🌟 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.

• 🏠 Runs locally using Docker
• 🔑 No API keys required - works out of the box!
• 🖼️ Saves screenshots locally instead of uploading to Google Cloud Storage
• 📥 Provides download URLs for saved screenshots
• 🌐 Converts web content to LLM-friendly formats

• 📄 Currently does not support parsing PDFs

The live demo is running on a VPS with the following specifications:

• CPU: 1 vCore
• RAM: 0.5 GB
• Web Server: nginx

This demonstrates that the Reader can run effectively even on minimal hardware resources.

1. Pull the latest image:

   docker pull ghcr.io/intergalacticalvariable/reader:latest

2. Run the container:

   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:
   • Maps port 3000 of the container to port 3000 on your host
   • Mounts a volume for local storage
   • Names the container reader-container
3. To stop the container:

   docker stop reader-container

4. To start the container again:

   docker start reader-container

1. Clone the repository:

   git clone https://github.com/intergalacticalvariable/reader.git
   cd reader

2. Build the Docker image:

   docker build -t reader .

3. Run the container:

   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:

1. 📝 Markdown (bypasses readability processing):

   curl -H "X-Respond-With: markdown" 'http://127.0.0.1:3000/https://google.com'

2. 🌐 HTML (returns documentElement.outerHTML):

   curl -H "X-Respond-With: html" 'http://127.0.0.1:3000/https://google.com'

3. 📄 Text (returns document.body.innerText):

   curl -H "X-Respond-With: text" 'http://127.0.0.1:3000/https://google.com'

4. 📸 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'

5. 📸 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:

1. The original Jina AI Reader project, which provided the foundation for this tool.
2. Harsh Gupta's adaptation, which served as the immediate basis for this Docker deployment version.

This project is licensed under Apache-2.0 same as the original Jina AI Reader project.