
Developed a production-ready chatbot application for the DrAlzahraniProjects/csusb_fall2024_cse6550_team4 repository, focusing on retrieval-augmented generation and scalable vector retrieval using Milvus. The project established a robust Streamlit-based user interface, integrating Python and HTML to deliver chat history, user feedback, and usage analytics. The technical approach included initializing a Milvus vector store to support embedding-based retrieval and implementing a RAG workflow to enhance response quality and relevance. UI scaffolding was created to enable future experimentation and rapid enhancements. The work emphasized maintainability and extensibility, laying a solid foundation for ongoing development in chatbot and data visualization capabilities.
October 2024 for DrAlzahraniProjects/csusb_fall2024_cse6550_team4 focused on delivering a production-ready chatbot experience that leverages retrieval-augmented generation (RAG) and Milvus-based vector retrieval. The work established a robust UI, data retrieval pipeline, and monitoring hooks while laying groundwork for UI experimentation.
October 2024 for DrAlzahraniProjects/csusb_fall2024_cse6550_team4 focused on delivering a production-ready chatbot experience that leverages retrieval-augmented generation (RAG) and Milvus-based vector retrieval. The work established a robust UI, data retrieval pipeline, and monitoring hooks while laying groundwork for UI experimentation.

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