RAG using Milvus, HuggingFace, LangChain, Ragas, with or without OpenAI

You’ve heard good data matters in Machine Learning, but does it matter for Generative AI applications? Corporate data often differs significantly from the general Internet data used to train most foundation models. Join me for a Python demo tutorial on building a customizable RAG (Retrieval Augmented Generation) stack using OSS Milvus vector database, LangChain, Ragas, HuggingFace, and optional Zilliz cloud and OpenAI.
Learn best practices and advanced techniques to optimize GenAI workflows with your own data.

What you’ll learn:
* Using Python, learn how to build a customizable open source RAG (Retrieval Augmented Generation) chatbot with Milvus vector database, LangChain, Ragas, and HuggingFace models, and optional Zilliz cloud and OpenAI.
* Best practices around embedding text data ("embedding" in AI is like "featurization" in ML).
* Best practices around vector indexing and search.
* Best practices around RAG evaluation with Ragas.

See weblink for additional details

Speaker: Christy Bergman, Ziliz