Computer Science

Welcome back to part 3 where we’ll take a look at LangChain agents. This is where LLMs like ChatGPT start to get really cool and also a bit scary at the same time. We’re going to give the decision-making powers to ChatGPT and let it decide which action it needs to take. An action can […]

Welcome back to part two, where we’re going to ‘chat’ with an entire book! We’ll be able to ask a textual question and we’ll receive a textual response based on the information inside the book. Besides being really cool, what are the practical real-world use cases for this? Imagine you’re a writer, and you are

Welcome to this tutorial series on LangChain. My name is Dirk van Meerveld, and it is my pleasure to be your host and guide for this tutorial series! We’re going to be using LangChain to further improve our ChatGPT superpowers and explore more cool ways in which we can put these powerful technologies to practical

Hi and welcome! I’m pleased to introduce this course on the Python LangChain library for working with Large Language Models. We’ll be using LangChain to greatly increase our ChatGPT superpowers and look at cool, but also real-world practical applications. Even though we’ll be using ChatGPT in this series, you will also be able to apply

💡 Full Course with Videos and Course Certificate (PDF): https://academy.finxter.com/university/openai-api-function-calls-and-embeddings/ Welcome back to the final part of this tutorial series. In this part, we’ll be looking at simple sentiment analysis using embeddings. For most text classification tasks, fine-tuned machine learning models will do better than embeddings, because they have been meticulously tuned and trained on

💡 Full Course with Videos and Course Certificate (PDF): https://academy.finxter.com/university/openai-api-function-calls-and-embeddings/ Welcome back to part 6 of this tutorial series. In this part, we’re going to be looking at embeddings. What is an embedding? Without getting into algorithmic arithmetic an embedding is basically a numerical (vector) representation of text that makes no sense to us, but

💡 Full Course with Videos and Course Certificate (PDF): https://academy.finxter.com/university/openai-api-function-calls-and-embeddings/ Welcome back to part 5, Where we’ll be calling functions that do not even exist. We’ll also briefly touch on pitfalls, failures, and retrying to make our code more robust as introducing AI tends to make our code a little more unpredictable and therefore potentially

💡 Full Course with Videos and Course Certificate (PDF): https://academy.finxter.com/university/openai-api-function-calls-and-embeddings/ Welcome back to part 4, where we’ll be doing something mind-blowing yet again! In this part, we will be connecting ChatGPT to a database and then using ChatGPT to extract information from the database using only natural language on the end user’s part. This is

💡 Full Course with Videos and Course Certificate (PDF): https://academy.finxter.com/university/openai-api-function-calls-and-embeddings/ Welcome back to part 3, where we’ll take things one step further and look at having multiple functions and even calling multiple functions in a row. First, let’s make a second function for ChatGPT to call. We have ChatGPT with access to the current weather

💡 Full Course with Videos and Course Certificate (PDF): https://academy.finxter.com/university/openai-api-function-calls-and-embeddings/ Welcome to the second part of the tutorial, where we’ll look at the shortcomings of ChatGPT and use our functions with parameters to overcome them. First, make a file in your base directory called ‘Ba_chatgpt_no_weather.py’ and copy/paste the following basic code into it: This is