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The rapid advancement in artificial intelligence (AI) and data management technologies has enabled the development of innovative solutions to complex data interaction challenges. This paper details the creation of a multimodal Retrieval Augmented Generation (RAG) chatbot that leverages a Dell Data Lakehouse to effectively manage and interact with diverse data types, including structured, semi-structured, and unstructured data. The Dell Data Lakehouse storage serves as the foundational storage solution, with the Dell Data Analytics Engine (DDAE) facilitating the efficient execution of SQL queries across federated data sources. This capability ensures seamless access to crucial tabular information, enabling the chatbot to provide accurate responses. By integrating vector databases, the system enhances its retrieval process, effectively managing multimodal data such as tabular data, text, images, audio, and video, all served from the Dell Data Lakehouse storage.
This approach addresses key limitations of LLMs, such as restricted context windows and outdated knowledge bases, by providing up-to-date and contextually relevant information. Furthermore, the chatbot employs AI to automate SQL query generation, bridging the gap between business users and data analysts by offering direct access to insights without requiring expertise in SQL or the organization's data schema. The result is a chatbot that delivers precise, contextually informed responses, significantly improving user interaction and data accessibility. This paper provides an in-depth exploration of the technologies and methodologies employed, demonstrating the potential of this integrated system across various applications.