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DocVerifyRAG: Document Verification and Anomaly Detection
Description
DocVerifyRAG is a revolutionary tool designed to streamline document verification processes in hospitals. It utilizes AI to classify documents and identify mistakes in metadata, ensuring accurate and efficient document management. Inspired by the need for improved data accuracy in healthcare, DocVerifyRAG provides automated anomaly detection to identify misclassifications and errors in document metadata, enhancing data integrity and compliance with regulatory standards.
Table of Contents
DocVerifyRAG
Video Demo
Web App
Screenshots
[Add screenshots here]
Technology Stack
Technology | Description |
---|---|
AI/ML | Artificial Intelligence and Machine Learning |
Python | Programming Language |
Flask | Web Framework |
Docker | Containerization |
Tech Name | Short description |
Features
Document Classification:
- Utilizes AI/ML algorithms to classify documents based on content and metadata.
- Provides accurate and efficient document categorization for improved data management.
Anomaly Detection:
- Identifies mistakes and misclassifications in document metadata through automated anomaly detection.
- Enhances data integrity and accuracy by flagging discrepancies in document metadata.
User-Friendly Interface:
- Offers a user-friendly web interface for easy document upload, classification, and verification.
- Simplifies the document management process for hospital staff, reducing manual effort and errors.
Install locally
Clone the repository:
$ git clone https://github.com/your-username/DocVerifyRAG.git
Navigate to the project directory:
$ cd DocVerifyRAG
Install dependencies:
$ pip install -r requirements.txt
Install using Docker
To deploy DocVerifyRAG using Docker, follow these steps:
Build a Docker image for the application:
$ docker build -t docverifyrag .
Run the Docker container:
$ docker run -d -p 5000:5000 docverifyrag
Usage
Access the web interface and follow the prompts to upload documents, classify them, and verify metadata. The AI-powered anomaly detection system will automatically flag any discrepancies or errors in the document metadata, providing accurate and reliable document management solutions for hospitals.