Building Multimodal RAG with LangChain, Gemini, and Chroma

Modern AI applications require more than basic text generation; they need the ability to search, reason over, and synthesize information across multiple formats including text and imagery. This course provides a clear, step-by-step introduction to Retrieval-Augmented Generation (RAG) and multimodal architectures. You will understand how to connect multimodal foundation models to private vector stores, enabling your applications to deliver grounded, accurate answers from complex source material. What you'll learn: - Understand core concepts of Retrieval-Augmented Generation and multimodal embeddings - Configure Chroma vector stores to store, index, and query text and visual data - Integrate LangChain to orchestrate context retrieval, prompt construction, and model execution - Connect Gemini Pro and Vertex AI to process multimodal queries seamlessly - Apply robust document processing techniques for chunking and structuring mixed media - Implement modern prompt engineering patterns and retrieval evaluation practices Starting with foundational terminology and architecture overviews, the text-based modules guide you through hands-on code examples and implementation patterns. This course is designed for software developers, data practitioners, and beginners in generative AI looking to expand beyond simple chatbots without requiring prior deep learning expertise. Start reading today to construct reliable, production-ready multimodal retrieval pipelines.