One of the major challenges in real estate is the ability to retrieve information from legal documents. We suggest building a customized chatbot that is able to query the real estate related legal pdf documents in this paper. We then use some recent progressions in Large Language Models and retrieval-based question answering to introduce a system that uses this information. It uses a sentence transformer on top of the text to let it talk to a FAISS vector index, so you can do a semantic fast search. Utilizing data up through October 2023, the chatbot's responses are crafted with the power of Google Gemini Pro, combining the TF-IDF process that ascertains relevant content; this also means that answers are accurate and contextual. It enhances engagement through fast and accurate response for complex queries. This solution demonstrates the true power of utilities like LangChain in the process of extracting unstructured legal text into something understandable and digestible which in turn boosts client communication, making the operational aspects of the firm more efficient. In addition, the paper discusses ethical issues pointed out using language models, other data storage methodologies and outlines central limitations. This new research illustrates the scalable and customizable chatbot solutions available to best support the broader ecosystem of real estate.