LangChain- Develop LLM powered applications with LangChain | Udemy [Update 02/2024]
English | Size: 3.27 GB
Genre: eLearning[/center]

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

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
Vectorestores/ Vector Databasrs (Pinecone, FAISS)

COURSE WAS RE-RECORDED ON MID APRIL 2024- LangChain Version 0.1.16 (No breaking changes in 0.2.0)

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

The topics covered in this course include:

LangChain

History

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

Chat Models

Prompts, PromptTemplates

Output Parsers

Chains: SequentialChain, LLMChain, RetrievalQA chain

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

OpenAI Functions

Tools, Toolkits

Memory

Vectorstores (Pinecone, FAISS)

DocumentLoaders, TextSplitters

Streamlit (for UI)

LCEL

LangSmith

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.

This is not just a course, it's  also  a community. Along with lifetime access to the course, you'll get:

Dedicated 1 on 1 troubleshooting support with me

Github links with additional AI resources, FAQ, troubleshooting guides

Access to an exclusive Discord community to connect with other learners (5000+ members)

No extra cost for continuous updates and improvements to the course

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-
ProxyURL, SerpAPI, 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
Backend Developers that want to learn how to build Generative AI based applications with LangChain
Fullstack engineers that want to learn how to build Generative AI based applications with LangChain

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