Langchain In Your Pocket

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Langchain In Your Pocket

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Author : Mehul Gupta
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-15

Langchain In Your Pocket written by Mehul Gupta 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 2024-05-15 with Computers categories.

Learn about LangChain and LLMs with "LangChain in your Pocket," a comprehensive guide to leveraging this innovative framework for building language-based applications. Key Features Step-by-step code explanations with expected outputs for each solution Practical examples and hands-on tutorials for real-world application Detailed discussions on managing and evaluating large language models Book Description"LangChain in your Pocket" offers a detailed exploration into the LangChain framework, designed to enhance your skills in developing sophisticated language understanding models and applications. This book begins with the basics, introducing you to the fundamental concepts of LangChain through a simple "Hello World" example. As you progress, you'll delve into various LangChain modules, learning how to create agents, manage memory, and utilize output parsers effectively. The journey continues as you explore the RAG Framework, vector databases, and their applications in natural language processing, providing you with the tools to tackle common NLP problems efficiently. The book also addresses critical aspects of working with large language models (LLMs), such as prompt engineering, handling hallucinations, and evaluating model outputs. Advanced topics like autonomous AI agents and the integration of LangSmith and LangServe are covered, giving you a holistic view of what you can achieve with LangChain. By the end of this book, you will not only understand the technical aspects of LangChain but also how to apply these principles in real-world scenarios, making it an essential resource for anyone looking to advance their capabilities in AI and language processing.What you will learn Navigate the basic to advanced features of LangChain Build and manage language understanding models and applications Employ advanced prompt engineering techniques Implement and evaluate large language models effectively Develop autonomous AI agents with LangChain Integrate LangSmith and LangServe for enhanced functionality Who this book is for The book "LangChain in your Pocket: Beginner's Guide to Building Generative AI Applications using LLMs" is an excellent resource for individuals new to the world of Generative AI. Whether you are a software developer, data scientist, or student, this beginner-friendly book provides a comprehensive introduction to the LangChain framework and its practical applications. Regardless of your prior experience, this book is a valuable asset for anyone interested in diving into the world of Generative AI and leveraging the power of LangChain.

Langchain In Your Pocket

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Author : Mehul Gupta
language : en
Publisher: Mehul Gupta
Release Date : 2024-01-28

Langchain In Your Pocket written by Mehul Gupta and has been published by Mehul Gupta this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-28 with Computers categories.

Unlock the full potential of Generative AI with "LangChain in your Pocket", a hands-on guide that takes you through the robust LangChain framework. This book provides a step-by-step journey into creating powerful applications, from Auto-SQL and NER to custom Agents and Chains, integrating Memory, OutputParsers, RAG for Q&A, Few-Shot Classification, Evaluators, Autonomous AI agents, Advanced Prompt Engineering and many more. NOTE: Drop an email to [email protected] with the transaction receipt for a free PDF version. Key Features: Step-by-step code explanations with expected outputs for each solution. No prerequisites: If you know Python, you're ready to dive in. Practical, hands-on guide with minimal mathematical explanations. Book Description: Since the arrival of ChatGPT in late 2022, the AI landscape has evolved dramatically. "LangChain in your Pocket" invites you to move beyond ChatGPT and explore the versatility of LangChain, a Python/JavaScript framework at the forefront of Large Language Models (LLMs). Whether you're building Classification models, Storyteller, or Internet-enabled GPT, LangChain empowers you to do more. This beginner-friendly introduction covers: Basics of Large Language Models (LLMs) and why LangChain is pivotal. Hello World tutorial for setting up LangChain and creating baseline applications. In-depth chapters on each LangChain module. Advanced problem-solving, including Multi-Document RAG, Hallucinations, NLP chains, and Evaluation for LLMs for supervised and unsupervised ML problems. Dedicated sections for Few-Shot Learning, Advanced Prompt Engineering using ReAct, Autonomous AI agents, and deployment using LangServe. Who should read it? This book is for anyone keen on exploring AI, especially Generative AI. Whether you're a Software Developer, Data Scientist, Student or Content Writer, the focus on diverse use cases in LangChain and GenAI makes it equally valuable to all. Table of Contents Introduction Hello World Different LangChain Modules Models & Prompts Chains Agents OutputParsers & Memory Callbacks RAG Framework & Vector Databases LangChain for NLP problems Handling LLM Hallucinations Evaluating LLMs Advanced Prompt Engineering Autonomous AI agents LangSmith & LangServe Additional Features

Model Context Protocol

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Author : Mehul Gupta
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-07-10

Model Context Protocol written by Mehul Gupta 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 2025-07-10 with Computers categories.

Explore AI Agents and Model Context Protocol with practical guides to setting up MCP servers across popular tools like Gmail, Slack, and Excel. Learn how AI can revolutionize task automation. Key Features In-depth coverage of generative AI and large language models Step-by-step installation guides for MCP servers across various tools Practical applications of AI agents with real-world use cases Book DescriptionThis book offers a detailed introduction to the groundbreaking field of AI agents and Model Context Protocol (MCP). The first section delves into generative AI and large language models (LLMs), exploring how these technologies power modern AI systems. From there, the book introduces the concept of AI agents—autonomous systems capable of executing tasks with varying levels of complexity. Moving into practical applications, the book focuses on Model Context Protocol, explaining its key components and how it enables effective interaction between AI and various software tools. Each chapter offers step-by-step instructions for setting up MCP servers for popular tools like Gmail, YouTube, GitHub, and more, empowering readers to automate tasks and streamline workflows. The book concludes by addressing the future of MCP, its potential risks, and how to stay safe while using these advanced technologies. Whether you're a beginner or experienced practitioner, this guide will deepen your understanding of AI and enhance your ability to leverage cutting-edge automation in daily operations.What you will learn Understand the principles of generative AI and LLMs Learn about the core concepts of AI agents and their roles Explore the importance of the Model Context Protocol Set up MCP servers for tools like Gmail, Excel, and Slack Apply MCP with local LLMs using Ollama Install MCP servers for platforms like YouTube and GitHub Who this book is for This book is ideal for AI enthusiasts, developers, and tech professionals interested in learning about AI agents, task automation, and Model Context Protocol. The audience should have a basic understanding of AI concepts and be familiar with popular software tools like Gmail, Slack, and Excel. While no advanced programming skills are required, readers should be comfortable following installation steps and exploring real-world applications. This guide is perfect for anyone looking to integrate AI into their business processes or personal projects.

Llms In Production

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Author : Christopher Brousseau
language : en
Publisher: Simon and Schuster
Release Date : 2025-02-11

Llms In Production written by Christopher Brousseau and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-11 with Computers categories.

Learn how to put Large Language Model-based applications into production safely and efficiently. This practical book offers clear, example-rich explanations of how LLMs work, how you can interact with them, and how to integrate LLMs into your own applications. Find out what makes LLMs so different from traditional software and ML, discover best practices for working with them out of the lab, and dodge common pitfalls with experienced advice. In LLMs in Production you will: • Grasp the fundamentals of LLMs and the technology behind them • Evaluate when to use a premade LLM and when to build your own • Efficiently scale up an ML platform to handle the needs of LLMs • Train LLM foundation models and finetune an existing LLM • Deploy LLMs to the cloud and edge devices using complex architectures like PEFT and LoRA • Build applications leveraging the strengths of LLMs while mitigating their weaknesses LLMs in Production delivers vital insights into delivering MLOps so you can easily and seamlessly guide one to production usage. Inside, you’ll find practical insights into everything from acquiring an LLM-suitable training dataset, building a platform, and compensating for their immense size. Plus, tips and tricks for prompt engineering, retraining and load testing, handling costs, and ensuring security. Foreword by Joe Reis. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Most business software is developed and improved iteratively, and can change significantly even after deployment. By contrast, because LLMs are expensive to create and difficult to modify, they require meticulous upfront planning, exacting data standards, and carefully-executed technical implementation. Integrating LLMs into production products impacts every aspect of your operations plan, including the application lifecycle, data pipeline, compute cost, security, and more. Get it wrong, and you may have a costly failure on your hands. About the book LLMs in Production teaches you how to develop an LLMOps plan that can take an AI app smoothly from design to delivery. You’ll learn techniques for preparing an LLM dataset, cost-efficient training hacks like LORA and RLHF, and industry benchmarks for model evaluation. Along the way, you’ll put your new skills to use in three exciting example projects: creating and training a custom LLM, building a VSCode AI coding extension, and deploying a small model to a Raspberry Pi. What's inside • Balancing cost and performance • Retraining and load testing • Optimizing models for commodity hardware • Deploying on a Kubernetes cluster About the reader For data scientists and ML engineers who know Python and the basics of cloud deployment. About the author Christopher Brousseau and Matt Sharp are experienced engineers who have led numerous successful large scale LLM deployments. Table of Contents 1 Words’ awakening: Why large language models have captured attention 2 Large language models: A deep dive into language modeling 3 Large language model operations: Building a platform for LLMs 4 Data engineering for large language models: Setting up for success 5 Training large language models: How to generate the generator 6 Large language model services: A practical guide 7 Prompt engineering: Becoming an LLM whisperer 8 Large language model applications: Building an interactive experience 9 Creating an LLM project: Reimplementing Llama 3 10 Creating a coding copilot project: This would have helped you earlier 11 Deploying an LLM on a Raspberry Pi: How low can you go? 12 Production, an ever-changing landscape: Things are just getting started A History of linguistics B Reinforcement learning with human feedback C Multimodal latent spaces

Building Llms For Production

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Author : Louis-François Bouchard
language : en
Publisher: Towards AI, Inc.
Release Date : 2024-05-21

Building Llms For Production written by Louis-François Bouchard and has been published by Towards AI, Inc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-21 with Computers categories.

“This is the most comprehensive textbook to date on building LLM applications - all essential topics in an AI Engineer's toolkit." - Jerry Liu, Co-founder and CEO of LlamaIndex (THE BOOK WAS UPDATED ON OCTOBER 2024) With amazing feedback from industry leaders, this book is an end-to-end resource for anyone looking to enhance their skills or dive into the world of AI and develop their understanding of Generative AI and Large Language Models (LLMs). It explores various methods to adapt "foundational" LLMs to specific use cases with enhanced accuracy, reliability, and scalability. Written by over 10 people on our Team at Towards AI and curated by experts from Activeloop, LlamaIndex, Mila, and more, it is a roadmap to the tech stack of the future. The book aims to guide developers through creating LLM products ready for production, leveraging the potential of AI across various industries. It is tailored for readers with an intermediate knowledge of Python. What's Inside this 470-page Book (Updated October 2024)? - Hands-on Guide on LLMs, Prompting, Retrieval Augmented Generation (RAG) & Fine-tuning - Roadmap for Building Production-Ready Applications using LLMs - Fundamentals of LLM Theory - Simple-to-Advanced LLM Techniques & Frameworks - Code Projects with Real-World Applications - Colab Notebooks that you can run right away Community access and our own AI Tutor Table of Contents - Chapter I Introduction to Large Language Models - Chapter II LLM Architectures & Landscape - Chapter III LLMs in Practice - Chapter IV Introduction to Prompting - Chapter V Retrieval-Augmented Generation - Chapter VI Introduction to LangChain & LlamaIndex - Chapter VII Prompting with LangChain - Chapter VIII Indexes, Retrievers, and Data Preparation - Chapter IX Advanced RAG - Chapter X Agents - Chapter XI Fine-Tuning - Chapter XII Deployment and Optimization Whether you're looking to enhance your skills or dive into the world of AI for the first time as a programmer or software student, our book is for you. From the basics of LLMs to mastering fine-tuning and RAG for scalable, reliable AI applications, we guide you every step of the way.

Ultimate Azure Ai Services For Gen Ai Solutions

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Author : Shanthababu Pandian
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2025-05-08

Ultimate Azure Ai Services For Gen Ai Solutions written by Shanthababu Pandian and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-08 with Computers categories.

TAGLINE Master Generative AI with Azure OpenAI, AI Services, and advanced tools for real-world applications! KEY FEATURES ● Step-by-step and structured content designed for beginners, intermediates, and experts alike. ● Master all facets of Generative AI development, including LLMs, LangChain, Prompt Engineering, and Vector Databases. ● Gain insights into implementation strategies through practical, real-world examples. DESCRIPTION Azure OpenAI provides unparalleled access to cutting-edge AI models, empowering enterprises to innovate, automate, and drive transformative business outcomes at scale. Ultimate Azure AI Services for Gen AI Solutions is your gateway to mastering Azure OpenAI and Azure AI services. Whether you’re just starting out or looking to refine your skills, this book covers everything from foundational concepts to advanced techniques. Dive into topics like Large Language Models (LLMs), LangChain, vector databases, embeddings, and Python programming, with a focus on key Azure components such as Storage, Search Services, Azure OpenAI Studio, and Prompt Flow. Through step-by-step hands-on examples, you’ll gain practical insights into the power of prompt engineering, advanced features of Azure’s AI capabilities, and how to implement solutions in language, speech, and vision. You’ll also explore ethical AI practices, ensuring responsible and impactful AI development. This book equips you with the skills to navigate the full Generative AI lifecycle—from development to deployment—ensuring your enterprise stays ahead in this fast-paced field. Don’t miss your chance to transform your business with Azure’s revolutionary AI tools—start building the future today! WHAT WILL YOU LEARN ● Understand core concepts, including Large Language Models (LLMs), LangChain, and embedding techniques. ● Utilize vector databases, embedding methods, and strategies for effective prompt design for Generative AI solutions. ● Gain hands-on experience with Azure Storage, Azure Search Service, Azure OpenAI Service, and Azure OpenAI Studio. ● Leverage Azure’s advanced AI capabilities, including Language, Speech, and Vision Studio, while adhering to responsible AI practices. ● Master the AI product lifecycle, from development to deployment, using Python for AI-driven applications. WHO IS THIS BOOK FOR? This book is tailored for Generative AI enthusiasts, professionals, and developers looking to upskill in Generative AI and integrate it into real-world applications. A basic understanding of Python and Azure is helpful but not required, as the book provides a structured approach to mastering AI implementation with Python and Azure services. TABLE OF CONTENTS 1. Introduction to Generative AI 2. Exploring LLMs and Its Capabilities 3. Vector Database and Embedding Techniques 4. Prompt Engineering and Its Significance 5. Azure Storage for Azure OpenAI Implementations 6. Azure AI Search Services for Azure OpenAI Implementations 7. Getting Started with Generative AI Using Azure OpenAI Services 8. Advanced Azure AI Studio–I 9. Advanced Azure AI Studio–II 10. Generative AI Use Cases for Industries–I 11. Gen AI Implementation Use Case with Azure OpenAI–II Index

Python Data Visualization Cookbook

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Author : Igor Milovanović
language : en
Publisher: Packt Publishing Ltd
Release Date : 2013-11-25

Python Data Visualization Cookbook written by Igor Milovanović 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 2013-11-25 with Computers categories.

This book is written in a Cookbook style targeted towards an advanced audience. It covers the advanced topics of data visualization in Python.Python Data Visualization Cookbook is for developers that already know about Python programming in general. If you have heard about data visualization but you don't know where to start, then this book will guide you from the start and help you understand data, data formats, data visualization, and how to use Python to visualize data.You will need to know some general programming concepts, and any kind of programming experience will be helpful, but the code in this book is explained almost line by line. You don't need maths for this book, every concept that is introduced is thoroughly explained in plain English, and references are available for further interest in the topic.

Machine Learning Design Patterns

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Author : Valliappa Lakshmanan
language : en
Publisher: O'Reilly Media
Release Date : 2020-10-15

Machine Learning Design Patterns written by Valliappa Lakshmanan and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-15 with Computers categories.

The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly

Building Machine Learning Pipelines

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Author : Hannes Hapke
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-07-13

Building Machine Learning Pipelines written by Hannes Hapke 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 2020-07-13 with Computers categories.

Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques

Programming Pytorch For Deep Learning

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Author : Ian Pointer
language : en
Publisher: O'Reilly Media
Release Date : 2019-09-20

Programming Pytorch For Deep Learning written by Ian Pointer and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-20 with Computers categories.

Take the next steps toward mastering deep learning, the machine learning method that’s transforming the world around us by the second. In this practical book, you’ll get up to speed on key ideas using Facebook’s open source PyTorch framework and gain the latest skills you need to create your very own neural networks. Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text,and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production. Learn how to deploy deep learning models to production Explore PyTorch use cases from several leading companies Learn how to apply transfer learning to images Apply cutting-edge NLP techniques using a model trained on Wikipedia Use PyTorch’s torchaudio library to classify audio data with a convolutional-based model Debug PyTorch models using TensorBoard and flame graphs Deploy PyTorch applications in production in Docker containers and Kubernetes clusters running on Google Cloud