Everything Retrieval-Augmented Generation (RAG) using LangChain and Pinecone

RSVP Webinar: https://www.eventbrite.com/e/webinarkubeflow-tensorflow-tfx-pytorch-gpu-spark-ml-amazonsagemaker-tickets-45852865154

Talk #0: Introductions and Meetup Announcements

By Chris Fregly and Antje Barth, Principal Developer Advocates, AI and machine learning @ AWS

Talk #1: Retrieval Augmented Generation using Langchain: An Answer-based Approach to Language Generation

By Giuseppe Zappia, Sr Solutions Architect @ AWS

In this technical overview, we will explore the concept of Retrieval Augmented Generation (RAG) and its role in improving the outputs for large language models (LLMs) by incorporating external data to user prompts to create a high-quality, contextually relevant output. You'll learn about the components of RAG workflows and how to use the open source framework Langchain to reduce complexity while increasing development velocity for building GenAI applications.

Talk #2: RAG with Pinecone Vector Store By Roie Schwaber-Cohen, Staff Developer Advocate @ Pinecone

Roie Schwaber-Cohen is a Staff Developer Advocate at Pinecone. He has an extensive background in software development and architecture with a specialization in large-scale applications and AI. A significant part of his work involves bridging the gap between the Python and TypeScript/JavaScript worlds in the field of AI. As a developer advocate, he regularly shares his work through his articles and demos for Pinecone.

RSVP Webinar: https://www.eventbrite.com/e/webinarkubeflow-tensorflow-tfx-pytorch-gpu-spark-ml-amazonsagemaker-tickets-45852865154

Zoom link: https://us02web.zoom.us/j/82308186562

Website: https://datascienceonaws.com

Meetup: https://meetup.datascienceonaws.com

GitHub Repo: https://github.com/data-science-on-aws/

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Slideshare: https://slideshare.datascienceonaws.com