From the course: Prompt Engineering with LangChain
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LangSmith deep dive
From the course: Prompt Engineering with LangChain
LangSmith deep dive
- [Instructor] In this notebook, we're going to do a bit of a deep dive into LangSmith. Now, this notebook was co-authored with my friends at the AI Makerspace. I highly recommend you check out their YouTube channel for hands-down the best educational content on all things LLM and all things AI engineering. Be sure to connect with Chris Alexiuk and Greg Loughnane on LinkedIn. I've learned so much from Chris and Greg about AI engineering and working with language models. So I'm going to really quickly breeze through everything in the first half of this notebook because you've seen this all before. We're instantiating an LLM, setting up Nest sync AO so that we could do asynchronous stuff in a Jupyter Notebook. We're instantiating a document loader. This document loader is actually just a site map loader. So it's going to go through here, the site map for LangChain and just essentially it's loading in all the docs. So we can take a look at this once it's done. All right, and we can see…
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