Applied Agentic AI Pipelines with LangChain

本课程是 Agentic AI Engineering 专项课程的一部分

位教师：Edureka

访问权限由 New York State Department of Labor 提供

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Design advanced workflows for intelligent agent systems with LangChain.
Apply multi-step reasoning and ReAct workflows to optimize AI agents.
Construct adaptive memory architectures and integrate multi-query retrieval.
Evaluate and apply error handling and output correction for pipeline reliability.

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作业

14 任务¹

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授课语言：英语（English）

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该课程共有4个模块

This program explores advanced techniques for designing intelligent agent pipelines using LangChain, equipping developers and AI enthusiasts with the skills to build scalable, reliable, and efficient AI systems. You’ll start by mastering LangChain’s core functionalities, including advanced workflow engineering, output correction, and data transformation for agent systems.

Next, you’ll dive into intelligent tooling, learning how to implement multi-step reasoning, ReAct-driven workflows, and complex tool orchestration. You’ll also explore cutting-edge retrieval techniques, multi-query reasoning, and adaptive memory architectures, building systems capable of handling dynamic, real-time data across multiple steps. By the end of this program, you will be able to: -Define the foundational concepts of LangChain and its role in intelligent agent design. -Master LangChain runnables, data transformations, and advanced error handling techniques. -Implement intelligent tool routing and multi-hop reasoning using ReAct workflows. -Build robust multi-query retrieval systems with adaptive memory and composite retrieval strategies. -Optimize knowledge query pipelines with self-correcting features for more accurate insights. -Design scalable, stateful agent systems with persistent memory and vector routing. This program is designed for developers and AI practitioners interested in building powerful agent-driven applications using LangChain. A background in Python, machine learning, and basic AI concepts will enhance your learning experience. Learners require a reliable internet connection, a modern web browser, and access to LangChain documentation and tools. No specialized hardware or software installation is necessary. Join us to explore the cutting-edge of intelligent agent design with LangChain, and gain the expertise needed to build the next generation of AI systems.

Design advanced LangChain workflows using runnable sequences, branching logic, and parallel execution to support complex agent pipelines. Engineer reliable workflows by applying output correction, structured error handling, and automated retry mechanisms. Stabilize LLM-driven systems by addressing common failure patterns and invalid outputs. Apply data transformation and post-processing techniques to normalize, score, and refine results.

涵盖的内容

12个视频5篇阅读材料4个作业

12个视频总计68分钟

Specialization Introduction 6分钟
Course Introduction 5分钟
Designing Runnable Sequences, Branching Logic, and Parallel Execution 5分钟
Demonstration: Creating a RunnableSequence for Data Enrichment 6分钟
Demonstration: Implementing Conditional Routing with RunnableBranch 7分钟
OutputFixingParser, Error Handling Techniques, and Automated Retries 5分钟
Demonstration: Auto-Correcting Invalid JSON Outputs - I 5分钟
Demonstration: Auto-Correcting Invalid JSON Outputs - II 4分钟
Demonstration: Applying Retry Logic for Pipeline Reliability 7分钟
TransformChain Workflows, Data Normalization, and Post-Processing Strategies 5分钟
Demonstration: Building a Text Normalization Pipeline 5分钟
Demonstration: Creating a Scoring and Ranking Processor 7分钟

5篇阅读材料总计70分钟

Course Syllabus 15分钟
Advanced Runnable Workflow Patterns for Reliable AI Pipelines 15分钟
Ensuring Accuracy and Consistency in Model Outputs 15分钟
Data Post-Processing Strategies for Scalable AI Systems 15分钟
Module Summary: Advanced Workflow Engineering and Reliability Techniques 10分钟

4个作业总计33分钟

Knowledge Check: Advanced Workflow Engineering and Reliability Techniques 15分钟
Practice Assignment: Mastering LangChain Runnables 6分钟
Practice Assignment: Output Correction and Pipeline Stabilization 6分钟
Practice Assignment: Data Transformation and Post-Processing Techniques 6分钟

Build intelligent agent pipelines that dynamically route tools, manage prioritization, and handle fallback execution. Implement advanced ReAct reasoning patterns using multi-step Thought-Action-Observation loops with verification and tool chaining. Enable deeper reasoning by applying multi-query retrieval, fusion strategies, and multi-hop RAG workflows. Coordinate reasoning, tooling, and retrieval across complex, multi-stage tasks.

涵盖的内容

14个视频4篇阅读材料4个作业

14个视频总计86分钟

Multi-Tool Routing Strategies, Fallback Handling, and Prioritization 6分钟
Demonstration: Building an Intelligent Tool Router - I 6分钟
Demonstration: Building an Intelligent Tool Router - II 7分钟
Thought–Action–Observation Loop and ReAct Enhancements 5分钟
Demonstration: Implementing a Multi-Hop ReAct Workflow - I 7分钟
Demonstration: Implementing a Multi-Hop ReAct Workflow - II 7分钟
Demonstration: Adding Verification to ReAct Tool-Chaining - I 7分钟
Demonstration: Adding Verification to ReAct Tool-Chaining - II 6分钟
Demonstration: Adding Verification to ReAct Tool-Chaining - III 4分钟
Multi-Query Retrieval, Fusion Techniques, and Multi-Hop Reasoning 5分钟
Demonstration: Generating Multi-Query Expansions with Fusion - I 8分钟
Demonstration: Generating Multi-Query Expansions with Fusion - II 5分钟
Demonstration: Building a Multi-Hop RAG Reasoning Chain - I 7分钟
Demonstration: Building a Multi-Hop RAG Reasoning Chain - II 7分钟

4篇阅读材料总计55分钟

Building Stateful and Context-Aware Tools 15分钟
ReAct Agents: Extensibility with Middleware and LangGraph 15分钟
Retrieval-Augmented Generation (RAG) Architecture 15分钟
Module Summary: Intelligent Tooling, ReAct Reasoning, and Multi-Step Retrieval 10分钟

4个作业总计33分钟

Knowledge Check: Intelligent Tooling, ReAct Reasoning, and Multi-Step Retrieval 15分钟
Practice Assignment: Intelligent Tool Routing and Orchestration 6分钟
Practice Assignment: Advanced ReAct Reasoning Patterns 6分钟
Practice Assignment: Multi-Query Retrieval and Multi-Hop RAG Workflows 6分钟

Develop advanced memory systems that enable intelligent agents to retain context and retrieve relevant knowledge over time. Apply vector memory and adaptive routing techniques to improve retrieval accuracy and efficiency. Combine vector, summary, and entity-based memory models to support layered context and long-term reasoning. Optimize knowledge retrieval using metadata-aware tools and self-correcting query pipelines.

涵盖的内容

9个视频4篇阅读材料4个作业

9个视频总计50分钟

Vector Memory Architecture and Adaptive Routing Techniques 6分钟
Demonstration: Building a Vector Memory Store with Adaptive Selection - I 5分钟
Demonstration: Building a Vector Memory Store with Adaptive Selection - II 5分钟
Hybrid Memory Systems with Layered Context 6分钟
Demonstration: Combining Memory Types for Composite Retrieval - I 6分钟
Demonstration: Combining Memory Types for Composite Retrieval - II 6分钟
Knowledge Tools and Metadata Ranking 6分钟
Demonstration: Building a Self-Correcting Knowledge Query Pipeline - I 5分钟
Demonstration: Building a Self-Correcting Knowledge Query Pipeline - II 5分钟

4篇阅读材料总计55分钟

Vector Memory Patterns for Stateful and Adaptive AI Systems 15分钟
Composite Memory Design for Persistent Deep Agents 15分钟
Integrating External Knowledge with LangChain Agents Using Retrievers 15分钟
Module Summary: Memory Architectures, Vector Routing, and Knowledge Pipelines 10分钟

4个作业总计33分钟

Knowledge Check: Memory Architectures, Vector Routing, and Knowledge Pipelines 15分钟
Practice Assignment: Vector Memory and Adaptive Retrieval 6分钟
Practice Assignment: Composite Memory: Vector, Summary, and Entity Models 6分钟
Practice Assignment: Knowledge Tools and Retrieval Optimization 6分钟

Review and consolidate the key concepts covered throughout the course, including advanced workflows, intelligent tooling, reasoning patterns, retrieval strategies, and memory architectures. Apply these skills in a hands-on practice project by building a multi-tool research agent that integrates end-to-end agent pipeline design. Demonstrate mastery through a final graded assignment focused on designing reliable and intelligent agent pipelines.

涵盖的内容

1个视频1篇阅读材料2个作业1个讨论话题

1个视频总计2分钟

Course Summary 2分钟

1篇阅读材料总计30分钟

Practice Project: Building a Research Assistant Agent for Strategy Teams 30分钟

2个作业总计60分钟

End Course Knowledge Check: Advanced Workflow Engineering and Reliability Techniques 30分钟
Building Scalable and Reliable Agent Pipelines with LangChain 30分钟