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Building Retrieval-Augmented Systems & Knowledge Graphs

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Building Retrieval-Augmented Systems & Knowledge Graphs

本课程是 Building AI Agents with LLMs, RAG, and Knowledge Graphs 专项课程的一部分

位教师：Packt - Course Instructors

包含在中

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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

5 小时完成

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

5 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Design and implement web scraping agents using LLMs for data collection.
Apply retrieval-augmented generation techniques to reduce hallucinations and improve accuracy.
Build and integrate knowledge graphs into AI systems for advanced reasoning and retrieval.

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本课程是 Building AI Agents with LLMs, RAG, and Knowledge Graphs 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

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该课程共有4个模块

This course teaches you how to enhance large language models (LLMs) by integrating retrieval-augmented generation (RAG) and structured knowledge through knowledge graphs. You'll learn how to design, optimize, and scale AI agents that combine external data sources with advanced reasoning capabilities. The practical applications of RAG and knowledge graphs are transforming the way intelligent agents operate, improving accuracy and reducing errors.

Throughout this course, you will acquire valuable skills in building and refining AI systems. We focus on practical outcomes, where you will develop the expertise to create agents that leverage web data, RAG pipelines, and knowledge graphs. These techniques will allow you to tackle real-world challenges in AI development. What sets this course apart is its unique combination of theory and real-world application. You'll work through hands-on projects that use cutting-edge technology, ensuring that you gain practical experience while reinforcing theoretical concepts. The course emphasizes scalability, security, and the implementation of best practices. This course is ideal for AI developers, data scientists, and anyone interested in the field of intelligent agent development. It is particularly suited for individuals with a background in AI, machine learning, or software development who want to specialize in advanced AI systems. This course is part two of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.

This module explores how large language models (LLMs) can serve as the core of intelligent web scraping agents. Learners will examine the architecture of such agents, compare single-agent and multi-agent systems, and gain hands-on familiarity with key frameworks like Haystack and AutoGen. By the end, you'll understand how to leverage LLMs and supporting libraries to automate web-based information retrieval tasks.

涵盖的内容

1个视频9篇阅读材料1个作业

1个视频总计1分钟

Overview1分钟

9篇阅读材料总计52分钟

Introduction7分钟
The Brain5分钟
The Perception6分钟
Classifying AI Agents7分钟
Understanding the Abilities of Single-Agent and Multiple-Agent Systems6分钟
Exploring the Principal Libraries5分钟
Haystack5分钟
AutoGen4分钟
Creating an Agent to Search the Web7分钟

1个作业总计16分钟

Building Intelligent Web Scraping Systems16分钟

This module explores how to enhance language model agents using Retrieval-Augmented Generation (RAG) to minimize hallucinations and improve reliability. Learners will examine RAG components, embedding strategies, vector databases, and evaluation metrics, and compare RAG with fine-tuning approaches. Practical application is demonstrated through building a movie recommendation agent.

涵盖的内容

1个视频7篇阅读材料1个作业

1个视频总计1分钟

Overview1分钟

7篇阅读材料总计49分钟

Introduction14分钟
Retrieval, Optimization, and Augmentation7分钟
Embedding Strategies8分钟
Embedding Databases5分钟
Evaluating the Output5分钟
Comparison Between RAG and Fine-Tuning5分钟
Using RAG to Build a Movie Recommendation Agent5分钟

1个作业总计16分钟

Enhancing Language Models with Retrieval-Augmented Generation16分钟

This module explores advanced Retrieval-Augmented Generation (RAG) techniques designed to enhance information retrieval and augmentation in AI systems. Learners will examine improvements over naïve RAG, including query transformation, reranking, modular architectures, scalability, and security considerations. By the end, participants will understand how to implement and optimize sophisticated RAG pipelines for real-world applications.

涵盖的内容

1个视频13篇阅读材料1个作业

1个视频总计1分钟

Overview1分钟

13篇阅读材料总计78分钟

Introduction6分钟
Exploring the Advanced RAG Pipeline4分钟
Hypothetical Questions and HyDE5分钟
Query Transformation4分钟
Query Routing4分钟
Reranking8分钟
Response Optimization6分钟
Modular RAG and Its Integration with Other Systems7分钟
Implementing an Advanced RAG Pipeline5分钟
Understanding the Scalability and Performance of RAG8分钟
Parallel Processing4分钟
Security and Privacy5分钟
Open Questions and Future Perspectives12分钟

1个作业总计16分钟

Advanced RAG Techniques and Information Retrieval16分钟

This module guides learners through the process of building, cleaning, and deploying knowledge graphs, and demonstrates how to connect them to AI agents using tools like Neo4j and LangChain. Learners will explore taxonomies, ontologies, and graph-based retrieval methods, as well as the challenges and applications of integrating knowledge graphs with large language models. The module also covers advanced topics such as graph reasoning, graph neural networks, and ongoing challenges in the field.

涵盖的内容

1个视频13篇阅读材料1个作业

1个视频总计1分钟

Overview1分钟

13篇阅读材料总计71分钟

Introduction4分钟
Introduction to Knowledge Graphs7分钟
Taxonomies and Ontologies4分钟
Knowledge Creation7分钟
Creating a Knowledge Graph with an LLM6分钟
Knowledge Cleaning4分钟
Knowledge Hosting and Deployment4分钟
Retrieving Information with a Knowledge Graph and an LLM4分钟
Graph-Guided Retrieval7分钟
GraphRAG Applications4分钟
Understanding Graph Reasoning5分钟
Graph Neural Networks8分钟
Ongoing Challenges in Knowledge Graphs and GraphRAG7分钟

1个作业总计16分钟

Knowledge Graphs and AI Agent Integration16分钟

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常见问题

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.