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Generative AI with LangChain
Generative AI with LangChain

Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs

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Generative AI with LangChain

Questions

If you've read and understood this chapter, you should be able to answer these questions:

  1. What is a generative model?
  2. Which applications exist for generative models?
  3. What's a large language model (LLM) and what does it do?
  4. How can we get bigger performance from LLMs?
  5. What are the conditions that make these models possible?
  6. Which companies and organizations are the big players in developing LLMs?
  7. What is a transformer and what does it consist of?
  8. What does GPT mean?
  9. How does stable diffusion work?
  10. How is stable diffusion trained?

If you struggle to answer these questions, please refer back to the corresponding sections in this chapter to make sure you've understood the material.

LangChain for LLM Apps

Large Language Models (LLMs) like GPT-4 have demonstrated immense capabilities in generating human-like text. However, simply accessing LLMs via APIs has limitations. Instead, combining them with other data sources and tools can enable more powerful applications. In this chapter, we will introduce LangChain as a way to overcome LLM limitations and build innovative language-based applications. We aim to demonstrate the potential of combining recent AI advancements with a robust framework like LangChain.

We will start by outlining some challenges faced when using LLMs on their own, like the lack of external knowledge, incorrect reasoning, and the inability to take action. LangChain provides solutions to these issues through different integrations and off-the-shelf components for specific tasks. We will walk through examples of how developers can use LangChain’s capabilities to create customized natural language processing solutions, outlining the components...

Going beyond stochastic parrots

LLMs have gained significant attention and popularity due to their ability to generate human-like text and understand natural language, which makes them useful in scenarios that revolve around content generation, text classification, and summarization. However, their apparent fluency obscures serious deficiencies that constrain real-world utility. The concept of stochastic parrots helps to elucidate this fundamental issue.

Stochastic parrots refers to LLMs that can produce convincing language but lack any true comprehension of the meaning behind words. Coined by researchers Emily Bender, Timnit Gebru, Margaret Mitchell, and Angelina McMillan-Major in their influential paper On the Dangers of Stochastic Parrots (2021), the term critiques models that mindlessly mimic linguistic patterns. Without being grounded in the real world, models can produce responses that are inaccurate, irrelevant, unethical, or make little logical sense.

Simply scaling...

What is LangChain?

Created in 2022 by Harrison Chase, LangChain is an open-source Python framework for building LLM-powered applications. It provides developers with modular, easy-to-use components for connecting language models with external data sources and services. The project has attracted millions in venture capital funding from the likes of Sequoia Capital and Benchmark, who supplied funding to Apple, Cisco, Google, WeWork, Dropbox, and many other successful companies.

LangChain simplifies the development of sophisticated LLM applications by providing reusable components and pre-assembled chains. Its modular architecture abstracts access to LLMs and external services into a unified interface. Developers can combine these building blocks to carry out complex workflows.

Building impactful LLM apps involves challenges like prompt engineering, bias mitigation, productionizing, and integrating external data. LangChain reduces this learning curve through its abstractions...

Exploring key components of LangChain

Chains, agents, memory, and tools enable the creation of sophisticated LLM applications that go beyond basic API calls to a single LLM. In the following dedicated subsections on these key concepts, we’ll consider how they enable the development of capable systems by combining language models with external data and services.

We won’t dive into implementation patterns in this chapter; however, we will discuss in more detail what some of these components are good for. By the end, you should have the level of understanding that’s required to architect systems with LangChain. Let’s start with chains!

What are chains?

Chains are a critical concept in LangChain for composing modular components into reusable pipelines. For example, developers can put together multiple LLM calls and other components in a sequence to create complex applications for things like chatbot-like social interactions, data extraction, and...

How does LangChain work?

The LangChain framework simplifies building sophisticated LLM applications by providing modular components that facilitate connecting language models with other data and services. The framework organizes capabilities into modules spanning from basic LLM interaction to complex reasoning and persistence.

These components can be combined into pipelines also called chains that sequence the following actions:

  • Loading documents
  • Embedding for retrieval
  • Querying LLMs
  • Parsing outputs
  • Writing memory

Chains match modules to application goals, while agents leverage chains for goal-directed interactions with users. They repeatedly execute actions based on observations, plan optimal logic chains, and persist memory across conversations.

The modules, ranging from simple to advanced, are:

  • LLMs and chat models: Provide interfaces to connect and query language models like GPT-3. Support async, streaming, and batch...

Comparing LangChain with other frameworks

LLM application frameworks have been developed to provide specialized tooling that can harness the power of LLMs effectively to solve complex problems. A few libraries have emerged that meet the requirements of effectively combining generative AI models with other tools to build LLM applications.

There are several open-source frameworks for building dynamic LLM applications. They all offer value in developing cutting-edge LLM applications. This graph shows their popularity over time (data source: GitHub star history, https://star-history.com/):

Figure 2.11: Comparison of popularity between different frameworks in Python

We can see the number of stars on GitHub over time for each project. Haystack is the oldest of the compared frameworks, having started in early 2020 (as per the earliest GitHub commits). It is also the least popular in terms of stars on GitHub. LangChain, LlamaIndex (previously called GPTIndex), and SuperAGI...

Summary

LLMs produce convincing language but have significant limitations in terms of reasoning, knowledge, and access to tools. The LangChain framework simplifies the building of sophisticated applications powered by LLMs that can mitigate these shortcomings. It provides developers with modular, reusable building blocks like chains for composing pipelines and agents for goal-oriented interactions. These building blocks fit together as LLM apps that come with extended capabilities.

As we saw in this chapter, chains allow sequencing calls to LLMs, databases, APIs, and more to accomplish multi-step workflows. Agents leverage chains to take actions based on observations for managing dynamic applications. Memory persists information across executions to maintain state. Together, these concepts enable developers to overcome the limitations of individual LLMs by integrating external data, actions, and context. In other words, LangChain reduces complex orchestration into customizable...

Questions

Please see if you can come up with answers to these questions. I’d recommend you go back to the corresponding sections of this chapter if you are unsure about any of them:

  1. What are the limitations of LLMs?
  2. What are stochastic parrots?
  3. What are LLM applications?
  4. What is LangChain and why should you use it?
  5. What are LangChain’s key features?
  6. What is a chain in LangChain?
  7. What is an agent?
  8. What is memory and why do we need it?
  9. What kind of tools are available in LangChain?
  10. How does LangChain work?

Join our community on Discord

Join our community’s Discord space for discussions with the authors and other readers:

https://packt.link/lang

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Key benefits

  • Learn how to leverage LangChain to work around LLMs’ inherent weaknesses
  • Delve into LLMs with LangChain and explore their fundamentals, ethical dimensions, and application challenges
  • Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality

Description

ChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Gemini. It demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications. Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.

Who is this book for?

The book is for developers, researchers, and anyone interested in learning more about LangChain. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs using LangChain. Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily.

What you will learn

  • Create LLM apps with LangChain, like question-answering systems and chatbots
  • Understand transformer models and attention mechanisms
  • Automate data analysis and visualization using pandas and Python
  • Grasp prompt engineering to improve performance
  • Fine-tune LLMs and get to know the tools to unleash their power
  • Deploy LLMs as a service with LangChain and apply evaluation strategies
  • Privately interact with documents using open-source LLMs to prevent data leaks

Product Details

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Publication date : Dec 22, 2023
Length: 368 pages
Edition : 1st
Language : English
ISBN-13 : 9781835088364
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Product Details

Publication date : Dec 22, 2023
Length: 368 pages
Edition : 1st
Language : English
ISBN-13 : 9781835088364
Category :
Languages :

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Table of Contents

12 Chapters
What Is Generative AI? Chevron down icon Chevron up icon
LangChain for LLM Apps Chevron down icon Chevron up icon
Getting Started with LangChain Chevron down icon Chevron up icon
Building Capable Assistants Chevron down icon Chevron up icon
Building a Chatbot Like ChatGPT Chevron down icon Chevron up icon
Developing Software with Generative AI Chevron down icon Chevron up icon
LLMs for Data Science Chevron down icon Chevron up icon
Customizing LLMs and Their Output Chevron down icon Chevron up icon
Generative AI in Production Chevron down icon Chevron up icon
The Future of Generative Models Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
(34 Ratings)
5 star 55.9%
4 star 17.6%
3 star 2.9%
2 star 14.7%
1 star 8.8%
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Josep Oriol Oct 13, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is as up-to-date as it can be, with lots of helpful code examples, and covering all aspects of LLM the development pipeline. It's my work companion, way more useful than official LangChain documentation. A must for everyone involved in LLMOps
Subscriber review Packt
Kam F Siu Jan 30, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Feefo Verified review Feefo
Andrew McVeigh May 01, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
i'm only 100 pages into this book, but boy is it well phrased and extremely clear. i write apps around LLMs, including RAG architectures. perhaps it's just the current state of my learning, but i've found this book to be extremely helpful and very logically organized. I'll revisit this review once i'm through the entire book, but so far 10/10. it's easily the best and most self-contained book i have on the subject.
Amazon Verified review Amazon
F. P. Dec 22, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
During my learning journey into large language model (LLM) development, I encountered several challenges:- The difficulty of providing precise instructions within specific contexts, which I found to be the most challenging and crucial aspect.- Switching between different LLM models with minimal programming effort.- Selectively saving chat history in memory.- Handling data efficiently, including managing input data of various modalities and making output data accessible.In overcoming these obstacles, I came across LangChain, a robust toolkit designed for LLM application development. The book "Generative AI with LangChain" by Ben Auffarth provides a comprehensive overview, covering the basics of LLM, LangChain, and its key components (chains, agents, memory, tools). The book also explores sample applications such as chatbots, customization of LLM models (conditioning, fine-tuning), and the deployment of LLM apps into production. Unlike theoretical research materials, this book serves as a practical, one-stop resource for understanding the current landscape of LLM applications.Some of the interesting points:- LangChain helps standardize prompts by providing prompt templates (LangChain Expression Language).- LangChain provides extensive integrations to other model APIs including Fake LLM, OpenAI, Hugging Face, GCP, Jina AI, Replicate, etc.- LangChain has "memory" which allows the model to be context-aware.- LangChain supports advanced data facilities such as map-reduce approach and output parser.This book has significantly saved me time, providing consolidated information without the need for extensive online searches or inquiries to ChatGPT. For those unsure about its content, I recommend checking out the free sample on Amazon – it's undoubtedly worth every penny.
Amazon Verified review Amazon
hawkinflight Jan 05, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I have not used LangChain before, and I am looking at this book to learn how to create an LLM app. I am really looking forward to trying it out for all three types of apps covered in the book - assistants/chatbot, code generation, and data science. The book is clear and straight to the point, so I expect to be able to try these out fairly quickly. I have gotten through the "setting up the dependencies" section. I cloned the book's github repo, and I tried three methods for variety's sake to create a python environment: pip, conda, and Docker, all on Windows, and I believe I have them all set up. I hit some bumps, but I was able to follow the onscreen error messages and get past them. For pip, I needed to install MSFT Build Tools to get C++. For the conda case, I had to modify the yaml file for two of the packages - ncurses and readline, which have different names for Windows. In Chapter 2 there is a comparison of LangChain with other frameworks, from which you get a feel that choosing LangChain at this moment is the best choice. I am happy to have found this book, and I can't wait to proceed w/the next steps. It's a lot of fun to be able to interact w/LLMs.
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