International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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AI-Based Code Generator and Context-Aware PDF Question Answering System

Author(s) Mr. Kh Dipayan Singha
Country India
Abstract Recent advancements in Natural Language Process-ing (NLP), particularly through Large Language Models (LLMs),have dramatically improved human-like text understanding and generation. However, these models often fall short when it comes to grounding responses in external knowledge, especially from
structured documents like PDFs. To address this, Retrieval- Augmented Generation (RAG) has emerged as a powerful hybrid architecture that combines semantic retrieval with generative reasoning. This paper presents the design and development of a Mul-tilingual AI Platform that leverages the RAG framework to
perform two key functions: contextual question answering from PDF documents and natural language-based code generation and optimization. The system employs PyMuPDF for PDF parsing, Sentence Transformers for multilingual embeddings, FAISS for vector-based similarity search, and Ollama-powered LLaMA 3 for response generation. It supports both English and Manipuri, providing accessibility in low-resource language settings. Fur-thermore, the assistant enables users to generate code snippets in languages like Python, JavaScript, C++ and refine them using an in-built optimization pipeline. By tightly integrating retrieval and generation, the platform delivers highly relevant answers and efficient, human-readable code—making it useful for developers, researchers and learners
Keywords Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Multilingual NLP, PDF Summariza- tion, Code Generation, FAISS, Sentence Transformers, Manipuri Language Processing, Ollama, Question Answering, Document Understanding
Field Computer Applications
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-12
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.63315
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