Obsidian Link Embed

This plugin allows you to convert URLs in your notes into embedded previews.

This is how it looks.

demo

Note Starting from 2.0.0, embeds are rendered with MarkdownCodeBlockProcessor, which avoids expanding the HTML block. You can convert your old embeds to new code blocks with the Convert button in the setting.

PS: If you happen to know any other website that provides free API for users to grab metadata from URLs, please let me know. This plugin can be more robust with your help.

Usage

The easiest way to use is by pasting your link and then creating an embed preview.

Additionally, there are three ways to pass the URL to this plugin.

Selecting the URL you want to parse

If nothing is selected

Put your cursor within the URL text that you want to parse

Copy the URL into the clipboard

Then

Open the command Palette
Select the command Link Embed: Embed link

In case some parsers are not working, you can also use the Link Embed: Embed link with ... to specify one parser.

Settings

If you enable Auto Embed in the setting, the plugin will automatically replace the link with an embed preview when you paste the link into an empty line. Although this option is quite convenient, I set the default setting to false in case someone doesn't know what happened.

You can change the default parser in the plugin settings.

And In Place means the selection in the editor will be removed.

The embedded preview will always be inserted into the next line.

Here is the table comparing the supported parsers.

Parser	Speed	Stable	Desc Length	Limitation
JSONLink	**	*	***	Unlimited
MicroLink(Default)	***	***	**	50/day
Iframely	**	**	*	1000/month

Thanks

Example results from different parsers

Example 1

https://arxiv.org/abs/2202.08434

JSONLink

{
    "title": "[2202.08434] A Survey of Explainable Reinforcement Learning",
    "image": "https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png",
    "description": "Explainable reinforcement learning (XRL) is an emerging subfield of\nexplainable machine learning that has attracted considerable attention in\nrecent years. The goal of XRL is to elucidate the decision-making process of\nlearning agents in sequential decision-making settings. In this survey, we\npropose a novel taxonomy for organizing the XRL literature that prioritizes the\nRL setting. We overview techniques according to this taxonomy. We point out\ngaps in the literature, which we use to motivate and outline a roadmap for\nfuture work.",
    "url": "https://arxiv.org/abs/2202.08434"
}

MicroLink

{
    "title": "A Survey of Explainable Reinforcement Learning",
    "image": "https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png",
    "description": "Explainable reinforcement learning (XRL) is an emerging subfield of\nexplainable machine learning that has attracted considerable attention in\nrecent years. The goal of XRL is to elucidate the decision-making process of\nlearning agents in sequential decision-making settings. In this survey, we\npropos…",
    "url": "https://arxiv.org/abs/2202.08434"
}

Iframely

{
    "title": "A Survey of Explainable Reinforcement Learning",
    "image": "https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png",
    "description": "Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine learning that has attracted considerable attention in recent years. The goal of XRL is to elucidate the...",
    "url": "https://arxiv.org/abs/2202.08434"
}

Example 2

https://www.redblobgames.com/articles/visibility/