Free Download LangChain Made Easy Generative AI with Python & OpenAI
Published 12/2025
Created by Vikas Munjal
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 57 Lectures ( 8h 13m ) | Size: 5.55 GB
Building with Langchain: Practical RAG & LLM Projects using the OpenAI API
What you'll learn
Understand the necessary components for building Generative AI applications and how to connect them using Langchain.
Learn how to easily set up your environment, acquire API Keys, and install all required Langchain libraries.
Master the fundamental steps for calling the OpenAI LLM directly in your Python applications.
Prompt Engineering-create effective prompts using System, Human, and AI messages.
Learn to create Static and Dynamic Prompts, utilize Prompt Templates, and structure them using JSON.
Understand the theory and practical application of Output Parsers (including JSON and the robust Pydantic Output Parser) for structured AI responses.
Explore the power of Runnables and Chains to build flexible, modular AI pipelines for real-world use cases.
Implement advanced Runnable functions like RunnableSequence, RunnableParallel, RunnableLambda, and RunnableBranch for complex logic.
Work with essential Document Loaders-including Text, CSV, PyPDF, and Web-based-to ingest data for RAG applications.
Apply various Text Splitting techniques, such as CharacterTextSplitter and RecursiveCharacterTextSplitter, for optimal LLM context.
Understand the distinction between Vector Stores and Vector Databases and implement Embeddings for data indexing.
Perform practical Vector Database operations like Similarity Search, Update, Delete, and MetaData Search.
Master sophisticated Retrievers including the Vector Store Retriever, MMR (Maximum Marginal Relevance), MultiQueryRetriever, and Contextual Compression Retrieve
Build a complete Retrieval-Augmented Generation (RAG) pipeline from document chunking to final context retrieval.
Requirements
A fundamental working knowledge of Python is required. You should be comfortable with Variables, data types, Defining functions , basic control flow , loops, classes and object concepts
No prior deep knowledge of Machine Learning or advanced LLM theory is required. This course is designed to teach you the Langchain framework and its application in Generative AI in a clear, step-by-step manner.
If you have experience with any other programming language, you may be able to understand this course with the help of Google searches.
You need to pay atleast 5 US Dollars to OpenAI to access the API key.
Description
The Easiest Path to Generative AI: Langchain & OpenAI API for BeginnersUnlock 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 JourneyYour learning journey is structured around building real-world components, ensuring deep understanding at every stage
art 1: Foundational Langchain & LLM InteractionSection 1: IntroductionBasic course introduction and setup.How to create API Keys and install necessary libraries.Calling the LLM (Large Language Model) directly.Section 2: PromptsUnderstanding 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 ParsersIn-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 RunnablesSection 4: Runnables and ChainsDetailed 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.Part 3: Data Management for RAG ApplicationsSection 5: Document LoadersUnderstanding the components required to build RAG-based applications.A foundational look at what a Document Loader is.Loading documents using TextLoader, PyPDFLoader, and a general Document Loader.Exploring Eager vs. Lazy Load strategies.Working with CSVLoader and WebBaseLoader for external content.Section 6: Text SplittingWhat is Text Splitting and why it is critical for LLMs.Using the basic CharacterTextSplitter.Implementing the RecursiveCharacterTextSplitter.Splitting text based on document structure using DocumentStructureBasedTextSplitting.Section 7: EmbeddingsWorking with Embeddings and Vector Search.Understanding the VectorStore vs. Vector Database distinction.Exploring ChromaDB Hierarchy.The practical steps for Creating Embeddings.Similarity Search Demo.Advanced operations: Update, Delete, and MetaData Search Operations on a Vector Database.Part 4: Retrieval and RAG ImplementationSection 8: RetrieversWhat is a Retriever and why it is needed.Implementing the Vector Store Retriever.Understanding and using the MMR (Maximum Marginal Relevance) Retriever.Theory and practical examples of the MultiQueryRetriever.Theory and practical examples of the Contextual Compression Retriever.Section 9: Retrieval-Augmented Generation (RAG)What is RAG and why it is needed in modern AI applications.A practical example: Extracting YouTube Video Transcript for use in RAG.The process of Document Chunking and Context Retrieval from a Vector Store in RAG.Final Steps in Building a RAG-Based Demo.Advanced implementation: Implementing Runnables in a RAG Pipeline.Who Is This Course For?Developers who want to move from basic scripting to building full-fledged AI applications.Anyone looking to leverage their private or custom data with powerful Generative AI models.Learners who prefer a clear, methodical, and simple explanation over dense academic theory.Engineers ready to use the OpenAI API within a scalable framework like Langchain.Stop struggling with complex documentation. Enroll today and start building production-ready GenAI applications simply and effectively!Who this course is for
Developers who want to learn from basic scripting to building full-fledged AI applications.
Anyone looking to leverage their private or custom data with powerful Generative AI models.
Learners who prefer a clear, methodical, and simple explanation over dense academic theory.
Engineers ready to use the OpenAI API within a scalable framework like Langchain
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