This is a Build with AI event, sponsored by Google. There is no charge for using Colab during this session for attendees.

In this codelab, we will embark on an exciting journey to explore the intricacies of a complete Retrieval-Augmented Generation (RAG) pipeline. RAG is a powerful approach that combines the strengths of retrieval-based methods and generative models to enhance the performance and capabilities of natural language processing tasks. By providing the entire RAG pipeline in code, this lab aims to give you a hands-on experience and a deep understanding of how to implement and customize your own RAG systems.

Throughout the lab, we will dive into the different components that make up a comprehensive RAG pipeline. We will start by laying the foundation and understanding the basic building blocks required for a functional RAG system. From there, we will gradually introduce enhancements and optimizations to improve the performance and efficiency of our pipeline. By incrementally adding these improvements, you will gain valuable insights into how your code can evolve to tackle increasingly complex and challenging problems in the realm of natural language processing.

To facilitate a seamless and immersive learning experience, we will leverage the power of the Colab Enterprise environment on Google Cloud Platform (GCP). Colab Enterprise provides a robust and user-friendly interface for developing and executing code, making it an ideal choice for this codelab. Additionally, we will explore the integration of other essential components of Vertex AI, such as Gemini and the model garden. Gemini is a powerful tool for efficient and scalable nearest neighbor search, which plays a crucial role in the retrieval phase of the RAG pipeline. By leveraging Gemini, we can enhance the speed and accuracy of retrieving relevant information from large-scale datasets. Furthermore, the model garden within Vertex AI offers a rich collection of pre-trained models that can be fine-tuned and adapted to specific tasks, enabling us to quickly prototype and experiment with different architectures and configurations. By combining these cutting-edge tools and services, we will create a comprehensive and powerful RAG pipeline that showcases the potential of this approach in various natural language processing applications.

Keith is a Senior Generative AI Data Scientist at Johnson & Johnson. He has accumulated over a decade of experience in data analytics and machine learning working across diverse projects in companies that range in size from start-ups to Fortune 50 companies, as well as an MBA from Babson College and a Master of Applied Data Science from the University of Michigan. More recently, he has been making significant contributions to healthcare innovation through his expertise in generative AI. His role in developing a sophisticated generative AI platform, which incorporates Retrieval-Augmented Generation (RAG) and various other advanced Generative AI (GAI) techniques like foundation model fine-tuning and AI Agents, led him to many insights for converting complex business challenges into scalable tech solutions, particularly when using RAG in a corporate setting.

Catering is provided by Marupo Eats: https://www.instagram.com/marupoeats/