Video RAG Foundations

This course introduces retrieval augmented generation and extends it from text documents to video. You build a working document-based RAG chatbot first, then learn to break video into the signals a retrieval system can search. You start with RAG architecture, embeddings, and vector stores, building a chatbot that answers questions from your own documents. You then process raw video, extracting frames, audio, transcripts, captions, and on-screen text, and combine those signals into structured, timestamped records. The course closes with multimodal embeddings, vector storage in ChromaDB, and your first natural-language video search application. By the end of this course, you will be able to: 1. Explain RAG architecture and the role of embeddings, retrievers, and vector stores. 2. Build a document-based RAG chatbot using LangChain and a vector database. 3. Extract frames, audio, transcripts, captions, and OCR text from raw video. 4. Combine multimodal signals into structured, timestamped video records. 5. Generate multimodal embeddings and store them for semantic retrieval. 6. Build a search application that returns video segments from a plain-language query. This course is intended for Python developers, data engineers, and AI practitioners. You should be comfortable writing Python and running notebooks. Turn raw video into searchable records you can query in plain language.