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Langchain For Beginners : Build Genai Llm Apps In Easy Steps
Published 8/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.49 GB | Duration: 3h 22m
A Step-by-Step Guide to Master LangChain
What you'll learn
Learn what LangChain is how it simplifies using LLMs in our applications
Use OpenAI LLMS in a python application
Use Open Source LLMS like Mistral,Gemma in a python application
Run Open Source LLMs on your local machine using OLLAMA
Use PromptTemplates to reuse and build dynamic prompts
Understand how to use the LangChain expression language
Create Simple and Regular Sequential chains using LCEL
Work with multiple LLMs in a single chain
Learn why and how to maintain Chat History
Learn what embeddings are and use the Embeddings Model to find text Similarity
Understand what a Vector Store is and use it to store and retrieve Embeddings
Understand the process of Retrieval Augmented Generation(RAG)
Implement (RAG) to use our own data with LLMs in simple steps
Analyze images using Multi Modal Models
Build multiple LLM APPs using Streamlit and LangChain
All in simple steps
Requirements
Knowledge of Python
OpenAI Account to work with OpenAI LLMs
Description
Welcome to LangChain for Beginners!This course is designed to provide a gentle, step-by-step introduction to LangChain, guiding youfrom the basics to more advanced concepts. Whether you're a complete novice or have someexperience with AI, this course will help you understand and leverage the power of LangChain forbuilding AI-powered applications.Course Goals:- Gradual Learning: Learn LangChain gradually from basic to advanced topics with clear andconcise instructions.- Comprehensive Understanding: Understand why LangChain is a powerful tool for building AIapplications and how it simplifies the integration of language models into your projects.- Hands-On Experience: Gain practical experience with essential LangChain features such asprompt templates, chains, agents, document loaders, output parsers, and model classes.What You Will Learn:- Introduction to LangChain: Get started with the basics of LangChain and understand its coreconcepts.- Building Blocks of LangChain: Learn about prompt templates, chains, agents, document loaders,output parsers, and model classes.- Creating AI Applications: See how these features come together to create a smart and flexible- Practical Coding: Write and run code examples to get a hands-on sense of how LangChaindevelopment looks like.Course Structure:- Concise Chapters: Each chapter focuses on a specific topic in LangChain programming,ensuring you gain a deep understanding of each concept.- Interactive Learning: Code along with the examples provided to reinforce your learning and buildyour skills.By the end of this course, you will:Learn what LangChain is how it simplifies using LLMs in our applicationsUse OpenAI LLMs in a python applicationUse Open Source LLMs like Mistral,Gemma in a python applicationRun Open Source LLMs on your local machine using OLLAMAUse PromptTemplates to reuse and build dynamic prompts Understand how to use the LangChain expression languageCreate Simple and Regular Sequential chains using LCEL Work with multiple LLMs in a single chainLearn why and how to maintain Chat HistoryLearn what embeddings are and use the Embeddings Model to find text SimilarityUnderstand what a Vector Store is and use it to store and retrieve EmbeddingsUnderstand the process of Retrieval Augmented Generation(RAG) Implement (RAG) to use our own data with LLMs in simple stepsAnalyze images using Multi Modal ModelsBuild multiple LLM APPs using Streamlit and LangChainAll in simple steps
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 How to make the best
Lecture 3 Download Completed Project
Section 2: The Fundamentals
Lecture 4 What is GenAI
Lecture 5 What is OpenAI
Lecture 6 Other LLMs
Lecture 7 What is Langchain
Section 3: Software Setup
Lecture 8 Setup OpenAI Account
Lecture 9 Setup Open Source LLMs
Section 4: Langchain in action
Lecture 10 Setup Project
Lecture 11 Langchain in action
Lecture 12 Use Open Source Models Locally
Lecture 13 What is Streamlit
Lecture 14 Use Streamlit GUI
Lecture 15 Turn on Debug
Section 5: Prompt Templates
Lecture 16 Introduction
Lecture 17 PromptTemplate in action
Lecture 18 Add two more place holders
Lecture 19 Improve the prompt
Lecture 20 Create a Travel Guide App
Section 6: Chains
Lecture 21 Introduction
Lecture 22 LCEL In Action
Lecture 23 UseCase and Code Walkthrough
Lecture 24 Simple Sequential Chain
Lecture 25 Display the title
Lecture 26 Using Multiple LLMs
Lecture 27 Sequential Chain
Lecture 28 Format Output
Lecture 29 Organize Files
Section 7: Maintaining ChatHistory
Lecture 30 Introduction
Lecture 31 Use ChatPromptTemplate
Lecture 32 Code Walk Through
Lecture 33 Use StreamlitChatMessageHistory
Lecture 34 Display History
Lecture 35 Use ChatMessageHistory
Section 8: Embeddings
Lecture 36 Introduction
Lecture 37 Using the Embeddings Model
Lecture 38 Similarity Finder
Section 9: Vector Stores
Lecture 39 Introduction
Lecture 40 Code Walk Through
Lecture 41 Implement Job Search Helper
Lecture 42 Test
Lecture 43 Use Retriever
Section 10: RAG - Working With Documents
Lecture 44 What is RAG
Lecture 45 UseCase and Code Walkthrough
Lecture 46 Implement RAG Part 1
Lecture 47 Implement RAG Part 2
Lecture 48 Test
Lecture 49 History Aware RAG Bot
Lecture 50 Test
Section 11: Image Processing
Lecture 51 Introduction
Lecture 52 Create Image Analyzer App
Lecture 53 Use Streamlit
Section 12: Agents
Lecture 54 Introduction
Lecture 55 Code Walk Through
Lecture 56 Setup Project
Lecture 57 Create an Agent
Lecture 58 Test
Section 13: Deployment
Lecture 59 Introduction
Lecture 60 Update Code
Lecture 61 Push to GitHub
Lecture 62 Deploy
Python Developers who want to use LangChain to build GenAI LLM applications,Any students who has completed my Python or OpenAI course and who want to master LanChain
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