Video RAG

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.

This course covers evaluation, optimization, and deployment for Video RAG systems. Measuring retrieval quality and serving the pipeline reliably are what separate a demonstration from a system other people can use. You start by improving retrieval quality with re-ranking, query reformulation, and chunking strategies, then address the problems that appear at scale, including index size, latency, and cost. You build an evaluation dataset and measure the system with precision, recall, and groundedness metrics, using the results to guide optimization rather than guesswork. The course closes by wrapping the pipeline in a FastAPI backend and a web interface for upload, search, and question answering. By the end of this course, you will be able to: 1. Apply re-ranking and query reformulation to improve retrieval relevance. 2. Scale retrieval across large video libraries while managing latency and cost. 3. Build an evaluation dataset for a Video RAG system. 4. Measure retrieval and answer quality using precision, recall, and groundedness. 5. Expose a Video RAG pipeline through a FastAPI backend. 6. Deliver a web interface for video upload, search, and question answering. Intended for learners who have completed the earlier courses in the Specialization. Enroll now to measure what your system retrieves, then deploy it.