimodal RAG** allows you to index these assets so your AI can answer questions like, "What did the graph on slide 14 of the Q3 presentation look like?"
1. Multimodal Embeddings: CLIP and Beyond
The foundation of multimodal search is a shared embedding space where text and images can be compared directly. We explore using models like OpenAI's CLIP or Google's SigLIP to generate vectors that represent the visual concepts in your media library.
2. Video Indexing and Keyframe Extraction
Searching video is a challenge of data density. We walkthrough a pipeline for extracting high-signal keyframes, generating textual descriptions of scenes, and indexing both to allow for precise temporal search (e.g., "Find the moment in the tutorial where the user clicks the settings button").
3. Prompting with Visual Context
Once you’ve retrieved the relevant media, how do you feed it to the LLM? We discuss strategies for passing images and video frames to multimodal models like GPT-4o or Gemini 1.5 Pro to ensure the model understands the visual evidence it’s being shown.