Mastering AI Development: Hands-On Projects & Deep Insights with Python, LangChain & OpenAI's Advanced LLMs

What you'll learn

Grasp LangChain & LLMs: Dive deep into their functionalities and core mechanisms.

Master LangChain Modules: Understand Parsers, Memory, Routers, and their interplay.

Hands-on Tool Creation: Learn to build tools using LangChain, Embeddings, and Document Splitting.

Craft Real-World AI Apps: Develop applications like Bill Extractor and Multi-doc Chatbot.

Optimize AI Performance: Learn best practices for efficient, scalable LangChain implementations.

Requirements

Some programming experience required

We'll be using Python in this course; although you don't need to know Python for this course, you do however need to have some programming experience

Description

Welcome to the ultimate guide on building autonomous AI tools using LangChain, OpenAI APIs and LLMs.Whether you're an AI novice or a tech enthusiast eager to upgrade your skills, this course will help you harness the power of large language models (LLMs) like GPT-4 to create next-generation applications.Dive deep into the transformative world of LangChain and Large Language Models (LLMs) with this comprehensive course tailored for novices and seasoned professionals. This meticulously designed curriculum offers you a step-by-step journey through the unique facets of LangChain — from understanding its intricate layers, such as Parsers, Memory, and Routers, to mastering the tools it offers like Vectorstores and Embeddings.But we don't stop at theory. Our hands-on approach ensures you apply your newfound knowledge through engaging real-world applications. Discover how to extract crucial information with a Bill Extractor Application, engage users through a Multi-document Chatbot, and convert imagery into textual data. What You'll Learn:Dive deep into the world of LangChain and LLMs.Unlock the mysteries of Large Language Models (LLMs) and their application.Craft several real-world projects that showcase the true potential of LangChain and LLMs.Gain insights from detailed case studies across diverse industries.By the end of this course, you won't just understand LangChain; you'll be ready to implement it in diverse scenarios, pushing the boundaries of what's possible with AI.

Overview

Section 1: Introduction

Lecture 1 Welcome

Lecture 2 Introduction & Course Pre-requisites

Lecture 3 What You'll Build in this Course - Demo

Lecture 4 Connect with Me

Section 2: Download Course Resources

Lecture 5 Download Code

Section 3: Development Environment Setup

Lecture 6 Setup OpenAI API - API Key

Lecture 7 Install Python - Full Instructions

Lecture 8 Setup VS Code and Python Extensions

Section 4: LangChain and LLMs - Deep Dive

Lecture 9 What's an LLM

Lecture 10 LangChain Deep Dive - How it Works and Benefits

Lecture 11 Setup Python Environment VS Code

Lecture 12 LangChain Building Blocks - Components - Chains - Agents

Lecture 13 LangChain Language Model Types

Lecture 14 LangChain Language Model Types

Section 5: Checkpoint

Lecture 15 Checkpoint - How are Things?

Section 6: LangChain Prompts Template

Lecture 16 LangChain Prompt Template - Introduction and Motivation

Lecture 17 Prompt Templates - Hands-on

Section 7: LangChain Parsers

Lecture 18 Parsers - Introduction

Lecture 19 Output Parsers - Hands-on

Lecture 20 Pydantic Output Parser - Introduction

Lecture 21 Pydantic Parser

Lecture 22 LangChain Building Blocks Summary

Section 8: LangChain Memory and Chains

Lecture 23 LangChain Memory - Introduction

Lecture 24 Memory Hands-On - ConversationBufferMemory

Lecture 25 LangChain Chains - Introduction

Lecture 26 LLMChain Hands-on

Lecture 27 LLMChain Input Variables - Hands-on

Lecture 28 Sequential Chain Hands-on

Lecture 29 Streamlit Application - Lullaby Generator - Demo

Lecture 30 Lullaby Application with Streamlit - Hands-on

Section 9: LangChain Routers, Document Loading and Document Splitting

Lecture 31 Router Chains - Introduction and Hands-on - Part 1

Lecture 32 Router Chains - Hands-on - Part 2

Lecture 33 LangChain Document Loading - Loading a PDF File

Lecture 34 Document Splitting - An Overview

Lecture 35 CharacterTextSplitter - Hands-on

Lecture 36 RecursiveCharacterTextSplitter - Hands-on

Section 10: LangChain Embeddings and Vectorstores

Lecture 37 Vectorstore & Embeddings - Full Overview

Lecture 38 Embeddings and Semantic Similarity Test - Hands-on

Lecture 39 Saving Embeddings to Chroma DB & Similarity Search

Lecture 40 LangChain Retrievers

Section 11: LangChain Agents - Deep Dive

Lecture 41 Agents - Introduction

Lecture 42 Agents - Motivation & Creating a Tool for an Agent

Lecture 43 Built-in Math Tool & Testing an Agent

Lecture 44 Adding a General Knowledge Tool for Our Agent

Lecture 45 Agents Types

Lecture 46 Looking Into the Agents Prompt Template

Lecture 47 Conversational Agent and Memory - Hands-on

Lecture 48 LangChain Docstore Agent

Lecture 49 Self-Ask-with-Search Agent

Lecture 50 What We've Learned So Far - Recap

Section 12: [REAL-WORLD] App - PDF Extractor

Lecture 51 Bill Extractor - Project Introduction and Functions Setpu

Lecture 52 Front-end Setup and Testing

Section 13: [REAL-WORLD] App - Newsletter Generator

Lecture 53 Newsletter Generator Demo

Lecture 54 Setup the Search Function with Serper API Key and Testing

Lecture 55 Picking the Best Articles Function and Testing

Lecture 56 Article Summary

Lecture 57 Fixing a Python Libmagic Bug

Lecture 58 Generating the Newsletter

Lecture 59 Creating the Frontend with Streamlit - Final Result

Section 14: [REAL-WORLD] App - Multi-document Chatbot

Lecture 60 Document Chatbot - Resumé Analyzer Bot

Lecture 61 Document Chatbot with LangChain QAChain

Lecture 62 Multi-Document Chatbot with Streamlit - Full Chatbot

Section 15: [REAL-WORLD] App - Image to Text

Lecture 63 Image to Recipe App - Demo

Lecture 64 Setup HuggingFace Token & Generating Text from an Image

Lecture 65 Text to Speech

Lecture 66 Generating Recipes from Image - Image Captioning

Lecture 67 Adding a Frontend with Streamlit - Text to Recipe Application - Final Result

Section 16: Next Steps

Lecture 68 Next Steps

Data Scientists: Individuals keen on integrating advanced AI models and LangChain tools into their data-driven projects for enhanced insights and automation.,Product Managers: Professionals looking to incorporate cutting-edge AI features into their products, enhancing user experience and solution capabilities.,AI Enthusiasts: Anyone passionate about the AI realm, eager to expand their knowledge horizon with the intricacies of LangChain and real-world applications.,Tech Innovators: Entrepreneurs and startup founders aiming to leverage LangChain's capabilities to pioneer next-generation solutions in the market.,Programmers: Coders and developers aiming to diversify their skill set by mastering LangChain, opening doors to novel AI-driven development opportunities.