Thanks to visit codestin.com
Credit goes to github.com

Skip to content

A web application to process and analyze academic documents using Ollama-hosted LLMs, providing markdown-formatted summaries and analyses

Notifications You must be signed in to change notification settings

poiley/recognizer

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

34 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Recognizer

Analyze, summarize, and explain information in PDF textbooks and whitepapers using OCR and AI analysis. Upload PDFs to get markdown summaries and analysis with real-time progress tracking.

Features

  • PDF text extraction with OCR
  • Real-time tracking of analysis progress
  • Chunked file upload
  • Automatic error recovery
  • Memory-efficient processing
  • Markdown formatted output

Quick Start

# Build and run with Docker
./scripts/build_local.sh

# Or clean and rebuild
./scripts/clean_and_build.sh

Requirements

  • Docker & Docker Compose
  • Node.js 20+
  • Python 3.12+
  • Bun 1.0+
  • uv package manager
  • Poppler Utils

Configuration

The application is configured through a central .env file. See Local Setup for details.

Key configuration groups:

  • Ports and networking
  • Version management
  • AI and processing settings
  • Timeouts and health checks

Version Management

  • Frontend: Controlled by package.json
  • Backend: Controlled by backend/version
  • Images: Tagged with respective component versions

Documentation

Development

# Frontend (default: http://localhost:5173)
cd frontend
bun install
bun run dev

# Backend (default: http://localhost:8000)
cd backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
uvicorn main:app --reload

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request

License

MIT License

About

A web application to process and analyze academic documents using Ollama-hosted LLMs, providing markdown-formatted summaries and analyses

Resources

Stars

Watchers

Forks