• 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

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 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 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