LangChain for Generative AI Pipelines
Published by Pearson
Develop LLM chatbots, prompts, and workflows with LangChain
- Explore different LLM APIs
- Understand the LangChain framework
- Build LLM applications using LangChain
LangChain is the state-of-the-art framework for developing Large Language Model (LLM) based applications. It provides a wide range of Lego-like components to streamline the integration of various LLM functionalities into functional pipelines without requiring in-depth expertise in ML.
In this course, students will get an in-depth view of the structure of LangChain and its various components. You will learn how to apply these components to Information Retrieval and the development of chatbots. An overview of the pros and cons of LLMs from OpenAI, HuggingFace, and Anthropic, as well as a primer on Prompt Engineering, will also be provided to empower students to make the best use possible of the capabilities that LangChain puts at their fingertips.
What you’ll learn and how you can apply it
- Learn the pros and cons of different LLMs
- Understand the structure of the LangChain library
- Build LLM apps with LangChain
- Integrate an LLM with external APIs
- Develop a Chatbot
This live event is for you because...
- You’re a data scientist or software engineer who wants to develop generative AI pipelines
- You want to finetune an LLM
- You’d like to integrate models from OpenAI, HuggingFace, or Anthropic
Prerequisites
- Python Programming
- Basic Understanding of Generative AI and LLMs
- Fundamentals of NLP
Course Set-up
- Complete Python distribution (like Anaconda, etc.)
- https://github.com/DataForScience/LangChain
Recommended Preparation
- Attend: Generative Artificial Intelligence with the OpenAI API for Developers by Bruno Goncalves
- Attend: ChatGPT and Competing LLMs by Bruno Goncalves
- Watch: GenAI Superstream: Possibilities and Pitfalls by Thomas Neild et al
Recommended Follow-up
- Watch: Generative AI with LangChain by Ben Auffarth
- Read: Natural Language Processing with Transformers, Revised Edition, by Lewis Tunstall, Leandro von Werra, & Thomas Wolf
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Segment 1: Generative AI (45 minutes)
- Overview of Generative Models
- Comparison of GPT to other LLMs
- Text to Image Models
Q&A (5 minutes)
Break (5 minutes)
Segment 2: LangChain (45 minutes)
- LangChain structure
- Understanding Chains
- Exploring Agents
- Using tools to interact with the world
Q&A (5 minutes)
Break (10 minutes)
Segment 3: Information Processing (30 minutes)
- Understanding Text Summarization
- Information Extraction Applications
- Developing a Question Answering
Q&A (5 minutes)
Segment 4: Chatbots (30 minutes)
- Information Retrieval and Vectors
- Retrievers in LangChain
- Implementing a simple Chatbot
Q&A (5 minutes)
Break (10 minutes)
Segment 5: Prompt Engineering (30 minutes)
- Overview of Prompt Engineering Techniques
- Comparison of Zero-Shot and Few-Shot Prompting
- Understanding Chain of Thought prompts
- Developing Tree of Thought prompts
Q&A (5 minutes)
Course wrap-up and next steps (10 minutes)
Your Instructor
Bruno Gonçalves
Bruno Gonçalves is an author, public speaker, corporate trainer, and consultant specializing in Generative AI, Blockchain Analytics, and Machine Learning. He has a diverse background that spans academia and industry, having previously served as a Data Science fellow at NYU's Center for Data Science while on leave from his tenured faculty position at Aix-Marseille Université. Bruno earned his PhD in the Physics of Complex Systems in 2008. He later focused his research on applying Data Science and Machine Learning to the large-scale analysis of online human behavior.