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generative_ai_with_langchain

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Basic Information

This repository is the companion code for the book Generative AI with LangChain, First Edition and provides practical, runnable examples and environment configurations to learn and build large language model applications with Python, ChatGPT and other LLMs. It contains notebook examples and supporting files that mirror the chapters of the book and demonstrate how to create LLM apps such as question-answering systems, chatbots, data-analysis automation and other production-ready patterns using the LangChain framework. The repo documents environment setup options (conda, pip, poetry, Docker), API key handling practices, and maintains two branches: main for the original book and softupdate for a newer LangChain version. The materials focus on prompt engineering, fine-tuning, deployment considerations and safe handling of credentials to help readers reproduce the book’s examples and extend them for their own projects.

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App URL
https://github.com/benman1/generative_ai_with_langchain
Github Repository
https://github.com/benman1/generative_ai_with_langchain/blob/main/README.md

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App Details

Features
Chapter-aligned, runnable code and notebooks that illustrate LangChain usage and generative AI patterns. Environment recipes including a conda YAML (langchain_ai.yaml), requirements.txt, pyproject.toml and a Dockerfile to reproduce the development environment. A sample config.py pattern and instructions for setting API keys and keeping credentials out of source control. A Makefile and validation guidance for static checks such as flake8, mypy and ruff. Branching strategy with main and softupdate to match LangChain versions. Guidance on installing pandoc and other prerequisites. Links to companion PDF and community resources plus instructions for contributing, raising issues and synchronizing dependency files.
Use Cases
The repository makes it straightforward to follow the book’s tutorials by providing the exact code, environment files and validation scripts used by the author. Users can recreate examples quickly with conda, pip, poetry or Docker and learn practical workflows for building LLM applications, including prompt engineering, fine-tuning, retrieval-based question answering, chatbots and automating data analysis. The repo emphasizes safe API key handling and reproducible environments to reduce setup friction. Code validation instructions and continuous updates to align with LangChain changes help maintain working examples. The dual-branch approach allows readers to pick examples that match their LangChain version and the README points to community channels for additional support and updates.

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