LangChain
Introduction
Course Introduction
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.
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 |
Note
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 |
Next Steps
By the end of this course, you’ll be proficient with LangChain’s core features and ready to build end-to-end generative AI applications. Join the KodeKloud Community Forum to ask questions, share your projects, and collaborate with fellow learners.
Links and References
Watch Video
Watch video content