AI Document Analyzer: An Intelligent Document Q&A System Based on Flask and Local LLM

The AI Document Analyzer is an intelligent document Q&A system developed based on the Flask framework. It supports multiple formats such as PDF, Word, and images. It runs completely offline via the Ollama local large language model, without relying on paid API services, providing intelligent Q&A functionality while protecting data privacy.

• Original Author/Maintainer: shyam1225
• Source Platform: GitHub
• Original Project Title: AI-Document-Analyser
• Original Link: https://github.com/shyam1225/AI-Document-Analyser
• Release Date: June 4, 2026

This project is a Flask-based intelligent document analysis application that allows users to upload multiple files and ask questions. Its core value lies in providing a completely offline AI processing solution, enabling intelligent Q&A without the need for paid APIs.

1. Supports multiple file formats: PDF (.pdf), Word (.docx), Text (.txt), Images (.png, .jpg, .jpeg, .webp)
2. Intelligent document chunking and retrieval: Selects the most relevant segments based on the question
3. Context-aware answer generation: Ensures answers are highly relevant to the question
4. Completely offline operation: Based on Ollama local LLM, no external API dependencies
5. Responsive web interface: Adapts to various devices, providing a good user experience

Tech Stack:

• Backend: Python + Flask
• AI Inference: Ollama (local LLM, compatible with OpenAI API format)
• Document Processing: PyPDF2 (PDF), python-docx (Word), OCR (Images)
• Frontend: HTML, CSS, JavaScript

Workflow:

1. User uploads a document
2. Extract text content and chunk it
3. User asks a question, the system retrieves relevant text chunks
4. Local LLM generates an answer and displays it

• Academic research: Quickly extract key information from literature
• Enterprise scenarios: Q&A retrieval for reports/manuals
• HR department: Resume analysis and screening
• Sensitive document processing: Data does not leave the local environment, ensuring privacy
• Researchers: Document exploration and associated insights

1. Introduce semantic search and FAISS indexing to improve retrieval efficiency
2. Add chat history and conversation memory to support multi-turn dialogues
3. Support larger document collections to meet enterprise-level needs
4. Enhance OCR and image understanding capabilities
5. Implement responsive streaming to improve user experience

This project demonstrates a practical development model for localized AI applications, integrating existing technologies to solve real document Q&A needs. Its completely offline feature has special significance in the context of increasing attention to data privacy, providing a safe and convenient solution for users handling sensitive documents, and has important reference value for AI application developers.