This work describes the design and the implementation of an AI document search chatbot using a Retrieval-Augmented Generation (RAG) architecture that combines generative modelling with information retrieval to provide an accurate and verifiable responses. The system supports PDF, DOCX, CSV, and TXT uploads, which are automatically processed, segmented, and indexed in a vector database for semantic search. It is implemented in Python using LangChain for orchestration, Facebook AI Similarity Search (FAISS) for vector retrieval, and multiple large-language-model Application Programming Interface (API), including OpenAI, Google Generative AI, and Hugging Face. For each query, embeddings are computed and the most relevant document segments are retrieved using cosine similarity before being passed to the selected model to ensure responses are grounded in user-supplied content. Owing to its modular design, the architecture is scalable, maintainable, and easily extensible for enterprise and academic knowledge-management applications.