Agentic RAG: Build Smarter AI Systems with Retrieval-Augmented Agents Using LangChain, OpenAI, and Vector Databases - Tapa blanda

Sinopsis

Master the art of building AI agents that deliver precise, context-aware, and actionable intelligence.

"Building Agentic RAG Systems: Design Patterns and Implementations" is your essential resource for developing smarter LLM applications. This book bridges the gap between foundational Retrieval-Augmented Generation (RAG) and advanced Agentic AI, showing you how to empower LLMs with dynamic reasoning and sophisticated tool use.

Whether you're an AI developer, data scientist, or an ML engineer focused on enterprise AI solutions, this guide provides the practical knowledge to excel.

Inside, You'll Discover:

• Comprehensive design patterns for orchestrating multi-step reasoning in agents.
• Practical strategies for data analysis and summarization by integrating internal documents and external APIs.
• In-depth setup and optimization for leading vector databases such as Pinecone, ChromaDB, and Weaviate.
• Robust approaches to deploying Agentic RAG in production, covering scalability, cost management, and observability.
• Critical guidance on security, data privacy, and ethical AI deployment, including preventing bias and ensuring human oversight.
• Forward-looking insights into multi-agent systems, self-improving agents, and advanced hybrid retrieval techniques.

Build production-ready Agentic RAG systems that solve real business problems with unparalleled accuracy and efficiency.

"Sinopsis" puede pertenecer a otra edición de este libro.