Product Context

Project Purpose

paperless-gpt is designed to enhance document management by integrating AI capabilities with paperless-ngx. Its primary purpose is to automate and improve the accuracy of document processing tasks that traditionally require manual intervention.

Problems Solved

Manual Document Organization
- Eliminates tedious manual tagging and titling
- Reduces time spent on document categorization
- Minimizes human error in classification
OCR Quality Issues
- Improves text extraction from poor quality scans
- Enhances accuracy through LLM-based OCR
- Provides context-aware text interpretation
Document Processing Automation
- Automates correspondent identification
- Streamlines document categorization
- Enables bulk processing capabilities

Core Functionality

AI-Powered Document Processing
- Title generation using LLMs
- Intelligent tag suggestions
- Automated correspondent detection
- Enhanced OCR capabilities
Integration Features
- Seamless paperless-ngx integration
- Support for multiple LLM providers
- Docker-based deployment
- Customizable prompt templates
User Experience
- Web-based interface
- Manual review capabilities
- Automatic processing options
- Flexible configuration options

Success Criteria

Accuracy Metrics
- High-quality OCR results
- Accurate document classification
- Relevant tag suggestions
- Correct correspondent identification
Performance Goals
- Fast processing times
- Reliable system operation
- Scalable document handling
- Efficient resource usage
User Satisfaction
- Intuitive interface
- Clear feedback mechanisms
- Minimal manual intervention
- Consistent results

Future Vision

Enhanced Capabilities
- Support for more AI providers
- Statistics and analytics features
- Advanced document analysis
- Improved processing algorithms
- Extended automation options
Community Growth
- Active contributor base
- Regular feature additions
- Strong documentation
- Responsive maintenance
Technical Evolution
- Improved architecture
- Enhanced performance
- Extended integrations
- Robust testing

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