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Building Retrieval-Augmented Systems & Knowledge Graphs

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Building Retrieval-Augmented Systems & Knowledge Graphs

Ce cours fait partie de Spécialisation "Building AI Agents with LLMs, RAG, and Knowledge Graphs"

Instructeur : Packt - Course Instructors

Inclus avec

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

5 heures à compléter

Planning flexible

Apprenez à votre propre rythme

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

5 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

Catégorie : Artificial Intelligence and Machine Learning (AI/ML)
Catégorie : System Design and Implementation
Catégorie : Retrieval-Augmented Generation
Catégorie : Large Language Modeling
Catégorie : Graph Theory
Catégorie : Scalability
Catégorie : Agentic systems
Catégorie : Machine Learning
Catégorie : LLM Application
Catégorie : Generative AI Agents
Catégorie : Artificial Intelligence
Catégorie : Taxonomy
Catégorie : AI Integrations

Outils que vous découvrirez

Catégorie : LangChain
Catégorie : Agentic Workflows
Catégorie : Python Programming

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Récemment mis à jour !

mai 2026

Évaluations

4 devoirs

Enseigné en Anglais

91%

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Ce cours fait partie de la Spécialisation "Building AI Agents with LLMs, RAG, and Knowledge Graphs"

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Il y a 4 modules dans ce cours

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.

Inclus

1 vidéo9 lectures1 devoir

1 vidéoTotal 1 minute

Overview1 minute

9 lecturesTotal 52 minutes

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

1 devoirTotal 16 minutes

Building Intelligent Web Scraping Systems16 minutes

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.

Inclus

1 vidéo7 lectures1 devoir

1 vidéoTotal 1 minute

Overview1 minute

7 lecturesTotal 49 minutes

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

1 devoirTotal 16 minutes

Enhancing Language Models with Retrieval-Augmented Generation16 minutes

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.

Inclus

1 vidéo13 lectures1 devoir

1 vidéoTotal 1 minute

Overview1 minute

13 lecturesTotal 78 minutes

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

1 devoirTotal 16 minutes

Advanced RAG Techniques and Information Retrieval16 minutes

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.

Inclus

1 vidéo13 lectures1 devoir

1 vidéoTotal 1 minute

Overview1 minute

13 lecturesTotal 71 minutes

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

1 devoirTotal 16 minutes

Knowledge Graphs and AI Agent Integration16 minutes

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Instructeur

Packt - Course Instructors

Packt

1 888 Cours517 752 apprenants

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Packt

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