The system processed tens of thousands of sentences, and integrates a vector database to enable contextual retrieval before passing results to a language model for grounded response generation. The architecture emphasizes scalable ingestion, efficient document storage, and modular retrieval logic.

This project strengthened my understanding of document-based data modeling, embedding generation, and end-to-end data pipeline design — from raw ingestion to semantic querying. Below is the output of a search.