Framework Docs AI

Framework Docs AI is a powerful SaaS solution that revolutionizes documentation management for various frameworks. It seamlessly scrapes framework documentation, creates a comprehensive knowledge base, and utilizes advanced language models to provide accurate, retrieval-based responses to user queries.

🚀 Features

• Automatic Documentation Scraping: Effortlessly gather documentation from popular frameworks.
• Intelligent Knowledge Base: Store and organize scraped data for efficient retrieval.
• AI-Powered Responses: Leverage state-of-the-art language models for accurate answers.
• Customizable Framework Support: Add your own frameworks or use existing ones.
• User-Friendly Interface: Built with Streamlit for a smooth user experience.

🛠️ Technologies Used

• UI: Streamlit
• Vector Database: Chroma DB
• Function Calling: Phi Data
• Language Model: GPT-4o-mini

📚 Supported Frameworks

1. Langchain
2. Next.js
3. Vue.js
4. PyTorch
5. Chainlit
6. MDN Web Docs

🚀 Getting Started

Prerequisites

• Python 3.7+
• pip

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/framework-docs-ai.git
   cd framework-docs-ai
   

2. Install required dependencies:

   pip install -r requirements.txt
   

3. Set up your OPENAI API key:

   • Create a .env file in the root directory
   • Add your OPENAI API key:

     OPENAI_API_KEY=your_api_key_here
     

Usage

1. Run the application:

   python app.py
   

2. Open your web browser and navigate to the provided local URL (usually http://localhost:8501).

3. Start exploring framework documentation and asking questions!

🔧 Adding Custom Frameworks

You can add your own custom frameworks to the Framework Docs AI. Follow these steps:

1. Crawl the URLs (crawler.py):

   • The crawler.py file uses a DomainCrawler class to crawl websites and collect URLs.
   • Key components to modify:

     CONFIGS = [
         {
             "start_url": "https://your-framework-docs-url.com",
             "docs_path": "/",
             "avoid_keywords": ["blog", "about", "community"]
         },
         # You can add multiple configurations for different sections of your documentation
     ]
     
     OUTPUT_PATH = "data/Crawled_url/"
     FILE_NAME = "YOUR_FRAMEWORK_crawled.json"

   • Customize the CONFIGS list:
     • start_url: The base URL of your framework's documentation.
     • docs_path: The specific path where the documentation starts.
     • avoid_keywords: List of keywords to avoid in URLs (e.g., blog posts, community pages).
   • Update OUTPUT_PATH and FILE_NAME as needed.
   • The crawler attempts to use a sitemap.xml if available, then falls back to crawling from the specified docs_path.
   • It uses multi-threading to crawl multiple configurations simultaneously.
   • Run crawler.py to save the crawled URLs in the specified output file.

2. Scrape the Content:

   • Use scrape.py to extract content from the crawled URLs.
   • Modify the file paths in scrape.py to match your new framework:

     if __name__ == "__main__":
         Scrape("data/Crawled_url/YOUR_FRAMEWORK_crawled.json", "data/Scraped_data/YOUR_FRAMEWORK_scraped.json")

   • Run scrape.py to save the scraped content in the data/Scraped_data directory.

3. Convert to Langchain Document Format:

   • Use save.py to convert the scraped JSON to a pickle file compatible with Langchain's Document schema.
   • Update the file paths in save.py:

     if __name__ == "__main__":
         convert_json_to_pkl('data/Scraped_data/YOUR_FRAMEWORK_scraped.json', 'data/Scraped_data/YOUR_FRAMEWORK_scraped.pkl')

   • Run save.py to create the pickle file.

4. Update app.py:

   • Initialize the knowledge base for your new framework:

     kb_manager_your_framework = initialize_kb("Your Framework Name", "./Vector_DB/YOUR_FRAMEWORK", "data/Scraped_data/YOUR_FRAMEWORK_scraped.pkl")

   • Create an assistant for your framework:

     assistant_your_framework = create_assistant("Your Framework Name", api_key, kb_manager_your_framework) if kb_manager_your_framework else None

   • Add your framework to the sidebar options:

     options=["Langchain Python", "Next.js", "Vue.js", "MDN_WEB", "Pytorch", "Chainlit", "Your Framework Name"],

   • Handle the selection of your framework:

     elif selected == "Your Framework Name" and assistant_your_framework:
         display_framework(assistant_your_framework, "Your Framework Name")

5. Restart the Application:

   • Run app.py again to see your new framework in action.

Remember to replace "YOUR_FRAMEWORK" and "Your Framework Name" with the appropriate names for your custom framework.

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for more details.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgements

• Streamlit for the intuitive UI framework
• All the open-source projects that made this possible

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