Welcome to the LangChain Course! I’m Janakiram MSV, your instructor for this journey into building modern AI applications. In this lesson, you’ll learn how to leverage LangChain’s orchestration framework to integrate large language models (LLMs) with databases, APIs, and the web, creating powerful generative AI solutions.Documentation Index
Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt
Use this file to discover all available pages before exploring further.
What You’ll Learn
- Core building blocks of LLM-based applications
- Shared architecture of ChatGPT, Google Gemini, and Microsoft Copilot
- Deep dive into LangChain’s modules: model I/O, the LangChain Expression Language (LCEL), chains, memory, tools, and agents
Course Structure
| Component | Description |
|---|---|
| Lecture | Theory and architectural patterns |
| Hands-On Demo | Live code walkthroughs in KodeKloud labs |
| Self-Practice | Interactive notebooks—bring your own OpenAI API key |
You’ll need your own OpenAI API key (and any other third-party credentials) to complete the KodeKloud labs.
LangChain Module Overview
| Module | Use Case |
|---|---|
| Model I/O | Query and manage LLMs for text generation and analysis |
| LangChain Expression Language (LCEL) | Create reusable prompt templates and transformations |
| Chains | Orchestrate multi-step LLM workflows |
| Memory | Maintain state and context across conversations |
| Tools | Connect to REST APIs, databases, and external services |
| Agents | Automate decision logic with LLM-driven agents |