3-4 June, 2025 | Bengaluru, India View More Details & Registration IMPORTANT NOTE: Timing of sessions and room locations are subject to change.
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Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for grounding large language models (LLMs) in external knowledge, enabling more accurate and contextually relevant responses. However, traditional RAG pipelines often operate in a static, pre-defined manner, limiting their ability to adapt to complex queries and dynamically explore information spaces. This talk introduces "Multimodal Agentic RAG", an innovative approach that integrates autonomous agents into the RAG framework to enhance its reasoning and retrieval capabilities. This talk will delve into the architecture and implementation of multimodal RAG with agentic workflows, showcasing its potential to address limitations of traditional RAG. The architecture is based on OpenSearch's multimodal capabilities, DeepSeek-R1's reasoning capabilities, and custom-tuned embedding models. Furthermore, we will discuss the challenges and future directions of Agentic RAG, including the development of robust planning mechanisms, efficient knowledge source management, and effective agent coordination.
Smita Singh is a senior solutions architect at AWS. She comes with 19 years of experience in the industry. She focuses on defining long- and short-term technical strategic vision and works on architecture, design, and implementation of modern, scalable platforms for large-scale global... Read More →
Hitesh Subnani is a Solutions Architect at AWS with specialized expertise in Search, Analytics, and Data Warehouse technologies. With a comprehensive background in data engineering and architecture, he brings a wealth of technical knowledge to complex cloud infrastructure challenges... Read More →
