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

LOCATION ADDRESS (Hybrid in person and zoom)
In-person at Hacker Dojo, 855 Maude Ave, Mountain View, CA 94043
For faster entry at the Dojo, please read the Dojo policies and when you sign up, state “I accept the Hacker Dojo policies“

Online on Zoom
https://acm-org.zoom.us/j/94167775732?pwd=RFZPQVA5RFpTTnE3RGlYU2VYejNtdz09
On YouTube:
https://youtube.com/live/LFx8K7oZkk8?feature=share

AGENDA
6:30 Door opens, food and networking (we invite honor system contributions)
7:00 SFBayACM upcoming events, introduce the speaker
7:10 speaker presentation starts
8:15 – 8:30 finish, depending on Q&A

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

Abstract:
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.

Tutorial notebook link will be linked here: https://github.com/milvus-io/bootcamp/tree/master/bootcamp

Tutorial instructions like this but more focused on running locally: https://docs.google.com/document/d/1yetuGEkYqh_1rAYEBXFAnwsFClMAIQFx1erLHHKXTLg
Slides like these: https://docs.google.com/presentation/d/1hpiaiVMHm4oQr5P86NhcrL0qXwBIdWhHEZOlWKySHyM

SPEAKER BIO:
6+ years building AI and ML systems with math and coding. My mission is to help developers and customers use those tools (with fewer heartaches than I had teaching myself) to organize and search unstructured data, such as images, videos, texts, and audios, using LLM and multi-modal apps. I enjoy learning new technologies and tools and solving challenging problems with math and coding.

As a Developer Advocate, I use my skills in Python, HuggingFace, PyTorch, Spark, RLlib, Ray distributed computing, and vector databases to create and share engaging and informative content, such as tutorials, demos, blogs, and talks. I also manage the Bay Area Unstructured Data meetup group, where I organize events and foster a community of enthusiasts and experts in the field.

Outside of work, I enjoy hiking and bird watching. In my background photo: Australian bustard, spotted near Cairns, Australia.