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DocuSnap-Backend Architecture Analysis

Welcome to the DocuSnap-Backend System Architecture Analysis documentation. This document provides a detailed analysis of the DocuSnap-Backend system architecture, technology stack, core modules, data flow, and deployment architecture, aiming to provide a comprehensive understanding of the system design and implementation.

Document Overview

This documentation is based on an in-depth analysis of the DocuSnap-Backend codebase and includes the following main sections:

  • Project Overview: Introduction to the project background, target users, and core features
  • Technology Stack: Details of the technologies and frameworks used in the system, along with selection rationale
  • Architecture Design: Analysis of the overall architectural style, layered structure, and component relationships
  • Core Modules: In-depth analysis of the system's five core functional modules
  • Typical Workflows: Illustration of key business process data flows and processing steps
  • Code Quality: Assessment of code quality, design pattern application, and improvement suggestions
  • Deployment Architecture: Description of production environment deployment plans and technology stack
  • Conclusions & Recommendations: Quick start guide and optimization directions

System Overview

DocuSnap-Backend is a Flask-based backend service for OCR recognition and AI analysis of documents and forms. The system employs a three-layer architecture: backend server, OCR server, and LLM provider. The main features include:

  1. Document Processing: Converting document images to structured text
  2. Form Processing: Extracting fields and values from form images
  3. Form Auto-filling: Automatically filling forms based on existing information

The system protects data transmission security through end-to-end encryption, improves concurrency through asynchronous task processing, and optimizes response speed for repeated requests using a caching mechanism.

Overall Architecture Diagram

Core Features

  • Three-layer Architecture: Backend server, OCR server, and LLM provider
  • Asynchronous Processing: Using task queues and worker threads to handle time-consuming operations
  • End-to-end Encryption: Using RSA and AES hybrid encryption to protect data security
  • Caching Mechanism: Using SQLite to store task status and results
  • Design Patterns: Applying various design patterns such as producer-consumer, strategy, and proxy

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