Learn how to best use OpenAI function calling within LangChain to do structured data extraction and build agents, with LangSmith access!
In this workshop, we will first go over what OpenAI function calling is. We will then go over two of the big use cases for it within LangChain: structured data extraction and tool usage (for agents)
This will include:
LangSmith access will be granted for all participants, in order that they can better debug their chains and agents.
Harrison Chase is the CEO and co-founder of LangChain, a company formed around the open source Python/Typescript packages that aim to make it easy to develop Language Model applications. Prior to starting LangChain, he led the ML team at Robust Intelligence (an MLOps company focused on testing and validation of machine learning models), led the entity linking team at Kensho (a fintech startup), and studied stats and CS at Harvard.
Logan currently leads developer relations at OpenAI, supporting developers building with DALL-E, the API, and ChatGPT. Outside of OpenAI, Logan is the Lead Developer Community Advocate for the Julia Programming Language, and a Teaching Fellow for Harvard University's Extension School course CSCI E-33A. Logan was previously a Applied Machine Learning Engineer and Software Engineer at Apple as well as the Community Manager for the Julia Programming Language. Additionally, Logan is on the Board of Directors at NumFOCUS and formerly on the board at DEFNA.
Lance recently joined LangChain after several years working on vision systems for self-driving bots, trucks, and cars at Nuro, Ike, and Uber ATG. Authored a few open-source packages, including the https://github.com/rlancemartin/auto-evaluator, which is hosted at https://autoevaluator.langchain.com/.
