GMC-4190 CellNucleiRAG - Smart Search Tool for Cell Nuclei Research

CellNucleiRAG is a specialized tool developed to address a significant challenge in medical research: the rapid retrieval and synthesis of detailed information on cell nuclei. Understanding cell nuclei characteristics is crucial in fields like pathology, oncology, and diagnostics, where detailed cell analysis can guide disease identification and treatment planning. However, accessing relevant, organized information on specific cell nuclei types, datasets, models, and methods is often time-consuming, requiring manual searches through multiple, disparate sources. CellNucleiRAG solves this problem by acting as a smart search engine, designed specifically for cell nuclei research, combining traditional retrieval methods with advanced AI capabilities. Built with an underlying Retrieval-Augmented Generation (RAG) architecture, CellNucleiRAG leverages MinSearch for rapid data retrieval, pulling relevant records from a curated dataset that contains information on various nuclei types, datasets, and analytical models. Once relevant data is retrieved, it is processed by an LLM (Large Language Model) to generate contextually accurate, human-readable responses. This dual approach ensures both precision and clarity, allowing researchers to receive comprehensive answers rather than isolated data points. Key technologies used in this project include Docker, for environment consistency; Flask, for a streamlined user interface; PostgreSQL, for storing interactions and user feedback; and Grafana, for real-time system performance monitoring. User feedback is incorporated to continually refine the tool, enhancing the accuracy and relevance of responses.