The main purpose of this workshop is to teach you how you can implement a RAG (Retrieval Augmented Generation) chatbot using vector similarity search and Generative AI / LLMs.
In today’s data-driven world, effectively managing and utilizing information is crucial for any organization. Whether dealing with customer support data, product documentation, or internal knowledge bases, organizing and retrieving relevant information from vast collections can be challenging. This tutorial demonstrates a comprehensive approach to processing, storing, and leveraging textual data using vectorization and retrieval techniques. By the end of this tutorial, you’ll understand how to transform raw documents into structured data, store them efficiently, and utilize advanced AI models to generate contextual responses.
Agenda
- Introduction
- Get Started
- Lab 1: Setup the vector database (Oracle Database 23ai)
- Lab 2: Setup the Python Environment
- Lab 3: Generate vector embeddings
- Lab 4: Vector retrieval and Large Language Model generation
- Lab 5: Next Steps
Prerequisites
- Familiarity with Oracle Cloud Infrastructure (OCI) is helpful
- All attendees need to have an Oracle account to be able to login to the workshop portal. Attendees who don't have one are advised to create an account at www.oracle.com before joining the workshop session. Please note that you only need to create an oracle account and NOT a cloud account.
NOTE: In order to access the session landing page, you will need to sign in with your oracle.com account. If you do not have one, you can create one here. A Join button will appear on the landing page 15 minutes prior the session (you may need to refresh the browser).
This curriculum is part of Cloud Customer Connect's series of live Workshops for OCI users, where architects and product managers provide technical deep dives, answer your questions, and deliver the latest updates.