Multimodal RAG with LlamaIndex, Gemini, and Qdrant

Modern AI applications need to retrieve and reason over diverse data sources, including text and images. This comprehensive text-based guide teaches you how to build robust multimodal retrieval-augmented generation systems step by step. You will learn how to connect cutting-edge vector search with multimodal language models to answer complex, context-rich questions. What you'll learn: Understand core principles, terminology, and workflows of multimodal RAG architectures; Configure and manage Qdrant vector databases to store and query multidimensional embeddings; Integrate Gemini models and LlamaIndex for seamless retrieval and generation; Process, chunk, and index documents containing both text and image data; Apply prompt engineering patterns and retrieval evaluation strategies for accurate outputs. The course starts with essential definitions and foundation concepts before guiding you through structured written code walkthroughs and implementation exercises. Designed for developers and AI enthusiasts looking to expand beyond text-only pipelines, with no prior RAG experience required. Start reading today and create your first multimodal retrieval system.