Langgraph Rag
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Unlocking Data With Generative Ai And Rag
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Author : Keith Bourne
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-09-27
Unlocking Data With Generative Ai And Rag written by Keith Bourne and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-27 with Computers categories.
Leverage cutting-edge generative AI techniques such as RAG to realize the potential of your data and drive innovation as well as gain strategic advantage Key Features Optimize data retrieval and generation using vector databases Boost decision-making and automate workflows with AI agents Overcome common challenges in implementing real-world RAG systems Purchase of the print or Kindle book includes a free PDF eBook Book Description Generative AI is helping organizations tap into their data in new ways, with retrieval-augmented generation (RAG) combining the strengths of large language models (LLMs) with internal data for more intelligent and relevant AI applications. The author harnesses his decade of ML experience in this book to equip you with the strategic insights and technical expertise needed when using RAG to drive transformative outcomes. The book explores RAG’s role in enhancing organizational operations by blending theoretical foundations with practical techniques. You’ll work with detailed coding examples using tools such as LangChain and Chroma’s vector database to gain hands-on experience in integrating RAG into AI systems. The chapters contain real-world case studies and sample applications that highlight RAG’s diverse use cases, from search engines to chatbots. You’ll learn proven methods for managing vector databases, optimizing data retrieval, effective prompt engineering, and quantitatively evaluating performance. The book also takes you through advanced integrations of RAG with cutting-edge AI agents and emerging non-LLM technologies. By the end of this book, you’ll be able to successfully deploy RAG in business settings, address common challenges, and push the boundaries of what’s possible with this revolutionary AI technique. What you will learn Understand RAG principles and their significance in generative AI Integrate LLMs with internal data for enhanced operations Master vectorization, vector databases, and vector search techniques Develop skills in prompt engineering specific to RAG and design for precise AI responses Familiarize yourself with AI agents' roles in facilitating sophisticated RAG applications Overcome scalability, data quality, and integration issues Discover strategies for optimizing data retrieval and AI interpretability Who this book is for This book is for AI researchers, data scientists, software developers, and business analysts looking to leverage RAG and generative AI to enhance data retrieval, improve AI accuracy, and drive innovation. It is particularly suited for anyone with a foundational understanding of AI who seeks practical, hands-on learning. The book offers real-world coding examples and strategies for implementing RAG effectively, making it accessible to both technical and non-technical audiences. A basic understanding of Python and Jupyter Notebooks is required.
Learning Langchain
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Author : Mayo Oshin
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2025-02-13
Learning Langchain written by Mayo Oshin and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-13 with Computers categories.
If you're looking to build production-ready AI applications that can reason and retrieve external data for context-awareness, you'll need to master--;a popular development framework and platform for building, running, and managing agentic applications. LangChain is used by several leading companies, including Zapier, Replit, Databricks, and many more. This guide is an indispensable resource for developers who understand Python or JavaScript but are beginners eager to harness the power of AI. Authors Mayo Oshin and Nuno Campos demystify the use of LangChain through practical insights and in-depth tutorials. Starting with basic concepts, this book shows you step-by-step how to build a production-ready AI agent that uses your data. Harness the power of retrieval-augmented generation (RAG) to enhance the accuracy of LLMs using external up-to-date data Develop and deploy AI applications that interact intelligently and contextually with users Make use of the powerful agent architecture with LangGraph Integrate and manage third-party APIs and tools to extend the functionality of your AI applications Monitor, test, and evaluate your AI applications to improve performance Understand the foundations of LLM app development and how they can be used with LangChain
Building Ai Agents With Llms Rag And Knowledge Graphs
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Author : Salvatore Raieli
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-07-11
Building Ai Agents With Llms Rag And Knowledge Graphs written by Salvatore Raieli and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-11 with Computers categories.
Master LLM fundamentals to advanced techniques like RAG, reinforcement learning, and knowledge graphs to build, deploy, and scale intelligent AI agents that reason, retrieve, and act autonomously Key Features Implement RAG and knowledge graphs for advanced problem-solving Leverage innovative approaches like LangChain to create real-world intelligent systems Integrate large language models, graph databases, and tool use for next-gen AI solutions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis 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.What you will learn 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 Who this book is for If you are a data scientist or researcher who wants to learn how to create and deploy an AI agent to solve limitless tasks, this book is for you. To get the most out of this book, you should have basic knowledge of Python and Gen AI. This book is also excellent for experienced data scientists who want to explore state-of-the-art developments in LLM and LLM-based applications.
Langgraph Rag
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Author : JAMES. WIGLOW
language : en
Publisher:
Release Date : 2025
Langgraph Rag written by JAMES. WIGLOW and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with categories.
Generative Ai On Google Cloud With Langchain
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Author : Leonid Kuligin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-12-20
Generative Ai On Google Cloud With Langchain written by Leonid Kuligin and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-20 with Computers categories.
Turn challenges into opportunities by mastering advanced techniques for text generation, summarization, and question answering using LangChain and Google Cloud tools Key Features Solve real-world business problems with hands-on examples of GenAI applications on Google Cloud Learn repeatable design patterns for Gen AI on Google Cloud with a focus on architecture and AI ethics Build and implement GenAI agents and workflows, such as RAG and NL2SQL, using LangChain and Vertex AI Purchase of the print or Kindle book includes a free PDF eBook Book Description The rapid transformation and enterprise adoption of GenAI has created an urgent demand for developers to quickly build and deploy AI applications that deliver real value. Written by three distinguished Google AI engineers and LangChain contributors who have shaped Google Cloud’s integration with LangChain and implemented AI solutions for Fortune 500 companies, this book bridges the gap between concept and implementation, exploring LangChain and Google Cloud’s enterprise-ready tools for scalable AI solutions. You'll start by exploring the fundamentals of large language models (LLMs) and how LangChain simplifies the development of AI workflows by connecting LLMs with external data and services. This book guides you through using essential tools like the Gemini and PaLM 2 APIs, Vertex AI, and Vertex AI Search to create sophisticated, production-ready GenAI applications. You'll also overcome the context limitations of LLMs by mastering advanced techniques like Retrieval-Augmented Generation (RAG) and external memory layers. Through practical patterns and real-world examples, you’ll gain everything you need to harness Google Cloud’s AI ecosystem, reducing the time to market while ensuring enterprise scalability. You’ll have the expertise to build robust GenAI applications that can be tailored to solve real-world business challenges. What you will learn Build enterprise-ready applications with LangChain and Google Cloud Navigate and select the right Google Cloud generative AI tools Apply modern design patterns for generative AI applications Plan and execute proof-of-concepts for enterprise AI solutions Gain hands-on experience with LangChain's and Google Cloud's AI products Implement advanced techniques for text generation and summarization Leverage Vertex AI Search and other tools for scalable AI solutions Who this book is for If you’re an application developer or ML engineer eager to dive into GenAI, this book is for you. Whether you're new to LangChain or Google Cloud, you'll learn how to use these tools to build scalable AI solutions. This book is ideal for developers familiar with Python and machine learning basics looking to apply their skills in GenAI. Professionals who want to explore Google Cloud's powerful suite of enterprise-grade GenAI products and their implementation will also find this book useful.
Langchain For Life Sciences And Healthcare
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Author : Ivan Reznikov
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2025-07-21
Langchain For Life Sciences And Healthcare written by Ivan Reznikov and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-21 with Computers categories.
Feeling overwhelmed by the volume of data in your research? Sifting through massive amounts of data to find useful insights is becoming increasingly difficult in drug discovery, genetics, and healthcare. Enter the era of generative AI with LangChain, whose groundbreaking tools are changing the way life scientists and researchers operate. In this groundbreaking book, Dr. Ivan Reznikov teaches you to harness the power of AI to elevate your research capabilities. Divided into two parts, the first is essential for any specialist, covering the transition from traditional statistics to generative AI, the fundamentals of large language models, and the practical uses of LangChain. The second part is designed for life science professionals who want to create AI applications for biology, chemistry, drug development, and more. By the end, you will: Learn how to easily create and integrate LangChain applications into research Discover how to substantially accelerate your experimental and data analysis operations Explore cutting-edge AI solutions designed to address complex research problems Gain the skills and knowledge to advance your career in AI-enhanced life sciences
Scaling Enterprise Solutions With Large Language Models
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Author : Arindam Ganguly
language : en
Publisher: Springer Nature
Release Date : 2025-05-20
Scaling Enterprise Solutions With Large Language Models written by Arindam Ganguly and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-20 with Computers categories.
Artificial Intelligence (AI) is the bedrock of today's applications, propelling the field towards Artificial General Intelligence (AGI). Despite this advancement, integrating such breakthroughs into large-scale production-grade enterprise applications presents significant challenges. This book addresses these hurdles in the domain of large language models within enterprise solutions. By leveraging Big Data engineering and popular data cataloguing tools, you’ll see how to transform challenges into opportunities, emphasizing data reuse for multiple AI models across diverse domains. You’ll gain insights into large language model behavior by using tools such as LangChain and LLamaIndex to segment vast datasets intelligently. Practical considerations take precedence, guiding you on effective AI Governance and data security, especially in data-sensitive industries like banking. This enterprise-focused book takes a pragmatic approach, ensuring large language models align with broader enterprise goals. From data gathering to deployment, it emphasizes the use of low code AI workflow tools for efficiency. Addressing the challenges of handling large volumes of data, the book provides insights into constructing robust Big Data pipelines tailored for Generative AI applications. Scaling Enterprise Solutions with Large Language Models will lead you through the Generative AI application lifecycle and provide the practical knowledge to deploy efficient Generative AI solutions for your business. What You Will Learn Examine the various phases of an AI Enterprise Applications implementation. Turn from AI engineer or Data Science to an Intelligent Enterprise Architect. Explore the seamless integration of AI in Big Data Pipelines. Manage pivotal elements surrounding model development, ensuring a comprehensive understanding of the complete application lifecycle. Plan and implement end-to-end large-scale enterprise AI applications with confidence. Who This Book Is For Enterprise Architects, Technical Architects, Project Managers and Senior Developers.
Generative Ai For Software Engineers The Journey Begins
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Author : Naresh Dulam
language : en
Publisher: Libertatem Media Private Limited
Release Date : 2023-07-23
Generative Ai For Software Engineers The Journey Begins written by Naresh Dulam and has been published by Libertatem Media Private Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-23 with Computers categories.
This book assists software engineers as they explore the realm of Artificial Intelligence (AI), providing essential tools and techniques for creating new AI-powered applications or adding AI functionalities to their current projects. Significant advancements in AI and machine learning (ML) frameworks have allowed software engineers to easily integrate intelligent capabilities into their products or projects in the last ten years. The introduction of ChatGPT in late 2022 brought Generative AI into the spotlight. Although abundant information is available online, software engineers need help finding a clear entry point like 101 to learn fundamentals. This guide simplifies the core concepts, gradually addressing more advanced topics to enable you to create practical, production-ready AI solutions with concrete code examples. Note: This book simplifies complex concepts by applying abstractions for software engineers to grasp the basics of Generative AI.
Agent
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Author : 高强文
language : zh-CN
Publisher: 机械工业出版社
Release Date : 2025-03-14
Agent written by 高强文 and has been published by 机械工业出版社 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-14 with Juvenile Nonfiction categories.
这是一本面向初中级读者的Agent学习指南，作者既是资深的AI技术专家，又是经验丰富的项目导师，融合作者亲身实践、培训反馈与官方资源，为Agent使用者和开发者提供了快速上手的实用指导。
Generative Ai With Python And Pytorch Second Edition
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Author : Joseph Babcock
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-03-28
Generative Ai With Python And Pytorch Second Edition written by Joseph Babcock and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-28 with Computers categories.
Master GenAI techniques to create images and text using variational autoencoders (VAEs), generative adversarial networks (GANs), LSTMs, and large language models (LLMs) Key Features Implement real-world applications of LLMs and generative AI Fine-tune models with PEFT and LoRA to speed up training Expand your LLM toolbox with Retrieval Augmented Generation (RAG) techniques, LangChain, and LlamaIndex Purchase of the print or Kindle book includes a free eBook in PDF format Book Description Become an expert in Generative AI through immersive, hands-on projects that leverage today’s most powerful models for Natural Language Processing (NLP) and computer vision. Generative AI with Python and PyTorch is your end-to-end guide to creating advanced AI applications, made easy by Raghav Bali, a seasoned data scientist with multiple patents in AI, and Joseph Babcock, a PhD and machine learning expert. Through business-tested approaches, this book simplifies complex GenAI concepts, making learning both accessible and immediately applicable. From NLP to image generation, this second edition explores practical applications and the underlying theories that power these technologies. By integrating the latest advancements in LLMs, it prepares you to design and implement powerful AI systems that transform data into actionable intelligence. You’ll build your versatile LLM toolkit by gaining expertise in GPT-4, LangChain, RLHF, LoRA, RAG, and more. You’ll also explore deep learning techniques for image generation and apply styler transfer using GANs, before advancing to implement CLIP and diffusion models. Whether you’re generating dynamic content or developing complex AI-driven solutions, this book equips you with everything you need to harness the full transformative power of Python and AI. What will you learn Grasp the core concepts and capabilities of LLMs Craft effective prompts using chain-of-thought, ReAct, and prompt query language to guide LLMs toward your desired outputs Understand how attention and transformers have changed NLP Optimize your diffusion models by combining them with VAEs Build text generation pipelines based on LSTMs and LLMs Leverage the power of open-source LLMs, such as Llama and Mistral, for diverse applications Who this book is for This book is for data scientists, machine learning engineers, and software developers seeking practical skills in building generative AI systems. A basic understanding of math and statistics and experience with Python coding is required.