Programming

LangChain

Build LLM applications with LangChain for prompts, chains, agents, memory, and retrieval-augmented generation (RAG).

Janakiram MSV

Technology Analyst, Advisor, Architect

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Description

LangChain is an innovative platform that enables developers and AI practitioners to harness the power of powerful LLMs such as GPT-4 for creating dynamic applications, ranging from personal projects to enterprise-level solutions.

This course introduces LangChain to beginners in an approachable and easy-to-understand format. Each lecture includes demonstrations that walk you through setting up and using LangChain with an emphasis on simplicity. Practical coding exercises are included to help you practice constructing prompts, managing model interactions, and integrating external data sources directly within your browser—no separate setup is required. These exercises will validate your understanding and ensure you're on the right track when using LangChain effectively.

By the end of this course, you will not only understand the fundamentals of LangChain but also how to effectively implement it in various use cases, enabling you to bring your AI-driven applications to life.

About the instructor

Janakiram MSV is an internationally recognized analyst, advisor and an architect in the field of Microservices, IoT, Edge Computing, and AI.

He was the founder and CTO of Get Cloud Ready Consulting, a niche Cloud Migration and Cloud Operations firm that was acquired by Aditi Technologies.

Through his speaking, writing, and analysis, he helps businesses take advantage of emerging technologies.

Janakiram is a regular contributor at Forbes and The New Stack. His articles are published at Business Insider, Computer Weekly, TechRepublic, and YourStory.

He is currently an advisor to Bay Area startups from the container storage, edge computing, and machine learning domains.

Janakiram is an adjunct faculty at the International Institute of Information Technology (IIIT-H) where he teaches Big Data, Cloud Computing, Containers, and DevOps to the students enrolled for the Master’s course.

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Course Content

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Introduction

Topics

Lesson Content

Course Introduction (02:00)

Join Our Community

Building Blocks of LLM Apps

Topics

Lesson Content

Building Blocks of an LLM Application (05:21)

Prompts and LLM (02:36)

LLM Context (03:46)

LLM Response (01:43)

History (02:32)

Feedback – Building Blocks of LLM Apps

Overview of LangChain

Topics

Lesson Content

What is LangChain? (03:50)

Why LangChain? (04:13)

Key Use Cases (01:30)

Feedback – Overview of LangChain

Key Components of LangChain

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Lesson Content

Building Blocks of LangChain (01:10)

Feedback – Key Components of LangChain

Interacting with LLMs

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Lesson Content

Setting up Environment (01:55)

Types of Language Models (01:39)

Getting Started with LLMs (04:40)

Messages in ChatModel (02:28)

Messages in ChatModel – Demo (03:54)

Lab: Invoking LLMs

Prompt Templates (02:02)

Prompt Templates – Demo (05:01)

Lab: Prompt Templates

Few-shot Prompt Templates (07:39)

Lab: Few-shot Prompt Templates

Parsing Model Output (02:34)

Parsing Model Output – Demo 1 (07:08)

Parsing Model Output – Demo 2 (04:36)

Parsing Model Output – Demo 3 (07:26)

Lab: Parsing Model Output

Feedback – Interacting with LLMs

Tips, Tricks, and Resources

Topics

Lesson Content

Key Libraries (04:02)

Key Libraries – Demo (05:00)

Tips & Tricks (01:13)

Debugging LangChain Applications (03:21)

Using Verbose Flag (01:59)

Callbacks (04:03)

Resources (07:29)

Lab: Tips & Tricks

Feedback – Tips, Tricks, and Resources

Introduction to LCEL

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Lesson Content

Overview of LCEL (07:06)

LCEL – Demo 1 (05:33)

LCEL – Demo 2 (01:41)

LCEL – Demo 3 (02:24)

LCEL – Demo 4 (09:36)

LCEL – Demo 5 (06:20)

LCEL – Demo 6 (08:52)

Lab: LCEL

Feedback – Introduction to LCEL

Adding Memory to LLM Apps

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Lesson Content

Adding Memory to LLM Apps (03:13)

Demo: Adding Short-Term Memory (06:24)

Exploring Configurable Parameters (06:09)

Dealing with Memory (12:09)

Lab: Memory for LLMs

Feedback – Adding Memory to LLM Apps

Performing Retrieval

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Lesson Content

Performing Retrieval (17:00)

Loading PDFs (03:18)

Loading Webpages (02:36)

Chunking Documents (05:52)

Generating Embeddings (04:37)

Performing Semantic Search (07:05)

RAG with PDFs (07:05)

RAG with Webpages (02:18)

Lab: Performing Retrieval

Feedback – Performing Retrieval

Performing Retrieval

Implementing Chains

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Lesson Content

Overview of Chains (02:06)

Using Document Chain (06:55)

Using Retrieval Chain (05:39)

Lab: Implementing Chains

Feedback – Implementing Chains

Using Tools

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Lesson Content

Understanding and Using tools (06:31)

Using Wikipedia Tool (04:25)

Using Tavily Search Tool (03:14)

Using Yahoo Finance News Tool (01:19)

Building a Custom Tool (06:03)

Lab: Using Tools

Feedback – Using Tools

Building Agents

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Lesson Content

What are Agents? (13:31)

Building an Agent with Search Tool (08:37)

Adding Python REPL to Tool (05:58)

Lab: Building Agents

Building a Real-time Flight Agent (11:10)

Conclusion

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Lesson Content

Conclusion (01:18)

Feedback – Langchain

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