Vector Space Models and Embeddings in RAGs
Discover the power of Retrieval-Augmented Generation (RAG) in modern NLP applications. This course will teach you how to implement a RAG-based chatbot using Python and TensorFlow, focusing on text embeddings and retrieval techniques.
What you'll learn
In the ever-evolving field of natural language processing, integrating robust retrieval mechanisms with generation models is crucial for creating advanced AI systems. In this course, Vector Space Models and Embeddings in RAGs, you’ll learn to implement effective RAG-based chatbots. First, you’ll explore the foundational concepts of Retrieval-Augmented Generation and understand its significance in enhancing language models. Next, you’ll discover how to represent text data using various embedding techniques, analyzing their properties and limitations. Finally, you’ll learn how to implement these embeddings in a practical RAG system to retrieve relevant information efficiently. When you’re finished with this course, you’ll have the skills and knowledge of RAG needed to develop advanced AI chatbots capable of sophisticated text retrieval and response generation.
Table of contents
About the author
Axel Sirota is a Microsoft Certified Trainer with a deep interest in Deep Learning and Machine Learning Operations. He has a Masters degree in Mathematics and after researching in Probability, Statistics and Machine Learning optimisation, he works as an AI and Cloud Consultant as well as being an Author and Instructor at Pluralsight, Develop Intelligence, and O'Reilly Media.