Building AI Agents with LLMs, RAG, and Knowledge Graphs (Code Fil...

Building AI Agents with LLMs, RAG, and Knowledge Graphs (Code Files)

https://WebToolTip.com

English | Code (.Rar) | 2025 | ISBN : 183508706X | 44.8 MB

This AI agents book addresses the challenge of building AI that not only generates text but also grounds its responses in real data and takes action. Authored by AI specialists with deep expertise in drug discovery and systems optimization, this guide empowers you to leverage retrieval-augmented generation (RAG), knowledge graphs, and agent-based architectures to engineer truly intelligent behavior. By combining large language models (LLMs) with up-to-date information retrieval and structured knowledge, you'll create AI agents capable of deeper reasoning and more reliable problem-solving. Inside, you'll find a practical roadmap from concept to implementation. You’ll discover how to connect language models with external data via RAG pipelines for increasing factual accuracy and incorporate knowledge graphs for context-rich reasoning. The chapters will help you build and orchestrate autonomous agents that combine planning, tool use, and knowledge retrieval to achieve complex goals. Concrete Python examples built on popular libraries, along with real-world case studies, reinforce each concept and show you how these techniques come together. By the end of this book, you’ll be well-equipped to build intelligent AI agents that reason, retrieve, and interact dynamically, empowering you to deploy powerful AI solutions across industries.

Key Learnings
Learn how LLMs work, their structure, uses, and limits, and design RAG pipelines to link them to external data
Build and query knowledge graphs for structured context and factual grounding
Develop AI agents that plan, reason, and use tools to complete tasks
Integrate LLMs with external APIs and databases to incorporate live data
Apply techniques to minimize hallucinations and ensure accurate outputs
Orchestrate multiple agents to solve complex, multi-step problems
Optimize prompts, memory, and context handling for long-running tasks
Deploy and monitor AI agents in production environments