AI-Document-Analyser: A Flask-based Intelligent Analysis System for Multi-format Documents

AI-Document-Analyser is an open-source intelligent document analysis system based on the Flask framework. It supports uploading multiple formats such as PDF, Word, text, and images. By combining RAG technology (Retrieval-Augmented Generation) with large language models, it enables intelligent Q&A interactions, solving the time-consuming and labor-intensive problem of daily document information retrieval and improving information acquisition efficiency.

In daily work and study, finding specific information when dealing with large amounts of PDF reports, Word documents, text files, and image materials is often time-consuming and labor-intensive. AI-Document-Analyser aims to solve this problem through technical means and provide a simple yet powerful intelligent document analysis solution.

Core Functional Features

1. Multi-format Support: Covers PDF (including scanned copies), Word, text, images (OCR extraction), etc.
2. Intelligent Content Extraction: Uses PDF parsing, OCR recognition, layout analysis, and other technologies to accurately extract content.
3. RAG Architecture: Document vectorization storage, intelligent semantic retrieval, and answer generation combined with LLM.
4. Web Interface: User-friendly file upload, dialogue interaction, and history record functions.

Technical Architecture

• Backend: Flask framework (lightweight, scalable).
• AI Components: Embedding models (e.g., text-embedding-ada-002), vector databases (e.g., FAISS), large language models (e.g., GPT series).
• Workflow: Upload → Parse → Split → Embed → Store → Query and generate answers.

Academic Research

Assists in literature review, paper reading, cross-document comparison;

Enterprise Document Management

Contract review, report analysis, knowledge base construction;

Education and Training

Textbook learning, homework assistance, exam review;

Personal Knowledge Management

Note organization, data archiving, reading assistance.

AI-Document-Analyser is a practical and well-designed open-source project with the following advantages:

1. Practicality: Solves real pain points in document retrieval;
2. Multi-format Support: Covers common document types;
3. Reasonable Technology Selection: The Flask+RAG combination is popular and effective;
4. Easy Deployment: Supports local, Docker, and cloud service deployment. It is suitable for individual users, researchers, and small teams.

1. Multimodal Support: Table parsing, chart understanding, video content extraction;
2. Collaboration Features: Shared knowledge base, permission management, annotation functions;
3. Advanced Retrieval: Hybrid retrieval, re-ranking, multi-hop reasoning;
4. Local Model Support: Reduce dependence on cloud services and enhance privacy protection.