| File Name: | LangChain Made Easy: Generative AI with Python & OpenAI |
| Content Source: | https://www.udemy.com/course/langchain-openai-rag-genrative-ai-beginners-course |
| Genre / Category: | Programming |
| File Size : | 5.6 GB |
| Publisher: | Vikas Munjal |
| Updated and Published: | December 5, 2025 |
Unlock the power of Generative AI and build intelligent, data-aware applications using Langchain (the leading framework for building sophisticated applications) and the OpenAI API—explained in the clearest, most straightforward way possible!
This course is designed to take you from a basic understanding of Langchain to build Retrieval-Augmented Generation (RAG) applications and complex LLM chains. By the end of this course, you will have a deep understanding of Langchain’s core modules and advanced techniques for working with modern Large Language Models (LLMs). We skip the confusing jargon and deliver practical, step-by-step instruction so you can confidently build and customize modern AI tools. If you want to move beyond simple prompts and learn how to make AI systems that can read your documents and answer complex questions, this is the course for you.
What You Will Master: A Practical Journey
Your learning journey is structured around building real-world components, ensuring deep understanding at every stage:
Part 1: Foundational Langchain & LLM Interaction
- Section 1: Introduction
- Basic course introduction and setup.
- How to create API Keys and install necessary libraries.
- Calling the LLM (Large Language Model) directly.
- Section 2: Prompts
- Understanding the structure of a chatbot interaction.
- Working with System and Human messages.
- Detailed theory and practical application of Static and Dynamic Prompts and Prompt Templates.
- Using Prompt Templates with JSON for reusability.
- Theory and practical application of Chat Prompts.
- Section 3: Output Parsers
- In-depth Output Parser Theory.
- Using Output Parsers with Chains for structured data output.
- Implementing the JSON Output Parser.
- Theory and practical application of the Pydantic Output Parser for robust data validation.
Part 2: Building Complex Applications with Runnables
- Section 4: Runnables and Chains
- Detailed Runnable Theory and its role in modern Langchain.
- Creating a Runnable Sequence to link steps together.
- Implementing Runnable Parallel execution for efficiency.
- Utilizing Runnable Passthrough for transparent data management.
- Introduction to Runnable Lambda and its usage inside a chain.
- Implementing Runnable Branch for conditional logic.
- Understanding the difference between Runnable and Chain architectures.
DOWNLOAD LINK: LangChain Made Easy: Generative AI with Python & OpenAI
LangChain_Made_Easy_Generative_AI_with_Python_OpenAI.part1.rar – 1000.0 MB
LangChain_Made_Easy_Generative_AI_with_Python_OpenAI.part2.rar – 1000.0 MB
LangChain_Made_Easy_Generative_AI_with_Python_OpenAI.part3.rar – 1000.0 MB
LangChain_Made_Easy_Generative_AI_with_Python_OpenAI.part4.rar – 1000.0 MB
LangChain_Made_Easy_Generative_AI_with_Python_OpenAI.part5.rar – 1000.0 MB
LangChain_Made_Easy_Generative_AI_with_Python_OpenAI.part6.rar – 696.5 MB
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