generative_ai_with_langchain

Build large language model (LLM) apps with Python, ChatGPT and other models. This is the companion repository for the book on generative AI with LangChain.

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Generative AI with LangChain is a code repository for building large language model (LLM) apps with Python, ChatGPT, and other LLMs. The repository provides code examples, instructions, and configurations for creating generative AI applications using the LangChain framework. It covers topics such as setting up the development environment, installing dependencies with Conda or Pip, using Docker for environment setup, and setting API keys securely. The repository also emphasizes stability, code updates, and user engagement through issue reporting and feedback. It aims to empower users to leverage generative AI technologies for tasks like building chatbots, question-answering systems, software development aids, and data analysis applications.

README:

Generative AI with LangChain, First Edition

Build large language model (LLM) apps with Python, ChatGPT, and other LLMs!

This is the code repository for Generative AI with LangChain, First Edition, written by Ben Auffarth and published by Packt.

Note to Readers

Thank you for choosing "Generative AI with LangChain"! We appreciate your enthusiasm and feedback.

Please note that we are working on a new release of the book that will include significant updates. These will be made available for anyone who bought the book until this point as an electronic copy. Consequently, there are two different branches for this repository:

main - this is the original version of the book.
softupdate - this is for the latest update of the book, corresponding to ver 0.1.13 of LangChain.

Please refer to the version that you are interested in or that corresponds to your version of the book.

Download a free PDF

If you have already purchased an up-to-date print or Kindle version of this book, you can get a DRM-free PDF version at no cost. Simply click on the link to claim your free PDF. Free-Ebook

We also provide a PDF file that has color images of the screenshots/diagrams used in this book at GraphicBundle

Commitment

Code Updates: Our commitment is to provide you with stable and valuable code examples. While LangChain is known for frequent updates, we understand the importance of aligning our code with the latest changes. The companion repository is regularly updated to harmonize with LangChain developments.

Expect Stability: For stability and usability, the repository might not match every minor LangChain update. We aim for consistency and reliability to ensure a seamless experience for our readers.

How to Reach Us: Encountering issues or have suggestions? Please don't hesitate to open an issue, and we'll promptly address it. Your feedback is invaluable, and we're here to support you in your journey with LangChain. Thank you for your understanding and happy coding!

Know more on the Discord server

You can get more engaged on the discord server for more latest updates and discussions in the community at Discord

Software and hardware list

This is the companion repository for the book. Here are a few instructions that help getting set up. Please also see chapter 3.

All chapters rely on Python.

Chapter	Software required	Link to the software	Hardware specifications	OS required
All chapters	Python 3.11	https://www.python.org/downloads/	Should work on any recent computer	Windows, MacOS, Linux (any), macOS, Windows

Please note that Python 3.12 might not work (see #11).

Environment

You can install your local environment with conda (recommended) or pip. The environment configurations for conda and pip are provided. Please note that if you choose pip as you installation tool, you might need additional tweaking.

If you have any problems with the environment, please raise an issue, where you show the error you got. If you feel confident, please go ahead and create a pull request.

For pip and poetry, make sure you install pandoc in your system. On MacOS use brew:

brew install pandoc

On Ubuntu or Debian linux, use apt:

sudo apt-get install pandoc

On Windows, you can use an installer.

Conda

This is the recommended method for installing dependencies. Please make sure you have anaconda installed.

First create the environment for the book that contains all the dependencies:

conda env create --file langchain_ai.yaml --force

The conda environment is called langchain_ai. You can activate it as follows:

conda activate langchain_ai

Pip

Pip is the default dependency management tool in Python. With pip, you should be able to install all the libraries from the requirements file:

pip install -r requirements.txt

If you are working with a slow internet connection, you might see a timeout with pip (this can also happen with conda and pip). As a workaround, you can increase the timeout setting like this:

export PIP_DEFAULT_TIMEOUT=100

Docker

There's a docker file for the environment as well. It uses the docker environment and starts an ipython notebook. To use it, first build it, and then run it:

docker build -t langchain_ai .
docker run -d -p 8888:8888 langchain_ai

You should be able to find the notebook in your browser at http://localhost:8888.

Poetry

Make sure you have poetry installed. On Linux and MacOS, you should be able to use the requirements file:

poetry install --no-root

This should take the pyproject.toml file and install all dependencies.

Setting API keys

Following best practices regarding safety, I am not committing my credentials to GitHub. You might see import statements mentioning a config.py file, which is not included in the repository. This module has a method set_environment() that sets all the keys as environment variables like this:

Example config.py:

import os

def set_environment():
     os.environ['OPENAI_API_KEY']='your-api-key-here'

Obviously, you'd put your API credentials here. Depending on the integration (Openai, Azure, etc) you need to add the corresponding API keys. The OpenAI API keys are the most often used across all the code.

You can find more details about API credentials and setup in chapter 3 of the book Generative AI with LangChain.

Contributing

If you find anything amiss with the notebooks or dependencies, please feel free to create a pull request.

If you want to change the conda dependency specification (the yaml file), you can test it like this:

conda env create --file langchain_ai.yaml --force

You can update the pip requirements like this:

pip freeze > requirements.txt

Please make sure that you keep these two ways of maintaining dependencies in sync.

Then make sure, you test the notebooks in the new environment to see that they run.

Code validation

I've included a Makefile that includes instructions for validation with flake8, mypy, and other tools. I have run mypy like this:

make typecheck

To run the code validation in ruff, please run

ruff check .

Generative AI with LangChain

Create generative AI apps with LangChain.

About the book

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 Bard. It also 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.

Key Takeaways

Understand LLMs, their strengths and limitations
Grasp generative AI fundamentals and industry trends
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

Outline and Chapter Summary

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

In the following table, you can find links to the files (directories) in this repository, and to notebook execution platforms such as Google Colab and Kaggle/Gradient Notebooks.

Chapter	Files	Colab	Kaggle	Gradient
01, What Is Generative AI
02, LangChain for LLM Apps
03, Getting Started with LangChain
04, Building Capable Assistants
05, Building a Chatbot like ChatGPT
06, Developing Software with Generative AI
07, LLMs for Data Science
08, Customizing LLMs and Their Output
09, Generative AI in Production
10, The Future of Generative Models

If you feel this book is for you, get your copy today!

Get to know the Author

Ben Auffarth A seasoned data science leader with a background and Ph.D. in computational neuroscience. Ben has analyzed terabytes of data, simulated brain activity on supercomputers with up to 64k cores, designed and conducted wet lab experiments, built production systems processing underwriting applications, and trained neural networks on millions of documents. He’s the author of the books Machine Learning for Time Series and Artificial Intelligence with Python Cookbook. He now works in insurance at Hastings Direct.

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