LangChain- Develop LLM powered applications with LangChain
- Offered byUDEMY
LangChain- Develop LLM powered applications with LangChain at UDEMY Overview
Duration | 7 hours |
Total fee | ₹399 |
Mode of learning | Online |
Difficulty level | Intermediate |
Credential | Certificate |
LangChain- Develop LLM powered applications with LangChain at UDEMY Highlights
- 30-Day Money-Back Guarantee
- Certificate of completion
- Full lifetime access
- Learn from 11 downloadable resources
LangChain- Develop LLM powered applications with LangChain at UDEMY Course details
- 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)
- 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:LangChainHistoryLLMs: Few shots prompting, Chain of Thought, ReAct promptingChat ModelsPrompts, PromptTemplatesOutput ParsersChains: SequentialChain, LLMChain, RetrievalQA chainAgents, Custom Agents, Python Agents, CSV Agents, Agent RoutersOpenAI FunctionsTools, ToolkitsMemoryVectorstores (Pinecone, FAISS)DocumentLoaders, TextSplittersStreamlit (for UI)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 meGithub links with additional AI resources, FAQ, troubleshooting guidesAccess to an exclusive Discord community to connect with other learners (1000+ members)No extra cost for continuous updates and improvements to the courseDISCLAIMERSPlease 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.
LangChain- Develop LLM powered applications with LangChain at UDEMY Curriculum
Introduction
Introduction
Course Structure + How to get the best of Udemy [PLEASE DO NOT SKIP]
What is LangChain?
Course's Discord Server
The GIST of LangChain- Get started by with your "Hello World" chain
Project Setup (Pycharm) recommend)
Project Setup (vscode) - optional
Your First LangChain application - Chaining a simple prompt
Quick Check In
Ice Breaker Real World Generative AI Agent application
Ice Breaker- What are we building here?
Integrating Linkedin Data Processing - Part 1 - Scraping
Linkedin Data Processing - Part 2 - Agents Theory
Linkedin Data Processing- Part 3: Tools, AgentType & initialize_agent
Linkedin Data Processing- Part 4: Custom Agent Implementation & Testing
[Optional] Twitter Data Processing- Part 1- Scraping
[Optional] Twitter Data Processing- Part 2- Agents (Optional)
Output Parsers- Getting Ready to work with a Frontend
FullsStack App- Building our LLM powered by LangChain FullStack Application
Diving Deep Into ReAct Agents- Whats is the magic?
What are we building? ReAct AgentExecutor from scratch
Environment Setup + ReAct Algorithm overview
Defining Tools for our ReAct agent
ReAct prompt, LLM Reasoning Engine, Output Parsing and Tool Execution
AgentAction, AgentFinish, ReAct Loop
CallbackHandlers, ReAct Prompt and finalizing the ReAct Agent loop
The GIST of Embeddings, Vector Databases and, VectorDBQA chain & RetrievalQA
Theoretical Introduction to embeddings, Vectorstores & RetrievalQA chain (RAG)
LangChain classes review: Pinecone, OpenAIEmbeddings, RetrievalQA, TextLoader
Medium Analyzer- Boilerplate Project Setup
Medium Analyzer- Implementation
Chat With Your PDF- FAISS Local Vectorstore
Building a documentation assistant (Embeddings, VectorDBs, RetrievalQA, Memory)
What are we building?
Building an AI LangChain Chat Assistant- Vectorstore Pincone Ingestion
Building an AI LangChain Chat Assistant- RetrievalQA chain (prompt augmentation)
Building an AI LangChain Chat Assistant- "Frontend" with Streamlit (UI)
Building an AI LangChain Chat Assistant- Memory & ConversationalRetrievalChain
Building a slim ChatGPT Code-Interpreter (Advanced Agents, OpenAI Functions)
What are we building? (A slim Version of GPT Code-Interpreter)
Project Setup
Python Agent
CSV Agent
Wrapping Everything: Router Agent + OpenAI functions
LangChain Theory
LangChain Token Limitation Handeling Strategies
LangChain Memory Deepdive
Prompt Engineering Theory
The GIST of LLMs
What is a Prompt? Composition of a formal prompt
Zero Shot Prompting
Few Shot Prompting
Chain of Thought Prompting
ReAct
Prompt Engineering Quick Tips
Troubleshooting Section
Have a technical issue? WATCH THIS FIRST. I Promise this will help!
Tweet API- tweepy.errors.Forbidden: 403 Forbidden
Wrapping Up
LLM Applications in Production
LLM Application Development landscape
Finished course? Whats next!
Useful tools when developing LLM Applications
LangChain Hub - Downloads prompt from the community
TextSplitting Playground
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