This commit introduces significant updates to the LinkedIn data discovery documentation by adding two new Jupyter notebooks that provide detailed insights into data discovery processes. The previous workshop notebook has been removed to streamline the content and avoid redundancy. Additionally, the URL seeder documentation has been expanded with a new tutorial and several enhancements to existing scripts, improving usability and clarity.
The changes include:
- Added and for comprehensive LinkedIn data discovery.
- Removed to eliminate outdated content.
- Updated to reflect new data visualization requirements.
- Introduced and to facilitate easier access to URL seeding techniques.
- Enhanced existing Python scripts and markdown files in the URL seeder section for better documentation and examples.
These changes aim to improve the overall documentation quality and user experience for developers working with LinkedIn data and URL seeding techniques.
Add new LinkedIn prospect discovery tool with three main components:
- c4ai_discover.py for company and people scraping
- c4ai_insights.py for org chart and decision maker analysis
- Interactive graph visualization with company/people exploration
Features include:
- Configurable LinkedIn search and scraping
- Org chart generation with decision maker scoring
- Interactive network graph visualization
- Company similarity analysis
- Chat interface for data exploration
Requires: crawl4ai, openai, sentence-transformers, networkx
