To develop a flexible Retrieval Augmented Generation (RAG) platform for the Sports Medicine domain that allows users to select any Large Language Model (LLM) and embedding method, experiment with different RAG approaches, and evaluate their design choices using an integrated performance assessment framework. This project delivers a customizable RAG-based chatbot designed to handle complex queries over domain-specific documents, such as medical research papers and case studies in sports medicine. Users can select from various LLMs (e.g., OpenAI, Gemini, LLaMA 3, Gamma 2) and embedding techniques to optimize retrieval performance. The system supports multiple RAG strategies, including Naive RAG and advanced GraphRAG variants, enabling experimentation with diverse retrieval and reasoning workflows. The platform incorporates agentic capabilities, allowing it to perform multi-step reasoning, query multiple data sources, and dynamically use tools and APIs to generate highly contextualized responses. A user-friendly Streamlit frontend enables real-time query input and results visualization, while a FastAPI backend ensures scalable, high-performance processing. Integrated with ARES (Automatic Retrieval Evaluation System), the platform continuously assesses relevance, accuracy, and coherence of generated answers, providing actionable feedback for system improvement. This approach empowers researchers, practitioners, and developers to fine-tune RAG systems for maximum effectiveness in sports medicine knowledge retrieval and decision support.