LangChain- Develop LLM powered applications with LangChain

Learn LangChain by building FAST a real world generative ai LLM powered application LLM (Python, Latest Version 0.3.0)

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What you'll learn

• Become proficient in LangChain
• Have 3 end to end working LangChain based generative AI applications
• Prompt Engineering Theory: Chain of Thought, ReAct, Few Shot prompting and understand how LangChain is build under the hood
• Understand how to navigate inside the LangChain opensource codebase
• Large Language Models theory for software engineers
• LangChain: Lots of chains Chains, Agents, DocumentLoader, TextSplitter, OutputParser, Memory
• RAG, Vectorestores/ Vector Databasrs (Pinecone, FAISS)
• Model Context Protocol

Description

COURSE WAS RE-RECORDED and supports- LangChain Version 0.3.0

Welcome to first LangChain Udemy course - Unleashing the Power of LLM!

This  course is designed to teach you how to QUICKLY harness the power the LangChain library for LLM applications.

This course will equip you with the skills and knowledge necessary to develop cutting-edge LLM solutions for a diverse range of topics.

Please note that this is not a course for beginners. This course assumes that you have a background in software engineering and are proficient in Python. I will be using Pycharm IDE but you can use any editor you'd like since we only use basic feature of the IDE like debugging and running scripts .

In this course, you will embark on a journey from scratch to building a real-world LLM powered application using LangChain.

We are going to do so by build 3 main applications:

Ice Breaker- LangChain agent that given a name, searches in google to find Linkedin and twitter profiles, scrape the internet for information about a name you provide and generate a couple of personalized ice breakers to kick off a conversation with the person.

Documentation Helper- Create chatbot over a python package documentation. (and over any other data you would like)

A slim version of ChatGPT Code-Interpreter

Prompt Engineering Theory Section

Introduction to LangGraph

Introduction to Model Context Protocol (MCP)

The topics covered in this course include:

LangChain

LLM + GenAI History

LLMs: Few shots prompting, Chain of Thought, ReAct prompting

Chat Models

Open Source Models

Prompts, PromptTemplates, langchainub

Output Parsers, Pydantic Output Parsers

Chains: create_retrieval_chain, create_stuff_documents_chain

Agents, Custom Agents, Python Agents, CSV Agents, Agent Routers

OpenAI Functions, Tool Calling

Tools, Toolkits

Memory

Vectorstores (Pinecone, FAISS)

RAG (Retrieval Augmentation Generation)

DocumentLoaders, TextSplitters

Streamlit (for UI)

LCEL

LangSmith

Intro to LangGraph

FireCrawl

GIST of Cursor IDE 

Cursor Composter

Curser Chat

MCP - Model Context Protocol & LangChain Ecosystem

Throughout the course, you will work on hands-on exercises and real-world projects to reinforce your understanding of the concepts and techniques covered. By the end of the course, you will be proficient in using LangChain to create powerful, efficient, and versatile LLM applications for a wide array of usages.

DISCLAIMERS

Please note that this is not a course for beginners. This course assumes that you have a background in software engineering and are proficient in Python.

I will be using Pycharm IDE but you can use any editor you'd like since we only use basic feature of the IDE like debugging and running scripts.

The first project of the course (Ice-Breaker) requires usage of 3rd party APIs-

Scrapin / ProxyURL, Tavily, Twitter API  which are generally paid services.

All of those 3rd parties have a free tier we will use to create stub responses development and testing.

Who this course is for:

• Software Engineers that want to learn how to build Generative AI based applications with LangChain
• Developers that want to learn how to build Generative AI based applications with LangChain
• Engineers that want to learn how to build Generative AI based applications with LangChain

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