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Keep shoppingBeginner-Friendly LangChain Course: Build an AI PDF Document
1,067 students
Beginner-Friendly LangChain Course: Build an AI PDF Document
Learn LangChain for AI Applications: Basics to Building PDF Document Search with Prompts, Chains, and Agents
Created byGurinder Ghotra
Last updated 12/2024
English
What you'll learn
- Build an LLM based App from scratch using Streamlit.
- Learn how Agents work. Understand the 3 components of Agents and code an Agent in LangChain
- Learn how Chains work. Understand and build Simple and Sequential Chains.
- Learn what are Prompts and how to use its structure
- Understand how LangChain works. What are the components that make this library so effective
Explore related topics
Course content
6 sections • 13 lectures • 1h 15m total length
Requirements
- There is no pre-requisite. I assume no knowledge of Langchain or Large Language Models
Description
Course Overview:
This beginner-friendly LangChain course is designed to help you start using LangChain to develop LLM (Large Language Model) applications with NO prior experience! Through hands-on coding examples, you'll learn the foundational concepts and build up to creating a functional AI app for PDF document search.
Why LangChain?
LangChain is poised to become as essential to LLM applications as Pandas is to Data Science. This core library will be invaluable for Data Scientists and Machine Learning professionals building applications with Large Language Models.
What You Will Learn:
LangChain Basics: Gain an understanding of Prompts, Chains, and Agents with easy-to-follow code examples.
Prompts: Learn what a Prompt is and how to create Prompt templates to automate inputs.
Chains: Discover how Prompts integrate into Chains, exploring both Simple and Sequential Chains.
Agents: Master the three components of Agents—Tools, LLMs, and Agent types. Explore Tools like Wikipedia, SerpAPI, and LLMmath to leverage the power of Agents.
Processing PDF Documents: Learn to load and process PDF documents with Large Language Models, utilizing embeddings and vector stores.
Enhancing Model Performance: Work with prompts to improve querying capabilities of GPT-3.5.
Deploying Your AI App: Serve your AI app using Streamlit, making it accessible for real-world applications.
Who This Course Is For:
Beginners interested in building applications with Large Language Models.
Anyone with a limited understanding of Python—this course breaks down the code step-by-step.
Course Highlights:
Step-by-step guidance on building a Large Language Model (LLM) based AI app for PDF document search.
Comprehensive understanding of LangChain's components.
Practical knowledge for deploying AI apps with Streamlit.
All necessary code files and data are provided.
Who this course is for:
- Beginner A.I. enthusiasts who want to understand how Large Language Models can be used by using Langchain
- Serve LLM based app using Streamlit