Langchain for beginners : Build GenAI LLM Apps in Easy Steps

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Bharath Thippireddy

3:18:32

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1. Introduction

1. Introduction.mp4

02:21

2. How to make the best.mp4

01:34

3. Download Completed Project.mp4

00:26

2. The Fundamentals

1. What is GenAI.mp4

02:32

2. What is OpenAI.mp4

01:50

3. Other LLMs.mp4

02:46

4. What is Langchain.mp4

03:04

5. The Fundamentals.html

3. Software Setup

1. Setup OpenAI Account.mp4

02:40

2. Setup Open Source LLMs.mp4

05:00

4. Langchain in action

1. Setup Project.mp4

01:42

2. Langchain in action.mp4

04:16

3. Use Open Source Models Locally.mp4

02:38

4. Use Mistral AI.html

5. What is Streamlit.mp4

02:41

6.1 Use Streamlit.mp4

04:27

6. Use Streamlit GUI.mp4

04:46

7. Turn on Debug.mp4

02:41

8. Langchain in action.html

5. Prompt Templates

1. Introduction.mp4

02:01

2. PromptTemplate in action.mp4

04:17

3. Add two more place holders.mp4

03:45

4. Improve the prompt.mp4

02:33

5. Create a Travel Guide App.mp4

05:32

6. Prompt Template.html

7. Interview Helper.html

6. Chains

1. Introduction.mp4

02:59

2. LCEL In Action.mp4

03:19

3. UseCase and Code Walkthrough.mp4

01:45

4. Simple Sequential Chain.mp4

06:19

5. Display the title.mp4

02:53

6. Blog Post Generator.html

7. Using Multiple LLMs.mp4

03:18

8. Sequential Chain.mp4

03:28

9. Format Output.mp4

03:50

10. Marketing Email Generator.html

11. Organize Files.mp4

01:08

12. LCEL and Chains.html

7. Maintaining ChatHistory

1. Introduction.mp4

01:39

2. Use ChatPromptTemplate.mp4

05:12

3. Code Walk Through.mp4

04:31

4. Use StreamlitChatMessageHistory.mp4

05:33

5. Display History.mp4

01:59

6. Use ChatMessageHistory.mp4

03:29

7. ChatHistory.html

8. Embeddings

1. Introduction.mp4

01:58

2. Using the Embeddings Model.mp4

02:21

3. Similarity Finder.mp4

04:04

4. Get Multiple Embeddings.html

9. Vector Stores

1. Introduction.mp4

02:53

2. Code Walk Through.mp4

04:31

3. Implement Job Search Helper.mp4

03:31

4. Test.mp4

03:31

5. Use Retriever.mp4

01:47

6. Use FAISS Vector Store.html

10. RAG - Working With Documents

1. What is RAG.mp4

03:55

2. UseCase and Code Walkthrough.mp4

04:14

3. Implement RAG Part 1.mp4

03:29

4. Implement RAG Part 2.mp4

02:38

5.1 rag questions.txt

5. Test.mp4

02:37

6. History Aware RAG Bot.mp4

04:18

7. Test.mp4

01:39

8. RAG.html

11. Image Processing

1. Introduction.mp4

01:36

2.1 images analysis.zip

2. Create Image Analyzer App.mp4

03:09

3. Use Streamlit.mp4

04:31

12. Agents

1. Introduction.mp4

03:12

2. Code Walk Through.mp4

03:59

3.1 agent demo.zip

3. Setup Project.mp4

01:27

4. Create an Agent.mp4

02:47

5. Test.mp4

04:36

13. Deployment

1. Introduction.mp4

02:37

2. Update Code.mp4

04:02

3.1 requirements.txt

3. Push to GitHub.mp4

01:39

4. Deploy.mp4

02:37

Description

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

Who is this for?

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

What You Need to Know?

Knowledge of Python

OpenAI Account to work with OpenAI LLMs

More details

Description
Welcome to LangChain for Beginners!
This course is designed to provide a gentle, step-by-step introduction to LangChain, guiding you
from the basics to more advanced concepts. Whether you're a complete novice or have some
experience with AI, this course will help you understand and leverage the power of LangChain for
building AI-powered applications.
Course Goals:
- Gradual Learning: Learn LangChain gradually from basic to advanced topics with clear and
concise instructions.
- Comprehensive Understanding: Understand why LangChain is a powerful tool for building AI
applications and how it simplifies the integration of language models into your projects.
- Hands-On Experience: Gain practical experience with essential LangChain features such as
prompt 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 core
concepts.
- 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 LangChain
development 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 build
your skills.
By the end of this course, you will:
Learn what LangChain is how it simplifiesÂ 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)
ImplementÂ (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

Who this course is for:
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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Natural Language Processing

Bharath Thippireddy

Instructor's Courses

Bharath Thippireddy is an Entrepreneur, Software Architect and Public Speaker who has trained 600000 + students across the planet. He is Oracle Certified Developer, Web Component Developer, Business Component Developer, Web Services Developer.He loves learning new things both in technology and personal development and shares them on YouTube and his web site. He have mentored students in class room trainings as well as in the corporate world both in India as well as in USA. He has spoken on technical topics at several agile conferences. While in India he also voluntarily teaches interview and soft skills at Vivekananda Kendra.His trainings will help you master Full Stack Development using Java,Python,JavaScript and DevOps, AWS, Docker, Kubernetes.From 30+ Courses which currently have 600K+ Learners you can pick a track and master Spring Boot Project Development using Angular and ReactAngular and React project creation with Java or Node backendComplete Python Stack from core Python to Django REST FrameworkDocker , Kubernetes, Maven, Jenkins ,GIT , AWS EC2 ,Elastic Beanstalk,ELB,Auto Scaling and more in easy stepsJava (Java Design Patterns,Java Web Services,Java Messaging Service) Spring modules ( Spring Security, Spring Boot,Spring Data using Hibernate,Spring Data REST)Serverless programming using AWS Lambda and more

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language english
Training sessions 63
duration 3:18:32
Release Date 2024/09/22

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