LangChain Lecture 1: Large Language Models, Prompt Templates, and Basic Chain Building
Education
Introduction
Hello everyone! My name is Rupang N, and I'm a professor at New York University. In this series of YouTube video recordings, I'm excited to teach you how to leverage the power of LangChain to construct retrieval-augmented generation (RAG) systems. These systems can utilize large language models (LLMs) while grounding them in a vector database or vector store that we will build together.
I anticipate that this will be a systematic series of videos, delving into how you can employ the LangChain ecosystem to create RAG applications. For those seeking a comprehensive resource, I highly recommend "Learning LangChain" by Mayo Aen and Nudo Campos. This book stands out as one of the most extensive and well-written introductions to the LangChain ecosystem. It’s currently under active development, but if you are already a reader, you can access the initial draft, which includes six completed chapters.
In this first video, we'll be focusing on the fundamentals of LLMs using LangChain. First, let me introduce you to the system we'll be using. You can use your own VS Code or Google Colab for this course, but I believe you'll find the online platform called Lightning AI Studio to be incredibly useful. It provides the capability to run VS Code and Jupyter Notebooks online, which makes it easier to share code and collaborate on building applications.
Lightning AI Studio offers a free account, where you'll be able to create multiple documents. The advantages of using this platform include the ability to store your code persistently without the need to reinstall libraries, collaborating with your team, and even deploying applications directly to the cloud.
Now, let's focus on the key functionalities of LangChain with LLMs. The first step is to import some necessary libraries, including LangChain, along with several of its subsidiary libraries such as open AI, LangChain community, and langchain.text_splitters. A significant benefit of Lightning AI is that unlike Google Colab, you won't need to install these libraries every session; they will remain available in your online studio.
After importing the necessary modules, including OS and dotenv for later use with the OpenAI API, you’ll set your API key in the environment for security. For demonstration purposes, we can use the inexpensive GPT-3.5 Turbo model, which is suitable for testing.
Once we invoke the model, I will show you how to ask a simple philosophical question—“What is the meaning of life?”—and observe the response. Following this, we will explore how to use the chat model from OpenAI and how to include human and AI messages, along with a system message that governs the behavior of the AI agent.
With the ability to create prompt templates and chat prompt templates, you’ll learn how to streamline your interactions with any model. We’ll also explore how to import data into a specific format, ensuring that the output complies with your requirements (for instance, CSV, JSON, etc.).
As we move through the tutorial, I will illustrate how to exert control over the model output by structuring responses with specific formats using PyIC's BaseModel class and output parsers. Additionally, you’ll learn how to invoke the model using specific commands, whether for single queries, batch processing, or using streaming methods.
After demonstrating the imperative way of combining LLM components, I will introduce you to the declarative method, which allows you to combine different functions using a pipe method. This method streamlines the chain-building process to make it more succinct and user-friendly.
In conclusion, this first session serves as an introduction to LangChain and provides foundational knowledge to move forward in our series. I will include a link to the code on GitHub in the show notes for your reference. The next session will discuss indexing, corresponding to chapter two of the "Learning LangChain" textbook.
Thank you for your attention, and I look forward to seeing you soon!
Keyword
LangChain, Large Language Models, Prompt Templates, Chain Building, RAG Systems, OpenAI API, GPT-3.5 Turbo, Lightning AI Studio, Declarative Method, Imperative Method.
FAQ
Q: What is LangChain?
A: LangChain is an ecosystem designed to help developers build applications that integrate Large Language Models (LLMs) with robust data retrieval systems.
Q: How do I install LangChain?
A: You need to import LangChain and its subsidiary libraries in your Python environment, either in an IDE like VS Code or an online platform like Lightning AI Studio.
Q: What is the purpose of Prompt Templates in LangChain?
A: Prompt Templates allow you to create reusable message structures that simplify interactions with LLMs, making it easier to manage and send requests.
Q: How does the declarative method of building chains differ from the imperative method?
A: The declarative method utilizes pipes to connect different functions succinctly, while the imperative method involves explicitly building functions that can be invoked later.
Q: What will the next session cover?
A: The next session will focus on indexing, aligned with chapter two of the "Learning LangChain" textbook, providing further insights and practical examples.