Developed an end-to-end multimodal retrieval-augmented generation system in the nikbearbrown/INFO_7390_Art_and_Science_of_Data repository, focusing on scalable data ingestion, processing, and retrieval workflows. Leveraged Python and Jupyter Notebook to implement data loading from Google Cloud Storage, PDF processing with automated image description generation, and robust URL scraping and normalization. Built a FastAPI service for user management and integrated vector search using Pinecone, ensuring efficient information retrieval. Containerized the application with Docker for streamlined deployment and included open-source licensing. The work established a production-ready pipeline, demonstrating depth in data engineering, document processing, and cloud-native API development within a single feature delivery.
April 2025 (nikbearbrown/INFO_7390_Art_and_Science_of_Data) — Delivered end-to-end multimodal retrieval-augmented generation (RAG) system setup, enabling scalable data ingestion, processing, and retrieval workflows. Implemented Python scripts and a Jupyter notebook for data loading from Google Cloud Storage, PDF processing with image description generation, and URL scraping/normalization. Built a FastAPI service for user management and vector search integration with Pinecone; included a Dockerfile for deployment and a LICENSE file for open-source readiness. Established a production-ready pipeline from data ingestion to retrieval with containerized deployment.
April 2025 (nikbearbrown/INFO_7390_Art_and_Science_of_Data) — Delivered end-to-end multimodal retrieval-augmented generation (RAG) system setup, enabling scalable data ingestion, processing, and retrieval workflows. Implemented Python scripts and a Jupyter notebook for data loading from Google Cloud Storage, PDF processing with image description generation, and URL scraping/normalization. Built a FastAPI service for user management and vector search integration with Pinecone; included a Dockerfile for deployment and a LICENSE file for open-source readiness. Established a production-ready pipeline from data ingestion to retrieval with containerized deployment.
Overview of all repositories you've contributed to across your timeline