AI-Powered Cross-Platform Data Extractor
Overview
This project implements an intelligent data extraction pipeline designed to identify high-value information across multiple platforms like GitHub and LinkedIn. Using a seed list of 180+ industry-specific keywords, the system performs targeted searches and employs the Voyage AI mini model to calculate semantic similarity. Only content meeting a strict 0.45 relevance threshold is processed, ensuring a high-signal, low-noise dataset.
The Solution
I engineered a containerized (Docker) architecture that orchestrates multiple extraction agents. The system uses a hybrid scraping approach (Requests for speed, Selenium for dynamic content) and stores results in a PostgreSQL database. A key innovation is the dynamic feedback loop: the system extracts new relevant keywords from high-scoring posts and feeds them back into the search engine, creating an increasingly powerful and targeted discovery mechanism.