🌟 Live Demo:
Example: https://reader.berlin.cx/https://github.com/intergalacticalvariable/reader/
This is an adapted version of Jina AI's Reader for local deployment using Docker.
It converts any URL to an LLM-friendly input with http://127.0.0.1:3000/https://google.com
. Get improved output for your agent and RAG systems at no cost. This tool helps you prepare web content for Large Language Models, making it easier to process and analyze online information.
The live demo is running on a VPS with the following specifications:
This demonstrates that the Reader can run effectively even on minimal hardware resources.
docker pull ghcr.io/intergalacticalvariable/reader:latest
docker run -d -p 3000:3000 -v /path/to/local-storage:/app/local-storage --name reader-container ghcr.io/intergalacticalvariable/reader:latest
Replace /path/to/local-storage
with the directory where you want to store screenshots.
This command does the following:
reader-container
docker stop reader-container
docker start reader-container
git clone https://github.com/intergalacticalvariable/reader.git
cd reader
docker build -t reader .
docker run -p 3000:3000 -v /path/to/local-storage:/app/local-storage reader
Once the Docker container is running, you can use curl to make requests. Here are examples for different response types:
📝 Markdown (bypasses readability processing):
curl -H "X-Respond-With: markdown" 'http://127.0.0.1:3000/https://google.com'
🌐 HTML (returns documentElement.outerHTML):
curl -H "X-Respond-With: html" 'http://127.0.0.1:3000/https://google.com'
📄 Text (returns document.body.innerText):
curl -H "X-Respond-With: text" 'http://127.0.0.1:3000/https://google.com'
📸 Screen-Size Screenshot (returns the URL of the webpage's screenshot):
curl -H "X-Respond-With: screenshot" 'http://127.0.0.1:3000/https://google.com'
📸 Full-Page Screenshot (returns the URL of the webpage's screenshot):
curl -H "X-Respond-With: pageshot" 'http://127.0.0.1:3000/https://google.com'
This project is based on the excellent work done by multiple contributors:
This project is licensed under Apache-2.0 same as the original Jina AI Reader project.