Reasoning Engine with Pre-Trained LLMs: An Operation GPT

Abstract: Recent developments in artificial intelligence, particularly with large language models (LLMs), have enabled machines to comprehend, generate, and respond to human language with unmatched precision. Nevertheless, most of these models are cloud-based and unsuitable for scenarios where data confidentiality, offline functionality, and immediate document comprehension are vital. This paper introduces Operation GPT, a robust, privacy-focused, and locally deployable system that allows users to interact intelligently with domain-specific documents using advanced language models. Operation GPT facilitates offline LLM inference by integrating Mistral 7B, Retrieval-Augmented Generation (RAG), and Ollama. Document indexing is performed using FAISS, while LangChain is employed to link retrieval and generation tasks. A user interface built with Gradio and secure backend services powered by Flask ensure a fluid and protected user experience. The system permits users to upload extensive technical documents and obtain highly pertinent, context-aware responses through a chat-like interface. The RAG mechanism enhances factual correctness by grounding responses in document content, while the local deployment model upholds data security. Tests indicate that Operation GPT is efficient, lightweight, and practical for implementation in knowledge-intensive sectors where accuracy and privacy are essential.