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https://github.com/hooked-on-mas/AutoGenBook

:robot: :ledger: AutoGenBook is a Python-based tool that automatically generates books using LLMs. It creates chapters, sections, and subsections recursively based on user-defined content and outputs the final book as a PDF or a Markdown using LaTeX (KaTeX).
https://github.com/hooked-on-mas/AutoGenBook

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:robot: :ledger: AutoGenBook is a Python-based tool that automatically generates books using LLMs. It creates chapters, sections, and subsections recursively based on user-defined content and outputs the final book as a PDF or a Markdown using LaTeX (KaTeX).

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README 

        
# AutoGenBook

AutoGenBook is a Python-based tool that automatically generates books using LLMs. It creates chapters, sections, and subsections recursively based on user-defined content and outputs the final book as a PDF using LaTeX.



## How to Use



🇯🇵 ➡️ [使い方](./READMEJP.md), 🇰🇷 ➡️ [사용법](./READMEKR.md), 🇨🇳 ➡️ [使用方法](./READMECN.md)



### Getting and Setting Up the OpenAI API Key



This tool requires an OpenAI API key. Once you obtain the API key, click on the key icon on the left-hand menu in Google Colab and register it with the name `openai_api`.



![image](https://github.com/user-attachments/assets/64e3ad1b-9eb9-4746-8485-e3318e269573)



### Running the Tool on Google Colab



Click the button below to open the tool in Google Colab:



[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/hooked-on-mas/AutoGenBook/blob/main/AutoGenBook.ipynb)



## Sample Textbook



You can view a sample of the generated textbook, "Linear Algebra for Machine Learning: A Practical Guide with Numpy" in PDF format here:



[Click here to view the sample textbook on Google Drive](https://drive.google.com/file/d/1hOh7vD9ftIzlfTQGm9zwaRSnoQJgq0Y0/view?usp=sharing)



## What’s Inside AutoGenBook



While you can dive into the code for all the details, I realize that it might be a bit hard to follow, so let me explain the basic idea and workflow behind the tool.



### Overview



Since ChatGPT has limitations on how much text it can generate at once, simply asking it to “write a textbook” results in only 1-2 pages of content. To overcome this, AutoGenBook recursively breaks down the structure of a book starting from the main topic or title. It goes from chapters → sections → subsections, and so on. This approach ensures that ChatGPT can generate meaningful, self-contained book for you without hitting its output limit.



Finally, the content for each subdivided section is generated using ChatGPT, and then output as a PDF.



### Workflow



Here’s an outline of the process. I've simplified it by only showing the flow up to the creation of subsections, but the same recursive structure continues for deeper levels.



```mermaid

graph TD

    N[Input specifications] --> A[Generate book title and overview
Generate chapter titles and overviews]

    A --> B[(Book title and overview)]

    A --> B1[(Chapter 1 title and overview)]

    A --> B2[(Chapter 2 title and overview)]

    B1 --> BB1[Generate sections for Chapter 1]

    B2 --> BB2[Generate sections for Chapter 2]

    BB1 --> C11[(Section 1.1's title, overview
and necessity of division)]

    BB1 --> C12[(Section 1.2's title, overview
and necessity of division)]

    BB2 --> C21[(Section 2.1's title, overview
and necessity of division)]

    BB2 --> C22[(Section 2.2's title, overview
and necessity of division)]

    C11 --> D11{Should it be devided?}

    C12 --> D12{Should it be devided?}

    C21 --> D21{Should it be devided?}

    C22 --> D22{Should it be devided?}

    D11 --> |Yes| E11[Generate subsections of Section 1.1]

    D11 --> |No| F11[Generate content of Section 1.1]

    E11 --> E111[(Subsection 1.1.1's title, overview
and necessity of division)]

    E11 --> E112[(Subsection 1.1.2's title, overview
and necessity of division)]

    E111 --> F111{Should it be devided?}

    E112 --> F112{Should it be devided?}

    F11 --> FF[(Content of Section 1.1)]

```