Computer Science

Welcome back to part 5, where we’ll set up our multi-agent team. So buckle up and let’s jump right in. Create a new file named multi_agent.py in your project root: Open up the multi_agent.py file and start with the imports: We have a lot of imports again, many of which will be familiar. We import […]

Hi and welcome back to part 4 of this tutorial series where we’ll once again be taking it up a step. We’ll basically compress the Agent and the Executor into a single node and then have multiple of these ‘agent and executor’ nodes inside of a team working together. First, we’ll cover the basic idea

Hello and welcome back to part 3 of this tutorial series. In this part, we’ll be getting started with LangGraph. Instead of having a lot of explanation before we start, we’ll see how stuff works as we go along. So without further ado, let’s just jump right in. Let’s start by actually installing LangGraph, as

Hi and welcome back to part 2 of the tutorial series where we will be having a look at LangSmith which will help us debug our LLM creations and also write tools that our powerful agents will be able to execute from part 3 onwards. LangSmith setup So what is LangSmith? LangSmith is another part

Hi and welcome to this course on LangGraph, LangChain, and LangSmith. My name is Dirk van Meerveld and I will be your host and guide as we go on this exploration together. So what is up with all these Lang-words? Well, in short: LangChain Let’s get started with LangChain🔗 first. Langchain is a framework designed

Hi and welcome to this course on building complex multi-agent teams and setups using LangGraph, LangChain, and LangSmith. In this course we’ll start from the ground up using LangChain, and then build and build, adding more complexity and tools as we go along. We will learn how to build a graph with paths, conditional paths,

Hi and welcome back to part 4, where we’ll be training and running ChrisGPT. In this part, we’ll finally be using the OpenAI fine-tuning API endpoints, which are fairly simple! There are two ways to use the fine-tuning API, both of which are very simple. The first way to do this is programmatically, using Python

Welcome back to part 3! This is where we’re going to do the last preparation and data validation steps on our dataset and also calculate how much it’s going to cost us to train the model. JSONL format Remember in part 1 where we discussed the training data? We discussed the data needing to be

Hi and welcome back to part 2, where we will be generating the training data for our own ChrisGPT model! We’ll pick up where we left off in part 1 and use our HTML to Markdown converter utility and more to write and run our training dataset generator that will build the ChrisGPT training data