EPAM School of Digital Technologies (capstones)

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MULTIMODAL RETRIEVAL AUGMENTED GENERATION SYSTEM
(Manuscript, 2025-01-23) Yuvzhenko, Denys
This study presents an asynchronous, web-based Retrieval-Augmented Generation (RAG) system that integrates multimodal inputs (text, images, tables) to enhance information retrieval and generation in various contexts. The system is developed in Python and hosted on AWS, combining Chroma DB for vector storage and Anthropic’s Claude-3-Haiku LLM model, which is accessible via AWS Bedrock. By leveraging modern cloud capabilities, the solution scales efficiently and handles diverse data modalities in real time. Through systematic experiments, this project highlights the effectiveness of multimodal embedding techniques for refining retrieval accuracy and providing context-aware responses. The architecture’s modular design supports seamless feature integration, making it adaptable for different use cases such as customer support, educational tools, and content creation. Key findings emphasize the role of vector databases in dynamic information updates and confirm that large language models, when appropriately curated and grounded, can produce high-quality, relevant outputs