Recently, Retrieval-Augmented Generation (RAG) has become a significant task in Large Language Models (LLMs). In multi-turn RAG, a good system must overcome the challenges of maintaining context as the dialogue turns progress and manage the issue of generating answers based on conversation history. In this work, we address the MTRAGEval task 8 at SemEval-2026, by presenting a high-performance, parallelised Multi-Turn RAG pipeline designed to address three subtasks: Retrieval (Subtask A), Generation (Subtask B), and End-to-End RAG (Subtask C). Our methodology utilises a Streamlit framework that allows users to embed diverse corpora with varying vector spaces and embedding models, facilitating configuration for each task based on its nature. Some key experiments focus on the performance of different vector databases and embedding models, the necessity of LLM-based query rewriting (QR) for non-standalone questions, the use of different rerankers, and the scale and performance of the selected LLM for answer generation. We conclude that a configuration utilising query rewriting along with reranking delivers the best results. The code is available on GitHub https://github.com/merttoprak1/MTRAGEval-Evaluating-Multi-Turn-RAG-Conversations.