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Developing LLM Applications with LangChain · LearnSpace
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Developing LLM Applications with LangChain

Курс от Edureka
Средний≈ 11.1 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course introduces the concepts, tools, and practical techniques behind LangChain, the leading framework for building intelligent applications powered by Large Language Models (LLMs). It blends conceptual understanding with hands-on implementation to help you design, build, and deploy context-aware, tool-using AI systems. Whether you’re a developer, data scientist, or AI practitioner, this course provides a clear roadmap for transforming LLMs into dynamic, reasoning-driven applications that interact with real-world data and APIs. Through guided lessons, structured demonstrations, and project-based learning, you’ll explore how LangChain connects prompts, models, memory, and tools into composable workflows. You’ll learn to build Retrieval-Augmented Generation (RAG) pipelines, integrate LangServe for deployment, and implement LangSmith for observability and evaluation. The course culminates with a capstone Knowledge Assistant project, where you’ll combine RAG, multi-agent systems, and secure API integrations into a fully functional, deployable AI assistant. By the end of this course, you will be able to: • Understand the architecture and components of LangChain for LLM development. • Build multi-step reasoning pipelines and retrieval-augmented generation (RAG) workflows. • Implement memory, tools, and agents to enable contextual, goal-oriented behavior. • Evaluate and optimize LLM applications for performance, safety, and scalability. This course is ideal for AI developers, data scientists, and software engineers seeking to go beyond prompt-based experimentation and build real-world, production-ready LLM applications. A working knowledge of Python and APIs is recommended, but the course provides guided support to help learners of all backgrounds master the LangChain ecosystem. Join us to master the framework that powers today’s most advanced generative AI applications.

Навыки, которые вы освоите

Retrieval-Augmented GenerationLangGraphLangChainLLM ApplicationPrompt EngineeringResponsible AIToken OptimizationAgentic systemsGenerative AIApplication Programming Interface (API)Data ProcessingApplication DeploymentLarge Language ModelingAI WorkflowsPandas (Python Package)Generative AI AgentsCloud DevelopmentAI OrchestrationNatural Language ProcessingPython Programming

Программа курса

4 модулей · 75 учебных материалов

01LangChain Fundamentals26 материалов

Introduction to LangChain - Setup and Core Concepts

Specialization IntroductionВидеоCourse IntroductionВидеоWelcome to Developing LLM Applications and LangChainЧтениеWhat do you know about LLM Applications and LangChainDIALOGUE

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Edureka

Преподаватель курса

Developing LLM Applications with LangChain
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Обучение на Coursera

≈ 11.1 ч

4 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский, Венгерский

Часть программы вашего университета
What Is LangChain and how it works with LLMs and LCELВидео
Understanding the LangChain Workflow and ArchitectureВидео
Demonstration: Installing and Setting Up LangChain Environment (Colab + API Keys)Видео
Demonstration: Building Your First LLM Chain Using LCELВидео
Introduce YourselfОбсуждение
LangChain Overview, LCEL Basics and Key ConceptsЧтение
Introduction to LangChain - Setup and Core ConceptsЗадание

Designing Dynamic AI Contexts

Prompt Templates and Memory in LangChainВидеоDesigning Adaptive Prompt Templates for Context Control and ValidationВидеоDemonstration: Adding Conversation Memory to an LLM Chat ChainВидеоDemonstration: Integrating Pydantic with LCEL for Structured OutputsВидеоReading: Prompt Design Patterns and Context Retention TechniquesЧтениеPractice Quiz : Designing Dynamic AI ContextsЗадание

Building and Combining Chains with LCEL/Runnables

Understanding LCEL and Runnables in LangChainВидеоChain Types in LangChain — Sequential, Router and CustomВидеоDesigning Modular Reasoning Workflows in LangChainВидеоDemonstration: Building a Multi-Step Reasoning Chain for QnA (LCEL)ВидеоDemonstration: Converting Chains to LCEL-Runnables for Efficient ExecutionВидеоBest Practices for Chain Composition and LCEL IntegrationЧтениеPractice Quiz : Building and Combining Chains with LCEL/RunnablesЗадание

Module Wrap-Up and Assessment

Summary of LangChain FundamentalsЧтениеKnowledge Check: LangChain FundamentalsЗадание
02Building Context-Aware Applications - RAG and Document Pipelines20 материалов

Retrieval-Augmented Generation Concepts

Why Context Matters in LLM ResponsesВидеоEmbeddings, Vectors and LCEL in RAG ВидеоDemonstration: Creating Text Embeddings Using Hugging Face or OpenAI ModelsВидеоDemonstration: Setting Up a Vector Store with FAISSВидеоOverview of RAG Architecture and LCEL for RAGЧтениеPractice Quiz : Retrieval-Augmented Generation ConceptsЗадание

Loading, Preprocessing, and Validating Documents

Working with Document Loaders and Text SplittersВидеоDocument Processing and Validation Workflow in LangChainВидеоDemonstration: Loading and Splitting Text Files for IndexingВидеоDemonstration: Validating Documents with Pydantic and LCELВидеоLangChain Document Loader Reference and Validation PatternsЧтениеParctice Quiz: Loading, Preprocessing, and Validating DocumentsЗадание

Building and Evaluating a Retrieval Pipeline

Designing the Retrieval WorkflowВидеоObservability and Evaluation in RAG Pipelines Using LangSmithВидеоDemonstration: Building Vector Stores to Retreiver ChainВидеоDemonstration: Querying Your Documents with a Context-Aware LLM ВидеоEvaluating RAG Performance, Observability with LangSmithЧтениеPractice Quiz : Building and Evaluating a Retrieval PipelineЗадание

Module Wrap-Up and Assessment

Summary of Building Context-Aware Applications - RAG and Document PipelinesЧтениеKnowledge Check: Building Context-Aware Applications - RAG and Document PipelinesЗадание
03Connecting Agents and Tools23 материалов

Understanding LangChain Agents and LangServe

What Are Agents and How They Use ToolsВидеоDeploying and Managing Agents via LangServe APIsВидеоDemonstration: Creating a Simple Tool-Using AgentВидеоDemonstration: Adding Your RAG Pipeline as a Tool ВидеоLangChain Agent Types, LangServe ConceptsЧтениеPractice Quiz : Understanding LangChain Agents and LangServeЗадание

Integrating APIs, LangGraph, and Observability

LangGraph Overview — Connecting Agents with Workflow Graphs (Brief Introduction)ВидеоConnecting APIs and Tracking Agents with LangSmithВидеоDemonstration: Connecting to a Weather API for Real-Time UpdatesВидеоDemonstration: Building Smarter Agents with Memory and Decision Flow - IВидеоDemonstration: Building Smarter Agents with Memory and Decision Flow - IIВидеоSecure API Integration, Key Management, and Observability with LangSmithЧтение

Capstone Project — Building and Testing Your Knowledge Assistant

Capstone Overview and Architecture PlanningВидеоDemonstration: Building AI Financial Agents with PythonВидеоDemonstration: Advanced Tools for AI Trading SystemsВидеоDemonstration: AI Trading Agents in ActionВидеоDemonstration: AI Orchestration with PythonВидеоDemonstration: Interactive AI Financial Dashboard with Streamlit Видео

Module Wrap-Up and Assessment

Summary of Connecting Agents and ToolsЧтениеKnowledge Check: Connecting Agents and ToolsЗадание
04Course Wrap-Up and Assessment6 материалов

Course Wrap-up and Assessments

Course Summary: Building LLM Applications with LangChainВидеоPractice Project: RAG-Enhanced Financial Multi-Agent System Using MCPЧтениеTest your knowledge about LLM Applications and LangchainDIALOGUEEnd Course Knowledge Check: Building LLM Applications with LangChainЗаданиеDesigning and Deploying Your LangChain Multi-Agent Knowledge SystemDIALOGUEDescribe your Learning JourneyОбсуждение
Practice Quiz : Integrating APIs, LangGraph, and ObservabilityЗадание
MCP, ACP, ANP, and A2A Protocol Reference NotesЧтение
Practice Quiz : Building and Testing Your Knowledge AssistantЗадание