The video provides an in-depth tutorial on leveraging Knowledge Graphs (KG) and Retrieval-Augmented Generation (RAG) for Q&A systems using tabular data. It begins by explaining the advantages of KGs, such as their suitability for structured and unstructured data, domain-specific applications, and their ability to bring explainability and traceability to data. The presenter emphasizes the importance of KGs in constructing powerful chatbots by leveraging shared knowledge across databases. The video then delves into the fundamentals of KGs, discussing nodes, relationships, and the knowledge required to construct a KG from datasets. It guides viewers through the process of constructing a KG, including the creation of nodes and relationships with properties and labels.
The tutorial also compares KGs with RAG, highlighting that while RAG is easier to implement and more mature, KGs offer more flexibility, scalability, and explainability, especially for sensitive data like medical reports. The presenter suggests that the choice between KG and RAG should be based on the specific needs of the project, and even a combination of both can be considered. The video further explores the role of large language models (LLMs) in constructing KGs, noting the importance of context length and cipher query knowledge.
In the practical section, the presenter demonstrates how to construct a KG for a chatbot using a movie dataset, which includes creating nodes for persons, movies, genres, and relationships between them. The video concludes with a brief overview of a Microsoft project that uses LLMs to construct a KG from unstructured medical data, showcasing the potential of KGs in various applications.